Human NKG2D ligand regulation of Natural Killer cell function and its implications in Cancer and Inflammation
Bibliographic record
Abstract
Abstract NKG2D is an activating lymphocyte receptor which plays an essential role in stimulating immune responses against tumors. The importance of ligand-induced activation of NKG2D in controlling tumor growth has been well established in various animal models. Amongst diverse NKG2D ligands, MHC I chain related molecule MICA/B is expressed most prevalently in human cancers, but absent in rodents. Proteolytically shed-form of MICA/B (sMIC) has been shown to correlate with advanced cancer stages in patients. Previous studies in our lab using two humanized bi-transgenic mice, one expressing native ligand (TRAMP/MICB) and alternatively expressing shedding-resistant mutant (TRAMP/MICB.A2) revealed opposite roles of the two forms of MIC ligand in tumor immunity. Mice with high levels of sMIC exhibited augmented tumor progression, whereas mice expressing MICB.A2 in tumors enjoyed tumor-free survival; raising an intriguing question of why do these similar forms of MIC have different effects on tumor immunity. To address this, we investigated the signaling events and functional outcomes in NK cells upon stimulation by the two forms of ligands. Co-culture studies of NK cells with tumor cells expressing sMICB and MICB.A2 revealed elevated pro-inflammatory cytokine production by NK cells upon stimulation with sMICB. In contrast, NK cells stimulated with MICB.A2 displayed enhanced expression of cytotoxicity mediators and signaling molecules of cytotoxicity pathway. This suggests that sMICB may polarize the NKG2D signaling pathways with preferential activation of inflammatory cytokine pathways. Our data has uncovered a potential mechanism by which sMIC promotes tumor progression and endorses sMIC as a viable target for cancer immunotherapy.
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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 | 0.000 |
| 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 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".