The Rat RT1-A1c MHC Molecule Is a Xenogeneic Ligand Recognized by the Mouse Activating Ly-49W and Inhibitory Ly-49G Receptors
Bibliographic record
Abstract
Mouse Ly-49 receptors are known to recognize xenogeneic ligands from hamster and rat. However, until now, there has been no description of a specific rat xenogeneic ligand for any mouse Ly-49 receptor. In this report, we identify RT1-A1c, a rat classical class I MHC molecule, as a ligand for the Ly-49G(BALB/c) inhibitory receptor and the closely related activating receptor, Ly-49W. Xenogeneic class I recognition of targets from PVG but not DA strain rats was mapped to the classical region of the RT1c haplotype by using Con A blasts from RT1c/RT1av1 intra-MHC recombinant rats as targets for RNK-16 cells expressing either Ly-49W or Ly-49G(BALB/c) receptors. Individual expression of class I molecules from PVG and DA rat strains in YB2/0 target cells demonstrate the xenogeneic recognition to be allele specific, because other class I molecules of the RT1c haplotype, RT1-A2c and RT1-U2c, and a classical class I molecule encoded by the RT1av1 haplotype, RT1-Aa, are not recognized by Ly-49W and -G(BALB/c). Furthermore, specificity for RT1-Ac can be transferred from Ly-49W to Ly-49P, which is normally unable to recognize RT1-Ac, by substitution of three residues shared by Ly-49W and -G(BALB/c) but not Ly-49P. These residues are located in the Ly-49 beta4-beta5 loop, which can determine class I allele specificity in mouse Ly-49 receptor interactions with mouse class I ligands, suggesting that mouse Ly-49 recognition of rat class I molecules follows similar principles of interaction. These findings have implications for xenotransplantation studies and for discerning Ly-49 recognition motifs present in MHC molecules.
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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) | 0.001 | 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".