329 research outputs found

    Bottleneck genes and community structure in the cell cycle network of S. pombe.

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    The identification of cell cycle-related genes is still a difficult task, even for organisms with relatively few genes such as the fission yeast. Several gene expression studies have been published on S. pombe showing similarities but also discrepancies in their results. We introduce a network in which the weight of each link is a function of the phase difference between the expression peaks of two genes. The analysis of the stability of the clustering through the computation of an entropy parameter reveals a structure made of four clusters, the first one corresponding to a robustly connected M-G1 component, the second to genes in the S phase, and the third and fourth to two G2 components. They are separated by bottleneck structures that appear to correspond to cell cycle checkpoints. We identify a number of genes that are located on these bottlenecks. They represent a novel group of cell cycle regulatory genes. They all show interesting functions, and they are supposed to be involved in the regulation of the transition from one phase to the next. We therefore present a comparison of the available studies on the fission yeast cell cycle and a general statistical bioinformatics methodology to find bottlenecks and gene community structures based on recent developments in network theory

    On Computable Protein Functions

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    Proteins are biological machines that perform the majority of functions necessary for life. Nature has evolved many different proteins, each of which perform a subset of an organism’s functional repertoire. One aim of biology is to solve the sparse high dimensional problem of annotating all proteins with their true functions. Experimental characterisation remains the gold standard for assigning function, but is a major bottleneck due to resource scarcity. In this thesis, we develop a variety of computational methods to predict protein function, reduce the functional search space for proteins, and guide the design of experimental studies. Our methods take two distinct approaches: protein-centric methods that predict the functions of a given protein, and function-centric methods that predict which proteins perform a given function. We applied our methods to help solve a number of open problems in biology. First, we identified new proteins involved in the progression of Alzheimer’s disease using proteomics data of brains from a fly model of the disease. Second, we predicted novel plastic hydrolase enzymes in a large data set of 1.1 billion protein sequences from metagenomes. Finally, we optimised a neural network method that extracts a small number of informative features from protein networks, which we used to predict functions of fission yeast proteins

    Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast

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    Background: Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction. Results: Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes. Conclusion: A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.Statistic

    Exploring long-term determinants of chronological lifespan using system-wide approaches

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    Ageing is a great research challenge. Age is the primary risk factor for many complex diseases, including cardiovascular disease, neurodegeneration and cancer. Anti-ageing interventions aim to delay the onset of these diseases and extend health span. Ageing remains enigmatic, however, and its proximal cause and mechanisms are not understood. This partly reflects the laborious nature of ageing experiments, typically requiring large timeframes and numerous individuals, which creates a bottleneck for systematic ageing studies. Yeast can be grown under highly parallelised experimental platforms and are well suited to systematic studies. However, ageing research is a notable exception, with the traditional colony-forming unit (CFU) assay for chronological lifespan being notoriously time- and resource-consuming. I present two alternative assays which circumnavigate this bottleneck. One is a high throughput CFU assay that is automated by robotics and supported by an R package to estimate culture viability by constructing a statistical model based on colony patterns. The second assay employs barcode sequencing to monitor strain viability in competitively ageing pools of deletion libraries, providing genome-scale functional insights into the genetics of lifespan. I employ this assay to dissect the genetic basis of rapamycin-mediated longevity, providing insights into the condition-specific nature of lifespan-extending mutations and the anti-ageing action of rapamycin. Experimental reproducibility is essential for research. Ageing studies, including those in yeast, are notably sensitive to batch effects: genetically identical cells grown under identical conditions can exhibit substantial phenotypic differences. I systematically test typically neglected factors, and demonstrate that chronological lifespan is strongly affected by pre-culture protocol such as the amount of colony picked for the pre-culture – suggesting a ‘memory’ which is passed across cell divisions from pre-culture to non-dividing, ageing cells. Hence, this work addresses key issues in yeast ageing research, both technological and biological, establishing a platform to robustly perform future studies at large scales

    Epigenetic inheritance of a phenotypically plastic epimutation

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    Organisms constantly have to adapt to changing environments in order to survive, thrive and successfully multiply. Phenotypic changes can be acquired by alterations of the deoxyribonucleic acid (DNA)sequence. If beneficial under natural selection, the DNA variation can become fixed permanently in a population and thereby drive its evolution. In addition to DNA sequence changes, a concept emerged that a soft, reversible layer could also potentially contribute to heritable adaptation. Epigenetic changes were shown to affect the development and complex phenotypic traits of almost isogenic organisms. Such changes can be inherited over many generations by strong self-reinforcing feedback loops without the initial trigger. Evidence for such a ‘soft’ inheritance is only just emerging and whether such phenomena are of physiological relevance in heritable adaptation though remains to be unraveled. Gene expression is regulated through several mechanisms. DNA does not exist as bare molecule, but is packaged into a highly complex structure called chromatin. Besides serving structural functions, chromatin also impacts gene expression. Chromatin can be broadly divided into transcriptionally active, gene-rich euchromatin and gene-poor, condensed heterochromatin, which serves as repressive structure for repetitive elements, such as transposons, and makes up most of the euchromatic genome. In some organisms, nuclear small ribonucleic acid (RNA) pathways are essential to initiate and maintain constitutive heterochromatin. The centerpiece of such pathways is a small RNA-bound Argonaute protein, which binds by complementary base-pairing to nascent transcripts and subsequently recruits effector complexes that mediate silencing. Given the appropriate small RNA, this pathway can theoretically target any expressed locus, thereby making it a versatile silencing strategy. In nematodes, small RNAs were shown to induce stable silencing of some protein coding genes that can be epigenetically maintained over tens of generations. During my PhD, I studied RNA interference (RNAi)-mediated epigenetic phenomena in the fission yeast Schizosaccharomyces pombe (S. pombe). In S. pombe, RNAi-mediated silencing is under strong negative control and can only be initiated in the presence of an enabling mutation, such as in genes encoding subunits of the RNA polymerase-associated factor 1 complex (Paf1C). On one hand, such mutations can have a detrimental effect on viability. On the other hand, the silencing phenotype observed in Paf1C mutants cannot be inherited to wild-type cells, suggesting that also all marks of the silencing event were erased. If RNAi-mediated epigenetic phenomena also exist in wild-type cells was not known. My main achievement during PhD was to discover that wild-type S. pombe cells remember a parental silencing event through acquiring a phenotypically neutral epimutation. I could show that such epimutation does not cause gene silencing when inherited by wild type cells. Yet, upon repeated mutation of Paf1C, the silencing phenotype was reinstated in subsequent generations. I could further show that the phenotypically neutral epimutation entails high levels of small interfering RNA (siRNA) and histone 3 lysine 9 tri-methylation (H3K9me3), and that its transgenerational inheritance depends on RNAi and H3K9 methylation. This finding is astounding, because H3K9me3 has commonly been associated with gene repression. That we have not observed silencing, despite high enrichments of this mark, was therefore highly unexpected. Based on my findings, I conclude that H3K9me3 is not repressive per se, but rather functions as stable epigenetic mark that can retain information of a previous gene-silencing event. Upon deposition of H3K9me3, the silencing phenotype is dependent on the modulation of Paf1C function. The discovery of this distinct form of epigenetic memory lets me speculate that it may have evolved to allow population adaptation to dynamic environments

    Broad functional profiling of fission yeast proteins using phenomics and machine learning

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    Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through ‘guilt by association’ with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions

    High-Throughput Chronological Lifespan Screening of the Fission Yeast Deletion Library Using Barcode Sequencing

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    Ageing is associated with the development of several chronic illnesses, including cardiovascular diseases, diabetes and cancer. To understand the genetic components driving cellular ageing in higher organisms, like ourselves, we study simple eukaryotic model systems which are more accessible and easier to manipulate than higher eukaryotes. This is possible due to the remarkably conserved ageing mechanisms that occurs between species. Here, we employ fission yeast one of the simplest eukaryotic model organisms to study cellular ageing. In this work, we de- coded the fission yeast deletion collection using our in-house developed pipeline, developed an improved version of Bar-seq along with a custom-developed analysis pipeline, determined a method for high-quality RNA extraction and RNA-seq from long-term quiescent yeast cells, and finally, performed a high-throughput Bar-seq screen to profile the chronological lifespan of our decoded strains. We describe bar- code decoding of 94% of the gene deletions; validation of our Bar-seq developed method; identification of ncRNAs as elements important for the cellular quiescence maintenance; Bar-seq screening of the competitively grown decoded strains which identified several long-lived and short-lived mutants following glucose-starvation and cellular culture re-growth; and also, validation of the top hits using isogenic cell cultures revealing eight novel gene deletions important for the early life maintenance, as well as ten novel gene deletion mutants with pro-ageing effects. Overall, in addition to providing rich datasets, we describe several high-throughput methods that can be used for future genome-wide studies, whereby the complementarity of genomics and transcriptomics can be coupled together to further advance our understanding of the genetic factors underpinning cellular ageing in humans

    Improving functional annotation for industrial microbes: a case study with Pichia pastoris.

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    The research communities studying microbial model organisms, such as Escherichia coli or Saccharomyces cerevisiae, are well served by model organism databases that have extensive functional annotation. However, this is not true of many industrial microbes that are used widely in biotechnology. In this Opinion piece, we use Pichia (Komagataella) pastoris to illustrate the limitations of the available annotation. We consider the resources that can be implemented in the short term both to improve Gene Ontology (GO) annotation coverage based on annotation transfer, and to establish curation pipelines for the literature corpus of this organism.We gratefully acknowledge funding from the Wellcome Trust (PomBase and Canto; WT090548MA to SGO), and the EU 7th Framework Programme (BIOLEDGE Contract No: 289126 to SGO).This is the published version distributed under a Creative Commons Attribution License 2.0, which can also be found on the publisher's website at: http://www.sciencedirect.com/science/article/pii/S0167779914001061

    Non-systemic transmission of tick-borne diseases: a network approach

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    Tick-Borne diseases can be transmitted via non-systemic (NS) transmission. This occurs when tick gets the infection by co-feeding with infected ticks on the same host resulting in a direct pathogen transmission between the vectors, without infecting the host. This transmission is peculiar, as it does not require any systemic infection of the host. The NS transmission is the main efficient transmission for the persistence of the Tick-Borne Encephalitis virus in nature. By describing the heterogeneous ticks aggregation on hosts through a \hyphenation{dynamical} bipartite graphs representation, we are able to mathematically define the NS transmission and to depict the epidemiological conditions for the pathogen persistence. Despite the fact that the underlying network is largely fragmented, analytical and computational results show that the larger is the variability of the aggregation, and the easier is for the pathogen to persist in the population.Comment: 15 pages, 4 figures, to be published in Communications in Nonlinear Science and Numerical Simulatio
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