Retinal disease: How to use proteomics to speed up diagnosis and metabolomics to slow down degeneration

Background Neurodegenerative diseases are incurable disorders caused by progressive neuronal cell death. Retinitis pigmentosa (RP) is a blinding neurodegenerative disease that results in photoreceptor death and progresses to the loss of the entire retinal network. We previously found that proteomic analysis of the adjacent vitreous served as way to indirectly biopsy the retina and identify changes in the retinal proteome. Methods We analyzed protein expression in liquid vitreous biopsies from autosomal recessive (ar)RP patients with PDE6A mutations and arRP mice with Pde6ɑ mutations. Proteomic analysis of retina and vitreous samples identified molecular pathways affected at the onset of photoreceptor death. Based on affected molecular pathways, arRP mice were treated with a ketogenic diet or metabolites involved in fatty-acid synthesis, oxidative phosphorylation, and the tricarboxylic acid (TCA) cycle. Findings Dietary supplementation of a single metabolite, ɑ-ketoglutarate, increased docosahexaeonic acid levels, provided neuroprotection, and enhanced visual function in arRP mice. A ketogenic diet delayed photoreceptor cell loss, while vitamin B supplementation had a limited effect. Finally, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) on ɑ-ketoglutarate-treated mice revealed restoration of metabolites that correlated with our proteomic findings: uridine, dihydrouridine, and thymidine (pyrimidine and purine metabolism), glutamine and glutamate (glutamine/glutamate conversion), and succinic and aconitic acid (TCA cycle). Interpretation This study demonstrates that replenishing TCA cycle metabolites via oral supplementation prolongs retinal function and provides a neuroprotective effect on the photoreceptor cells and inner retinal network. Funding NIH grants [R01EY026682, R01EY024665, R01EY025225, R01EY024698, R21AG050437, P30EY026877, 5P30EY019007, R01EY018213, F30EYE027986, T32GM007337, 5P30CA013696], NSF grant CHE-1734082.