# A modified arrestin1 to enhance glycolysis in photoreceptors as a therapeutic approach to slowing retinal degeneration

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2024 · $391,460

## Abstract

SUMMARY/ABSTRACT
There are currently more than 280 genes in which defects are known to cause retinal
degeneration. Thus, there is a critical need to develop broad spectrum approaches to treating
these diseases. The hypothesis of this proposal is that if we can selectively increase glycolysis in
rod and cone photoreceptors, then this increase in metabolic potential should slow retinal
degeneration across a broad spectrum of etiologies. Our findings strongly support the idea that
we can specifically modify arrestin1 such that the catalytic rate of enolase1 in glycolysis is
selectively increased in rods and cones. Significantly, this increase in glycolysis slows the loss of
photoreceptors and improves photoreceptor function in at least one animal model of retinal
degeneration. Accordingly, the specific aims of this proposal are to determine the mechanistic
properties of ArrGG’s effect, examining metabolites and gene expression profiles of treated
animals (Aim1). We will then establish ArrGG as a gene-agnostic approach to slowing retinal
degeneration, testing the therapeutic benefit of ArrGG in diverse models of inherited retinal
degeneration (Aim 2).

## Key facts

- **NIH application ID:** 10782168
- **Project number:** 1R01EY035768-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** W CLAY SMITH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $391,460
- **Award type:** 1
- **Project period:** 2024-01-01 → 2028-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10782168

## Citation

> US National Institutes of Health, RePORTER application 10782168, A modified arrestin1 to enhance glycolysis in photoreceptors as a therapeutic approach to slowing retinal degeneration (1R01EY035768-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10782168. Licensed CC0.

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