Endonucleases: tools to correct the dystrophin gene
Bibliographic record
Abstract
BACKGROUND: Various endonucleases can be engineered to induce double-strand breaks (DSBs) in chosen DNA sequences. These DSBs are spontaneously repaired by nonhomologous-end-joining, resulting in micro-insertions or micro-deletions (INDELs). We detected, characterized and quantified the frequency of INDELs produced by one meganuclease (MGN) targeting the RAG1 gene, six MGNs targeting three introns of the human dystrophin gene and one pair of zinc finger nucleases (ZFNs) targeting exon 50 of the human dystrophin gene. The experiments were performed in human cells (i.e. 293 T cells, myoblasts and myotubes). METHODS: To analyse the INDELs produced by the endonucleases the targeted region was polymerase chain reaction amplified and the amplicons were digested with the Surveyor enzyme, cloned in bacteria or deep sequenced. RESULTS: Endonucleases targeting the dystrophin gene produced INDELs of different sizes but there were clear peaks in the size distributions. The positions of these peaks were similar for MGNs but not for ZFNs in 293 T cells and in myoblasts. The size of the INDELs produced by these endonucleases in the dystrophin gene would have permitted a change in the reading frame. In a subsequent experiment, we observed that the frequency of INDELs was increased by re-exposition of the cells to the same endonuclease. CONCLUSIONS: Endonucleases are able to: (i) restore the normal reading of a gene with a frame shift mutation; (ii) delete a nonsense codon; and (iii) knockout a gene. Endonucleases could thus be used to treat Duchenne muscular dystrophy and other hereditary diseases that are the result of a nonsense codon or a frame shift mutation.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".