A Proteomics-Based Analysis Reveals Predictive Biological Patterns in Fabry Disease.

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Auteur: 
Tebani, A; Mauhin, W; Abily-Donval, L; Lidove, O; Bekri, S
Date Publication: 
2020
Mois: 
Mai
Revue: 
Journal of clinical medicine
ISSN: 
2077-0383
NLM-ID: 
101606588
Volume: 
9
Num: 
5
Résumé: 
Background: Fabry disease (FD) is an X-linked progressive lysosomal disease (LD) due to glycosphingolipid metabolism impairment. Currently, plasmatic globotriaosylsphingosine (LysoGb3) is used for disease diagnosis and monitoring. However, this biomarker is inconstantly increased in mild forms and in some female patients. Materials andMethods: We applied a targeted proteomic approach to explore disease-related biological patterns that might explain the disease pathophysiology. Forty proteins, involved mainly in inflammatory and angiogenesis processes, were assessed in 69 plasma samples retrieved from the French Fabry cohort (FFABRY) and from 83 healthy subjects. For predictive performance assessment, we also included other LD samples (Gaucher, Pompe and Niemann Pick C). Results: The study yielded four discriminant proteins that include three angiogenesis proteins (fibroblast growth factor 2 (FGF2), vascular endothelial growth factor A (VEGFA), vascular endothelial growth factor C (VEGFC)) and one cytokine interleukin 7 (IL-7). A clear elevation of FGF2 and IL-7 concentrations was observed in FD compared to other LD samples. No correlation was observed between these proteins and globotriaosylsphingosine (LysoGb3). A significant correlation exists between IL-7 and residual enzyme activity in a non-classical phenotype. This highlights the orthogonal biological information yielded by these proteins that might help in stratifying Fabry patients. Conclusion: This work highlights the potential of using proteomics approaches in exploring FD and enhancing FD diagnosis and therapeutic monitoring performances.
Mots clés auteurs: 
/Fabry disease/Inborn errors of metabolism/lysosomal storage diseases/machine learning/proteomics/systems biology
DOI: 
10.3390/jcm9051325
PMID: 
32370284