Ly49 receptors: evolution, genetic diversity, and impact on immunity
Bibliographic record
Abstract
Natural killer (NK) cells express cell surface receptors that recognize class I major histocompatibility complex (MHC-I) molecules to distinguish between healthy and unhealthy cells. The multigenic and polymorphic nature of the MHC-I genes has influenced the convergent evolution of similarly polymorphic and diversified NK cell receptor families: the C-type lectin-like Ly49 receptors in mice, and the killer cell immunoglobulin-like receptors (KIRs) in humans. Although structurally distinct, both receptor families have similar functions in terms of MHC-I recognition and downstream signal transduction, and they regulate multiple aspects of NK cell biology during development and after maturation as fully differentiated and functionally competent cells. The Ly49 gene locus has undergone rapid, lineage-specific expansions and contractions resulting in multiple distinct haplotypes of variable gene number, allelic diversity, and MHC-I ligand specificity. This in turn has influenced the type and degree of Ly49 receptor expression on NK cells, and their contribution to immunity in different mouse strains. In this review, we have attempted to describe the evolutionary processes that have shaped strain-specific Ly49 receptor repertoires, and their impact on NK cell functions during health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".