Neutrophils promote T-cell activation through the regulated release of CD44-bound Galectin-9 from the cell surface during HIV infection
Bibliographic record
Abstract
The interaction of neutrophils with T cells has been the subject of debate and controversies. Previous studies have suggested that neutrophils may suppress or activate T cells. Despite these studies, the interaction between neutrophils and T cells has remained a largely unexplored field. Here, based on our RNA sequencing (RNA-seq) analysis, we found that neutrophils have differential transcriptional and functional profiling depending on the CD4 T-cell count of the HIV-infected individual. In particular, we identified that neutrophils in healthy individuals express surface Galectin-9 (Gal-9), which is down-regulated upon activation, and is consistently down-regulated in HIV-infected individuals. However, down-regulation of Gal-9 was associated with CD4 T-cell count of patients. Unstimulated neutrophils express high levels of surface Gal-9 that is bound to CD44, and, upon stimulation, neutrophils depalmitoylate CD44 and induce its movement out of the lipid raft. This process causes the release of Gal-9 from the surface of neutrophils. In addition, we found that neutrophil-derived exogenous Gal-9 binds to cell surface CD44 on T cells, which promotes LCK activation and subsequently enhances T-cell activation. Furthermore, this process was regulated by glycolysis and can be inhibited by interleukin (IL)-10. Together, our data reveal a novel mechanism of Gal-9 shedding from the surface of neutrophils. This could explain elevated plasma Gal-9 levels in HIV-infected individuals as an underlying mechanism of the well-characterized chronic immune activation in HIV infection. This study provides a novel role for the Gal-9 shedding from neutrophils. We anticipate that our results will spark renewed investigation into the role of neutrophils in T-cell activation in other acute and chronic conditions, as well as improved strategies for modulating Gal-9 shedding.
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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 | 0.001 |
| 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 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".