Shaken not stirred: a global research cocktail served in Hinxton

A report of the 2007 Cold Spring Harbor Laboratory/Wellcome Trust Conference on Functional Genomics and Systems Biology, Hinxton, UK, 10-13 October 2007

Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Motivation: Gene expression is characterized 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 are 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 nonallele-specific scRNA-seq data. // Availability and implementation: 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

Differential patterns of intronic and exonic DNA regions with respect to RNA polymerase II occupancy, nucleosome density and H3K36me3 marking in fission yeast

BACKGROUND: The generation of mature mRNAs involves interconnected processes, including transcription by RNA polymerase II (Pol II), modification of histones, and processing of pre-mRNAs through capping, intron splicing, and polyadenylation. These processes are thought to be integrated, both spatially and temporally, but it is unclear how these connections manifest at a global level with respect to chromatin patterns and transcription kinetics. We sought to clarify the relationships between chromatin, transcription and splicing using multiple genome-wide approaches in fission yeast. RESULTS: To investigate these functional interdependencies, we determined Pol II occupancy across all genes using high-density tiling arrays. We also performed ChIP-chip on the same array platform to globally map histone H3 and its H3K36me3 modification, complemented by formaldehyde-assisted isolation of regulatory elements (FAIRE). Surprisingly, Pol II occupancy was higher in introns than in exons, and this difference was inversely correlated with gene expression levels at a global level. Moreover, introns showed distinct distributions of histone H3, H3K36me3 and FAIRE signals, similar to those at promoters and terminators. These distinct transcription and chromatin patterns of intronic regions were most pronounced in poorly expressed genes. CONCLUSIONS: Our findings suggest that Pol II accumulates at the 3 ends of introns, leading to substantial transcriptional delays in weakly transcribed genes. We propose that the global relationship between transcription, chromatin remodeling, and splicing may reflect differences in local nuclear environments, with highly expressed genes being associated with abundant processing factors that promote effective intron splicing and transcriptional elongation

Growth-rate-dependent and nutrient-specific gene expression resource allocation in fission yeast

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 determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on 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 from RNA 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, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression

Proportionality: a valid alternative to correlation for relative data

In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative-or compositional-data, differential expression needs careful interpretation, and correlation-a statistical workhorse for analyzing pairwise relationships-is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic. which can be used instead of correlation as the basis of familiar analyses and visualisation methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.Peer ReviewedPostprint (published version

Size-Dependent Expression of the Mitotic Activator Cdc25 as a Mechanism of Size Control in Fission Yeast [preprint]

Proper cell size is essential for cellular function (Hall et al., 2004). Nonetheless, despite more than 100 years of work on the subject, the mechanisms that maintain cell size homeostasis are largely mysterious (Marshall et al., 2012). 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 (Marshall et al., 2012; Turner et al., 2012; Amodeo and Skotheim, 2015). 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 (Schmoller et al., 2015), while 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, a mechanism also suggested for budding yeast (Campos et al., 2014; Jun and Taheri-Araghi, 2015; Taheri-Araghi et al., 2015; Tanouchi et al., 2015; Soifer et al., 2016). 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. The 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 cell grow and providing a mechanism for cell to trigger cell division when they reach a threshold concentration of Cdc25. Since regulation of mitotic entry by Cdc25 is well conserved, this mechanism may provide a wide spread solution to the problem of size control in eukaryotes

Genetic effects on molecular network states explain complex traits

The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes

Data-driven spatio-temporal modelling of glioblastoma

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 summarise 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. Finally, 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.Comment: 30 pages, 3 figures, 3 table

Transcriptional activation by bidirectional RNA polymerase II elongation over a silent promoter

Transcriptional interference denotes negative cis effects between promoters. Here, we show that promoters can also interact positively. Bidirectional RNA polymerase II (Pol II) elongation over the silent human endogenous retrovirus ( HERV)-K18 promoter ( representative of 2.5 +/- 10(3) similar promoters genome-wide) activates transcription. In tandem constructs, an upstream promoter activates HERV-K18 transcription. This is abolished by inversion of the upstream promoter, or by insertion of a poly( A) signal between the promoters; transcription is restored by poly( A) signal mutants. TATA-box mutants in the upstream promoter reduce HERV-K18 transcription. Experiments with the same promoters in a convergent orientation produce similar effects. A small promoter deletion partially restores HERV-K18 activity, consistent with activation resulting from repressor repulsion by the elongating Pol II. Transcriptional elongation over this class of intragenic promoters will generate co-regulated sense - antisense transcripts, or, alternatively initiating transcripts, thus expanding the diversity and complexity of the human transcriptome

Long noncoding RNA repertoire and targeting by nuclear exosome, cytoplasmic exonuclease, and RNAi in fission yeast.

Long noncoding RNAs (lncRNAs), which are longer than 200 nucleotides but often unstable, contribute a substantial and diverse portion to pervasive noncoding transcriptomes. Most lncRNAs are poorly annotated and understood, although several play important roles in gene regulation and diseases. Here we systematically uncover and analyze 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× 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