Differential contribution of education through KIR2DL1, KIR2DL3, and KIR3DL1 to antibody-dependent (AD) NK cell activation and ADCC
Bibliographic record
Abstract
Abstract The engagement of activating NK receptors (aNKR) stimulates NK cell activity, provided that interactions between inhibitory NK receptors (iNKR) with their HLA ligands do not override them. Abs bound to target cells can also activate NK cells by engaging the CD16 aNKR. NK cell education status is an important factor for Ab-dependent NK cell activation (ADNKA) of some NK cell subsets. However, whether NK cell education also influences Ab-dependent cellular cytotoxicity (ADCC) levels is not fully known. ADCC-GranToxiLux (GTL) assays measured ADCC activity as the frequency of granzyme B positive (%GzB+) target cells. Target cells were anti-HIV Immunoglobulin G (HIVIG)-opsonized CEM-NKr.CCR5 (CEM) cells. Lymphocytes and sorted single positive (SP) NKG2A+, KIR2DL1+, KIR2DL3+, and KIR3DL1+ NK cells, to self- and nonself HLA, were used as effectors in ADCC-GTL assays to examine how education status influenced ADCC activity. ADNKA activity was assessed by stimulating lymphocytes with HIVIG-opsonized CEMs and measuring the frequency of NK cell populations defined by their expression of iNKRs, along with IFN-γ, CCL4, and CD107a functions. ADCC: the %GzB+ CEM cells generated by self- versus nonself HLA-specific SPiNKR did not differ. ADNKA: More NK cells educated through KIR2DL1 and KIR3DL1, but not KIR2DL3, responded to ADNKA than their uneducated counterparts. CD16 engagement induced ADCC and ADNKA activity. With the proviso that groups’ sizes were small, our results support the notion that NK cell education does not influence ADCC levels but does contribute to ADNKA activity.
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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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".