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Fibroblast-Specific Genome-Scale Modelling Predicts an Imbalance in Amino Acid Metabolism in Refsum Disease

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Fibroblast-Specific Genome-Scale Modelling Predicts an Imbalance in Amino Acid Metabolism in Refsum Disease bioRxiv preprint doi: https://doi.org/10.1101/776575; this version posted October 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease Agnieszka B. Wegrzyn1,2,$, Katharina Herzog3,4,$, Albert Gerding1, Marcel Kwiatkowski5,6, Justina C. Wolters7, Amalia M. Dolga8, Alida E. M. van Lint3, Ronald J. A. Wanders3, Hans R. Waterham3, Barbara M. Bakker1,# 1 Systems Medicine of Metabolism and Signalling, Laboratory of Paediatrics, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands 2 Analytical Biosciences and Metabolomics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands 3 Laboratory Genetic Metabolic Diseases, Department of Clinical Chemistry, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands 4 Centre for Analysis and Synthesis, Department of Chemistry, Lund University, 223 62 Lund, Sweden 5 Pharmacokinetics, Toxicology and Targeting, Groningen Research Institute of Pharmacy (GRIP), University of Groningen 6 Mass Spectrometric Proteomics and Metabolomics, Institute of Biochemistry, University of Innsbruck 7 Laboratory of Paediatrics, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands 8 Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy, University of Groningen $ Shared first authors # Corresponding author Running title: Changes in amino acid metabolism in Refsum disease Databases: Transcriptomics - available via GEO database with identifier GSE138379, Proteomics - available via ProteomeXchange with identifier PXD015518 Keywords: Refsum disease, genome-scale modelling, fibroblast, metabolism, amino acids Conflict of Interest Statement: Authors declare no conflict of interests regarding the contents of this manuscript. Corresponding author: Prof. dr. Barbara M. Bakker, Systems Medicine of Metabolism and Signalling, Laboratory of Pediatrics, University Medical Center Groningen, University of Groningen, Postbus 196, NL- 9700 AD Groningen, The Netherlands, Tel: +31-50-3611542, E-mail: [email protected] recently published, FAD-curated model, based ABSTRACT on Recon3D reconstruction. We used transcriptomics (available via GEO database with Refsum disease is an inborn error of metabolism identifier GSE138379), metabolomics, and that is characterised by a defect in peroxisomal proteomics data (available via ProteomeXchange α-oxidation of the branched-chain fatty acid with identifier PXD015518), which we obtained phytanic acid. The disorder presents with late- from healthy controls and Refsum disease patient onset progressive retinitis pigmentosa and fibroblasts incubated with phytol, a precursor of polyneuropathy and can be diagnosed phytanic acid. biochemically by elevated levels of phytanic acid Our model correctly represents the metabolism in plasma and tissues of patients. To date, no of phytanic acid and displays fibroblast-specific cure exists for Refsum disease, but phytanic acid metabolic functions. Using this model, we levels in patients can be reduced by investigated the metabolic phenotype of Refsum plasmapheresis and a strict diet. disease at the genome-scale, and we studied the In this study, we reconstructed a fibroblast- effect of phytanic acid on cell metabolism. We specific genome-scale model based on the identified 53 metabolites that were predicted to 1 bioRxiv preprint doi: https://doi.org/10.1101/776575; this version posted October 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. discriminate between Healthy and Refsum metabolic networks. One type of computational disease patients, several of which with a link to models is genome-scale models of metabolism, amino acid metabolism. Ultimately, these which contain all currently known stoichiometric insights in metabolic changes may provide leads information of metabolic reactions, together with for pathophysiology and therapy. enzyme and pathway annotation [7]. These models can further be constrained and validated by incorporation of different types of data, 1. Introduction including mRNA and metabolite profiles, as well as biochemical and phenotypic information [8]. Peroxisomes are organelles that, among other To date, the most comprehensive human models functions, are crucial for cellular lipid are Recon 3D [9] and HMR 2.0 [10], which are metabolism. They perform both anabolic and consensus metabolic reconstructions that were catabolic processes, including the α- and β- built to describe all known metabolic reactions oxidation of very-long-chain fatty acids, within the human body. Besides, a few tissue- dicarboxylic acids, and methyl-branched-chain and cell-type-specific models have been fatty acids [1]. Furthermore, peroxisomes are developed by incorporating tissue- or cell- involved in the biosynthesis of ether specific transcriptomics and proteomics data. phospholipids, including plasmalogens, bile These models can be used to predict possible acids, and essential polyunsaturated fatty acids ranges of metabolic fluxes for all enzymes in the such as docosahexaenoic acid [2]. network. Flux ranges in diseased and control models can be compared to discover functional Refsum disease is an inborn error of metabolism changes in the metabolic network. These may be (IEM) that is caused by biallelic mutations in the used as biomarkers or give insight into the gene encoding phytanoyl-CoA 2-hydroxylase biochemical origin of disease symptoms [11–15]. (PHYH), resulting in defective α-oxidation of the branched-chain fatty acid phytanic acid In the last decade, a paradigm shift occurred in (3,7,11,15-tetramethylhexadecanoic acid) [3]. the field of IEMs. Today, IEMs are no longer Phytanic acid contains a 3-methyl group and is viewed according to the “one gene, one disease” therefore not a substrate for peroxisomal β- paradigm as proposed more than 100 years ago, oxidation. Consequently, phytanic acid first but recognised to be complex diseases [16]. needs to undergo α-oxidation, thereby producing However, only few studies using systems biology pristanic acid, which then can be further and multi-omics approaches that are widely used degraded by β-oxidation [2]. An alternative for complex diseases have been published for metabolic pathway for the breakdown of IEMs [8,9,14,17–22]. phytanic acid is ω-oxidation, which takes place in the endoplasmic reticulum [4]. The end In this study, we aim to investigate the metabolic product of ω-oxidation of phytanic acid is 3- phenotype of Refsum disease at the genome- methyladipic acid (3-MAA), and ω-oxidation has scale, and to study the effect of phytanic acid on been described to be upregulated in patients with cell metabolism. Cultured fibroblasts contain Refsum disease [5]. Refsum disease was first most metabolic functions present in the human described in 1945 and is clinically characterised body, and biochemical and functional studies in by progressive retinitis pigmentosa, cultured skin fibroblasts are important tools for polyneuropathy, cerebellar ataxia, and deafness the diagnosis of patients with a peroxisomal [5]. Biochemically, Refsum disease is diagnosed disorder [23]. Therefore, we reconstructed a by elevated levels of phytanic acid in plasma and fibroblast-specific genome-scale model based on tissues. Phytanic acid solely derives from the fibroblast specific transcriptomics, diet, and patients with Refsum disease are mostly metabolomics, and proteomics data, and starting diagnosed in late childhood [3,5]. To date, from the recently published Recon3D-based patient management focuses on the reduction of model. We obtained these data from healthy phytanic acid levels by plasmapheresis and a controls and Refsum disease patient fibroblasts strict diet to reduce the intake of dairy-derived incubated with phytol, a precursor of phytanic fat [6]. acid. Since flavoproteins play a crucial role in lipid metabolism, we integrated our recently Recently, computational models have become curated set of FAD-related reactions [20]. The valuable tools to study the complex behaviour of resulting model reflects the in vivo situation in 2 bioRxiv preprint doi: https://doi.org/10.1101/776575; this version posted October 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. fibroblasts and demonstrates the physiological harvested cells after 96 h to isolate RNA and effects of a defective α-oxidation. Ultimately, protein. The cells were either incubated with such insights in metabolic changes may provide phytol, a precursor of phytanic acid, or with the leads for pathophysiology and therapy. solvent ethanol (Fig. 1B). Our primary dataset consisted of the data obtained from transcriptomics (RNAseq) and proteomics (shot- 2. Results gun) measurements. In the principal component analysis (PCA), no separation was seen between Model curation and
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