SLAMF7 selectively favors degranulation to promote cytotoxicity in human NK cells
Bibliographic record
Abstract
NK cells play an important role in immunity by recognizing and eliminating cells undergoing infection or malignant transformation. This role is dependent on the ability of NK cells to lyse targets cells in a perforin-dependent mechanism and by secreting inflammatory cytokines. Both effector functions are controlled by several cell surface receptors. The Signaling Lymphocyte Activation Molecule (SLAM) family of receptors plays an essential role in regulating NK cell activation. Several studies have demonstrated that SLAMF7 regulates NK cell activation. However, the molecular and cellular mechanisms by which SLAMF7 influences NK effector functions are unknown. Here, we present evidence that physiological ligation of SLAMF7 in human NK cells enhances the lysis of target cells expressing SLAMF7. This effect was dependent on the ability of SLAMF7 to promote NK cell degranulation rather than cytotoxic granule polarization or cell adhesion. Moreover, SLAMF7-dependent NK cell degranulation was predominantly dependent on PLC-γ when compared to PI3K. These data provide novel information on the cellular mechanism by which SLAMF7 regulates human NK cell activation. Finally, this study supports a model for NK cell activation where activated receptors contribute by regulating specific discrete cellular events rather than multiple cellular processes.
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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) | 0.002 | 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".