Therapeutic Vaccination with Simian Immunodeficiency Virus (SIV)-DNA+IL-12 or IL-15 Induces Distinct CD8 Memory Subsets in SIV-Infected Macaques
Bibliographic record
Abstract
DNA vaccination is an invaluable approach for immune therapy in that it lacks vector interference and thus permits repeated vaccination boosts. However, by themselves, DNA-based vaccines are typically poor inducers of Ag-specific immunity in humans and non-human primates. Cytokines, such as IL-12 and IL-15, have been shown to be potent adjuvants for the induction and maintenance of cellular immune responses, in particular during HIV infection. In this study, we examined the ability of therapeutic vaccination with SIV-DNA+IL-12 or IL-15 as molecular adjuvants to improve DNA vaccine potency and to enhance memory immune responses in SIV-infected macaques. Our results demonstrate that incorporating IL-12 into the vaccine induces SIV-specific CD8 effector memory T cell (T(EM)) functional responses and enhances the capacity of IFN-gamma-producing CD8 T(EM) cells to produce TNF. Lower levels of PD-1 were expressed on T cells acquiring dual function upon vaccination as compared with mono-functional CD8 T(EM) cells. Finally, a boost with SIV-DNA+IL-15 triggered most T cell memory subsets in macaques primed with either DNA-SIV or placebo but only CD8 T(EM) in macaques primed with SIV-DNA+IL-12. These results indicate that plasmid IL-12 and IL-15 cytokines represent a significant addition to enhance the ability of therapeutic DNA vaccines to induce better immunity.
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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".