Reading Frame Correction by Targeted Genome Editing Restores Dystrophin Expression in Cells From Duchenne Muscular Dystrophy Patients
Bibliographic record
Abstract
Genome editing with engineered nucleases has recently emerged as an approach to correct genetic mutations by enhancing homologous recombination with a DNA repair template. However, many genetic diseases, such as Duchenne muscular dystrophy (DMD), can be treated simply by correcting a disrupted reading frame. We show that genome editing with transcription activator-like effector nucleases (TALENs), without a repair template, can efficiently correct the reading frame and restore the expression of a functional dystrophin protein that is mutated in DMD. TALENs were engineered to mediate highly efficient gene editing at exon 51 of the dystrophin gene. This led to restoration of dystrophin protein expression in cells from Duchenne patients, including skeletal myoblasts and dermal fibroblasts that were reprogrammed to the myogenic lineage by MyoD. Finally, exome sequencing of cells with targeted modifications of the dystrophin locus showed no TALEN-mediated off-target changes to the protein-coding regions of the genome, as predicted by in silico target site analysis. This strategy integrates the rapid and robust assembly of active TALENs with an efficient gene-editing method for the correction of genetic diseases caused by mutations in non-essential coding regions that cause frameshifts or premature stop codons.
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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".