Quantitative Antisense Screening and Optimization for Exon 51 Skipping in Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD), the most common lethal genetic disorder, is caused by mutations in the dystrophin (DMD) gene. Exon skipping is a therapeutic approach that uses antisense oligonucleotides (AOs) to modulate splicing and restore the reading frame, leading to truncated, yet functional protein expression. In 2016, the US Food and Drug Administration (FDA) conditionally approved the first phosphorodiamidate morpholino oligomer (morpholino)-based AO drug, eteplirsen, developed for DMD exon 51 skipping. Eteplirsen remains controversial with insufficient evidence of its therapeutic effect in patients. We recently developed an in silico tool to design antisense morpholino sequences for exon skipping. Here, we designed morpholino AOs targeting DMD exon 51 using the in silico tool and quantitatively evaluated the effects in immortalized DMD muscle cells in vitro. To our surprise, most of the newly designed morpholinos induced exon 51 skipping more efficiently compared with the eteplirsen sequence. The efficacy of exon 51 skipping and rescue of dystrophin protein expression were increased by up to more than 12-fold and 7-fold, respectively, compared with the eteplirsen sequence. Significant in vivo efficacy of the most effective morpholino, determined in vitro, was confirmed in mice carrying the human DMD gene. These findings underscore the importance of AO sequence optimization for exon skipping.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".