Genome editing without nucleases confers proliferative advantage to edited hepatocytes and corrects Wilson disease
Bibliographic record
Abstract
Application of classic liver-directed gene replacement strategies is limited in genetic diseases characterized by liver injury due to hepatocyte proliferation, resulting in decline of therapeutic transgene expression and potential genotoxic risk. Wilson disease (WD) is a life-threatening autosomal disorder of copper homeostasis caused by pathogenic variants in copper transporter ATP7B and characterized by toxic copper accumulation, resulting in severe liver and brain diseases. Genome editing holds promise for the treatment of WD; nevertheless, to rescue copper homeostasis, ATP7B function must be restored in at least 25% of the hepatocytes, which surpasses by far genome-editing correction rates. We applied a liver-directed, nuclease-free genome editing approach, based on adeno-associated viral vector-mediated (AAV-mediated) targeted integration of a promoterless mini-ATP7B cDNA into the albumin (Alb) locus. Administration of AAV-Alb-mini-ATP7B in 2 WD mouse models resulted in extensive liver repopulation by genome-edited hepatocytes holding a proliferative advantage over nonedited ones, and ameliorated liver injury and copper metabolism. Furthermore, combination of genome editing with a copper chelator, currently used for WD treatment, achieved greater disease improvement compared with chelation therapy alone. Nuclease-free genome editing provided therapeutic efficacy and may represent a safer and longer-lasting alternative to classic gene replacement strategies for WD.
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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 | 0.000 | 0.001 |
| 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".