RNA-based therapies in animal models of Leber congenital amaurosis causing blindness
Bibliographic record
Abstract
Abstract Leber congenital amaurosis (LCA) is a severe, genetically heterogeneous recessive eye disease in which ~ 35% of gene mutations are in-frame nonsense mutations coding for loss-of-function premature termination codons (PTCs) in mRNA. Nonsense suppression therapy allows read-through of PTCs leading to production of full-length protein. A limitation of nonsense suppression is that nonsense-mediated decay (NMD) degrades PTC-containing RNA transcripts. The purpose of this study was to determine whether inhibition of NMD could improve nonsense suppression efficacy in vivo. Using a high-throughput approach in the recessive cep290 zebrafish model of LCA (cep290;Q1223X), we first tested the NMD inhibitor Amlexanox in combination with the nonsense suppression drug Ataluren. We observed reduced retinal cell death and improved visual function. With these positive data, we next investigated whether this strategy was also applicable across species in two mammalian models: Rd12 (rpe65;R44X) and Rd3 (rd3;R107X) mouse models of LCA. In the Rd12 model, cell death was reduced, RPE65 protein was produced, and in vivo visual function testing was improved. We establish for the first time that the mechanism of action of Amlexanox in Rd12 retina was through reduced UPF1 phosphorylation. In the Rd3 model, however, no beneficial effect was observed with Ataluren alone or in combination with Amlexanox. This variation in response establishes that some forms of nonsense mutation LCA can be targeted by RNA therapies, but that this needs to be verified for each genotype. The implementation of precision medicine by identifying better responders to specific drugs is essential for development of validated retinal therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".