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Open AccessArticle

A Data Mining Metabolomics Exploration of Glaucoma

1
Faculté de santé, Institut MITOVASC, UMR CNRS 6015, INSERM U1083, Université d'Angers, 49933 Angers, France
2
Faculté de Pharmacie de Paris, CiTCoM UMR 8038 CNRS, Université Paris Descartes, 75270 Paris, France
3
Département d’Ophtalmologie, Centre Hospitalier Universitaire, 49933 Angers, France
4
Centre de Ressources Biologiques, BB-0033-00038, Centre Hospitalier Universitaire, 49933 Angers, France
5
Département de Biochimie et Génétique, Centre Hospitalier Universitaire, 49933 Angers, France
6
Singapore Eye Research Institute, Singapore National Eye Centre, Duke-NUS, Singapore 168751, Singapore
*
Authors to whom correspondence should be addressed.
Metabolites 2020, 10(2), 49; https://doi.org/10.3390/metabo10020049
Received: 2 December 2019 / Revised: 10 January 2020 / Accepted: 24 January 2020 / Published: 28 January 2020
(This article belongs to the Special Issue Metabolomics in Human Tissues and Materials)
Glaucoma is an age related disease characterized by the progressive loss of retinal ganglion cells, which are the neurons that transduce the visual information from the retina to the brain. It is the leading cause of irreversible blindness worldwide. To gain further insights into primary open-angle glaucoma (POAG) pathophysiology, we performed a non-targeted metabolomics analysis on the plasma from POAG patients (n = 34) and age- and sex-matched controls (n = 30). We investigated the differential signature of POAG plasma compared to controls, using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). A data mining strategy, combining a filtering method with threshold criterion, a wrapper method with iterative selection, and an embedded method with penalization constraint, was used. These strategies are most often used separately in metabolomics studies, with each of them having their own limitations. We opted for a synergistic approach as a mean to unravel the most relevant metabolomics signature. We identified a set of nine metabolites, namely: nicotinamide, hypoxanthine, xanthine, and 1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline with decreased concentrations and N-acetyl-L-Leucine, arginine, RAC-glycerol 1-myristate, 1-oleoyl-RAC-glycerol, cystathionine with increased concentrations in POAG; the modification of nicotinamide, N-acetyl-L-Leucine, and arginine concentrations being the most discriminant. Our findings open up therapeutic perspectives for the diagnosis and treatment of POAG.
Keywords: data mining; metabolomics; mitochondrial dysfunction; optic neuropathy; primary open-angle glaucoma data mining; metabolomics; mitochondrial dysfunction; optic neuropathy; primary open-angle glaucoma
MDPI and ACS Style

Kouassi Nzoughet, J.; Guehlouz, K.; Leruez, S.; Gohier, P.; Bocca, C.; Muller, J.; Blanchet, O.; Bonneau, D.; Simard, G.; Milea, D.; Procaccio, V.; Lenaers, G.; Chao de la Barca, J.M.; Reynier, P. A Data Mining Metabolomics Exploration of Glaucoma. Metabolites 2020, 10, 49.

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