From Metabolomics to Identification of a new therapeutic approach for Age-Related Macular Degeneration (AMD)

Abstract

Age-related macular degeneration (AMD) is the leading cause of vision loss in the western world among people aged 50 or older. 90% of all vision loss due to AMD result from the exudative form, which is characterized by choroidal neovascularization (CNV). Age-related changes that induce pathologic CNV are incompletely understood and critical issues remain to be addressed. Metabolomics is defined as the comprehensive study of endogenous metabolites changes in various biological systems. This newly emerging “omic” science provides a unique opportunity to correlate variation of the metabolome with pathological occurrence or progression and/or to identify metabolites that are implicated in the disease. We apply a 1H NMR metabolomics approach on sera collected from AMD patient and healthy volunteers and form a mice model of laser-induced CNV which mimics the effect of exudative AMD. After post-processing treatments, the different spectra were analyzed by statistical discriminant methodologies (PCA, ICA, PLS-DA, O-PLS-DA). These approaches allow the differentiation between control and AMD patients and between laser-induced mice and the control mice group. Moreover, the same discriminating spectral zones have been identified in human and mice model, leading to the emergence of different putative biomarkers. Among these markers, lactate emerges as a key metabolite in both settings. Mechanistically, lactate produced locally and by inflammatory cells, plays a critical role in the onset of the inflammatory and angiogenic phases. In mice model of laser-induced CNV, normalization of circulating lactate by dichloroacetate a pyruvate dehydrogenase kinase (PDK) inhibitor, decreases CNV development. Our data support the innovative concept of lactate as a parainflammation- and angio-metabolite associated to AMD and CNV progression. Moreover, control of blood lactate level via inhibition of PDK provides new options for the treatment of exudative AMD. This study demonstrates the ability of metabolomics for drug target discovery and opens new perspectives for AMD treatment and patient follow-up