TIGIT blockade enhances NK cell activity against autologous HIV‐1‐infected CD4<sup>+</sup> T cells
Bibliographic record
Abstract
Abstract Objectives During chronic human immunodeficiency virus (HIV)‐1 infection, inhibitory molecules upregulated on lymphocytes contribute to effector cell dysfunction and immune exhaustion. People living with HIV (PLWH) are at greater risk for age‐related morbidities, an issue magnified by human cytomegalovirus (CMV) coinfection. As CMV infection modifies natural killer (NK) cell properties and NK cells contribute to protection against HIV‐1 infection, we considered the role of T‐cell immunoreceptor with immunoglobulin and intracellular tyrosine inhibitory motif domains (TIGIT) in NK cell‐based HIV‐1 immunotherapy and elimination strategies. Methods We measured TIGIT expression on immune cell subsets of 95 PLWH and assessed its impact on NK cell function, including elimination of autologous CD4+ T cells infected through reactivation of endogenous HIV‐1. Results TIGIT was expressed on CD4+ T cells, CD8+ T cells and NK cells from PLWH. Although TIGIT levels on T cells correlated with HIV‐1 disease progression, the extent of TIGIT expression on NK cells more closely paralleled adaptation to CMV. TIGIT interacts with its predominant ligand, poliovirus receptor (PVR), to inhibit effector cell functions. Circulating CD4+ T cells from PLWH more frequently expressed PVR than HIV‐seronegative controls, and PVR expression was enriched in CD4+ T cells replicating HIV‐1 ex vivo. Treatment with anti‐TIGIT monoclonal antibodies increased NK cell HIV‐1‐specific antibody‐dependent cytotoxicity in vitro and ex vivo. Conclusion Blocking TIGIT may be an effective strategy to invigorate antibody‐dependent NK cell activity against HIV‐1 activated in cellular reservoirs for cure or treatment strategies.
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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".