Enhancement of adeno‐associated virus serotype 6 transduction into T cells with cell‐penetrating peptides
Bibliographic record
Abstract
BACKGROUND: Adeno-associated viruses (AAVs) are gaining interest in the development of cellular immunotherapy. Compared to other viral vectors, AAVs can reduce the risk of insertional oncogenesis. AAV serotype 6 (AAV6) shows the highest efficiency for transducing T cells. Nevertheless, a multiplicity of infection (MOI) of up to one million viral genomes per cell is required to transduce the target cells effectively. Cell-penetrating peptides (CPPs) are short, positively charged peptides that easily translocate the plasma membranes and can facilitate the cellular uptake of a wide variety of cargoes, including small molecules, nucleic acids, drugs, proteins and viral vectors. METHODS: The present study evaluated five CPPs (Antp, TAT-HA2, LAH4, TAT1 and TAT2) on their effects on enhancing transduction of AAV6 packaging a green fluorescent protein transgene into Jurkat T cell line. RESULTS: ), using LAH4 resulted in a 10-fold increase in transduction efficiency. The peptide LAH4 increased the uptake of AAV6 viral particles in both Jurkat cells and mouse primary T cells. Regardless of the large size of the AAV6-LAH4 complexes, their internalization does not appear to depend on macropinocytosis. CONCLUSIONS: Overall, the present study reports an approach to significantly improve the delivery of transgenes into T cells using AAV6 vectors. Notably, the peptides TAT-HA2 and LAH4 contribute to improving the use of AAV6 as a gene delivery vector for the engineering of T cells.
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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 | 0.000 |
| 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".