Activating Ly-49 Receptors Regulate LFA-1-Mediated Adhesion by NK Cells
Bibliographic record
Abstract
NK cells are important for innate resistance to tumors and viruses. Engagement of activating Ly-49 receptors expressed by NK cells leads to rapid NK cell activation resulting in target cell lysis and cytokine production. The ITAM-containing DAP12 adapter protein stably associates with activating Ly-49 receptors, and couples receptor recognition with generation of NK responses. Activating Ly-49s are potent stimulators of murine NK cell functions, yet how they mediate such activities is not well understood. We demonstrate that these receptors trigger LFA-1-dependent tight conjugation between NK cells and target cells. Furthermore, we show that activating Ly-49 receptor engagement leads to rapid DAP12-dependent up-regulation of NK cell LFA-1 adhesiveness to ICAM-1 that is also dependent on tyrosine kinases of the Syk and Src families. These results indicate for the first time that activating Ly-49s control adhesive properties of LFA-1, and by DAP12-dependent inside-out signaling. Ly-49-driven mobilization of LFA-1 adhesive function may represent a fundamental proximal event during NK cell interactions with target cells involving activating Ly-49 receptors, leading to target cell death.
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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) | 0.003 | 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".