TIGIT is upregulated by HIV-1 infection and marks a highly functional adaptive and mature subset of natural killer cells
Bibliographic record
Abstract
OBJECTIVE: Our objective was to investigate the mechanisms that govern natural killer (NK)-cell responses to HIV, with a focus on specific receptor--ligand interactions involved in HIV recognition by NK cells. DESIGN AND METHODS: We first performed a mass cytometry-based screen of NK-cell receptor expression patterns in healthy controls and HIV individuals. We then focused mechanistic studies on the expression and function of T cell immunoreceptor with Ig and ITIM domains (TIGIT). RESULTS: The mass cytometry screen revealed that TIGIT is upregulated on NK cells of untreated HIV women, but not in antiretroviral-treated women. TIGIT is an inhibitory receptor that is thought to mark exhausted NK cells; however, blocking TIGIT did not improve anti-HIV NK-cell responses. In fact, the TIGIT ligands CD112 and CD155 were not upregulated on CD4 T cells in vitro or in vivo, providing an explanation for the lack of benefit from TIGIT blockade. TIGIT expression marked a unique subset of NK cells that express significantly higher levels of NK-cell-activating receptors (DNAM-1, NTB-A, 2B4, CD2) and exhibit a mature/adaptive phenotype (CD57, NKG2C, LILRB1, FcRγ, Syk). Furthermore, TIGIT NK cells had increased responses to mock-infected and HIV-infected autologous CD4 T cells, and to PMA/ionomycin, cytokine stimulation and the K562 cancer cell line. CONCLUSION: TIGIT expression is increased on NK cells from untreated HIV individuals. Although TIGIT does not participate directly to the response to HIV-infected cells, it marks a population of mature/adaptive NK cells with increased functional responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".