CD1d1 Displayed on Cell Size Beads Identifies and Enriches an NK Cell Population Negatively Regulated by CD1d1
Bibliographic record
Abstract
NK cells destroy microbe-infected cells while sparing healthy cells, and are controlled, in part, by inhibitory receptors specific for class I Ag-presenting molecules. CD1d1, a beta(2)-microglobulin-associated class I-like molecule, binds glycolipids and stimulates NKT cells. We previously demonstrated that target cell lysis by IL-2-activated mouse NK cells is inhibited by target cell expression of CD1d1, suggesting that IL-2-activated NK cells may express a CD1d1-specific inhibitory receptor. We now report that a significant subset of mouse IL-2-activated NK cells specifically binds cell size beads displaying either naturally expressed or recombinant CD1d1. In contrast, although tetramers of soluble recombinant CD1d1 loaded with alpha-galactosylceramide identify NKT cells, binding of this reagent to resting or IL-2-activated NK cells was undetectable, even with activated NK cells sorted with CD1d1 beads. Cytotoxicity by the CD1d1 bead-separated NK subset was strongly inhibited by CD1d1, compared with the NK cell subset not bound to CD1d1 beads. An Ab that blocks NKT cell recognition of CD1d1 also reverses CD1d1 inhibition of NK lysis, suggesting that TCRs of NKT cells and NK inhibitory receptor(s) may interact with a similar site on CD1d1. These results provide direct evidence for a physical interaction of NK cells with CD1d1, mediated by a functional, CD1d1-specific low-affinity inhibitory NK receptor. Display of ligands on cell size beads to maximize multivalent interaction may offer an alternative approach to examine NK cell receptor-ligand interactions, particularly those of lower expression and/or lower affinity/avidity that may go undetected using tetrameric reagents.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".