Exon skipping peptide-conjugated morpholinos downregulate dynamin 2 to rescue centronuclear myopathy
Bibliographic record
Abstract
Centronuclear myopathies (CNM) are rare congenital disorders characterized by muscle weakness and disorganization of myofibres. These conditions can result from dominant mutations in the DNM2 gene encoding the GTPase dynamin, making them potential targets for antisense therapy. Preclinical studies suggested decreasing DNM2 as a therapy but a recent clinical trial with antisense oligonucleotides did not effectively address the disease and showed some non-muscle toxicity. Here, to promote DNM2 downregulation in muscle versus other tissues, we used an exon skipping peptide-conjugated phosphorodiamidate morpholino (PPMO) targeting Dnm2 exon 6 splicing in the Dnm2R369W/+ mouse model for the moderate CNM form. Intravenous administration of PPMOs at an early age (4 weeks) significantly downregulated intact (i.e. normally spliced) Dnm2 mRNA (∼50%) and DNM2 protein levels in muscle. This intervention led to a rescue of muscle force, thereby preventing disease progression. PPMO administration at a later age (8 weeks), when mice demonstrated established phenotypes, efficiently decreased intact Dnm2 mRNA and protein levels in muscle, resulting in reversal of the disease phenotype and significant improvement in muscle force (from 11 mN/mg to nearly 16 mN/mg). Overall, our results indicate that PPMOs targeting Dnm2 splicing effectively decrease intact Dnm2 mRNA and protein levels in muscle and rescue muscle force in Dnm2R369W/+ mice, suggesting a promising translational approach for patients with DNM2 mutations and potentially other forms of CNM. More generally, it provides the concept of using the exon skipping strategy to decrease the protein expression of a target gene, rather than producing a shorter functional protein as is generally done.
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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 | 0.001 |
| 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".