Receptor/ligand avidity determines the capacity of Ly49 inhibitory receptors to interfere with T‐cell receptor‐mediated activation
Bibliographic record
Abstract
The specificity and the relative affinity of many Ly49 receptors for major histocompatibility complex class I ligands have been studied in detail in various adhesion and binding assays. However, how the level of cell surface expression of a given Ly49 receptor and its ligand affinity influence the strength of the inhibition signal is not well documented. To address this issue, we developed a series of human Jurkat T-cell transfectants expressing the whole range of Ly49A and Ly49C levels found in vivo on natural killer and T cells and evaluated their capacity to alter superantigen-induced NF-AT activation and interleukin-2 production. We show that the strength of the inhibition induced by Ly49A/H-2Dd interaction correlates with Ly49A density up to a certain level after which increasing expression does not further inhibit significantly the T-cell receptor-induced activation. This system also represents a valuable tool for the determination of the relative strength of the inhibitory signals of Ly49 receptors following their interactions with different ligands. Even at high levels of expression there was no evidence that engagement of Ly49A with H-2b class I molecules provided an inhibitory signal. Moreover, we showed that functional inhibitory interactions of Ly49C with H-2b class I molecules were only the result of H-2Kb and that H-2d represent lower affinity ligands for Ly49C than H-2b. Therefore, depending on the relative affinity of Ly49 receptors for their ligands, the modulation of their expression level will be determinant for the functional outcome of activated T cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".