Treatment of a genetic liver disease in mice through transient prime editor expression
Bibliographic record
Abstract
Abstract Prime editing is a versatile genome editing technology that does not rely on DNA double-strand break formation and homology-directed repair (HDR). This makes it a promising tool for correcting pathogenic mutations in tissues consisting predominantly of postmitotic cells, such as the liver. While recent studies have already demonstrated proof-of-concept for in vivo prime editing, the use of viral delivery vectors resulted in prolonged prime editor (PE) expression, posing challenges for clinical application. Here, we developed an in vivo prime editing approach where we delivered the pegRNA using self-complementary adeno-associated viral (scAAV) vectors and the prime editor using nucleoside-modified mRNA encapsulated in lipid nanoparticles (LNPs). This methodology led to transient expression of the PE for 48h and 26% editing at the Dnmt1 locus using AAV doses of 2.5×10 13 vector genomes (vg)/kg and a single dose of 3mg/kg mRNA-LNP. When targeting the pathogenic mutation in the Pah enu2 mouse model of phenylketonuria (PKU), we achieved 4.3% gene correction using an AAV dose of 2.5×10 13 vg/kg and three doses of 2 mg/kg mRNA-LNP. Editing was specific to the liver and the intended locus, and was sufficient to reduce blood L-phenylalanine (Phe) levels from over 1500 µmol/l to below the therapeutic threshold of 600 µmol/l. Our study demonstrates the feasibility of in vivo gene correction in the liver with transient PE expression, bringing prime editing closer to clinical application.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".