Effect of Ly49 Haplotype Variance on NK Cell Function and Education
Bibliographic record
Abstract
The class I MHC-specific receptors expressed by murine NK cells exhibit remarkable variation. Specific activating killer Ig-related receptor/Ly49 have major effects on autoimmune and infectious disease induction and outcome in humans and mice. However, these studies are greatly affected by individual background genetics. Furthermore, the educational impact of variable inhibitory KIR/Ly49 gene numbers on NK cell development and the subsequent ability to survey for MHC class I (MHC-I) expression remain unknown. To address these questions, Ly49 congenic mice were generated that maintain a 129-derived Ly49 gene cluster on a C57BL/6 genetic background (B6.Ly49(129) mice), and the in vitro and in vivo NK cell function of these mice was compared with their inbred parental 129S1 and C57BL/6 counterparts. Notably, target cell recognition directed by activating Ly49 receptors was profoundly affected by allelic variation in B6.Ly49(129) congenic cells versus C57BL/6 NK cells. Furthermore, when assessing NK cell function based on education and subsequent recognition of the C57BL/6 MHC-I haplotype by inhibitory Ly49 receptors, B6.Ly49(129) congenic mice exhibited robust NK cell activity, demonstrating efficient NK cell education by the 129S1 Ly49 cluster during development. The responsiveness of NK cells expressing 129S1 Ly49 was shown to be mediated by subsets expressing one or more self-MHC receptors, including Ly49I, Ly49O, Ly49V, and NKG2A. These findings demonstrate that the genetically segregating and diverse MHC-I and Ly49 loci in mice exhibit independent and epistatic effects on NK cell education that can be uncoupled during the intercrossing of inbred strains.
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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 | 0.001 |
| 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".