Biology subjects

Oliver, S. G.

Publications and source records attributed to Oliver, S. G..

10 recordsLinked to original sources

bioRxiv2026

Understanding the impact of sodium sulfide on the invasive growth of wine yeast

Yeasts ability to invade surfaces has important implications for infections and food contamination. Invasive growth in yeast is influenced by genetic and environmental factors. In this exploratory study, we used a systematic experimental design to identify conditions under which sulfide-induced invasive growth can be reliably observed and quantified and investigated the effects of sulfide, gene deletions, and environmental conditions on the invasive behaviour of the wine yeast strain AWRI 796. Sulfide enhanced invasion in the (parent) AWRI 796 strain under nitrogen-limiting conditions, although its effect was obscured by experimental variability and pre-culture conditions. Genetic factors had a major effect on the overall invasive phenotype, with deletion of key genes suppressing invasion. Most gene-deletion mutants did not significantly affect how the colony responded to sulfide. In addition to sulfide and genotype, environmental conditions also influenced invasive behaviour. The pre-2xSLAD pre-culture condition was best for detecting sulfide-induced growth, and later plate washing time and decreased nutrient levels enhanced invasiveness. Our experimental design and findings provide a framework for understanding the determinants of yeast invasiveness, which may inform future studies on filamentous yeast behaviour.

microbiology↗

bioRxiv2024

Enhancing the accuracy of genome-scale metabolic models with kinetic information

Metabolic models can be used to analyze and predict cellular features such as growth, gene essentiality, and product formation. There are several metabolic models but two of the main types are the constraint-based models and the kinetic models. Constraint-based models usually account for a large subset of the metabolic reactions of the organism and, in addition to the reaction stoichiometry, these models can accommodate gene regulation and constant flux bounds of the reactions. Constraint-based models are mostly limited to the steady state and it is challenging to optimize competing objective functions. On the other hand, kinetic models contain detailed kinetic information of a relatively small subset of metabolic reactions; thus, they can only provide precise predictions of a reduced part of an organisms metabolism. We propose an approach that combines these two types of modeling to enrich metabolic genome-scale constraint-based models by re-defining their flux bounds. We apply our approach to the constraint-based model of E. coli, both as a wild-type and when genetically modified to produce citramalate. We show that the enriched model has more realistic reaction flux boundaries. We also resolve a bifurcation of fluxes between growth and citramalate production present in the genetically modified model by fixing the growth rate to the value computed according to kinetic information, enabling us to predict the rate of citramalate production. IMPORTANCEThe investigation addressed in this manuscript is crucial for biotechnology and metabolic engineering, as it enhances the predictive power of metabolic models, which are essential tools in these disciplines. Constraint-based metabolic models, while comprehensive, are limited by their steady-state assumption and difficulty in optimizing competing objectives, whereas kinetic models, though detailed, only cover a small subset of reactions. By integrating these two approaches, our novel methodology refines flux bounds in genome-scale models, leading to more accurate and realistic metabolic predictions. Key highlights include improved predictive accuracy through more realistic flux boundaries, application to both wild-type and genetically modified E. coli for citramalate production, successful resolution of the bifurcation between growth and product formation, and broad applicability to other organisms and metabolic engineering projects, paving the way for more efficient bioproduction processes.

systems biology↗

bioRxiv2022

Synthetic yeast chromosome XI design enables extrachromosomal circular DNA formation on demand

We describe construction of the 660 kilobase synthetic yeast chromosome XI (synXI) and reveal how synthetic redesign of non-coding DNA elements impact the cell. To aid construction from synthesized 5 to 10 kilobase DNA fragments, we implemented CRISPR-based methods for synthetic crossovers in vivo and used these methods in an extensive process of bug discovery, redesign and chromosome repair, including for the precise removal of 200 kilobases of unexpected repeated sequence. In synXI, the underlying causes of several fitness defects were identified as modifications to non-coding DNA, including defects related to centromere function and mitochondrial activity that were subsequently corrected. As part of synthetic yeast chromosome design, loxPsym sequences for Cre-mediated recombination are inserted between most genes. Using the GAP1 locus from chromosome XI, we show here that targeted insertion of these sites can be used to create extrachromosomal circular DNA on demand, allowing direct study of the effects and propagation of these important molecules. Construction and characterization of synXI has uncovered effects of non-coding and extrachromosomal circular DNA, contributing to better understanding of these elements and informing future synthetic genome design.

synthetic biology↗

bioRxiv2021

Fission stories: Using PomBase to understand Schizosaccharomyces pombe biology

PomBase (www.pombase.org), the model organism database (MOD) for the fission yeast Schizosaccharomyces pombe, supports research within and beyond the S. pombe community by integrating and presenting genetic, molecular, and cell biological knowledge into intuitive displays and comprehensive data collections. With new content, novel query capabilities, and biologist-friendly data summaries and visualisation, PomBase also drives innovation in the MOD community.

bioinformatics↗

bioRxiv2019

A multi-omic analysis of optineurin proteinopathy in a yeast model suggests the involvement of lipid metabolism in Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is an incurable fatal neurodegenerative disease for which the precise mechanisms of toxicity remain unclear despite some significant advances in our understanding of the underlying genetic basis. A holistic, integrated view of cellular changes will be critical to understanding the processes of neurodegeneration and the development of effective treatments. Mutant forms of optineurin (a ubiquitin-binding protein involved in autophagy, membrane trafficking, and NF-{kappa}B activation) are found associated with cytoplasmic inclusions containing TDP43 or SOD1 in some ALS patients. We have taken a multi-omics approach to understand the cellular response to OPTN overexpression in a yeast model of ALS. We found that genetic interaction screens and metabolomics provided parallel, highly complementary data on OPTN toxicity. Genetic enhancers of OPTN toxicity in yeast relate directly to the native function of OPTN in vesicular trafficking and intracellular transport, suggesting the human OPTN protein is functional when expressed in yeast even though there is no yeast ortholog. Crucially, we find that the genetic modifiers and the metabolic response are distinct for different ALS-linked genes expressed in yeast. This lends strong support to the use of yeast as a model system and omics platform to study ALS.

systems biology↗

bioRxiv2019

Comparative analyses of metabolic pathways in mycobacteria: Insights into the identification and characterisation of new drug targets in M. tuberculosis, M. leprae and M. abscessus

Three related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance. Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO; M. abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; and M. leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential to Mycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared by M. tuberculosis, M. leprae and M. abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared between M. tuberculosis and M. abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification. AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

bioinformatics↗

bioRxiv2018

Saccharomyces cerevisiae adapted to grow in the presence of low-dose rapamycin exhibit altered amino acid metabolism

Rapamycin is a potent inhibitor of the highly conserved TOR kinase, the nutrient-sensitive controller of growth and aging. It has been utilised as a chemotherapeutic agent due to its anti-proliferative properties and as an immunosuppressive drug, and is also known to extend lifespan in a range of eukaryotes from yeast to mammals. However, the mechanisms through which eukaryotic cells adapt to sustained exposure to rapamycin have not yet been thoroughly investigated. Here, S. cerevisiae response to long-term rapamycin exposure was investigated by identifying the physiological, transcriptomic and metabolic differences observed for yeast populations inoculated into low-dose rapamycin-containing environment. The effect of oxygen availability and acidity of extracellular environment on this response was further deliberated by controlling or monitoring the dissolved oxygen level and pH of the culture. Yeast populations grown in the presence of rapamycin reached higher cell densities complemented by an increase in their chronological lifespan, and these physiological adaptations were associated with a rewiring of the amino acid metabolism, particularly that of arginine. The ability to synthesise amino acids emerges as the key factor leading to the major mechanistic differences between mammalian and microbial TOR signalling pathways in relation to nutrient recognition. Furthermore, oxygen levels and extracellular acidity of the culture were observed to conjointly affect yeast populations, virtually acting as coupled physiological effectors; cells were best adapted when maximal oxygenation of the culture was maintained in slightly acidic pH, any deviation necessitated more extensive readjustment to additional stress factors.

biochemistry↗

bioRxiv2017

Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

The topological analyses of many large-scale molecular interaction networks often provide only limited insights into network function or evolution. In this paper, we argue that the functional heterogeneity of network components, rather than network size, is the main factor limiting the utility of topological analysis of large cellular networks. We have analysed large epistatic, functional, and transcriptional regulatory networks of genes that were attributed to the following biological process groupings: protein transactions, gene expression, cell cycle, and small molecule metabolism. Control analyses were performed on networks of randomly selected genes. We identified novel biological features emerging from the analysis of functionally homogenous biological networks irrespective of their size. In particular, direct regulation by transcription as an underrepresented feature of protein transactions. The analysis also demonstrated that the regulation of the genes involved in protein transactions at the transcriptional level was orchestrated by only a small number of regulators. Quantitative proteomic analysis of nuclear- and chromatin-enriched sub-cellular fractions of yeast provided supportive evidence for the conclusions generated by network analyses.

systems biology↗

bioRxiv2017

Estimating global enzyme abundance levels from cofactor requirements: a model-based analysis of the iron metabolism in yeast

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. Unfortunately such data are only available on a fraction of the enzyme and transporter pools, and this leaves us distant from generating the full picture. We here propose a strategy to quantify metabolic protein requirements for cofactor-bearing enzymes and transporters through constraint-based modelling. In this work, we constructed the first eukaryotic genome-scale metabolic model with a comprehensive iron metabolism and investigated partial functional impairment of the genes involved in iron-sulphur (Fe-S) cluster maturation, which revealed extensive rewiring of the fluxes in silico, despite only marginal phenotypic dissimilarities. The yeast metabolism at steady state was determined to employ a constant iron-recruiting enzyme turnover at a rate of 3.02 x 10-11 mM / (g biomass)-1 h-1. Furthermore, we employed this model to identify and study a mutualistic relationship between Streptomyces coelicolor and Saccharomyces cerevisiae, which involves iron and folate sharing economies between the two microbes.

systems biology↗

bioRxiv2017

Heterogeneous chromatin mobility derived from chromatin states is a determinant of genome organisation in S. cerevisiae

Spatial organisation of the genome is essential for regulating gene activity, yet the mechanisms that shape this three-dimensional organisation in eukaryotes are far from understood. Here, we combine bioinformatic determination of chromatin states during normal growth and heat shock, and computational polymer modelling of genome structure, with quantitative microscopy and Hi-C to demonstrate that differential mobility of yeast chromosome segments leads to spatial self-organisation of the genome. We observe that more than forty percent of chromatin-associated proteins display a poised and heterogeneous distribution along the chromosome, creating a heteropolymer. This distribution changes upon heat shock in a concerted, state-specific manner. Simulating yeast chromosomes as heteropolymers, in which the mobility of each segment depends on its cumulative protein occupancy, results in functionally relevant structures, which match our experimental data. This thermodynamically driven self-organisation achieves spatial clustering of poised genes and mechanistically contributes to the directed relocalisation of active genes to the nuclear periphery upon heat shock.\n\nOne Sentence SummaryUnequal protein occupancy and chromosome segment mobility drive 3D organisation of the genome.

systems biology↗