Reading Frame Correction by Targeted Genome Editing Restores Dystrophin Expression in Cells From Duchenne Muscular Dystrophy Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it