Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets
Project description:Myceliophthora thermophila is a thermophilic fungus with great biotechnological characteristics for industrial applications, which can degrade and utilize all major polysaccharides in plant biomass. Nowadays, it has been developing into a platform for production of enzyme, commodity chemicals and biofuels. Therefore, an accurate genome-scale metabolic model would be an accelerator for this fungus becoming a universal chassis for biomanufacturing. Here we present a genome-scale metabolic model for M. thermophila constructed using an auto-generating pipeline with consequent thorough manual curation. Temperature plays a basic and critical role for the microbe growth. we are particularly interested in the genome wide response at metabolic layer of M. thermophilia as it is a thermophlic fungus. To study the effects of temperature on metabolic characteristics of M. thermophila growth, the fungus was cultivated under different temperature. The metabolic rearrangement predicted using context-specific GEMs integrating transcriptome data.The developed model provides new insights into thermophilic fungi metabolism and highlights model-driven strain design to improve biotechnological applications of this thermophilic lignocellulosic fungus.
Project description:The rapid rise in antibiotic-resistance of microbial pathogens has brought the attention to new, heterologous approaches to better exploit the vast repertoire of biosynthetic gene clusters in Actinobacteria genomes and the large number of potentially novel bioactive compounds encoded in these. To enable and optimize production of these compounds, a better understanding of -among others- the interplay between primary and secondary metabolism in the selected suitable heterologous production hosts is needed, in our case the model Streptomycete Streptomyces coelicolor. In this study, a genome-scale metabolic model is reconstructed based on several previous metabolic models and refined by including experimental data, in particular proteome data. This new consensus model provides not only a valuable and more accurate mathematical representation to predict steady-state flux distributions in this strain, but also provides a new framework for interpretation and integration of different 'omics' data by the Streptomyces research community for improved strain-specific systems-scale knowledge to be used in targeted strain development, e.g. for efficient new antibiotics production.
Project description:We investigated the covS-regulation of S. pyogenes in a hypervirulent M23 strain using RNA-sequencing. The differential gene expression comparison between the covS- mutant and isogenic wild type covS+ identified altered expression of 349 (18%) genes, including a broad spectrum of virulence genes and diverse metabolic genes. The data showed that the strain achieved hypervirulence by enhancing the expression of genes responsible for invasiveness and antiphagocytosis (i.e., hasABC), by abrogating the expression of toxic genes (i.e., speB), and by compromising gene products with dispensable functions (i.e., sfb1). Meanwhile, we found that covS also regulated diverse kinds of metabolic genes that maximized nutrient utilization and energy metabolism during growth and dissemination. From constructing a genome-scale metabolic model, we identified fourteen non-redundant metabolic gene modules, which constitute unique sources for specific nutrients. These genes were probably essential for pathogen growth and virulence. In general, this study provided quantitative transcriptomic signatures of the covS regulation in a hypervirulent S. pyogenes strain and addressed major aspects of the covS-modulated virulent mechanisms.
Project description:We investigated the covS-regulation of S. pyogenes in a hypervirulent M23 strain using RNA-sequencing. The differential gene expression comparison between the covS- mutant and isogenic wild type covS+ identified altered expression of 349 (18%) genes, including a broad spectrum of virulence genes and diverse metabolic genes. The data showed that the strain achieved hypervirulence by enhancing the expression of genes responsible for invasiveness and antiphagocytosis (i.e., hasABC), by abrogating the expression of toxic genes (i.e., speB), and by compromising gene products with dispensable functions (i.e., sfb1). Meanwhile, we found that covS also regulated diverse kinds of metabolic genes that maximized nutrient utilization and energy metabolism during growth and dissemination. From constructing a genome-scale metabolic model, we identified fourteen non-redundant metabolic gene modules, which constitute unique sources for specific nutrients. These genes were probably essential for pathogen growth and virulence. In general, this study provided quantitative transcriptomic signatures of the covS regulation in a hypervirulent S. pyogenes strain and addressed major aspects of the covS-modulated virulent mechanisms. Examination of expression profiling of covS- mutant strain and isogenic wild type covS+ at different growth stages.
Project description:Gas fermentation is emerging as an economically attractive option for the sustainable production of fuels and chemicals from gaseous waste feedstocks. Clostridium autoethanogenum can use CO and/or CO2 + H2 as its sole carbon and energy sources. Fermentation of C. autoethanogenum is currently being deployed on a commercial scale for ethanol production. Expanding the product spectrum of acetogens will enhance the economics of gas fermentation. To achieve efficient heterologous product synthesis, limitations in redox and energy metabolism must be overcome. Here, we engineered and characterised at a systems-level, a recombinant poly-3-hydroxybutyrate (PHB)-producing strain of C. autoethanogenum. Cells were grown in CO-limited steady-state chemostats on two gas mixtures, one resembling syngas (20% H2) and the other steel mill off-gas (2% H2). Results were characterised using metabolomics and transcriptomics, and then integrated using a genome-scale metabolic model reconstruction. PHB-producing cells had an increased expression of the Rnf complex, suggesting energy limitations for heterologous production. Subsequent optimisation of the bioprocess led to a 12-fold increase in the cellular PHB content. The data suggest that the cellular redox state, rather than the acetyl-CoA pool, was limiting PHB production. Integration of the data into the genome-scale metabolic model showed that ATP availability limits PHB production. Altogether, the data presented here advances the fundamental understanding of heterologous product synthesis in gas-fermenting acetogens.
Project description:In this study, we reconstructed a fibroblast-specific genome-scale model based on the recently published, FAD-curated model, based on Recon3D reconstruction. To constrain the model we used transcriptomics, and proteomics data, which we obtained from healthy controls and Refsum disease patient fibroblasts incubated with phytol, a precursor of phytanic acid. Using this model, we investigated the metabolic phenotype of Refsum disease at the genome-scale, and we studied the effect of phytanic acid on cell metabolism. We identified 20 metabolites that were predicted to discriminate between Healthy and Refsum disease patients, several of which with a link to amino acid metabolism.
Project description:We have integrated nucleotide resolution genome-scale measurements of the transcriptome and translatome of the Streptomyces coelicolor A3(2), the model antibiotic-producing actinomycete. Our systematic study determined 3,473 transcription start sites, leading to discovery of a high proportion (~21%) of leaderless mRNAs and 230 non-coding RNAs; this enabled deduction of promoter architecture on a genome-scale. Ribosome profiling analysis revealed that the translation efficiency was negatively correlated for secondary metabolic genes. These results provide novel fundamental insights into translational regulation of secondary metabolism that enables rational synthetic biology approaches to awaken such ‘silent’ secondary metabolic pathways.