Biology subjects

Marguerat, S. B.

Publications and source records attributed to Marguerat, S. B..

3 recordsLinked to original sources

bioRxiv2021

Functional profiling of long intergenic non-coding RNAs in fission yeast

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.

genetics↗

bioRxiv2019

Size-dependent increase in RNA Polymerase II initiation rates mediates gene expression scaling with cell size

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.

molecular biology↗

bioRxiv2018

bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

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 methods 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.

bioinformatics↗