# Transcriptional control of stress-induced resistance to retinal degeneration

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2022 · $372,156

## Abstract

Abstract:
 Retinal degenerations are a large cluster of diseases characterized by the irreversible loss of
photoreceptors. The death of these cells results in a permanent loss of vision that can have debilitating
impacts on an individual's quality of life. Despite the diversity among triggers for retinal degenerations, the
mechanisms surrounding photoreceptor death are often similar, suggesting the possibility of developing
gene/mutation-independent approaches to reduce blindness from multiple forms of retinal degeneration. We
and others have shown that STAT3 is activated in all retinal cells, including photoreceptors and Müller cells
during inherited retinal degeneration. Additional work has shown that activation of STAT3 plays an essential
role in promoting a wide array of gene expression changes to increase the cell’s capacity to resist cell death.
However, despite these impressive findings, little progress has been made in identifying the mechanisms by
which STAT3 regulates protection. In this project, we will use state of the art techniques including single-cell
RNA-seq and integrating the data with cell-specific ChIP-seq to comprehensively identify all genes and
transcriptional networks regulated by STAT3 in retinal Müller cells and rods.

## Key facts

- **NIH application ID:** 10477262
- **Project number:** 5R01EY032051-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** John D Ash
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $372,156
- **Award type:** 5
- **Project period:** 2021-09-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10477262

## Citation

> US National Institutes of Health, RePORTER application 10477262, Transcriptional control of stress-induced resistance to retinal degeneration (5R01EY032051-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10477262. Licensed CC0.

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