STAT3 promotes survival of mutant photoreceptors in inherited photoreceptor degeneration models
Bibliographic record
Abstract
Inherited photoreceptor degenerations (IPDs), a group of incurable progressive blinding diseases, are caused by mutations in more than 200 genes, but little is known about the molecular pathogenesis of photoreceptor (PR) death. Increased retinal expression of STAT3 has been observed in response to many retinal insults, including IPDs, but the role of this increase in PR death is unknown. Here, we show that the expression of Stat3 is increased in PRs of the Tg(RHO P347S) and Prph2(rds) (/+) mouse models of IPD and is activated by tyrosine phosphorylation. PR-specific deletion of Stat3 substantially accelerated PR degeneration in both mutant strains. In contrast, increased PR-specific expression of ROSA26 (R26) alleles encoding either WT STAT3 (Stat3(wt)) or the gain-of-function variant STAT3(C) (Stat3(C)) improved PR survival in both models. Moreover, PR signaling in Tg(RHO P347S) mice carrying either a R26-Stat3(wt) or R26-Stat3(C) allele demonstrated increased a-wave amplitude of the scotopic electroretinogram. Phosphorylation of STAT3 at tyrosine 705 was required for the prosurvival effect because an R26-Stat3(Y705F) allele was not protective. The prosurvival role of enhanced Stat3 activity was validated using recombinant adenoassociated virus (rAAV) vector-mediated PR Stat3 expression in Tg(RHO P347S) mice. Our findings (i) establish that the increase in endogenous PR Stat3 expression is a protective response in IPDs, (ii) suggest that therapeutic augmentation of PR Stat3 expression has potential as a common neuroprotective therapy for these disorders, and (iii) indicate that prosurvival molecules whose expression is increased in mutant PRs may have promise as novel therapies for IPDs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".