Publications
72 results found
Tang W, Jørgensen ACS, Marguerat S, et al., 2023, Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics, Bioinformatics, Vol: 39, Pages: 1-9, ISSN: 1367-4803
MOTIVATION: Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. RESULTS: Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and non-allele-specific scRNA-seq data. AVAILABILITY: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Simpson Ragdale H, Clements M, Tang W, et al., 2023, Injury primes mutation-bearing astrocytes for dedifferentiation in later life., Curr Biol, Vol: 33, Pages: 1082-1098.e8
Despite their latent neurogenic potential, most normal parenchymal astrocytes fail to dedifferentiate to neural stem cells in response to injury. In contrast, aberrant lineage plasticity is a hallmark of gliomas, and this suggests that tumor suppressors may constrain astrocyte dedifferentiation. Here, we show that p53, one of the most commonly inactivated tumor suppressors in glioma, is a gatekeeper of astrocyte fate. In the context of stab-wound injury, p53 loss destabilized the identity of astrocytes, priming them to dedifferentiate in later life. This resulted from persistent and age-exacerbated neuroinflammation at the injury site and EGFR activation in periwound astrocytes. Mechanistically, dedifferentiation was driven by the synergistic upregulation of mTOR signaling downstream of p53 loss and EGFR, which reinstates stemness programs via increased translation of neurodevelopmental transcription factors. Thus, our findings suggest that first-hit mutations remove the barriers to injury-induced dedifferentiation by sensitizing somatic cells to inflammatory signals, with implications for tumorigenesis.
Jørgensen ACS, Hill CS, Sturrock M, et al., 2023, Data-driven spatio-temporal modelling of glioblastoma., R Soc Open Sci, Vol: 10, ISSN: 2054-5703
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
Robles-Rebollo I, Cuartero S, Canellas-Socias A, et al., 2022, Cohesin couples transcriptional bursting probabilities of inducible enhancers and promoters, Nature Communications, Vol: 13, ISSN: 2041-1723
Innate immune responses rely on inducible gene expression programmes which, in contrast to steady-state transcription, are highly dependent on cohesin. Here we address transcriptional parameters underlying this cohesin-dependence by single-molecule RNA-FISH and single-cell RNA-sequencing. We show that inducible innate immune genes are regulated predominantly by an increase in the probability of active transcription, and that probabilities of enhancer and promoter transcription are coordinated. Cohesin has no major impact on the fraction of transcribed inducible enhancers, or the number of mature mRNAs produced per transcribing cell. Cohesin is, however, required for coupling the probabilities of enhancer and promoter transcription. Enhancer-promoter coupling may not be explained by spatial proximity alone, and at the model locus Il12b can be disrupted by selective inhibition of the cohesinopathy-associated BET bromodomain BD2. Our data identify discrete steps in enhancer-mediated inducible gene expression that differ in cohesin-dependence, and suggest that cohesin and BD2 may act on shared pathways.
Kleijn IT, Martínez-Segura A, Bertaux F, et al., 2022, Growth-rate dependent and nutrient-specific gene expression resource allocation in fission yeast, Life Science Alliance, Vol: 5, ISSN: 2575-1077
Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determinedtheimportanceof the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombegrownon non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart fromRNA polymerase II-dependent transcription.Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass,showedmostly condition-specificregulation.In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our 19basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.
Brooks LJ, Clements MP, Burden JJ, et al., 2022, The white matter is a pro-differentiative niche for glioblastoma (vol 12, 2184, 2021), NATURE COMMUNICATIONS, Vol: 13
Rodriguez-Lopez M, Anver S, Cotobal C, et al., 2022, Functional profiling of long intergenic non-coding RNAs in fission yeast (vol 11, e76000, 2022), ELIFE, Vol: 11, ISSN: 2050-084X
Rodriguez-Lopez M, Anver S, Cotobal C, et al., 2022, Functional profiling of long intergenic non-coding RNAs in fission yeast, ELIFE, Vol: 11, ISSN: 2050-084X
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Ellis DA, Reyes-Martin F, Rodriguez-Lopez M, et al., 2021, R-loops and regulatory changes in chronologically ageing fission yeast cells drive non-random patterns of genome rearrangements, PLOS GENETICS, Vol: 17, ISSN: 1553-7404
Rodriguez-Lopez M, Anver S, Cotobal C, et al., 2021, Functional profiling of long intergenic non-coding RNAs in fission yeast
<jats:p>Eukaryotic genomes express numerous long intergenic non-coding RNAs (lincRNAs) that do not overlap any coding genes. Some lincRNAs function in various aspects of gene regulation, but it is not clear in general to what extent lincRNAs contribute to the information flow from genotype to phenotype. To explore this question, we systematically analyzed cellular roles of lincRNAs in Schizosaccharomyces pombe. Using seamless CRISPR/Cas9-based genome editing, we deleted 141 lincRNA genes to broadly phenotype these mutants, together with 238 diverse coding-gene mutants for functional context. We applied high-throughput colony-based assays to determine mutant growth and viability in benign conditions and in response to 145 different nutrient, drug and stress conditions. These analyses uncovered phenotypes for 47.5% of the lincRNAs and 96% of the protein-coding genes. For 110 lincRNA mutants, we also performed high-throughput microscopy and flow-cytometry assays, linking 37% of these lincRNAs with cell-size and/or cell-cycle control. With all assays combined, we detected phenotypes for 84 (59.6%) of all lincRNA deletion mutants tested. For complementary functional inference, we analyzed colony growth of strains ectopically overexpressing 113 lincRNA genes under 47 different conditions. Of these overexpression strains, 102 (90.3%) showed altered growth under certain conditions. Clustering analyses provided further functional clues and relationships for some of the lincRNAs. These rich phenomics datasets associate lincRNA mutants with hundreds of phenotypes, indicating that most of the lincRNAs analyzed exert cellular functions in specific environmental or physiological contexts. This study provides groundwork to further dissect the roles of these lincRNAs in the relevant conditions.</jats:p>
Brooks LJ, Clements MP, Burden JJ, et al., 2021, The white matter is a pro-differentiative niche for glioblastoma, NATURE COMMUNICATIONS, Vol: 12
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- Citations: 15
Bertaux F, von Kugelgen J, Marguerat S, et al., 2020, A bacterial size law revealed by a coarse-grained model of cell physiology, PLOS COMPUTATIONAL BIOLOGY, Vol: 16, ISSN: 1553-734X
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- Citations: 10
Sun X-M, Bowman A, Priestman M, et al., 2020, Size-Dependent Increase in RNA Polymerase II Initiation Rates Mediates Gene Expression Scaling with Cell Size., Curr Biol, Vol: 30, Pages: 1217-1230.e7
Cell size varies during the cell cycle and in response to external stimuli. This requires the tight coordination, or "scaling," of mRNA and protein quantities with the cell volume in order to maintain biomolecule concentrations and cell density. Evidence in cell populations and single cells indicates that scaling relies on the coordination of mRNA transcription rates with cell size. Here, we use a combination of single-molecule fluorescence in situ hybridization (smFISH), time-lapse microscopy, and mathematical modeling in single fission yeast cells to uncover the precise molecular mechanisms that control transcription rates scaling with cell size. Linear scaling of mRNA quantities is apparent in single fission yeast cells during a normal cell cycle. Transcription of both constitutive and periodic genes is a Poisson process with transcription rates scaling with cell size and without evidence for transcriptional off states. Modeling and experimental data indicate that scaling relies on the coordination of RNA polymerase II (RNAPII) transcription initiation rates with cell size and that RNAPII is a limiting factor. We show using real-time quantitative imaging that size increase is accompanied by a rapid concentration-independent recruitment of RNAPII onto chromatin. Finally, we find that, in multinucleated cells, scaling is set at the level of single nuclei and not the entire cell, making the nucleus a determinant of scaling. Integrating our observations in a mechanistic model of RNAPII-mediated transcription, we propose that scaling of gene expression with cell size is the consequence of competition between genes for limiting RNAPII.
Sun X-M, Bowman A, Priestman M, et al., 2020, Size-dependent increase in RNA Polymerase II initiation rates mediates gene expression scaling with cell size, Current Biology, Vol: 30, Pages: 1217-1230.e7, ISSN: 0960-9822
Cell size varies during the cell cycle and in response to external stimuli. This requires the tight coordination, or “scaling”, of mRNA and protein quantities with the cell volume in order to maintain biomolecules concentrations and cell density. Evidence in cell populations and single cells indicates that scaling relies on the coordination of mRNA transcription rates with cell size. Here we use a combination of single-molecule fluorescence in situ hybridisation (smFISH), time-lapse microscopy and mathematical modelling in single fission yeast cells to uncover the precise molecular mechanisms that control transcription rates scaling with cell size. Linear scaling of mRNA quantities is apparent in single fission yeast cells during a normal cell cycle. Transcription rates of both constitutive and regulated genes scale with cell size without evidence for transcriptional bursting. Modelling and experimental data indicate that scaling relies on the coordination of RNAPII transcription initiation rates with cell size and that RNAPII is a limiting factor. We show using real-time quantitative imaging that size increase is accompanied by a rapid concentration independent recruitment of RNAPII onto chromatin. Finally, we find that in multinucleated cells, scaling is set at the level of single nuclei and not the entire cell, making the nucleus the transcriptional scaling unit. Integrating our observations in a mechanistic model of RNAPII mediated transcription, we propose that scaling of gene expression with cell size is the consequence of competition between genes for limiting RNAPII.
Tang W, Bertaux F, Thomas P, et al., 2020, bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data, Bioinformatics, Vol: 36, Pages: 1174-1181, ISSN: 1367-4803
Motivation:Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to theirinterpretation. The marked technical variability, high amounts of missing observations and batch effecttypical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient andunified approach for normalisation, imputation and batch effect correction.Results:Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priorsare estimated from expression values across cells using an empirical Bayes approach. We first validateour assumptions by showing this model can reproduce different statistics observed in real scRNA-seqdata. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data thatbayNorm allows robust imputation of missing values generating realistic transcript distributions that matchsingle molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNormimproves accuracy and sensitivity of differential expression analysis and reduces batch effect comparedto other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scalingnormalisation, imputation and true count recovery of gene expression measurements from scRNA-seqdata.Availability:The R package “bayNorm” is available at https://github.com/WT215/bayNorm. The code foranalysing data in this paper is available at https://github.com/WT215/bayNorm_papercode.Contact:samuel.marguerat@imperial.ac.uk or v.shahrezaei@imperial.ac.ukSupplementary information:Supplementary data are available atBioinformaticsonline.
Ragdale HS, Clements M, Zakka L, et al., 2019, Injury signals drive lineage conversion of premalignant astrocytes: insights into tumour initiation, 14th European Meeting on Glial Cells in Health and Disease (GLIA), Publisher: WILEY, Pages: E147-E147, ISSN: 0894-1491
Saint M, Bertaux F, Tang W, et al., 2019, Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation, Nature Microbiology, Vol: 4, Pages: 480-491, ISSN: 2058-5276
Phenotypic cell-to-cell variability is a fundamental determinant of microbial fitness that contributes to stress adaptation and drug resistance. Gene expression heterogeneity underpins this variability but is challenging to study genome-wide. Here we examine the transcriptomes of >2,000 single fission yeast cells exposed to various environmental conditions by combining imaging, single-cell RNA sequencing and Bayesian true count recovery. We identify sets of highly variable genes during rapid proliferation in constant culture conditions. By integrating single-cell RNA sequencing and cell-size data, we provide insights into genes that are regulated during cell growth and division, including genes whose expression does not scale with cell size. We further analyse the heterogeneity of gene expression during adaptive and acute responses to changing environments. Entry into the stationary phase is preceded by a gradual, synchronized adaptation in gene regulation that is followed by highly variable gene expression when growth decreases. Conversely, sudden and acute heat shock leads to a stronger, coordinated response and adaptation across cells. This analysis reveals that the magnitude of global gene expression heterogeneity is regulated in response to different physiological conditions within populations of a unicellular eukaryote.
Ellis DA, Reyes-Martín F, Rodríguez-López M, et al., 2019, R-loops and regulatory changes in chronologically ageing fission yeast cells drive non-random patterns of genome rearrangements
<jats:title>Abstract</jats:title><jats:p>Aberrant repair of DNA double-strand breaks can recombine distant pairs of chromosomal breakpoints. Such chromosomal rearrangements are a hallmark of ageing and compromise the structure and function of genomes. Rearrangements are challenging to detect in non-dividing cell populations, because they reflect individually rare, heterogeneous events. The genomic distribution of<jats:italic>de novo</jats:italic>rearrangements in non-dividing cells, and their dynamics during ageing, remain therefore poorly characterized. Studies of genomic instability during ageing have focussed on mitochondrial DNA, small genetic variants, or proliferating cells. To gain a better understanding of genome rearrangements during cellular ageing, we focused on a single diagnostic measure – DNA breakpoint junctions – allowing us to interrogate the changing genomic landscape in non-dividing cells of fission yeast (<jats:italic>Schizosaccharomyces pombe</jats:italic>). Aberrant DNA junctions that accumulated with age were associated with microhomology sequences and R-loops. Global hotspots for age-associated breakpoint formation were evident near telomeric genes and linked to remote breakpoints on the same or different chromosomes, including the mitochondrial chromosome. An unexpected mechanism of genomic instability caused more local hotspots: age-associated reduction in an RNA-binding protein could trigger R-loop formation at target loci. This finding suggests that biological processes other than transcription or replication can drive genome rearrangements. Notably, we detected similar signatures of genome rearrangements that accumulated in old brain cells of humans. These findings provide insights into the unique patterns and potential mechanisms of genome rearrangements in non-dividing cells, which can be triggered by ageing-related changes in gene-regulatory proteins.</jats:p>
Hocquet C, Robellet X, Modolo L, et al., 2018, Condensin controls cellular RNA levels through the accurate segregation of chromosomes instead of directly regulating transcription, eLife, Vol: 7, ISSN: 2050-084X
Condensins are genome organisers that shape chromosomes and promote theiraccurate transmission. Several studies have also implicated condensins in gene expression,although any mechanisms have remained enigmatic. Here, we report on the role of condensin ingene expression in fission and budding yeasts. In contrast to previous studies, we providecompelling evidence that condensin plays no direct role in the maintenance of the transcriptome,neither during interphase nor during mitosis. We further show that the changes in gene expressionin post-mitotic fission yeast cells that result from condensin inactivation are largely a consequenceof chromosome missegregation during anaphase, which notably depletes the RNA-exosome fromdaughter cells. Crucially, preventing karyotype abnormalities in daughter cells restores a normaltranscriptome despite condensin inactivation. Thus, chromosome instability, rather than a directrole of condensin in the transcription process, changes gene expression. This knowledge challengesthe concept of gene regulation by canonical condensin complexes.
Atkinson S, Marguerat S, Bitton D, et al., 2018, Long non-coding RNA repertoire and targeting by nuclear exosome, cytoplasmic exonuclease and RNAi in fission yeast, RNA, Vol: 24, Pages: 1195-1213, ISSN: 1355-8382
Long non-coding RNAs (lncRNAs), which are longer than 200 nucleotides but often unstable, contribute a substantial and diverse portion to pervasive non-coding transcriptomes. Most lncRNAs are poorly annotated and understood, although several play important roles in gene regulation and diseases. Here we systematically uncover and analyse lncRNAs in Schizosaccharomyces pombe. Based on RNA-seq data from twelve RNA-processing mutants and nine physiological conditions, we identify 5775 novel lncRNAs, nearly 4-times the previously annotated lncRNAs. The expression of most lncRNAs becomes strongly induced under the genetic and physiological perturbations, most notably during late meiosis. Most lncRNAs are cryptic and suppressed by three RNA-processing pathways: the nuclear exosome, cytoplasmic exonuclease, and RNAi. Double-mutant analyses reveal substantial coordination and redundancy among these pathways. We classify lncRNAs by their dominant pathway into cryptic unstable transcripts (CUTs), Xrn1-sensitive unstable transcripts (XUTs), and Dicer-sensitive unstable transcripts (DUTs). XUTs and DUTs are enriched for antisense lncRNAs, while CUTs are often bidirectional and actively translated. The cytoplasmic exonuclease, along with RNAi, dampens the expression of thousands of lncRNAs and mRNAs that become induced during meiosis. Antisense lncRNA expression mostly negatively correlates with sense mRNA expression in the physiological, but not the genetic conditions. Intergenic and bidirectional lncRNAs emerge from nucleosome-depleted regions, upstream of positioned nucleosomes. Our results highlight both similarities and differences to lncRNA regulation in budding yeast. This broad survey of the lncRNA repertoire and characteristics in S. pombe, and the interwoven regulatory pathways that target lncRNAs, provides a rich framework for their further functional analyses.
Tang W, Bertaux F, Thomas P, et al., 2018, bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data
<jats:p>Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.</jats:p>
Bertaux F, Marguerat S, Shahrezaei V, 2018, Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits, ROYAL SOCIETY OPEN SCIENCE, Vol: 5, ISSN: 2054-5703
The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.
Björklund M, Marguerat S, 2017, Editorial: Determinants of Cell Size., Frontiers in Cell and Developmental Biology, Vol: 5, ISSN: 2296-634X
Clements MP, Byrne E, Camarillo Guerrero LF, et al., 2017, The wound microenvironment reprogrammes Schwann cells to invasive mesenchymal- like cells to drive peripheral nerve regeneration, Neuron, Vol: 96, Pages: 98-114.e7, ISSN: 0896-6273
Schwann cell dedifferentiation from a myelinating to a progenitor-like cell underlies the remarkable ability of peripheral nerves to regenerate following injury. However, the molecular identity of the differentiated and dedifferentiated states in vivo has been elusive. Here, we profiled Schwann cells acutely purified from intact nerves and from the wound and distal regions of severed nerves. Our analysis reveals novel facets of the dedifferentiation response, including acquisition of mesenchymal traits and a Myc module. Furthermore, wound and distal dedifferentiated Schwann cells constitute different populations, with wound cells displaying increased mesenchymal character induced by localized TGFβ signaling. TGFβ promotes invasion and crosstalks with Eph signaling via N-cadherin to drive collective migration of the Schwann cells across the wound. Consistently, Tgfbr2 deletion in Schwann cells resulted in misdirected and delayed reinnervation. Thus, the wound microenvironment is a key determinant of Schwann cell identity, and it promotes nerve repair through integration of multiple concerted signals.
Keifenheim D, Sun X-M, D'Souza E, et al., 2017, Size-Dependent Expression of the Mitotic Activator Cdc25 Suggests a Mechanism of Size Control in Fission Yeast, Current Biology, Vol: 27, Pages: 1491-1497.e4, ISSN: 1879-0445
Proper cell size is essential for cellular function. Nonetheless, despite more than 100 years of work on the subject, the mechanisms that maintain cell-size homeostasis are largely mysterious [ 1 ]. Cells in growing populations maintain cell size within a narrow range by coordinating growth and division. Bacterial and eukaryotic cells both demonstrate homeostatic size control, which maintains population-level variation in cell size within a certain range and returns the population average to that range if it is perturbed [ 1, 2 ]. Recent work has proposed two different strategies for size control: budding yeast has been proposed to use an inhibitor-dilution strategy to regulate size at the G1/S transition [ 3 ], whereas bacteria appear to use an adder strategy, in which a fixed amount of growth each generation causes cell size to converge on a stable average [ 4–6 ]. Here we present evidence that cell size in the fission yeast Schizosaccharomyces pombe is regulated by a third strategy: the size-dependent expression of the mitotic activator Cdc25. cdc25 transcript levels are regulated such that smaller cells express less Cdc25 and larger cells express more Cdc25, creating an increasing concentration of Cdc25 as cells grow and providing a mechanism for cells to trigger cell division when they reach a threshold concentration of Cdc25. Because regulation of mitotic entry by Cdc25 is well conserved, this mechanism may provide a widespread solution to the problem of size control in eukaryotes.
Lemay JF, Marguerat S, Larochelle M, et al., 2016, The Nrd1-like protein Seb1 coordinates cotranscriptional 3′ end processing and polyadenylation site selection, Genes & Development, Vol: 30, Pages: 1558-1572, ISSN: 1549-5477
Termination of RNA polymerase II (RNAPII) transcription is associated with RNA 3′ end formation. For coding genes, termination is initiated by the cleavage/polyadenylation machinery. In contrast, a majority of noncoding transcription events in Saccharomyces cerevisiae does not rely on RNA cleavage for termination but instead terminates via a pathway that requires the Nrd1–Nab3–Sen1 (NNS) complex. Here we show that the Schizosaccharomyces pombe ortholog of Nrd1, Seb1, does not function in NNS-like termination but promotes polyadenylation site selection of coding and noncoding genes. We found that Seb1 associates with 3′ end processing factors, is enriched at the 3′ end of genes, and binds RNA motifs downstream from cleavage sites. Importantly, a deficiency in Seb1 resulted in widespread changes in 3′ untranslated region (UTR) length as a consequence of increased alternative polyadenylation. Given that Seb1 levels affected the recruitment of conserved 3′ end processing factors, our findings indicate that the conserved RNA-binding protein Seb1 cotranscriptionally controls alternative polyadenylation.
Ahrne E, Martinez-Segura A, Syed AP, et al., 2015, Exploiting the multiplexing capabilities of tandem mass tags for high-throughput estimation of cellular protein abundances by mass spectrometry, METHODS, Vol: 85, Pages: 100-107, ISSN: 1046-2023
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Shahrezaei V, Marguerat S, 2015, Connecting growth with gene expression: of noise and numbers., Current Opinion in Microbiology, Vol: 25, Pages: 127-135, ISSN: 1879-0364
Growth is a dynamic process whereby cells accumulate mass. Growth rates of single cells are connected to RNA and protein synthesis rates, and therefore with biomolecule numbers. Noise in gene expression depends on these numbers, and is thus linked with cellular growth. Whether these global attributes of the cell participate in gene regulation is still largely unexplored. New experimental and modelling studies suggest that systemic variations in biomolecule numbers can coordinate cellular processes, including growth itself, through global regulatory feedback that acts in addition to genetic regulatory networks. Here, we review these findings and speculate on possible implications of this less appreciated layer of gene regulation for cellular physiology and adaptation to changing environments.
Bitton DA, Atkinson SR, Rallis C, et al., 2015, Widespread exon skipping triggers degradation by nuclear RNA surveillance in fission yeast., Genome Research, Vol: 25, Pages: 884-896, ISSN: 1549-5469
Exon skipping is considered a principal mechanism by which eukaryotic cells expand their transcriptome and proteome repertoires, creating different splice variants with distinct cellular functions. Here we analyze RNA-seq data from 116 transcriptomes in fission yeast (Schizosaccharomyces pombe), covering multiple physiological conditions as well as transcriptional and RNA processing mutants. We applied brute-force algorithms to detect all possible exon-skipping events, which were widespread but rare compared to normal splicing events. Exon-skipping events increased in cells deficient for the nuclear exosome or the 5'-3' exonuclease Dhp1, and also at late stages of meiotic differentiation when nuclear-exosome transcripts decreased. The pervasive exon-skipping transcripts were stochastic, did not increase in specific physiological conditions, and were mostly present at less than one copy per cell, even in the absence of nuclear RNA surveillance and during late meiosis. These exon-skipping transcripts are therefore unlikely to be functional and may reflect splicing errors that are actively removed by nuclear RNA surveillance. The average splicing rate by exon skipping was ∼0.24% in wild type and ∼1.75% in nuclear exonuclease mutants. We also detected approximately 250 circular RNAs derived from single or multiple exons. These circular RNAs were rare and stochastic, although a few became stabilized during quiescence and in splicing mutants. Using an exhaustive search algorithm, we also uncovered thousands of previously unknown splice sites, indicating pervasive splicing; yet most of these splicing variants were cryptic and increased in nuclear degradation mutants. This study highlights widespread but low frequency alternative or aberrant splicing events that are targeted by nuclear RNA surveillance.
Lovell D, Pawlowsky-Glahn V, Egozcue JJ, et al., 2015, Proportionality: A Valid Alternative to Correlation for Relative Data, Plos Computational Biology, Vol: 11, ISSN: 1553-7358
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