Efficient Rescue of Retinal Degeneration in <i>Pde6a</i> Mice by Engineered Base Editing and Prime Editing
Bibliographic record
Abstract
Retinitis pigmentosa (RP) is a complex spectrum of inherited retinal diseases marked by the gradual loss of photoreceptor cells, ultimately leading to blindness. Among these, mutations in PDE6A, responsible for encoding a cGMP-specific phosphodiesterase, stand out as pivotal in autosomal recessive RP (RP43). Unfortunately, no effective therapy currently exists for this specific form of RP. However, recent advancements in genome editing, such as base editing (BE) and prime editing (PE), offer a promising avenue for precise and efficient gene therapy. Here, it is illustrated that the engineered BE and PE systems, particularly PE, exhibit high efficiency in rescuing a target point mutation with minimal bystander effects in an RP mouse model carrying the Pde6a (c.2009A > G, p.D670G) mutation. The optimized BE and PE systems are first screened in N2a cells and subsequently assessed in electroporated mouse retinas. Notably, the optimal PE system, delivered via dual adeno-associated virus (AAV), precisely corrects the pathogenic mutation with average 9.4% efficiency, with no detectable bystander editing. This correction restores PDE6A protein expression, preserved photoreceptors, and rescued retinal function in Pde6a mice. Therefore, this study offers a proof-of-concept demonstration for the treatment of Pde6a-related retinal degeneration using BE and PE systems.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".