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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; both teacher heads agree on what is shown here.
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".