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Reprogramming the metabolome rescues retinal degeneration

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Abstract

Metabolomics studies in the context of ophthalmology have largely focused on identifying metabolite concentrations that characterize specific retinal diseases. Studies involving mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have shown that individuals suffering from retinal diseases exhibit metabolic profiles that markedly differ from those of control individuals, supporting the notion that metabolites may serve as easily identifiable biomarkers for specific conditions. An emerging branch of metabolomics resulting from biomarker studies, however, involves the study of retinal metabolic dysfunction as causes of degeneration. Recent publications have identified a number of metabolic processes—including but not limited to glucose and oxygen metabolism—that, when perturbed, play a role in the degeneration of photoreceptor cells. As a result, such studies have led to further research elucidating methods for prolonging photoreceptor survival in an effort to halt degeneration in its early stages. This review will explore the ways in which metabolomics has deepened our understanding of the causes of retinal degeneration and discuss how metabolomics can be used to prevent retinal degeneration from progressing to its later disease stages.

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Acknowledgements

Supported in part by Grants from the National Eye Institute, NIH; P30EY019007, R01EY018213, R01EY024698, R01EY026682, R21AG050437, National Cancer Institute Core (5P30CA013696), the Research to Prevent Blindness (RPB) Physician-Scientist Award, and unrestricted funds from RPB. S.H.T. is a member of the RD-CURE Consortium and is supported by the Tistou and Charlotte Kerstan Foundation, the Schneeweiss Stem Cell Fund, New York State (C029572), the Foundation Fighting Blindness New York Regional Research Center Grant (C-NY05-0705-0312), the Crowley Family Fund, and the Gebroe Family Foundation.

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Park, K.S., Xu, C.L., Cui, X. et al. Reprogramming the metabolome rescues retinal degeneration. Cell. Mol. Life Sci. 75, 1559–1566 (2018). https://doi.org/10.1007/s00018-018-2744-9

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