Influence of Xenogeneic β2-Microglobulin on Functional Recognition of H-2Kb by the NK Cell Inhibitory Receptor Ly49C
Bibliographic record
Abstract
NK cells maintain self-tolerance through expression of inhibitory receptors that bind MHC class I (MHC-I) molecules. MHC-I can exist on the cell surface in several different forms, including "peptide-receptive" or PR-MHC-I that can bind exogenous peptide. PR-MHC-I molecules are short lived and, for H-2K(b), comprise approximately 10% of total MHC-I. In the present study, we confirm that signaling through the mouse NK inhibitory receptor Ly49C requires the presence of PR-K(b) and that this signaling is prevented when PR-K(b) is ablated by pulsing with a peptide that can bind to it with high affinity. Although crystallographic data indicate that Ly49C can engage H-2K(b) loaded with high-affinity peptide, our data suggest that this interaction does not generate an inhibitory signal. We also show that no signaling occurs when the PR-K(b) complex has mouse beta(2)-microglobulin (beta(2)m) replaced with human beta(2)m, although replacement with bovine beta(2)m has no effect. Furthermore, we show that beta(2)m exchange occurs preferentially in the PR-K(b) component of total H-2K(b). These conclusions were reached in studies modulating the sensitivity to lysis of both NK-resistant syngeneic lymphoblasts and NK-sensitive RMA-S tumor cells. We also show, using an in vivo model of lymphocyte recirculation, that engrafted lymphocytes are unable to survive NK attack when otherwise syngeneic lymphocytes express human beta(2)m. These findings suggest a qualitative extension of the "missing self" hypothesis to include NK inhibitory receptors that are restricted to the recognition of unstable forms of MHC-I, thus enabling NK cells to respond more quickly to events that decrease MHC-I synthesis.
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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".