The Tat Protein Enhances CTL Responses and Therapeutic Immunity of Gag-Specific Exosome-Targeted T Cell-Based Gag/Tat-Texo Vaccine in Transgenic HLA-A2 Mice
Bibliographic record
Abstract
Human immunodeficiency virus type-1 (HIV-1) chronic infection causes millions of deaths each year. We previously developed a novel HIV-1 Gag-spe cific exosome (EXO)-targeted T cell-based vaccine (Gag-Texo) using ConAstimulated polyclonal CD8+T (ConA-T) cells armed with Gag-specific dendritic cell (DC)-released EXOs, and showed that Gag-Texo stimulated more efficient cytotoxic T lymphocyte (CTL) responses than DCs. Tat HIV-1 early regulatory protein possesses immunomodulatory and adjuvant properties. To enhance Gag-Texo immunogenicity, we generated Tat-engineered OVA/Tat Texo and Gag/Tat-Texo vaccines using ConA-T cells armed with EXOs release by DCs infected with recombinant OVA/Tat- and Gag/Tat-expressing adenoviruses (AdVOVA/Tat and AdVGag/Tat). We then assessed vaccination-stimulated CTL responses in naive mice, and therapeutic immunity in transgenic HLA-A2 mice bearing Gag/HLA-A2-expressing BL6-10OVA/A2 melanoma lung metastases. We demonstrate that the OVA/Tat-Texo vaccine enhances functional OVA-specific CTL responses, compared to the OVA-Texo vaccine, and broadens CTL responses recognizing the cryptic OVA epitope in C57BL/6 mice. Furthermore, we determine that the Gag/Tat-Texo not only stimulates more efficient CTL responses than Gag-Texo, but also induces enhanced therapeutic immunity. We show that, 30% of Gag/Tat-Texo-immunized mice are free of tumor lung-metastases, compared to all Gag-Texo-immunized mice displaying lung-metastasis. In addition, the average number of tumor lung metastases colonies (32/lung) in the Gag/Tat-Texo-immunized mice was also significantly lower than that (78/lung) observed in Gag-Texo-immunized mice. Taken together, this indicates that HIV-1 Gag/Tat-Texo capable of stimulating enhanced Gag-specific CTL responses and therapeutic immunity may become a new immunotherapeutic vaccine candidate for controlling virus in HIV-1 patients.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".