Metabolomics a Novel Tool for Investigating the Pathogenesis of Age-related Macular Degeneration

Massachusetts Eye And Ear Infirmary, Boston MA

Investigators

Linked publications & trials

Abstract

PROJECT SUMMARY: Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly.1 Currently, there are no treatments for its early and intermediate forms and there is a lack of reliable biomarkers to predict risk of disease progression. Therefore, there is a great unmet need for better understanding the mechanisms of AMD and its wide phenotypic variability. The metabolome is downstream of the genome and is simultaneously impacted by a wide range of environmental exposures thus being considered a close representation of the phenotype in multifactorial diseases.2,3 Indeed, our work has shown that metabolomics can address these current unmet needs4 in AMD. We developed a prospective study including patients with AMD and controls that were followed over 5 years (R01EY030088). We identified changes in the metabolomic profile of patients with AMD compared to controls, differences across AMD severity stages5 mostly in specific lipid and amino acid pathways,6 as well as changes related to AMD progression.5,7 Additionally, we identified strong associations between glycerophospholipids and specific AMD single nucleotide polymorphisms (SNPs).8,9 The current application is of the natural progression of our R01EY030088 research. We aim to further characterize the disease-causing mechanisms of AMD by quantifying previously identified metabolomic signals using targeted metabolomics. Direct quantification is crucial to confirm that these pathways are indeed dysregulated in AMD. To do so, we will validate the AMD associated metabolites and determine AMD risk by performing targeted metabolomic assays to: (Aim 1A) compare plasma metabolite quantifications between patients with AMD and controls, and across stages of disease; (Aim 1B) characterize the metabolome of eyes with AMD on retinal tissues samples from in donor eyes with AMD and controls; (Aim 1C) develop and create novel polymetabolomic risk scores for AMD and explore plasma-tissue metabolic correlations. Additionally, we will (Aim 2) characterize the metabolome of AMD progression longitudinally by comparing the profiles of AMD-progressors versus non progressors at 5- years; and (Aim 3) identify key mechanistic pathways and endotypes of AMD using multiomics: In particular, (Aim 3A); we will characterize metabolite-genetic correlations of AMD and its progression and (Aim 3B) use machine learning to identify endotypes of AMD to better classify the phenotypic diversity of the disease and point to potential treatment targets. By using plasma and tissue targeted metabolomics and studying metabolomics- gene interactions, in this proposal, we will increase the current understanding of the mechanisms that cause AMD and identify possible new targets for prevention and earlier intervention. The experiments outlined in this proposal represent the essential next phase to identify biological biomarkers of AMD and AMD progression, contributing to improved screening and risk assessment and ushering in an era of precision medicine for this blinding condition.