Exons 45–55 Skipping Using Mutation-Tailored Cocktails of Antisense Morpholinos in the DMD Gene
Bibliographic record
Abstract
Mutations in the dystrophin (DMD) gene and consequent loss of dystrophin cause Duchenne muscular dystrophy (DMD). A promising therapy for DMD, single-exon skipping using antisense phosphorodiamidate morpholino oligomers (PMOs), currently confronts major issues in that an antisense drug induces the production of functionally undefined dystrophin and may not be similarly efficacious among patients with different mutations. Accordingly, the applicability of this approach is limited to out-of-frame mutations. Here, using an exon-skipping efficiency predictive tool, we designed three different PMO cocktail sets for exons 45-55 skipping aiming to produce a dystrophin variant with preserved functionality as seen in milder or asymptomatic individuals with an in-frame exons 45-55 deletion. Of them, the most effective set was composed of select PMOs that each efficiently skips an assigned exon in cell-based screening. These combinational PMOs fitted to different deletions of immortalized DMD patient muscle cells significantly induced exons 45-55 skipping with removing 3, 8, or 10 exons and dystrophin restoration as represented by western blotting. In vivo skipping of the maximum 11 human DMD exons was confirmed in humanized mice. The finding indicates that our PMO set can be used to create mutation-tailored cocktails for exons 45-55 skipping and treat over 65% of DMD patients carrying out-of-frame or in-frame deletions.
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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) | 0.000 | 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".