Killer cell immunoglobulin-like receptor 3DL1 licenses CD16-mediated effector functions of natural killer cells
Bibliographic record
Abstract
Activating receptor-mediated recognition of stress-induced ligands or IgG antibody bridging of tumor or pathogen-associated antigens to the FcγRIII CD16 triggers NK cells to kill transformed and infected cells with reduced HLA-I expression. According to the licensing hypothesis, NK cells become competent for activating receptor-mediated triggering after a formative encounter between a NK inhibitory receptor and its ligand. This general hypothesis is supported by murine and human studies, but to date, evidence of a role for such licensing in human ADCC is ambiguous. Inhibitory receptor interactions with HLA-C promote NK cell ADCC licensing, but interactions between KIR3DL1 and its HLA-Bw4 ligand may be insufficient. We investigated the impact of KIR3DL1 and HLA-Bw4 coexpression on NK cell ADCC using a robust, genuine target system of antibody-bearing EBV-transformed B lymphocytes. Although numbers of KIR3DL1(+) NK cells were similar in HLA-Bw4(+) and HLA-Bw4(-) individuals, general levels of ADCC mediated against target cells were significantly higher in a group of HLA-Bw4(+)KIR3DL1(+) individuals than in a comparable HLA-Bw4(-) group. Flow cytometry demonstrated directly that a significantly higher fraction of KIR3DL1(+) NK cells derived from HLA-Bw4(+) compared with HLA-Bw4(-) individuals produced IFN-γ following stimulation with ADCC targets. Murine FcR-bearing P815 target cells also triggered higher levels of CD16-mediated cytotoxicity by NK cells from HLA-Bw4(+)KIR3DL1(+) individuals. These results indicate a prominent role for KIR3DL1/HLA-Bw4 interactions in licensing NK cells for CD16-mediated effector function.
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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".