Degenerate Recognition of MHC Class I Molecules with Bw4 and Bw6 Motifs by a Killer Cell Ig-like Receptor 3DL Expressed by Macaque NK Cells
Bibliographic record
Abstract
The killer cell Ig-like receptors (KIRs) expressed on the surface of NK cells recognize specific MHC class I (MHC-I) molecules and regulate NK cell activities against pathogen-infected cells and neoplasia. In HIV infection, survival is linked to host KIR and MHC-I genotypes. In the SIV macaque model, however, the role of NK cells is unclear due to the lack of information on KIR-MHC interactions. In this study, we describe, to our knowledge, the first in-depth characterization of KIR-MHC interactions in pigtailed macaques (Macaca nemestrina). Initially, we identified three distinct subsets of macaque NK cells that stained ex vivo with macaque MHC-I tetramers loaded with SIV peptides. We then cloned cDNAs corresponding to 15 distinct KIR3D alleles. One of these, KIR049-4, was an inhibitory KIR3DL that bound MHC-I tetramers and prevented activation, degranulation, and cytokine production by macaque NK cells after engagement with specific MHC-I molecules on the surface of target cells. Furthermore, KIR049-4 recognized a broad range of MHC-I molecules carrying not only the Bw4 motif, but also Bw6 and non-Bw4/Bw6 motifs. This degenerate, yet peptide-dependent, MHC reactivity differs markedly from the fine specificity of human KIRs.
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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) | 0.001 | 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".