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Parsing Fabry Disease Metabolic Plasticity Using Metabolomics.
- Source :
-
Journal of personalized medicine [J Pers Med] 2021 Sep 08; Vol. 11 (9). Date of Electronic Publication: 2021 Sep 08. - Publication Year :
- 2021
-
Abstract
- Background: Fabry disease (FD) is an X-linked lysosomal disease due to a deficiency in the activity of the lysosomal α-galactosidase A (GalA), a key enzyme in the glycosphingolipid degradation pathway. FD is a complex disease with a poor genotype-phenotype correlation. FD could involve kidney, heart or central nervous system impairment that significantly decreases life expectancy. The advent of omics technologies offers the possibility of a global, integrated and systemic approach well-suited for the exploration of this complex disease.<br />Materials and Methods: Sixty-six plasmas of FD patients from the French Fabry cohort (FFABRY) and 60 control plasmas were analyzed using liquid chromatography and mass spectrometry-based targeted metabolomics (188 metabolites) along with the determination of LysoGb3 concentration and GalA enzymatic activity. Conventional univariate analyses as well as systems biology and machine learning methods were used.<br />Results: The analysis allowed for the identification of discriminating metabolic profiles that unambiguously separate FD patients from control subjects. The analysis identified 86 metabolites that are differentially expressed, including 62 Glycerophospholipids, 8 Acylcarnitines, 6 Sphingomyelins, 5 Aminoacids and 5 Biogenic Amines. Thirteen consensus metabolites were identified through network-based analysis, including 1 biogenic amine, 2 lysophosphatidylcholines and 10 glycerophospholipids. A predictive model using these metabolites showed an AUC-ROC of 0.992 (CI: 0.965-1.000).<br />Conclusion: These results highlight deep metabolic remodeling in FD and confirm the potential of omics-based approaches in lysosomal diseases to reveal clinical and biological associations to generate pathophysiological hypotheses.
Details
- Language :
- English
- ISSN :
- 2075-4426
- Volume :
- 11
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- Journal of personalized medicine
- Publication Type :
- Academic Journal
- Accession number :
- 34575675
- Full Text :
- https://doi.org/10.3390/jpm11090898