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

dsRNA formation leads to preferential nuclear export and gene expression

When mRNAs have been transcribed and processed in the nucleus, they are exported to the cytoplasm for translation. This export is mediated by the export receptor heterodimer Mex67-Mtr2 in the yeast Saccharomyces cerevisiae (TAP-p15 in humans)1,2. Interestingly, many long non-coding RNAs (lncRNAs) also leave the nucleus but it is currently unclear why they move to the cytoplasm3. Here we show that antisense RNAs (asRNAs) accelerate mRNA export by annealing with their sense counterparts through the helicase Dbp2. These double-stranded RNAs (dsRNAs) dominate export compared with single-stranded RNAs (ssRNAs) because they have a higher capacity and affinity for the export receptor Mex67. In this way, asRNAs boost gene expression, which is beneficial for cells. This is particularly important when the expression program changes. Consequently, the degradation of dsRNA, or the prevention of its formation, is toxic for cells. This mechanism illuminates the general cellular occurrence of asRNAs and explains their nuclear export.

Size fractionated NET-Seq reveals a conserved architecture of transcription units around yeast genes

Genomes from yeast to humans are subject to pervasive transcription. A single round of pervasive transcription is sufficient to alter local chromatin conformation, nucleosome dynamics and gene expression, but is hard to distinguish from background signals. Size fractionated native elongating transcript sequencing (sfNET-Seq) was developed to precisely map nascent transcripts independent of expression levels. RNAPII-associated nascent transcripts are fractionation into different size ranges before library construction. When anchored to the transcription start sites (TSS) of annotated genes, the combined pattern of the output metagenes gives the expected reference pattern. Bioinformatic pattern matching to the reference pattern identified 9542 transcription units in Saccharomyces cerevisiae, of which 47% are coding and 53% are noncoding. In total, 3113 (33%) are unannotated noncoding transcription units. Anchoring all transcription units to the TSS or polyadenylation site (PAS) of annotated genes reveals distinctive architectures of linked pairs of divergent transcripts approximately 200nt apart. The Reb1 transcription factor is enriched 30nt downstream of the PAS only when an upstream (TSS -60nt with respect to PAS) noncoding transcription unit co-occurs with a downstream (TSS +150nt) coding transcription unit and acts to limit levels of upstream antisense transcripts. The potential for extensive transcriptional interference is evident from low abundance unannotated transcription units with variable TSS (median -240nt) initiating within a 500nt window upstream of, and transcribing over, the promoters of protein-coding genes. This study confirms a highly interleaved yeast genome with different types of transcription units altering the chromatin landscape in distinctive ways, with the potential to exert extensive regulatory control.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.

Maintaining transcriptional homeostasis during cell cycle

The preservation of gene expression patterns that define cellular identity throughout the cell division cycle is essential to perpetuate cellular lineages. However, the progression of cells through different phases of the cell cycle severely disrupts chromatin accessibility, epigenetic marks, and the recruitment of transcriptional regulators. Notably, chromatin is transiently disassembled during S-phase and undergoes drastic condensation during mitosis, which is a significant challenge to the preservation of gene expression patterns between cell generations. This article delves into the specific gene expression and chromatin regulatory mechanisms that facilitate the preservation of transcriptional identity during replication and mitosis. Furthermore, we emphasize our recent findings revealing the unconventional role of yeast centromeres and mitotic chromosomes in maintaining transcriptional fidelity beyond mitosis.

Puzzling out nuclear pore complex assembly

Nuclear pore complexes (NPCs) are sophisticated multiprotein assemblies embedded within the nuclear envelope and controlling the exchanges of molecules between the cytoplasm and the nucleus. In this review, we summarize the mechanisms by which these elaborate complexes are built from their subunits, the nucleoporins, based on our ever-growing knowledge of NPC structural organization and on the recent identification of additional features of this process. We present the constraints faced during the production of nucleoporins, their gathering into oligomeric complexes, and the formation of NPCs within nuclear envelopes, and review the cellular strategies at play, from co-translational assembly to the enrolment of a panel of cofactors. Remarkably, the study of NPCs can inform our perception of the biogenesis of multiprotein complexes in general - and vice versa.

Mitochondria and the cell cycle in budding yeast

As centers for energy production and essential biosynthetic activities, mitochondria are vital for cell growth and proliferation. Accumulating evidence suggests an integrated regulation of these organelles and the nuclear cell cycle in distinct organisms. In budding yeast, a well-established example of this coregulation is the coordinated movement and positional control of mitochondria during the different phases of the cell cycle. The molecular determinants involved in the inheritance of the fittest mitochondria by the bud also seem to be cell cycle-regulated. In turn, loss of mtDNA or defects in mitochondrial structure or inheritance often lead to a cell cycle delay or arrest, indicating that mitochondrial function can also regulate cell cycle progression, possibly through the activation of cell cycle checkpoints. The up-regulation of mitochondrial respiration at G2/M, presumably to fulfil energetic requirements for progression at this phase, also supports a mitochondria-cell cycle interplay. Cell cycle-linked mitochondrial regulation is accomplished at the transcription level and through post-translational modifications, predominantly protein phosphorylation. Here, we address mitochondria-cell cycle interactions in the yeast Saccharomyces cerevisiae and discuss future challenges in the field.

Cell cycle-dependent Cu uptake explained the heterogenous responses of Chlamydomonas to Cu exposure

Growing evidence suggested that microorganisms exhibited heterogeneous sensitivity to toxicants, but their underlying mechanisms remain largely unknown. The asynchronous cell cycle progression in natural population implies the connection between cell cycle and heterogeneity. Here, the heterogenous responses of Chlamydomonas reinhardtii upon Cu stress were confirmed with the aid of a fluorometric probe for imaging Cu(I), implying the connection with cell cycle. Our results further indicated that the increase of labile Cu(I) was related to the cell division, leading to the fluctuation of labile Cu(I) with diurnal cycle and cell cycle, respectively. However, lack of Cu mainly influenced the cell division. We demonstrated that G2/M phase was the critical stage requiring high Cu quota during cell division. Specifically, algae at G2/M phase required 10-fold of Cu quota compared with that at G1 phase, which was related to the mitochondrial replication. Eventually, the heterogeneous Cu uptake ability of algae at different cell phases led to the heterogeneous responses to Cu exposure. Overall, Cu could influence the cell cycle through mediating the cell division, and in turn algae at different cell phases exhibited different Cu sensitivities. This study firstly uncovered the underlying mechanisms of heterogeneous Cu sensitivity for phytoplankton, which could help to evaluate the potential ecological risks of Cu.

Mitotic chromosome condensation resets chromatin to safeguard transcriptional homeostasis during interphase

Mitotic entry correlates with the condensation of the chromosomes, changes in histone modifications, exclusion of transcription factors from DNA, and the broad downregulation of transcription. However, whether mitotic condensation influences transcription in the subsequent interphase is unknown. Here, we show that preventing one chromosome to condense during mitosis causes it to fail resetting of transcription. Rather, in the following interphase, the affected chromosome contains unusually high levels of the transcription machinery, resulting in abnormally high expression levels of genes in cis, including various transcription factors. This subsequently causes the activation of inducible transcriptional programs in trans, such as the GAL genes, even in the absence of the relevant stimuli. Thus, mitotic chromosome condensation exerts stringent control on interphase gene expression to ensure the maintenance of basic cellular functions and cell identity across cell divisions. Together, our study identifies the maintenance of transcriptional homeostasis during interphase as an unexpected function of mitosis and mitotic chromosome condensation.

Transcriptome analysis of the binucleate ciliate Tetrahymena thermophila with asynchronous nuclear cell cycles

Tetrahymena thermophila harbors two functionally and physically distinct nuclei within a shared cytoplasm. During vegetative growth, the "cell cycles" of the diploid micronucleus and polyploid macronucleus are offset. Micronuclear S phase initiates just before cytokinesis and is completed in daughter cells before onset of macronuclear DNA replication. Mitotic micronuclear division occurs mid-cell cycle, while macronuclear amitosis is coupled to cell division. Here we report the first RNA-seq cell cycle analysis of a binucleated ciliated protozoan. RNA was isolated across 1.5 vegetative cell cycles, starting with a macronuclear G1 population synchronized by centrifugal elutriation. Using MetaCycle, 3244 of the 26,000+ predicted genes were shown to be cell cycle regulated. Proteins present in both nuclei exhibit a single mRNA peak that always precedes their macronuclear function. Nucleus-limited genes, including nucleoporins and importins, are expressed before their respective nucleus-specific role. Cyclin D and A/B gene family members exhibit different expression patterns that suggest nucleus-restricted roles. Periodically expressed genes cluster into seven cyclic patterns. Four clusters have known PANTHER gene ontology terms associated with G1/S and G2/M phase. We propose that these clusters encode known and novel factors that coordinate micro- and macronuclear-specific events such as mitosis, amitosis, DNA replication, and cell division.

Single-cell RNA-seq reveals early heterogeneity during aging in yeast

The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has relatively short lifespan and is genetically tractable, making it a widely used model organism in aging research. Here, we carried out a systematic and quantitative investigation of yeast aging with single-cell resolution through transcriptomic sequencing. We optimized a single-cell RNA sequencing (scRNA-seq) protocol to quantitatively study the whole transcriptome profiles of single yeast cells at different ages, finding increased cell-to-cell transcriptional variability during aging. The single-cell transcriptome analysis also highlighted key biological processes or cellular components, including oxidation-reduction process, oxidative stress response (OSR), translation, ribosome biogenesis and mitochondrion that underlie aging in yeast. We uncovered a molecular marker of FIT3, indicating the early heterogeneity during aging in yeast. We also analyzed the regulation of transcription factors and further characterized the distinctive temporal regulation of the OSR by YAP1 and proteasome activity by RPN4 during aging in yeast. Overall, our data profoundly reveal early heterogeneity during aging in yeast and shed light on the aging dynamics at the single cell level.

Consensus clustering for Bayesian mixture models

BACKGROUND

Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness.

RESULTS

In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three 'omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis.

CONCLUSTIONS

Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

Cell Cycle-Dependent Transcription: The Cyclin Dependent Kinase Cdk1 Is a Direct Regulator of Basal Transcription Machineries

The cyclin-dependent kinase Cdk1 is best known for its function as master regulator of the cell cycle. It phosphorylates several key proteins to control progression through the different phases of the cell cycle. However, studies conducted several decades ago with mammalian cells revealed that Cdk1 also directly regulates the basal transcription machinery, most notably RNA polymerase II. More recent studies in the budding yeast Saccharomyces cerevisiae have revisited this function of Cdk1 and also revealed that Cdk1 directly controls RNA polymerase III activity. These studies have also provided novel insight into the physiological relevance of this process. For instance, cell cycle-stage-dependent activity of these complexes may be important for meeting the increased demand for various proteins involved in housekeeping, metabolism, and protein synthesis. Recent work also indicates that direct regulation of the RNA polymerase II machinery promotes cell cycle entry. Here, we provide an overview of the regulation of basal transcription by Cdk1, and we hypothesize that the original function of the primordial cell-cycle CDK was to regulate RNAPII and that it later evolved into specialized kinases that govern various aspects of the transcription machinery and the cell cycle.

Multiple kernel learning for integrative consensus clustering of omic datasets

Motivation

Diverse applications—particularly in tumour subtyping—have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets is unclear.

Results

We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery.

Availability and implementation

R packages klic and coca are available on the Comprehensive R Archive Network.

Supplementary information

Supplementary data are available at Bioinformatics online.

Regulation of the conserved 3'-5' exoribonuclease EXOSC10/Rrp6 during cell division, development and cancer

The conserved 3'-5' exoribonuclease EXOSC10/Rrp6 processes and degrades RNA, regulates gene expression and participates in DNA double-strand break repair and control of telomere maintenance via degradation of the telomerase RNA component. EXOSC10/Rrp6 is part of the multimeric nuclear RNA exosome and interacts with numerous proteins. Previous clinical, genetic, biochemical and genomic studies revealed the protein's essential functions in cell division and differentiation, its RNA substrates and its relevance to autoimmune disorders and oncology. However, little is known about the regulatory mechanisms that control the transcription, translation and stability of EXOSC10/Rrp6 during cell growth, development and disease and how these mechanisms evolved from yeast to human. Herein, we provide an overview of the RNA- and protein expression profiles of EXOSC10/Rrp6 during cell division, development and nutritional stress, and we summarize interaction networks and post-translational modifications across species. Additionally, we discuss how known and predicted protein interactions and post-translational modifications influence the stability of EXOSC10/Rrp6. Finally, we explore the idea that different EXOSC10/Rrp6 alleles, which potentially alter cellular protein levels or affect protein function, might influence human development and disease progression. In this review we interpret information from the literature together with genomic data from knowledgebases to inspire future work on the regulation of this essential protein's stability in normal and malignant cells.

Accumulation of cis- and trans-regulatory variations is associated with phenotypic divergence of a complex trait between yeast species

Gene regulatory variations accumulate during evolution and alter gene expression. While the importance of expression variation in phenotypic evolution is well established, the molecular basis remains largely unknown. Here, we examine two closely related yeast species, Saccharomyces cerevisiae and Saccharomyces paradoxus, which show phenotypical differences in morphology and cell cycle progression when grown in the same environment. By profiling the cell cycle transcriptome and binding of key transcription factors (TFs) in the two species and their hybrid, we show that changes in expression levels and dynamics of oscillating genes are dominated by upstream trans-variations. We find that multiple cell cycle regulators show both cis- and trans-regulatory variations, which alters their expression in favor of the different cell cycle phenotypes. Moreover, we show that variations in the cell cycle TFs, Fkh1, and Fkh2 affect both the expression of target genes, and the binding specificity of an interacting TF, Ace2. Our study reveals how multiple variations accumulate and propagate through the gene regulatory network, alter TFs binding, contributing to phenotypic changes in cell cycle progression.

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.

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.

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.

Transcriptional responses to DNA damage

In response to the threat of DNA damage, cells exhibit a dramatic and multi-factorial response spanning from transcriptional changes to protein modifications, collectively known as the DNA damage response (DDR). Here, we review the literature surrounding the transcriptional response to DNA damage. We review differences in observed transcriptional responses as a function of cell cycle stage and emphasize the importance of experimental design in these transcriptional response studies. We additionally consider topics including structural challenges in the transcriptional response to DNA damage as well as the connection between transcription and protein abundance.

Cdk1 gates cell cycle-dependent tRNA synthesis by regulating RNA polymerase III activity

tRNA genes are transcribed by RNA polymerase III (RNAPIII). During recent years it has become clear that RNAPIII activity is strictly regulated by the cell in response to environmental cues and the homeostatic status of the cell. However, the molecular mechanisms that control RNAPIII activity to regulate the amplitude of tDNA transcription in normally cycling cells are not well understood. Here, we show that tRNA levels fluctuate during the cell cycle and reveal an underlying molecular mechanism. The cyclin Clb5 recruits the cyclin dependent kinase Cdk1 to tRNA genes to boost tDNA transcription during late S phase. At tDNA genes, Cdk1 promotes the recruitment of TFIIIC, stimulates the interaction between TFIIIB and TFIIIC, and increases the dynamics of RNA polymerase III in vivo. Furthermore, we identified Bdp1 as a putative Cdk1 substrate in this process. Preventing Bdp1 phosphorylation prevented cell cycle-dependent recruitment of TFIIIC and abolished the cell cycle-dependent increase in tDNA transcription. Our findings demonstrate that under optimal growth conditions Cdk1 gates tRNA synthesis in S phase by regulating the RNAPIII machinery, revealing a direct link between the cell cycle and RNAPIII activity.

Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations

Single-cell RNA-seq has revealed extensive cellular heterogeneity within many organisms, but few methods have been developed for microbial clonal populations. The yeast genome displays unusually dense transcript spacing, with interleaved and overlapping transcription from both strands, resulting in a minuscule but complex pool of RNA protected by a resilient cell wall. Here, we have developed a sensitive, scalable, and inexpensive yeast single-cell RNA-seq (yscRNA-seq) method that digitally counts transcript start sites in a strand- and isoform-specific manner. YscRNA-seq detects the expression of low-abundant, non-coding RNAs, and at least half of the protein-coding genome in each cell. Within clonal cells, we observed a negative correlation for the expression of sense/antisense pairs, while paralogs and divergent transcripts co-express. Combining yscRNA-seq with index sorting, we uncovered a linear relationship between cell size and RNA content. Although we detected an average of ~3.5 molecules/gene, the number of expressed isoforms are restricted at the single-cell level. Remarkably, the expression of metabolic genes is highly variable, while their stochastic expression primes cells for increased fitness towards the corresponding environmental challenge. These findings suggest that functional transcript diversity acts as a mechanism for providing a selective advantage to individual cells within otherwise transcriptionally heterogeneous populations.

Visualizing Post-Translational Modifications in Protein Interaction Networks Using PTMOracle

Post-translational modifications (PTMs) of proteins act as key regulators of protein activity, including the regulation of protein-protein interactions (PPIs). However, exploring functional links between PTMs and PPIs can be difficult. PTMOracle is a Cytoscape app that facilitates the co-visualization and co-analysis of PTMs in the context of PPI networks. PTMOracle also allows extensive data to be integrated and co-analyzed, allowing the role of domains, motifs, and disordered regions to be considered. Here, we describe several PTMOracle protocols investigating complex PTM-associated relationships and their role in PPIs. This is assisted by OraclePainter for coloring proteins by the modifications present and visualizing these in the context of networks, by OracleTools for cross-matching PTMs with sequence feature for all nodes in the network, and by OracleResults for exploring specific proteins and visualizing their PTMs in the context of protein sequences. This unit aims to demonstrate how PTMOracle can be used to systematically explore network visualizations and generate testable hypotheses regarding the functional role of PTMs in PPIs, and how the results can be analyzed to better understand the regulatory role of PTMs in PPIs. © 2019 by John Wiley & Sons, Inc.

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.

Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates

Motivation

Computational models in biology are frequently underdetermined, due to limits in our capacity to measure biological systems. In particular, mechanistic models often contain parameters whose values are not constrained by a single type of measurement. It may be possible to achieve better model determination by combining the information contained in different types of measurements. Bayesian statistics provides a convenient framework for this, allowing a quantification of the reduction in uncertainty with each additional measurement type. We wished to explore whether such integration is feasible and whether it can allow computational models to be more accurately determined.

Results

We created an ordinary differential equation model of cell cycle regulation in budding yeast and integrated data from 13 different studies covering different experimental techniques. We found that for some parameters, a single type of measurement, relative time course mRNA expression, is sufficient to constrain them. Other parameters, however, were only constrained when two types of measurements were combined, namely relative time course and absolute transcript concentration. Comparing the estimates to measurements from three additional, independent studies, we found that the degradation and transcription rates indeed matched the model predictions in order of magnitude. The predicted translation rate was incorrect however, thus revealing a deficiency in the model. Since this parameter was not constrained by any of the measurement types separately, it was only possible to falsify the model when integrating multiple types of measurements. In conclusion, this study shows that integrating multiple measurement types can allow models to be more accurately determined.

Availability and implementation

The models and files required for running the inference are included in the Supplementary information.

Contact

l.wessels@nki.nl.

Supplementary information

Supplementary data are available at Bioinformatics online.

The evolution of a G1/S transcriptional network in yeasts

The G1-to-S cell cycle transition is promoted by the periodic expression of a large set of genes. In Saccharomyces cerevisiae G1/S gene expression is regulated by two transcription factor (TF) complexes, the MBF and SBF, which bind to specific DNA sequences, the MCB and SCB, respectively. Despite extensive research little is known regarding the evolution of the G1/S transcription regulation including the co-evolution of the DNA binding domains with their respective DNA binding sequences. We have recently examined the co-evolution of the G1/S TF specificity through the systematic generation and examination of chimeric Mbp1/Swi4 TFs containing different orthologue DNA binding domains in S. cerevisiae (Hendler et al. in PLoS Genet 13:e1006778. doi: 10.1371/journal.pgen.1006778 , 2017). Here, we review the co-evolution of G1/S transcriptional network and discuss the evolutionary dynamics and specificity of the MBF-MCB and SBF-SCB interactions in different fungal species.

Untranslated Parts of Genes Interpreted: Making Heads or Tails of High-Throughput Transcriptomic Data via Computational Methods: Computational methods to discover and quantify isoforms with alternative untranslated regions

In this review we highlight the importance of defining the untranslated parts of transcripts, and present a number of computational approaches for the discovery and quantification of alternative transcription start and poly-adenylation events in high-throughput transcriptomic data. The fate of eukaryotic transcripts is closely linked to their untranslated regions, which are determined by the position at which transcription starts and ends at a genomic locus. Although the extent of alternative transcription starts and alternative poly-adenylation sites has been revealed by sequencing methods focused on the ends of transcripts, the application of these methods is not yet widely adopted by the community. We suggest that computational methods applied to standard high-throughput technologies are a useful, albeit less accurate, alternative to the expertise-demanding 5' and 3' sequencing and they are the only option for analysing legacy transcriptomic data. We review these methods here, focusing on technical challenges and arguing for the need to include better normalization of the data and more appropriate statistical models of the expected variation in the signal.

Reconciling conflicting models for global control of cell-cycle transcription

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

Transcriptomic Characterization of the Human Cell Cycle in Individual Unsynchronized Cells

The highly fine-tuned dynamics of cell cycle gene expression have been intensely studied for several decades. However, some previous observations may be difficult to fully decouple from artifacts induced by traditional cell synchronization procedures. In addition, bulk cell measurements may have disguised intricate details. Here, we address this by sorting and transcriptomic sequencing of single cells progressing through the cell cycle without prior synchronization. Genes and pathways with known cell cycle roles are confirmed, associated regulatory sequence motifs are determined, and we also establish ties between other biological processes and the unsynchronized cell cycle. Importantly, we find the G1 phase to be surprisingly heterogeneous, with transcriptionally distinct early and late time points. We additionally note that mRNAs accumulate to reach maximum total levels at mitosis and find that stable transcripts show reduced cell-to-cell variability, consistent with the transcriptional burst model of gene expression. Our study provides the first detailed transcriptional profiling of an unsynchronized human cell cycle.

Reconstructing cell cycle pseudo time-series via single-cell transcriptome data

Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.In single-cell RNA sequencing data of heterogeneous cell populations, cell cycle stage of individual cells would often be informative. Here, the authors introduce a computational model to reconstruct a pseudo-time series from single cell transcriptome data, identify the cell cycle stages, identify candidate cell cycle-regulated genes and recover the methylome changes during the cell cycle.

Reconstructing cell cycle pseudo time-series via single-cell transcriptome data

Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.

In single-cell RNA sequencing data of heterogeneous cell populations, cell cycle stage of individual cells would often be informative. Here, the authors introduce a computational model to reconstruct a pseudo-time series from single cell transcriptome data, identify the cell cycle stages, identify candidate cell cycle-regulated genes and recover the methylome changes during the cell cycle.

PTMOracle: A Cytoscape App for Covisualizing and Coanalyzing Post-Translational Modifications in Protein Interaction Networks

Post-translational modifications of proteins (PTMs) act as key regulators of protein activity and of protein-protein interactions (PPIs). To date, it has been difficult to comprehensively explore functional links between PTMs and PPIs. To address this, we developed PTMOracle, a Cytoscape app for coanalyzing PTMs within PPI networks. PTMOracle also allows extensive data to be integrated and coanalyzed with PPI networks, allowing the role of domains, motifs, and disordered regions to be considered. For proteins of interest, or a whole proteome, PTMOracle can generate network visualizations to reveal complex PTM-associated relationships. This is assisted by OraclePainter for coloring proteins by modifications, OracleTools for network analytics, and OracleResults for exploring tabulated findings. To illustrate the use of PTMOracle, we investigate PTM-associated relationships and their role in PPIs in four case studies. In the yeast interactome and its rich set of PTMs, we construct and explore histone-associated and domain-domain interaction networks and show how integrative approaches can predict kinases involved in phosphodegrons. In the human interactome, a phosphotyrosine-associated network is analyzed but highlights the sparse nature of human PPI networks and lack of PTM-associated data. PTMOracle is open source and available at the Cytoscape app store: http://apps.cytoscape.org/apps/ptmoracle .

Cdc15 Phosphorylates the C-terminal Domain of RNA Polymerase II for Transcription during Mitosis

In eukaryotes, the basal transcription in interphase is orchestrated through the regulation by kinases (Kin28, Bur1, and Ctk1) and phosphatases (Ssu72, Rtr1, and Fcp1), which act through the post-translational modification of the C-terminal domain (CTD) of the largest subunit of RNA polymerase II. The CTD comprises the repeated Tyr-Ser-Pro-Thr-Ser-Pro-Ser motif with potential epigenetic modification sites. Despite the observation of transcription and periodic expression of genes during mitosis with entailing CTD phosphorylation and dephosphorylation, the associated CTD specific kinase(s) and its role in transcription remains unknown. Here we have identified Cdc15 as a potential kinase phosphorylating Ser-2 and Ser-5 of CTD for transcription during mitosis in the budding yeast. The phosphorylation of CTD by Cdc15 is independent of any prior Ser phosphorylation(s). The inactivation of Cdc15 causes reduction of global CTD phosphorylation during mitosis and affects the expression of genes whose transcript levels peak during mitosis. Cdc15 also influences the complete transcription of clb2 gene and phosphorylates Ser-5 at the promoter and Ser-2 toward the 3' end of the gene. The observation that Cdc15 could phosphorylate Ser-5, as well as Ser-2, during transcription in mitosis is in contrast to the phosphorylation marks put by the kinases in interphase (G₁, S, and G₂), where Cdck7/Kin28 phosphorylates Ser-5 at promoter and Bur1/Ctk1 phosphorylates Ser-2 at the 3' end of the genes.

Connecting virulence pathways to cell-cycle progression in the fungal pathogen Cryptococcus neoformans

Proliferation and host evasion are critical processes to understand at a basic biological level for improving infectious disease treatment options. The human fungal pathogen Cryptococcus neoformans causes fungal meningitis in immunocompromised individuals by proliferating in cerebrospinal fluid. Current antifungal drugs target "virulence factors" for disease, such as components of the cell wall and polysaccharide capsule in C. neoformans. However, mechanistic links between virulence pathways and the cell cycle are not as well studied. Recently, cell-cycle synchronized C. neoformans cells were profiled over time to identify gene expression dynamics (Kelliher et al., PLoS Genet 12(12):e1006453, 2016). Almost 20% of all genes in the C. neoformans genome were periodically activated during the cell cycle in rich media, including 40 genes that have previously been implicated in virulence pathways. Here, we review important findings about cell-cycle-regulated genes in C. neoformans and provide two examples of virulence pathways-chitin synthesis and G-protein coupled receptor signaling-with their putative connections to cell division. We propose that a "comparative functional genomics" approach, leveraging gene expression timing during the cell cycle, orthology to genes in other fungal species, and previous experimental findings, can lead to mechanistic hypotheses connecting the cell cycle to fungal virulence.

Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans

The pathogenic yeast Cryptococcus neoformans causes fungal meningitis in immune-compromised patients. Cell proliferation in the budding yeast form is required for C. neoformans to infect human hosts, and virulence factors such as capsule formation and melanin production are affected by cell-cycle perturbation. Thus, understanding cell-cycle regulation is critical for a full understanding of virulence factors for disease. Our group and others have demonstrated that a large fraction of genes in Saccharomyces cerevisiae is expressed periodically during the cell cycle, and that proper regulation of this transcriptional program is important for proper cell division. Despite the evolutionary divergence of the two budding yeasts, we found that a similar percentage of all genes (~20%) is periodically expressed during the cell cycle in both yeasts. However, the temporal ordering of periodic expression has diverged for some orthologous cell-cycle genes, especially those related to bud emergence and bud growth. Genes regulating DNA replication and mitosis exhibited a conserved ordering in both yeasts, suggesting that essential cell-cycle processes are conserved in periodicity and in timing of expression (i.e. duplication before division). In S. cerevisiae cells, we have proposed that an interconnected network of periodic transcription factors (TFs) controls the bulk of the cell-cycle transcriptional program. We found that temporal ordering of orthologous network TFs was not always maintained; however, the TF network topology at cell-cycle commitment appears to be conserved in C. neoformans. During the C. neoformans cell cycle, DNA replication genes, mitosis genes, and 40 genes involved in virulence are periodically expressed. Future work toward understanding the gene regulatory network that controls cell-cycle genes is critical for developing novel antifungals to inhibit pathogen proliferation.

Coordination of Cell Cycle Progression and Mitotic Spindle Assembly Involves Histone H3 Lysine 4 Methylation by Set1/COMPASS

Methylation of histone H3 lysine 4 (H3K4) by Set1 complex/COMPASS is a hallmark of eukaryotic chromatin, but it remains poorly understood how this post-translational modification contributes to the regulation of biological processes like the cell cycle. Here, we report a H3K4 methylation-dependent pathway in Saccharomyces cerevisiae that governs toxicity toward benomyl, a microtubule destabilizing drug. Benomyl-sensitive growth of wild-type cells required mono- and dimethylation of H3K4 and Pho23, a PHD-containing subunit of the Rpd3L complex. Δset1 and Δpho23 deletions suppressed defects associated with ipl1-2 aurora kinase mutant, an integral component of the spindle assembly checkpoint during mitosis. Benomyl resistance of Δset1 strains was accompanied by deregulation of all four tubulin genes and the phenotype was suppressed by tub2-423 and Δtub3 mutations, establishing a genetic link between H3K4 methylation and microtubule function. Most interestingly, sine wave fitting and clustering of transcript abundance time series in synchronized cells revealed a requirement for Set1 for proper cell-cycle-dependent gene expression and Δset1 cells displayed delayed entry into S phase. Disruption of G1/S regulation in Δmbp1 and Δswi4 transcription factor mutants duplicated both benomyl resistance and suppression of ipl1-2 as was observed with Δset1 Taken together our results support a role for H3K4 methylation in the coordination of cell-cycle progression and proper assembly of the mitotic spindle during mitosis.

Ndt80 activates the meiotic ORC1 transcript isoform and SMA2 via a bi-directional middle sporulation element in Saccharomyces cerevisiae

The origin of replication complex subunit ORC1 is important for DNA replication. The gene is known to encode a meiotic transcript isoform (mORC1) with an extended 5'-untranslated region (5'-UTR), which was predicted to inhibit protein translation. However, the regulatory mechanism that controls the mORC1 transcript isoform is unknown and no molecular biological evidence for a role of mORC1 in negatively regulating Orc1 protein during gametogenesis is available. By interpreting RNA profiling data obtained with growing and sporulating diploid cells, mitotic haploid cells, and a starving diploid control strain, we determined that mORC1 is a middle meiotic transcript isoform. Regulatory motif predictions and genetic experiments reveal that the activator Ndt80 and its middle sporulation element (MSE) target motif are required for the full induction of mORC1 and the divergently transcribed meiotic SMA2 locus. Furthermore, we find that the MSE-binding negative regulator Sum1 represses both mORC1 and SMA2 during mitotic growth. Finally, we demonstrate that an MSE deletion strain, which cannot induce mORC1, contains abnormally high Orc1 levels during post-meiotic stages of gametogenesis. Our results reveal the regulatory mechanism that controls mORC1, highlighting a novel developmental stage-specific role for the MSE element in bi-directional mORC1/SMA2 gene activation, and correlating mORC1 induction with declining Orc1 protein levels. Because eukaryotic genes frequently encode multiple transcripts possessing 5'-UTRs of variable length, our results are likely relevant for gene expression during development and disease in higher eukaryotes.

Msa1 and Msa2 Modulate G1-Specific Transcription to Promote G1 Arrest and the Transition to Quiescence in Budding Yeast

Yeast that naturally exhaust their glucose source can enter a quiescent state that is characterized by reduced cell size, and high cell density, stress tolerance and longevity. The transition to quiescence involves highly asymmetric cell divisions, dramatic reprogramming of transcription and global changes in chromatin structure and chromosome topology. Cells enter quiescence from G1 and we find that there is a positive correlation between the length of G1 and the yield of quiescent cells. The Swi4 and Swi6 transcription factors, which form the SBF transcription complex and promote the G1 to S transition in cycling cells, are also critical for the transition to quiescence. Swi6 forms a second complex with Mbp1 (MBF), which is not required for quiescence. These are the functional analogues of the E2F complexes of higher eukaryotes. Loss of the RB analogue, Whi5, and the related protein Srl3/Whi7, delays G1 arrest, but it also delays recovery from quiescence. Two MBF- and SBF-Associated proteins have been identified that have little effect on SBF or MBF activity in cycling cells. We show that these two related proteins, Msa1 and Msa2, are specifically required for the transition to quiescence. Like the E2F complexes that are quiescence-specific, Msa1 and Msa2 are required to repress the transcription of many SBF target genes, including SWI4, the CLN2 cyclin and histones, specifically after glucose is exhausted from the media. They also activate transcription of many MBF target genes. msa1msa2 cells fail to G1 arrest and rapidly lose viability upon glucose exhaustion. msa1msa2 mutants that survive this transition are very large, but they attain the same thermo-tolerance and longevity of wild type quiescent cells. This indicates that Msa1 and Msa2 are required for successful transition to quiescence, but not for the maintenance of that state.

In spite of the many differences between yeast and humans, the basic strategies that regulate the cell division cycle are fundamentally conserved. In this study, we extend these parallels to include a common strategy by which cells transition from proliferation to quiescence. The decision to divide is made in the G1 phase of the cell cycle. During G1, the genes that drive DNA replication are repressed by the E2F/RB complex. When a signal to divide is received, RB is removed and the complex is activated. When cells commit to a long term, but reversible G1 arrest, or quiescence, they express a novel E2F/RB-like complex, which promotes and maintains a stable repressive state. Yeast cells contain a functional analog of E2F/RB, called SBF/Whi5, which is activated by a similar mechanism in proliferating yeast cells. In this study, we identify two novel components of the SBF/Whi5 complex whose activity is specific to the transition to quiescence. These factors, Msa1 and Msa2, repress SBF targets and are required for the long term, but reversible G1 arrest that is critical for achieving a quiescent state.

A graph theoretical approach to data fusion

The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. Therefore, it is increasingly being recognized that we can gain deeper understanding about underlying biology by combining the insights obtained from multiple, diverse datasets. Thus we propose a novel scalable computational approach to unsupervised data fusion. Our technique exploits network representations of the data to identify similarities among the datasets. We may work within the Bayesian formalism, using Bayesian nonparametric approaches to model each dataset; or (for fast, approximate, and massive scale data fusion) can naturally switch to more heuristic modeling techniques. An advantage of the proposed approach is that each dataset can initially be modeled independently (in parallel), before applying a fast post-processing step to perform data integration. This allows us to incorporate new experimental data in an online fashion, without having to rerun all of the analysis. We first demonstrate the applicability of our tool on artificial data, and then on examples from the literature, which include yeast cell cycle, breast cancer and sporadic inclusion body myositis datasets.

Regulation of Budding Yeast CENP-A levels Prevents Misincorporation at Promoter Nucleosomes and Transcriptional Defects

The exclusive localization of the histone H3 variant CENP-A to centromeres is essential for accurate chromosome segregation. Ubiquitin-mediated proteolysis helps to ensure that CENP-A does not mislocalize to euchromatin, which can lead to genomic instability. Consistent with this, overexpression of the budding yeast CENP-A^Cse4 is lethal in cells lacking Psh1, the E3 ubiquitin ligase that targets CENP-A^Cse4 for degradation. To identify additional mechanisms that prevent CENP-A^Cse4 misincorporation and lethality, we analyzed the genome-wide mislocalization pattern of overexpressed CENP-A^Cse4 in the presence and absence of Psh1 by chromatin immunoprecipitation followed by high throughput sequencing. We found that ectopic CENP-A^Cse4 is enriched at promoters that contain histone H2A.Z^Htz1 nucleosomes, but that H2A.Z^Htz1 is not required for CENP-A^Cse4 mislocalization. Instead, the INO80 complex, which removes H2A.Z^Htz1 from nucleosomes, promotes the ectopic deposition of CENP-A^Cse4. Transcriptional profiling revealed gene expression changes in the psh1Δ cells overexpressing CENP-A^Cse4. The down-regulated genes are enriched for CENP-A^Cse4 mislocalization to promoters, while the up-regulated genes correlate with those that are also transcriptionally up-regulated in an htz1Δ strain. Together, these data show that regulating centromeric nucleosome localization is not only critical for maintaining centromere function, but also for ensuring accurate promoter function and transcriptional regulation.

Chromosomes carry the genetic material in cells. When cells divide, each daughter cell must inherit a single copy of each chromosome. The centromere is the locus on each chromosome that ensures the equal distribution of chromosomes during cell division. One essential protein involved in this task is CENP-A^Cse4, which normally localizes exclusively to centromeres. Here, we investigated where CENP-A^Cse4 spreads in the genome when parts of its regulatory machinery are removed. We found that CENP-A^Cse4 becomes mislocalized to promoters, the region upstream of each gene that controls the activity of the gene. Consistent with this, the mislocalization of CENP-A^Cse4 to promoters leads to problems with gene activity. Our work shows that mislocalization of centromeric proteins can have effects beyond chromosome segregation defects, such as interfering with gene expression on chromosome arms.

Using Formal Concept Analysis to Identify Negative Correlations in Gene Expression Data

Recently, many biological studies reported that two groups of genes tend to show negatively correlated or opposite expression tendency in many biological processes or pathways. The negative correlation between genes may imply an important biological mechanism. In this study, we proposed a FCA-based negative correlation algorithm (NCFCA) that can effectively identify opposite expression tendency between two gene groups in gene expression data. After applying it to expression data of cell cycle-regulated genes in yeast, we found that six minichromosome maintenance family genes showed the opposite changing tendency with eight core histone family genes. Furthermore, we confirmed that the negative correlation expression pattern between these two families may be conserved in the cell cycle. Finally, we discussed the reasons underlying the negative correlation of six minichromosome maintenance (MCM) family genes with eight core histone family genes. Our results revealed that negative correlation is an important and potential mechanism that maintains the balance of biological systems by repressing some genes while inducing others. It can thus provide new understanding of gene expression and regulation, the causes of diseases, etc.

The Late S-Phase Transcription Factor Hcm1 Is Regulated through Phosphorylation by the Cell Wall Integrity Checkpoint

The cell wall integrity (CWI) checkpoint in the budding yeast Saccharomyces cerevisiae coordinates cell wall construction and cell cycle progression. In this study, we showed that the regulation of Hcm1, a late-S-phase transcription factor, arrests the cell cycle via the cell wall integrity checkpoint. Although the HCM1 mRNA level remained unaffected when the cell wall integrity checkpoint was induced, the protein level decreased. The overproduction of Hcm1 resulted in the failure of the cell wall integrity checkpoint. We identified 39 Hcm1 phosphorylation sites, including 26 novel sites, by tandem mass spectrometry analysis. A mutational analysis revealed that phosphorylation of Hcm1 at S61, S65, and S66 is required for the proper onset of the cell wall integrity checkpoint by regulating the timely decrease in its protein level. Hyperactivation of the CWI mitogen-activated protein kinase (MAPK) signaling pathway significantly reduced the Hcm1 protein level, and the deletion of CWI MAPK Slt2 resulted in a failure to decrease Hcm1 protein levels in response to stress, suggesting that phosphorylation is regulated by CWI MAPK. In conclusion, we suggest that Hcm1 is regulated negatively by the cell wall integrity checkpoint through timely phosphorylation and degradation under stress to properly control budding yeast proliferation.

Inferring orthologous gene regulatory networks using interspecies data fusion

Motivation: The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved ‘hypernetwork’. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression.

Results: Results on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase.

Availability and implementation: MATLAB code is available from http://go.warwick.ac.uk/systemsbiology/software/.

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

Supplementary information:Supplementary data are available at Bioinformatics online.

Loss of the Yeast SR Protein Npl3 Alters Gene Expression Due to Transcription Readthrough

Yeast Npl3 is a highly abundant, nuclear-cytoplasmic shuttling, RNA-binding protein, related to metazoan SR proteins. Reported functions of Npl3 include transcription elongation, splicing and RNA 3’ end processing. We used UV crosslinking and analysis of cDNA (CRAC) to map precise RNA binding sites, and strand-specific tiling arrays to look at the effects of loss of Npl3 on all transcripts across the genome. We found that Npl3 binds diverse RNA species, both coding and non-coding, at sites indicative of roles in both early pre-mRNA processing and 3’ end formation. Tiling arrays and RNAPII mapping data revealed 3’ extended RNAPII-transcribed RNAs in the absence of Npl3, suggesting that defects in pre-mRNA packaging events result in termination readthrough. Transcription readthrough was widespread and frequently resulted in down-regulation of neighboring genes. We conclude that the absence of Npl3 results in widespread 3' extension of transcripts with pervasive effects on gene expression.

Npl3 is a yeast mRNA binding protein with many reported functions in RNA processing. We wanted to identify direct targets and therefore combined analyses of the transcriptome-wide effects of the loss of Npl3 on gene expression with UV crosslinking and bioinformatics to identify RNA-binding sites for Npl3. We found that Npl3 binds diverse sites on large numbers of transcripts, and that the loss of Npl3 results in transcriptional readthrough on many genes. One effect of this transcription readthrough is that the expression of numerous flanking genes is strongly down regulated. This underlines the importance of faithful termination for the correct regulation of gene expression. The effects of the loss of Npl3 are seen on both mRNAs and non-protein coding RNAs. These have distinct but overlapping termination mechanisms, with both classes requiring Npl3 for correct RNA packaging.