PD-1 inhibition results in reduced dendritic cell (DC) activation of HIV-1-specific de novo CTL and enhancement of DC-induced CTL memory recall responses
Bibliographic record
Abstract
Abstract Combination strategies consisting of DC vaccines and suppression of immunoregulatory pathways such as PD-1 blockade have been advocated to elicit optimal CTL responses in chronic diseases including cancer and HIV infection. We have shown that inactivated HIV-1 virus loaded, high IL-12 producing, mature, type-1 polarized DC (DC1) can activate CTL from naïve CD8+ T cell precursors that more effectively kill HIV-1-infected cells than CTL derived from memory CD8+ T cells. Here we tested DC1 transfected with an adenoviral (Ad) vector encoding anti-PD-1 antibody (DC.αPD1) enhancement of both primary as well as memory CTL responses to HIV-1. Ad-αPD1-transduced DC1 (DC.αPD1) secreted high levels of functional anti-PD1 Ab without affecting their phenotype or IL-12p70-producing capacity. We next compared HIV Gag peptide epitope-loaded DC.αPD1 to DC1 that were transfected with an empty vector for their ability to activate either purified, autologous naïve or memory HIV-1-specific CD8+ T cells from chronic HIV-1-infected participants of the Multicenter AIDS Cohort Study. When compared to the control DC1, antigen-loaded DC.αPD1 enhanced the overall magnitude of HIV-1-specific CTL responses induced, as determined by the expansion of HIV-1-peptide responsive CD107a and IFNγ expressing T cells. In contrast, the overall number HIV-1 antigen-reactive CTL derived from the naïve CD8+ T cell fraction sharply decreased when using the DC.αPD1-based approach. These results suggest previously unrecognized, opposing roles of the inhibitory receptor PD-1 in DC1-induced primary versus memory recall CD8+ T cell responses to HIV-1.
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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".