A temperature-sensitive and less immunogenic Sendai virus for efficient gene editing
Bibliographic record
Abstract
ABSTRACT The therapeutic potential of gene editing technologies hinges on the development of safe and effective delivery methods. In this study, we developed a temperature-sensitive and less immunogenic Sendai virus (ts SeV) as a novel delivery vector for CRISPR-Cas9 and for efficient gene editing in sensitive human cell types with limited induction of an innate immune response. ts SeV demonstrates high transduction efficiency in human CD34 + hematopoietic stem and progenitor cells (HSPCs) including transduction of the CD34 + /CD38 − /CD45RA − /CD90 + (Thy1 + )/CD49f high stem cell enriched subpopulation. The frequency of CCR5 editing exceeded 90% and bi-allelic CCR5 editing exceeded 70% resulting in significant inhibition of HIV-1 infection in primary human CD14 + monocytes. These results demonstrate the potential of the ts SeV platform as a safe, efficient, and flexible addition to the current gene-editing tool delivery methods, which may help further expand the possibilities in personalized medicine and the treatment of genetic disorders. IMPORTANCE Gene editing has the potential to be a powerful tool for the treatment of human diseases including HIV, β-thalassemias, and sickle cell disease. Recent advances have begun to overcome one of the major limiting factors of this technology, namely delivery of the CRISPR-Cas9 gene editing machinery, by utilizing viral vectors. However, gene editing therapies have yet to be implemented due to inherent risks associated with the DNA viral vectors typically used for delivery. As an alternative strategy, we have developed an RNA-based Sendai virus CRISPR-Cas9 delivery vector that does not integrate into the genome, is temperature sensitive, and does not induce a significant host interferon response. This recombinant SeV successfully delivered CRISPR-Cas9 in primary human CD14+ monocytes ex vivo resulting in a high level of CCR5 editing and inhibition of HIV infection.
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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) | 0.000 | 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".