Murine CD160, Ig-Like Receptor on NK Cells and NKT Cells, Recognizes Classical and Nonclassical MHC Class I and Regulates NK Cell Activation
Bibliographic record
Abstract
Human and mouse NK cells use different families of receptors to recognize MHC class I (MHC I) on target cells. Although human NK cells express both Ig-like receptors and lectin-like receptors specific for MHC I, all the MHC I-specific receptors identified on mouse NK cells to date are lectin-like receptors, and no Ig-like receptors recognizing MHC I have been identified on mouse NK cells. In this study we report the first MHC I-specific Ig-like receptor on mouse NK cells, namely, murine CD160 (mCD160). The expression of mCD160 is restricted to a subset of NK cells, NK1.1+ T cells, and activated CD8+ T cells. The mCD160-Ig fusion protein binds to rat cell lines transfected with classical and nonclassical mouse MHC I, including CD1d. Furthermore, the level of mCD160 on NK1.1+ T cells is modulated by MHC I of the host. Overexpression of mCD160 in the mouse NK cell line KY-2 inhibits IFN-gamma production induced by phorbol ester plus ionomycin, whereas it enhances IFN-gamma production induced by NK1.1 cross-linking or incubation with dendritic cells. Cross-linking of mCD160 also inhibits anti-NK1.1-mediated stimulation of KY-2 cells. Anti-mCD160 mAb alone has no effect. Thus, mCD160, the first MHC I-specific Ig-like receptor on mouse NK cells, regulates NK cell activation both positively and negatively, depending on the stimulus.
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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 | 0.001 | 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 | 0.001 |
| 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".