Identification of Novel Antisense-Mediated Exon Skipping Targets in DYSF for Therapeutic Treatment of Dysferlinopathy
Bibliographic record
Abstract
Dysferlinopathy is a progressive myopathy caused by mutations in the dysferlin (DYSF) gene. Dysferlin protein plays a major role in plasma-membrane resealing. Some patients with DYSF deletion mutations exhibit mild symptoms, suggesting some regions of DYSF can be removed without significantly impacting protein function. Antisense-mediated exon-skipping therapy uses synthetic molecules called antisense oligonucleotides to modulate splicing, allowing exons harboring or near genetic mutations to be removed and the open reading frame corrected. Previous studies have focused on DYSF exon 32 skipping as a potential therapeutic approach, based on the association of a mild phenotype with the in-frame deletion of exon 32. To date, no other DYSF exon-skipping targets have been identified, and the relationship between DYSF exon deletion pattern and protein function remains largely uncharacterized. In this study, we utilized a membrane-wounding assay to evaluate the ability of plasmid constructs carrying mutant DYSF, as well as antisense oligonucleotides, to rescue membrane resealing in patient cells. We report that multi-exon skipping of DYSF exons 26-27 and 28-29 rescues plasma-membrane resealing. Successful translation of these findings into the development of clinical antisense drugs would establish new therapeutic approaches that would be applicable to ∼5%-7% (exons 26-27 skipping) and ∼8% (exons 28-29 skipping) of dysferlinopathy patients worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".