Project description:Genome-scale metabolic model of Rickettsia helvetica generated with the CarveMe reconstruction tool with accompanying MEMOTE and FROG analysis reports.
Project description:The kidneys are metabolically active organs that are important for several physiological tasks such as the secretion of soluble wastes into urine. Other functions of the kidneys include synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to five compounds with varying levels of nephrotoxicity (acetaminophen, carbon tetrachloride, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene for six and twenty-four hours), and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. We observed changes in fatty acid metabolism and amino acid metabolism, consistent with changes in existing markers of kidney function such as Lcn2 (lipocalin), Clu (clusterin), and the carnitine transporter Octn2 (solute carrier family 22, member 5). The iRno metabolic network reconstruction was able to predict alterations in these same pathways after integrating transcriptomics data, and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism to provide a step towards identifying novel biomarkers of kidney function and dysfunction.
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: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:This genome-scale metabolic model (GEM) of Corynebacterium tuberculostearicum strain DSM 44922 (Taxon ID 38304) was initially built with CarveMe version 1.5.1 based on the genome assembly with NCBI accession GCF_013408445.1 and then underwent a series of careful semi-automatic and manual curation. It is the first model curated using the Python tool MCC for mass and charge curation.
Project description:By integrating sequence information from closely related bacteria with a compendium of high-throughput gene expression datasets, a large-scale transcriptional regulatory networks was constructed for Rhodobacter sphaeroides. Predictions from this network were validated in part using genome-wide analysis for 3 transcription factors (PpsR, RSP_0489 and RSP_3341). Genome-wide protein-DNA interaction analysis of 3 transcription factors predicted to be involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341) were used to validate predictions from a large-scale reconstruction of R. sphaeroides transcriptional regulatory network.
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:Rhodobacter sphaeroides is the best studied photosynthetic bacterium, yet much remains unknown about its transcriptional regulatory processes on a genome-scale. We developed a work-flow for genome-scale reconstruction of transcriptional regulatory networks and applied it to sequence and gene expression data sets available for R. sphaeroides. To assess the predictive performance of our reconstructed model, we generated global transcript level and/or protein-DNA interaction data for 3 transcription factors (PpsR, RSP_0489 and RSP_3341). This dataset contains global transcript level analyses for RSP_0489 and RSP_3341 deletion strains, as well as matching wild type controls.
Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets