Immune Checkpoint Blockade Restores HIV-Specific CD4 T Cell Help for NK Cells
Bibliographic record
Abstract
Immune exhaustion is an important feature of chronic infections, such as HIV, and a barrier to effective immunity against cancer. This dysfunction is in part controlled by inhibitory immune checkpoints. Blockade of the PD-1 or IL-10 pathways can reinvigorate HIV-specific CD4 T cell function in vitro, as measured by cytokine secretion and proliferative responses upon Ag stimulation. However, whether this restoration of HIV-specific CD4 T cells can improve help to other cell subsets impaired in HIV infection remains to be determined. In this study, we examine a cohort of chronically infected subjects prior to initiation of antiretroviral therapy (ART) and individuals with suppressed viral load on ART. We show that IFN-γ induction in NK cells upon PBMC stimulation by HIV Ag varies inversely with viremia and depends on HIV-specific CD4 T cell help. We demonstrate in both untreated and ART-suppressed individuals that dual PD-1 and IL-10 blockade enhances cytokine secretion of NK cells via restored HIV-specific CD4 T cell function, that soluble factors contribute to these immunotherapeutic effects, and that they depend on IL-2 and IL-12 signaling. Importantly, we show that inhibition of the PD-1 and IL-10 pathways also increases NK degranulation and killing of target cells. This study demonstrates a previously underappreciated relationship between CD4 T cell impairment and NK cell exhaustion in HIV infection, provides a proof of principle that reversal of adaptive immunity exhaustion can improve the innate immune response, and suggests that immune checkpoint modulation that improves CD4/NK cell cooperation can be used as adjuvant therapy in HIV infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".