Parsing Fabry Disease Metabolic Plasticity Using Metabolomics

Journal of Personalized Medicine

Tony Pereira¹  Wladimir Mauhin²  Olivier Lidove²  Olivier Benveniste³  Soumeya Bekri⁴  Carine Pilon⁴  Raphaël Aubert⁴  Abdellah Tebani⁴  Franklin Ducatez⁴  Stéphane Marret⁵  Agnès Boullier⁶ 

[1] CHU Rouen, Institut de Biologie Clinique, 76000 Rouen, France;Department of Internal Medicine, Groupe Hospitalier Diaconesses Croix Saint Simon, Site Avron & UMRS 974, 75013 Paris, France;Department of Internal Medicine, Hôpital Pitié-Salpêtrière & INSERM U 974, 75013 Paris, France;Department of Metabolic Biochemistry, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France;Department of Neonatal Pediatrics, Intensive Care, and Neuropediatrics, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France;MP3CV-UR7517, CURS-Université de Picardie Jules Verne, Avenue de la Croix Jourdain, 80054 Amiens, France;

关键词: inborn errors of metabolism;    Fabry disease;    lysosomal storage diseases;    metabolomics;    systems biology;    machine learning;   

DOI  :  10.3390/jpm11090898

来源: DOAJ

【 摘 要 】

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. 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. 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). 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.

【 授权许可】

Unknown