Challenges of Assessing Exon 53 Skipping of the Human <i>DMD</i> Transcript with Locked Nucleic Acid-Modified Antisense Oligonucleotides in a Mouse Model for Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Antisense oligonucleotide (AON)-mediated exon skipping is a promising therapeutic approach for Duchenne muscular dystrophy (DMD) patients to restore dystrophin expression by reframing the disrupted open reading frame of the DMD transcript. However, the treatment efficacy of the already conditionally approved AONs remains low. Aiming to optimize AON efficiency, we assessed exon 53 skipping of the DMD transcript with different chemically modified AONs, all with a phosphorothioate backbone: 2′-O-methyl (2′OMe), locked nucleic acid (LNA)-2′OMe, 2′-fluoro (FRNA), LNA-FRNA, αLNA-FRNA, and FANA-LNA-FRNA. Efficient exon 53 skipping was observed with the FRNA, LNA-FRNA, and LNA-2′OMe AONs in human control myoblast cultures. Weekly subcutaneous injections (50 mg/kg AON) for a duration of 6 weeks were well tolerated by hDMDdel52/ mdx males. Treatment with the LNA-FRNA and LNA-2′OMe AONs resulted in pronounced exon 53 skip levels in skeletal muscles and heart up to 90%, but no dystrophin restoration was observed. This discrepancy was mainly ascribed to the strong binding nature of LNA modifications to RNA, thereby interfering with the amplification of the unskipped product resulting in artificial overamplification of the exon 53 skip product. Our study highlights that treatment effect on RNA and protein level should both be considered when assessing AON efficiency.
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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) | 0.000 | 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 | 0.001 | 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".