Galectin-9 Expression Defines a Subpopulation of NK Cells with Impaired Cytotoxic Effector Molecules but Enhanced IFN-γ Production, Dichotomous to TIGIT, in HIV-1 Infection
Bibliographic record
Abstract
Abstract NK cell functions are tightly regulated by the balance between the inhibitory and stimulatory surface receptors. We investigated the surface expression of galectin-9 (Gal-9) and its function in NK cells from HIV-infected individuals on antiretroviral therapy, long-term nonprogressors, and progressors compared with healthy controls. We also measured the expression of TIGIT and TIM-3 on different NK cell subpopulations and compared their functionality to Gal-9+ NK cells. Our data demonstrated significant upregulation of Gal-9 on NK cells in HIV-infected groups versus healthy controls. Gal-9 expression was associated with impaired expression of cytotoxic effector molecules granzyme B, perforin, and granulysin. In contrast, Gal-9 expression significantly enhanced IFN-γ expression in NK cells of HIV-1–infected individuals. We also found an expansion of TIGIT+ NK cells in HIV-infected individuals; however, dichotomous to Gal-9+ NK cells, TIGIT+ NK cells expressed significantly higher amounts of cytotoxic molecules but lower IFN-γ. Moreover, lower expression of cytotoxic effector molecules in Gal-9+ NK cells was associated with higher CD107a expression, which suggests indiscriminate degranulation. Importantly, a positive correlation between the plasma viral load and Gal-9+ NK cells was observed in progressors. Finally, we found that a cytokine mixture (IL-12, IL-15, and IL-18) can improve effector functions of Gal-9+ NK cells in HIV-infected individuals, although, such an effect was observed for Gal-9− NK cells, as well. Overall, our data highlight the important role of Gal-9 in dysfunctional NK cells and, more importantly, a dichotomy for the role of Gal-9 versus TIGIT and suggest a potential new avenue for the development of therapeutic approaches.
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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 | 0.001 |
| 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) | 0.000 | 0.001 |
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".