Antisense oligonucleotide-mediated exon 27 skipping restores dysferlin function in dysferlinopathy patient-derived muscle cells
Bibliographic record
Abstract
Dysferlinopathies are debilitating autosomal recessive muscular dystrophies caused by mutations in the DYSF gene, encoding dysferlin, a protein crucial for sarcolemmal homeostasis and membrane resealing. Currently, no therapies exist for dysferlinopathies. Dysferlin features a modular structure with multiple calcium-dependent C2 lipid-binding domains. Clinical reports of mild, late-onset phenotypes suggest partial retention of functionality despite missing C2 domains, supporting exon-skipping therapies using antisense oligonucleotides (ASOs). In this study, we identified a patient-derived muscle cell line with a splice site mutation in DYSF intron 26, causing exon 26 exclusion, an out-of-frame transcript, and no detectable dysferlin protein. We hypothesized that skipping DYSF exon 27 could restore the reading frame and membrane repair function. Using an in-house in silico tool, we designed ASOs targeting exon 27. Treatment resulted in 65%–92% exon 27 skipping in myoblasts and myotubes, leading to a 39%–51% rescue of normal dysferlin expression, demonstrating robust efficacy of our designed ASOs. Two-photon laser-based assays indicated functional membrane repair. Additionally, we observed improved myotube fusion, cell vitality, and reduced apoptosis levels post-treatment. These findings provide proof of concept that DYSF exon 27 skipping restores functional dysferlin in patient-derived cells, paving the way for future in vivo and clinical studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".