ArticleThe FEBS journal2020
Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease.
Article in The FEBS journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 13 citations in OpenAlex.
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- Metabolomics Effects of Folding Correction in Retinitis Pigmentosa Rhodopsin Mutant P23A.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Pathological extracellular matrix changes in decellularized normal appearing gray matter and subpial multiple sclerosis lesions.iScience · 2026Article
- FGF1 ameliorates hepatic steatosis through acute activation of the unfolded protein response and VLDL production.JHEP reports : innovation in hepatology · 2026Article
- Personalized Genome-Scale Modeling Reveals Metabolic Perturbations in Fibroblasts of Methylmalonic Aciduria Patients.Journal of inherited metabolic disease · 2025Article
- Article
- Review
- Metabolic Profiling in Human Fibroblasts Enables Subtype Clustering in Glycogen Storage Disease.Frontiers in endocrinology · 2020Article
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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