HLA-F on HLA-Null 721.221 Cells Activates Primary NK Cells Expressing the Activating Killer Ig-like Receptor KIR3DS1
Bibliographic record
Abstract
Abstract NK cells elicit important responses against transformed and virally infected cells. Carriage of the gene encoding the activating killer Ig-like receptor KIR3DS1 is associated with slower time to AIDS and protection from HIV infection. Recently, open conformers of the nonclassical MHC class Ib Ag HLA-F were identified as KIR3DS1 ligands. In this study, we investigated whether the interaction of KIR3DS1 on primary NK cells with HLA-F on the HLA-null cell line 721.221 (221) stimulated KIR3DS1+ NK cells. We used a panel of Abs to detect KIR3DS1+CD56dim NK cells that coexpressed the inhibitory NK cell receptors KIR2DL1/L2/L3, 3DL2, NKG2A, and ILT2; the activating NK cell receptors KIR2DS1/S2/S3/S5; and CCL4, IFN-γ, and CD107a functions. We showed that both untreated and acid-pulsed 221 cells induced a similar frequency of KIR3DS1+ cells to secrete CCL4/IFN-γ and express CD107a with a similar intensity. A higher percentage of KIR3DS1+ than KIR3DS1− NK cells responded to 221 cells when either inclusive or exclusive (i.e., coexpressing none of the other inhibitory NK cell receptors and activating NK cell receptors detected by the Ab panel) gating strategies were employed to identify these NK cell populations. Blocking the interaction of HLA-F on 221 cells with KIR3DS1-Fc chimeric protein or anti–HLA-F Abs on exclusively gated KIR3DS1+ cells reduced the frequency of functional cells compared with that of unblocked conditions for stimulated KIR3DS1+ NK cells. Thus, ligation of KIR3DS1 activates primary NK cells for several antiviral functions.
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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) | 0.004 | 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".