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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.
TL;DRA fibroblast-specific genome-scale model is reconstructed based on the recently published, FAD-curated model, and 53 metabolites that were predicted to discriminate between Healthy and Refsum disease patients are identified, several of which with a link to amino acid metabolism. (Semantic Scholar)
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a
Analytical Biosciences and Metabolomics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
bSystems Medicine of Metabolism and Signalling, Laboratory of Paediatrics, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
cAnalytical Biosciences and Metabolomics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
dSystems Medicine of Metabolism and Signalling, Laboratory of Paediatrics, University of Groningen, University Medical Centre Groningen, The Netherlands
eCentre for Analysis and Synthesis, Department of Chemistry, Lund University, 223 62 Lund, Sweden
fLaboratory Genetic Metabolic Diseases, Department of Clinical Chemistry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
gCentre for Analysis and Synthesis, Department of Chemistry, Lund University, Sweden
hLaboratory Genetic Metabolic Diseases, Department of Clinical Chemistry, Amsterdam UMC, Location AMC, University of Amsterdam, The Netherlands
iDepartment of Laboratory Medicine, University of Groningen, University Medical Center Groningen, The Netherlands
jMass Spectrometric Proteomics and Metabolomics, Institute of Biochemistry, University of Innsbruck
kPharmacokinetics, Toxicology and Targeting, Groningen Research Institute of Pharmacy (GRIP), University of Groningen
lMass Spectrometric Proteomics and Metabolomics, Institute of Biochemistry, University of Innsbruck, Austria
mPharmacokinetics, Toxicology and Targeting, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, The Netherlands
nLaboratory of Paediatrics, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
oLaboratory of Paediatrics, University Medical Centre Groningen, University of Groningen, The Netherlands
pDepartment of Molecular Pharmacology, Groningen Research Institute of Pharmacy, University of Groningen
qDepartment of Molecular Pharmacology, Groningen Research Institute of Pharmacy, University of Groningen, The Netherlands
