June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Metabolic pathways associated with Age-related Macular Degeneration (AMD)
Author Affiliations & Notes
  • L. Goodwin Burgess
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
  • Youngja Park
    Department of Medicine, Emory University, Atlanta, GA
  • Megan Parks
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
  • Kichun Lee
    Department of Industrial Engineering, Hanyang University, Seoul, Republic of Korea
  • Paul Sternberg
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
  • Dean Jones
    Department of Medicine, Emory University, Atlanta, GA
  • Milam Brantley
    Vanderbilt Eye Institute, Vanderbilt University, Nashville, TN
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 4117. doi:
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      L. Goodwin Burgess, Youngja Park, Megan Parks, Kichun Lee, Paul Sternberg, Dean Jones, Milam Brantley; Metabolic pathways associated with Age-related Macular Degeneration (AMD). Invest. Ophthalmol. Vis. Sci. 2013;54(15):4117.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To identify the metabolic pathways that are altered in neovascular age-related macular degeneration (NVAMD).

Methods: We performed metabolomic analysis using liquid chromatography with Fourier-transform mass spectrometry on plasma samples from 26 NVAMD patients and 19 controls. Data were collected from mass/charge ratio (m/z) 85 to 850 on a Thermo LTQ-Orbitrap Velos mass spectrometer, and individual m/z features were matched to the Metlin metabolomics database. Metabolic features were extracted using an adaptive processing software package; both non-transformed and log2-transformed data were corrected using Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Principal Component Analysis and Orthogonal Partial Least Squares-Discriminant Analysis were performed to determine metabolic features that distinguished AMD patients from controls. To identify the metabolic pathways and enzyme-gene networks associated with NVAMD, we used MetScape, a plug-in for an open source software platform for visualizing complex networks (Cytoscape).

Results: A total of 94 metabolic features differed between NVAMD and control patients with FDR (q=0.05) while 132 discriminating features were found using log2 transformation (q=0.2). Metscape analysis of the non-transformed 94 features identified 17 affected metabolic pathways, including tyrosine metabolism; the urea cycle and metabolisms of related amino acids arginine, proline, glutamate, aspartate, and asparagine; and several carbohydrate metabolism pathways. Compound-reaction-enzyme-gene analysis of the log2-transformed data confirmed the network associations to tyrosine and urea metabolism pathways, indicating their importance. Features matching to tyrosine pathways included L-tyrosine, L-phenylalanine, and dopaquinone. The matches for the urea pathway included L-glutamate, L-/D-aspartate, carnitine, and O-acyetylcarnitine.

Conclusions: Metabolomics can identify metabolites that discriminate NVAMD cases from controls, and subsequent pathway analysis can indicate which metabolic pathways are involved in creating these disparities. Single metabolites from the tyrosine and urea metabolism pathways have already been suggested as involved in retinal equilibrium, and pathway analysis is now able to substantiate evidence of their involvement in NVAMD.

Keywords: 412 age-related macular degeneration • 592 metabolism • 636 pathobiology  
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