Optimized Tetramer Analysis Reveals Ly49 Promiscuity for MHC Ligands
Bibliographic record
Abstract
Murine Ly49 receptors, which are expressed mainly on NK and NKT cells, interact with MHC class I (MHC-I) molecules with varying specificity. Differing reports of Ly49/MHC binding affinities may be affected by multiple factors, including cis versus trans competition and species origin of the MHC-I L chain (β2-microglobulin). To determine the contribution of each of these factors, Ly49G, Ly49I, Ly49O, Ly49V, and Ly49Q receptors from the 129 mouse strain were expressed individually on human 293T cells or the mouse cell lines MHC-I-deficient C1498, H-2(b)-expressing MC57G, and H-2(k)-expressing L929. The capacity to bind to H-2D(b)- and H-2K(b)-soluble MHC-I tetramers containing either human or murine β2-microglobulin L chains was tested for all five Ly49 receptors in all four cell lines. We found that most of these five inhibitory Ly49 receptors show binding for one or both self-MHC-I molecules in soluble tetramer binding assays when three conditions are fulfilled: 1) lack of competing cis interactions, 2) tetramer L chain is of mouse origin, and 3) Ly49 is expressed in mouse and not human cell lines. Furthermore, Ly49Q, the single known MHC-I receptor on plasmacytoid dendritic cells, was shown to bind H-2D(b) in addition to H-2K(b) when the above conditions were met, suggesting that Ly49Q functions as a pan-MHC-Ia receptor on plasmacytoid dendritic cells. In this study, we have optimized the parameters for soluble tetramer binding analyses to enhance future Ly49 ligand identification and to better evaluate specific contributions by different Ly49/MHC-I pairs to NK cell education and function.
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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) | 0.002 | 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".