Natural killer cell education does not affect the magnitude of granzyme B delivery to target cells by antibody-dependent cellular cytotoxicity
Bibliographic record
Abstract
OBJECTIVE: Interest in the role of antibody-dependent cellular cytotoxicity (ADCC) in protection from HIV infection has grown since analyses of the RV144 HIV vaccine trial results found ADCC correlated with protection. Natural killer (NK) cells are among the effector cells that mediate ADCC. The level of antibody-induced NK cell activation depends on NK cell education through inhibitory NK cell receptor human leukocyte antigen (HLA) ligand interactions. Here, we investigated the impact of NK cell education on the delivery of Granzyme B (GzB) to target cells. DESIGN: Lymphocytes from 50 HIV-uninfected [30 Bw4 (Bw4) and 20 Bw4 (Bw6)] KIR3DL1 homozygote persons were used as effectors and cocultured with gp120-coated target cells in the presence of a single source of anti-HIV gp120 antibody to ascertain whether NK cell education status influenced the level of GzB delivered to target cells. METHODS: The GTL assay assessed the frequency of GzB-positive (%GzB) CEM.NKr.CCR5 target cells generated by effectors from each individual. The frequency of CD107a, interferon (IFN)-γ and CCL4 NK cells was assessed as a measure of antibody-induced NK cell activation. RESULTS: KIR3DL1 NK cells from the Bw4 group were more functional than KIR3DL1 NK cells. Despite this, the %GzB target cells generated in the GTL assay did not differ according to the KIR3DL1-HLA-B genotype of the effector cells. The %GzB cells positively correlated with the frequency of CD16KIR3DL1 NK cells in the effector population. CONCLUSION: ADCC potency does not depend on NK cell education.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".