Impaired natural killer cell self-education and “missing-self” responses in Ly49-deficient mice
Bibliographic record
Abstract
Ly49-mediated recognition of MHC-I molecules on host cells is considered vital for natural killer (NK)-cell regulation and education; however, gene-deficient animal models are lacking because of the difficulty in deleting this large multigene family. Here, we describe NK gene complex knockdown (NKC(KD)) mice that lack expression of Ly49 and related MHC-I receptors on most NK cells. NKC(KD) NK cells exhibit defective killing of MHC-I-deficient, but otherwise normal, target cells, resulting in defective rejection by NKC(KD) mice of transplants from various types of MHC-I-deficient mice. Self-MHC-I immunosurveillance by NK cells in NKC(KD) mice can be rescued by self-MHC-I-specific Ly49 transgenes. Although NKC(KD) mice display defective recognition of MHC-I-deficient tumor cells, resulting in decreased in vivo tumor cell clearance, NKG2D- or antibody-dependent cell-mediated cytotoxicity-induced tumor cell cytotoxicity and cytokine production induced by activation receptors was efficient in Ly49-deficient NK cells, suggesting MHC-I education of NK cells is a single facet regulating their total potential. These results provide direct genetic evidence that Ly49 expression is necessary for NK-cell education to self-MHC-I molecules and that the absence of these receptors leads to loss of MHC-I-dependent "missing-self" immunosurveillance by NK 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".