Combination Immune Checkpoint Blockade Enhances IL-2 and CD107a Production from HIV-Specific T Cells Ex Vivo in People Living with HIV on Antiretroviral Therapy
Bibliographic record
Abstract
Abstract In people with HIV (PWH) on antiretroviral therapy (ART), immune dysfunction persists, including elevated expression of immune checkpoint (IC) proteins on total and HIV-specific T cells. Reversing immune exhaustion is one strategy to enhance the elimination of HIV-infected cells that persist in PWH on ART. We aimed to evaluate whether blocking CTL-associated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1), T cell Ig domain and mucin domain 3 (TIM-3), T cell Ig and ITIM domain (TIGIT) and lymphocyte activation gene-3 (LAG-3) alone or in combination would enhance HIV-specific CD4+ and CD8+ T cell function ex vivo. Intracellular cytokine staining was performed using human PBMCs from PWH on ART (n = 11) and expression of CD107a, IFN-γ, TNF-α, and IL-2 was quantified with HIV peptides and Abs to IC. We found the following: 1) IC blockade enhanced the induction of CD107a and IL-2 but not IFN-γ and TNF-α in response to Gag and Nef peptides; 2) the induction of CD107a and IL-2 was greatest with multiple combinations of two Abs; and 3) Abs to LAG-3, CTLA-4, and TIGIT in combinations showed synergistic induction of IL-2 in HIV-specific CD8+ and CD107a and IL-2 production in HIV-specific CD4+ and CD8+ T cells. These results demonstrate that the combination of Abs to LAG-3, CTLA-4, or TIGIT can increase the frequency of cells expressing CD107a and IL-2 that associated with cytotoxicity and survival of HIV-specific CD4+ and CD8+ T cells in PWH on ART. These combinations should be further explored for an HIV cure.
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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) | 0.002 | 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".