Functional Requirements for Signaling through the Stimulatory and Inhibitory Mouse NKR-P1 (CD161) NK Cell Receptors
Bibliographic record
Abstract
The NK cell receptor protein 1 (NKR-P1) (CD161) molecules represent a family of type II transmembrane C-type lectin-like receptors expressed predominantly by NK cells. Despite sharing a common NK1.1 epitope, the mouse NKR-P1B and NKR-P1C receptors possess opposing functions in NK cell signaling. Engagement of NKR-P1C stimulates cytotoxicity of target cells, Ca2+ flux, phosphatidylinositol turnover, kinase activity, and cytokine production. In contrast, NKR-P1B engagement inhibits NK cell cytotoxicity. Nonetheless, it remains unclear how different signaling outcomes are mediated at the molecular level. Here, we demonstrate that both NKR-P1B and NKR-P1C associate with the tyrosine kinase, p56(lck). The interaction is mediated through the di-cysteine CxCP motif in the cytoplasmic domains of NKR-P1B/C. Disrupting this motif leads to abrogation of both stimulatory and inhibitory NKR-P1 signals. In addition, mutation of the consensus ITIM (LxYxxL) in NKR-P1B abolishes both its Src homology 2-containing protein tyrosine phosphatase-1 recruitment and inhibitory function. Strikingly, engagement of NKR-P1C on NK cells obtained from Lck-deficient mice failed to induce NK cytotoxicity. These results reveal a role for Lck in the initiation of NKR-P1 signals, and demonstrate a requirement for the ITIM in NKR-P1-mediated inhibition.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".