NK Cells Expressing the Inhibitory Killer Immunoglobulin-Like Receptors (iKIR) KIR2DL1, KIR2DL3 and KIR3DL1 Are Less Likely to Be CD16+ than Their iKIR Negative Counterparts
Bibliographic record
Abstract
Natural Killer (NK) cell education, which requires the engagement of inhibitory NK cell receptors (iNKRs) by their ligands, is important for generating self-tolerant functional NK cells. While the potency of NK cell education is directly related to their functional potential upon stimulation with HLA null cells, the influence of NK cell education on the potency of the antibody dependent cellular cytotoxicity (ADCC) function of NK cells is unclear. ADCC occurs when the Fc portion of an immunoglobulin G antibody bridges the CD16 Fc receptor on NK cells and antigen on target cells, resulting in NK cell activation, cytotoxic granule release, and target cell lysis. We previously reported that education via the KIR3DL1/HLA-Bw4 iNKR/HLA ligand combination supported higher KIR3DL1+ than KIR3DL1- NK cell activation levels but had no impact on ADCC potency measured as the frequency of granzyme B positive (%GrB+) targets generated in an ADCC GranToxiLux assay. A lower frequency of KIR3DL1+ compared to KIR3DL1- NK cells were CD16+, which may in part explain the discrepancy between NK cell activation and target cell effects. Here, we investigated the frequency of CD16+ cells among NK cells expressing other iNKRs. We found that CD16+ cells were significantly more frequent among NK cells negative for the inhibitory KIR (iKIR) KIR2DL1, KIR2DL3, and KIR3DL1 than those positive for any one of these iKIR to the exclusion of the others, making iKIR+ NK cells poorer ADCC effectors than iKIR- NK cells. The education status of these iKIR+ populations had no effect on the frequency of CD16+ cells.
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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) | 0.003 | 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".