Deletion of <i>Slam</i> locus in mice reveals inhibitory role of SLAM family in NK cell responses regulated by cytokines and LFA-1
Bibliographic record
Abstract
Signaling lymphocytic activation molecule (SLAM) family receptors (SFRs) can mediate either activating or inhibitory effects during natural killer cell (NK cell) activation. In this study, we addressed the global role, regulation, and mechanism of action of the SLAM family in NK cells by analyzing a mouse lacking the entire ∼400-kilobase Slam locus, which encodes all six SFRs and CD48, the ligand of SFR 2B4. This mouse displayed enhanced NK cell activation responses toward hematopoietic target cells. Analyses of mice lacking individual SFRs showed that the inhibitory function of the Slam locus was due solely to 2B4 and was not influenced positively or negatively by other SFRs. Differences in NK cell responses between recognition of targets expressing or lacking ligands for SFRs were enhanced by IL-12 but suppressed by type I interferon. Cytokines also changed the levels of SLAM-associated protein adaptors, which prevent the inhibitory function of SFRs. The enhanced activation responses of SFR-deficient NK cells were dependent on integrin LFA-1 but not on DNAM-1 or NKG2D. SFR-mediated inhibition prevented the generation of activated forms of LFA-1. Hence, the Slam locus has an overall inhibitory role during NK cell activation that is solely dependent on 2B4. This effect is influenced by cytokines and leads to suppression of LFA-1 activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".