Evaluation of Novel Enhancer Compounds in Gentamicin-Mediated Readthrough of Nonsense Mutations in Rett Syndrome
Bibliographic record
Abstract
Rett syndrome (RTT), a severe X-linked neurodevelopmental disorder, is primarily caused by mutations in the methyl CpG binding protein 2 gene (MECP2). Over 35% RTT patients carry nonsense mutation in MECP2, making it a suitable candidate disease for nonsense suppression therapy. In our previous study, gentamicin was found to induce readthrough of MECP2 nonsense mutations with modest efficiency. Given the recent discovery of readthrough enhancers, CDX compounds, we herein evaluated the potentiation effect of CDX5-1, CDX5-288, and CDX6-180 on gentamicin-mediated readthrough efficiency in transfected HeLa cell lines bearing the four most common MECP2 nonsense mutations. We showed that all three CDX compounds potentiated gentamicin-mediated readthrough and increased full-length MeCP2 protein levels in cells expressing the R168X, R255X, R270X, and R294X nonsense mutations. Among all three CDX compounds, CDX5-288 was the most potent enhancer and enabled the use of reduced doses of gentamicin, thus mitigating the toxicity. Furthermore, we successfully demonstrated the upregulation of full-length Mecp2 protein expression in fibroblasts derived from Mecp2R255X/Y mice through combinatorial treatment. Taken together, findings demonstrate the feasibility of this combinatorial approach to nonsense suppression therapy for a subset of RTT patients.
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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 | 0.000 |
| 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".