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: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:In this study, we developed a workflow to systematically and selectively induce increases in metabolites by knocking down enzymes with CRISPR interference (CRISPRi). Therefore, we created a sorted CRISPRi library targeting all 1,515 metabolic genes in the most recent genome-scale metabolic model of E. coli (iML151515). In a first step, we screened the metabolome of the CRISPRi library with a fast flow-injection mass spectrometry, which revealed strong and specific accumulation of 36% of the predicted metabolites in the iML1515 model. The accumulating metabolites were unique to certain knockdowns, especially those metabolites associated with the CRISPRi targeted-pathway.
Project description:Lactobacillus reuteri is a heterofermentative lactic acid bacterium best known for its ability to co-ferment glucose and glycerol. Its genome sequence has recently been deduced enabling the implementation of genome-wide analysis. In this study we developed a dedicated cDNA microarray platform and a genome-scale metabolic network model of L. reuteri and use them to revisit the co-fermentation of glucose and glycerol. The model was used to simulate experimental conditions and to visualize and integrate experimental data in particular the global transcriptional response of L. reuteri to the presence of glycerol. We show how the presence of glycerol affects cell physiology and triggers specific regulatory mechanisms allowing simultaneously a better yield and more efficient biomass formation. Furthermore we were able to predict and demonstrate for this well-studied condition the involvement of previously unsuspected metabolic pathways for instance related to amino acids and vitamins. These could be used as leads in future studies aiming at the increased production of industrially relevant compounds such as vitamin B12 or 1 3- propanediol. Keywords: cell type comparison Dye swap
Project description:Lactobacillus reuteri is a heterofermentative lactic acid bacterium best known for its ability to co-ferment glucose and glycerol. Its genome sequence has recently been deduced enabling the implementation of genome-wide analysis. In this study we developed a dedicated cDNA microarray platform and a genome-scale metabolic network model of L. reuteri and use them to revisit the co-fermentation of glucose and glycerol. The model was used to simulate experimental conditions and to visualize and integrate experimental data in particular the global transcriptional response of L. reuteri to the presence of glycerol. We show how the presence of glycerol affects cell physiology and triggers specific regulatory mechanisms allowing simultaneously a better yield and more efficient biomass formation. Furthermore we were able to predict and demonstrate for this well-studied condition the involvement of previously unsuspected metabolic pathways for instance related to amino acids and vitamins. These could be used as leads in future studies aiming at the increased production of industrially relevant compounds such as vitamin B12 or 1 3- propanediol. Keywords: cell type comparison Loop design
Project description:Refsum disease is an inborn error of metabolism that is characterized by a defect in peroxisomal α-oxidation of the branched-chain fatty acid phytanic acid. After clinical suspicion of this disorder, including progressive retinitis pigmentosa and polyneuropathy, Refsum disease is biochemically diagnosed by elevated levels of phytanic acid in plasma and tissues of the patient. To date, no cure exists for Refsum disease, but phytanic acid levels in patients can be reduced by plasmapheresis and a strict diet. In recent years, computational models have become valuable tools to provide insight into the complex behaviour of metabolic networks. Besides the comprehensive models that contain all known metabolic reactions within the human body, several tissue- and cell-type-specific models have been developed. However, while systems biology approaches are widely used for complex diseases, only few studies have been published for inborn errors of metabolism. In this study, we reconstructed a fibroblast-specific genome-scale model based on the recently published, FAD-curated model, based on Recon3D reconstruction. We used transcriptomics, exo-metabolomics, and proteomics data, which we obtained from healthy controls and Refsum disease patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanic acid and displays fibroblast-specific metabolic functions. 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’s Disease patients, whereof several with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.
Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions.