Novel photoreceptor-specific promoters for gene therapy in mid- to late-stage retinal degeneration
Bibliographic record
Abstract
Inherited retinal degenerations (IRDs) cause progressive photoreceptor loss, leading to vision impairment. Gene therapy using adeno-associated viral (AAV) vectors holds immense promise for treating these conditions. However, achieving optimal gene expression at mid to late stages of retinal degeneration remains challenging due to scarcity of efficient photoreceptor-specific promoters expressed at these disease stages. This study aimed to identify and validate novel promoters capable of robust and specific transgene expression when ≥50% of photoreceptors are lost. Analysis of transcriptomic data from two naturally occurring canine IRD models, laser capture microdissection of retinal cryosections followed by qPCR, and RNA in situ hybridization identified six promising genes with sustained or upregulated expression in photoreceptors in late-stage disease. Upstream cis-regulatory elements of both canine and human orthologs were identified and characterized using in silico analyses and dual-luciferase assays. Short promoters (≤840 base pairs) derived from GNGT2, IMPG2, and PDE6H genes exhibited robust reporter gene expression in photoreceptors when delivered via AAV to the subretinal space of two non-allelic canine IRD models at mid and late disease stages. These findings provide a strategy to enhance AAV-mediated gene therapy by enabling sustained transgene expression in degenerating retinas, improving treatment outcomes for patients with progressive vision loss.
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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 | 0.001 | 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".