The licensing of natural killer cells (113.3)
Bibliographic record
Abstract
Abstract The interaction between inhibitory Ly49 receptors and MHCI is important for the functional maturation of NK cells, known as “NK cell licensing.” NK cells from MHCI-deficient β2microglobulin (β2m)-KO mice are not cytotoxic and cannot efficiently kill their targets, as they are not licensed. Our goal is to determine the mechanism through which β2m-KO NK cells are kept hyporesponsive to otherwise NK-sensitive targets. Comparison through cytotoxicity assays confirmed that ex-vivo NK cells from poly-IC-injected wild type (WT) mice kill the prototypic YAC1 target, whereas NK cells from β2m-KO mice do not. Interestingly, both express similar levels of granzyme B and adhere to immobilized ICAM-1, the ligand for LFA-1. Further, confocal analysis of NK cells interacting with YAC1 showed that WT NK cells aggregate and polarize lytic granules toward their targets, while β2m-KO NK cells are deficient in granule polarization. Once stimulated by plastic beads coated with ICAM-1, both WT and β2m-KO NK cells reorganize their actin cytoskeleton. Stimulation by beads coated with ICAM-1, H60 and CD48 (ligands for stimulatory NKG2D and 2B4 receptors), induces aggregation and polarization of lytic granules in WT but not in β2m-KO NK cells. This suggests that critical components of the cytotoxicity pathway generated by stimulatory receptor interactions are impaired in β2m-KO NK cells, preventing aggregation and polarization of cytotoxic granules, and the release of their contents towards targets.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".