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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".