Prime Editing Permits the Introduction of Specific Mutations in the Gene Responsible for Duchenne Muscular Dystrophy
Bibliographic record
Abstract
The Prime editing technique derived from the CRISPR/Cas9 discovery permits the modification of selected nucleotides in a specific gene. We used it to insert specific point mutations in exons 9, 20, 35, 43, 55 and 61 of the Duchenne Muscular Dystrophy (DMD) gene coding for the dystrophin protein, which is absent in DMD patients. Up to 11% and 21% desired mutations of the DMD gene in HEK293T cells were obtained with the PRIME Editor 2 (PE2) and PE3, respectively. Three repeated treatments increased the percentage of specific mutations with PE2 to 16%. An additional mutation in the protospacer adjacent motif (PAM) sequence improved the PE3 result to 38% after a single treatment. We also carried out the correction of c.428 G>A point mutation in exon 6 of the DMD gene in a patient myoblast. Myoblast electroporation showed up to 8% and 28% modifications, respectively, for one and three repeated treatments using the PE3 system. The myoblast correction led to dystrophin expression in myotubes detected by Western blot. Thus, prime editing can be used for the correction of point mutations in the DMD gene.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".