Treatment of a metabolic liver disease in mice with a transient prime editing approach
Bibliographic record
Abstract
Abstract Prime editing is a versatile genome editing technology that circumvents the need for DNA double-strand break formation and homology-directed repair, making it particularly suitable for in vivo correction of pathogenic mutations. Here we developed liver-specific prime editing approaches with temporally restricted prime editor (PE) expression. We first established a dual-delivery approach where the prime editor guide RNA is continuously expressed from adeno-associated viral vectors and only the PE is transiently delivered as nucleoside-modified mRNA encapsulated in lipid nanoparticles (LNP). This strategy achieved 26.2% editing with PEmax and 47.4% editing with PE7 at the Dnmt1 locus using a single 2 mg kg −1 dose of mRNA–LNP. When targeting the pathogenic Pah enu2 mutation in a phenylketonuria mouse model, gene correction rates reached 4.3% with PEmax and 20.7% with PE7 after three doses of 2 mg kg −1 mRNA–LNP, effectively reducing blood l -phenylalanine levels from over 1,500 µmol l −1 to below the therapeutic threshold of 360 µmol l −1 . Encouraged by the high efficiency of PE7, we next explored a simplified approach where PE7 mRNA was co-delivered with synthetic prime editor guide RNAs encapsulated in LNP. This strategy yielded 35.9% editing after two doses of RNA–LNP at the Dnmt1 locus and 8.0% editing after three doses of RNA–LNP at the Pah enu2 locus, again reducing l -phenylalanine levels below 360 µmol l −1 . These findings highlight the therapeutic potential of mRNA–LNP-based prime editing for treating phenylketonuria and other genetic liver diseases, offering a scalable and efficient platform for future clinical translation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".