Genome-scale metabolic network reconstructions of diverse Escherichia strains reveal strain-specific adaptations

Bottom-up approaches to systems biology rely on constructing a mechanistic basis for the biochemical and genetic processes that underlie cellular functions. Genome-scale network reconstructions of metabolism are built from all known metabolic reactions and metabolic genes in a target organism. A network reconstruction can be converted into a mathematical format and thus lend itself to mathematical analysis. Genome-scale models (GEMs) of metabolism enable a systems approach to characterize the pan and core metabolic capabilities of the Escherichia genus. In this work, GEMs were constructed for 222 representative strains of Escherichia across HC1100 levels spanning the known Escherichia phylogeny. The models were used to study Escherichia metabolic diversity and speciation on a large scale. The results show that unique strain-specific metabolic capabilities correspond to different species and nutrient niches. This work is a first step towards a curated reconstruction of pan-Escherichia metabolism.

This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’.

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

Refsum disease (RD) is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. The disorder presents with late‐onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanate in plasma and tissues of patients. To date, no cure exists for RD, but phytanate levels in patients can be reduced by plasmapheresis and a strict diet. 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 (available via GEO database with identifier GSE138379), metabolomics and proteomics (available via ProteomeXchange with identifier PXD015518) data, which we obtained from healthy controls and RD patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanate and displays fibroblast‐specific metabolic functions. Using this model, we investigated the metabolic phenotype of RD at the genome scale, and we studied the effect of phytanate on cell metabolism. We identified 53 metabolites that were predicted to discriminate between healthy and RD patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.

Databases

Transcriptomics data are available via GEO database with identifier GSE138379, and proteomics data are available via ProteomeXchange with identifier PXD015518.