Galectin-9 promotes natural killer cells activity via interaction with CD44
Bibliographic record
Abstract
Natural killer (NK) cells are a potent innate source of cytokines and cytoplasmic granules. Their effector functions are tightly synchronized by the balance between the stimulatory and inhibitory receptors. Here, we quantified the proportion of NK cells and the surface presence of Galectin-9 (Gal-9) from the bone marrow, blood, liver, spleen, and lungs of adult and neonatal mice. We also examined the effector functions of Gal-9+NK cells compared with their Gal-9- counterparts. Our results revealed that Gal-9+NK cells are more abundant in tissues, in particular, in the liver than in the blood and bone marrow. We found Gal-9 presence was associated with enhanced cytotoxic effector molecules granzyme B (GzmB) and perforin expression. Likewise, Gal-9 expressing NK cells displayed greater IFN-γ and TNF-α expression than their negative counterparts under hemostatic circumstances. Notably, the expansion of Gal-9+NK cells in the spleen of mice infected with E. coli implies that Gal-9+NK cells may provide a protective role against infection. Similarly, we found the expansion of Gal-9+NK cells in the spleen and tumor tissues of melanoma B16-F10 mice. Mechanistically, our results revealed the interaction of Gal-9 with CD44 as noted by their co-expression/co-localization. Subsequently, this interaction resulted in enhanced expression of Phospho-LCK, ERK, Akt, MAPK, and mTOR in NK cells. Moreover, we found Gal-9+NK cells exhibited an activated phenotype as evidenced by increased CD69, CD25, and Sca-1 but reduced KLRG1 expression. Likewise, we found Gal-9 preferentially interacts with CD44high in human NK cells. Despite this interaction, we noted a dichotomy in terms of effector functions in NK cells from COVID-19 patients. We observed that the presence of Gal-9 on NK cells resulted in a greater IFN-γ expression without any changes in cytolytic molecule expression in these patients. These observations suggest differences in Gal-9+NK cell effector functions between mice and humans that should be considered in different physiological and pathological conditions. Therefore, our results highlight the important role of Gal-9 via CD44 in NK cell activation, which suggests Gal-9 is a potential new avenue for the development of therapeutic approaches to modulate NK cell effector functions.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".