Non-viral Delivery of Zinc Finger Nuclease mRNA Enables Highly Efficient In Vivo Genome Editing of Multiple Therapeutic Gene Targets
Bibliographic record
Abstract
It has previously been shown that engineered zinc finger nucleases (ZFNs) can be packaged into adeno-associated viruses (AAVs) and delivered intravenously into mice, non-human primates, and most recently, humans to induce highly efficient therapeutic genome editing in the liver. Lipid nanoparticles (LNPs) are synthetic delivery vehicles that enable repeat administration and are not limited by the presence of preexisting neutralizing antibodies in patients. Here, we show that mRNA encoding ZFNs formulated into LNP can enable >90% knockout of gene expression in mice by targeting the TTR or PCSK9 gene, at mRNA doses 10-fold lower than has ever been reported. Additionally, co-delivering mRNA-LNP containing ZFNs targeted to intron 1 of the ALB locus with AAV packaged with a promoterless human IDS or FIX therapeutic transgene can result in high levels of targeted integration and subsequent therapeutically relevant levels of protein expression in mice. Finally, we show repeat administration of ZFN mRNA-LNP after a single AAV donor dose results in significantly increased levels of genome editing and transgene expression compared to a single dose. These results demonstrate LNP-mediated ZFN mRNA delivery can drive highly efficient levels of in vivo genome editing and can potentially offer a new treatment modality for a variety of diseases.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".