Toward an all-in-one recombinant adeno-associated virus vector for functionally ablating the prion gene using CRISPR-Cas technology
Bibliographic record
Abstract
Any strategy that can selectively and persistently lower the brain levels of the cellular prion protein (PrPC) is expected to extend survival in prion diseases. Recent advances in the virus-mediated delivery of gene therapies prompted us to explore if a recombinant adeno-associated virus (rAAV) vector delivering a CRISPR-Cas-based gene editor can be devised that induces a functional knockout of the prion gene. Whereas the eventual objective is to assess the therapeutic potency of an optimized vector in prion-infected mice, in this proof-of-concept study, we evaluated tools and methods that are suited to achieve this goal. The result of these efforts is a first-generation all-in-one rAAV vector that codes for a prion gene-specific guide RNA and a small Cas9 endonuclease, whose expression is controlled by a truncated neural cell adhesion molecule 1 (NCAM1) promoter that is active in PrPC expressing cells. We also constructed a second rAAV vector coding for a prion gene-specific 'traffic light reporter' (TLR). The TLR can be used to monitor prion gene-editing efficacy by coding for red and green fluorescent proteins separated by a segment of the prion gene that is targeted by the gene editor. For the purification of AAVs, we adopted a robust and scalable rAAV vector assembly pipeline and undertook proof-of-concept prion gene editing experiments in human cells and mice, which to date yielded prion gene editing rates of approximately 20% and 5%, respectively. Finally, we compared brain distributions of rAAV vectors following intrathalamic versus retro-orbital injection, and selected the 9P31 capsid for future studies based on a 7.5-fold higher heterologous gene expression level as compared to the PHP.eB capsid.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".