bioRxivCell BiologyPreprint
Perturbations of glutathione and sphingosine metabolites in Port Wine Birthmark patient-derived induced pluripotent stem cells
V. Nguyen, J. Kravitz, C. Gao, M. L. Hochman, D. Meng, D. Chen, Y. Wang, A. G. Jegga, S. Nelson, W. Tan
Abstract
Port Wine Birthmark (PWB) is a congenital vascular malformation in the skin, occurring in 1-3 per 1,000 live births. We recently generated PWB-derived induced pluripotent stem cells (iPSCs) as clinically relevant disease models. The metabolites associated with the pathological phenotypes of PWB-derived iPSCs are unknown, which we aimed to explore in this study. Metabolites were separated by ultra-performance liquid chromatography and were screened with electrospray ionization mass spectrometry. Orthogonal partial least-squares discriminant analysis, multivariate and univariate analysis were used to identify differential metabolites (DMs). KEGG analysis was used for the enrichment of metabolic pathways. A total of 339 metabolites were identified. There were 22 DMs confirmed with 9 downregulated DMs including sphingosine and 13 upregulated DMs including glutathione in PWB iPSCs as compared to controls. Pathway enrichment analysis confirmed the upregulation of glutathione and downregulation of sphingolipid metabolism in PWB-derived iPSCs as compared to normal ones. We next examined the expression patterns of the key factors associated with glutathione metabolism in PWB lesions. We found that hypoxia-inducible factor 1 (HIF1), glutathione S-transferase Pi 1 (GSTP1), {gamma}-glutamyl transferase 7 (GGT7), and glutamate cysteine ligase modulatory subunit (GCLM) were upregulated in PWB vasculatures as compared to blood vessels in normal skins. Our data demonstrate that there are perturbations in sphingolipid and cellular redox homeostasis in the PWB vasculature, which may facilitate cell survival and pathological progression. Our data imply that upregulation of glutathione may contribute to laser-resistant phenotypes in the PWB vasculature.
- organism: human
- assay: metabolomics
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
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