DG9 boosts PMO nuclear uptake and exon skipping to restore dystrophic muscle and cardiac function
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a severe neuromuscular disorder caused by DMD gene mutations, leading to the loss of functional dystrophin. While antisense oligonucleotide (ASO)-mediated exon skipping offers therapeutic potential, its efficacy in cardiac muscle remains limited. Here, we investigate DG9, a cell-penetrating peptide derived from human polyhomeotic 1 homolog (Hph-1) transcription factor, as an enhancer of phosphorodiamidate morpholino oligomer (PMO)-based therapy targeting exon 44. In a humanized DMD mouse model (hDMDdel45;mdx), DG9-PMO significantly increases exon skipping, restores dystrophin expression, and improves muscle function, particularly in the heart. Mechanistically, DG9-PMO enhances intracellular uptake through multiple endocytic pathways and achieves superior nuclear localization. Compared to the benchmark R6G peptide, DG9-PMO exhibits greater efficacy in cardiac tissue with no detectable toxicity. These findings highlight DG9-PMO as a promising next-generation exon-skipping therapy with potential clinical relevance for improving both skeletal and cardiac outcomes in DMD patients. The authors show that the cell-penetrating peptide DG9 enhances PMO delivery to skeletal and cardiac muscles via multiple endocytic pathways. DG9-PMO significantly boosts dystrophin restoration and cardioprotection, addressing a key challenge in the therapy of Duchenne muscular dystrophy.
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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 | 0.000 |
| 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".