Genetic Investigation of MHC-Independent Missing-Self Recognition by Mouse NK Cells Using an In Vivo Bone Marrow Transplantation Model
Bibliographic record
Abstract
MHC-I-specific receptors play a vital role in NK cell-mediated "missing-self" recognition, which contributes to NK cell activation. In contrast, MHC-independent NK recognition mechanisms are less well characterized. In this study, we investigated the role of NKR-P1B:Clr-b (Klrb1:Clec2d) interactions in determining the outcome of murine hematopoietic cell transplantation in vivo. Using a competitive transplant assay, we show that Clr-b(-/-) bone marrow (BM) cells were selectively rejected by wild-type B6 recipients, to a similar extent as H-2D(b-/-) MHC-I-deficient BM cells. Selective rejection of Clr-b(-/-) BM cells was mitigated by NK depletion of recipient mice. Competitive rejection of Clr-b(-/-) BM cells also occurred in allogeneic transplant recipients, where it was reversed by selective depletion of NKR-P1B(hi) NK cells, leaving the remaining NKR-P1B(lo) NK subset and MHC-I-dependent missing-self recognition intact. Moreover, competitive rejection of Clr-b(-/-) hematopoietic cells was abrogated in Nkrp1b-deficient recipients, which lack the receptor for Clr-b. Of interest, similar to MHC-I-deficient NK cells, Clr-b(-/-) NK cells were hyporesponsive to both NK1.1 (NKR-P1C)-stimulated and IL-12/18 cytokine-primed IFN-γ production. These findings support a unique and nonredundant role for NKR-P1B:Clr-b interactions in missing-self recognition of normal hematopoietic cells and suggest that optimal BM transplant success relies on MHC-independent tolerance mechanisms. These findings provide a model for human NKR-P1A:LLT1 (KLRB1:CLEC2D) interactions in human hematopoietic cell transplants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".