Differential Role of Hematopoietic and Nonhematopoietic Cell Types in the Regulation of NK Cell Tolerance and Responsiveness
Bibliographic record
Abstract
Many NK cells express inhibitory receptors that bind self-MHC class I (MHC I) molecules and prevent killing of self-cells, while enabling killing of MHC I-deficient cells. But tolerance also occurs for NK cells that lack inhibitory receptors for self-MHC I, and for all NK cells in MHC I-deficient animals. In both cases, NK cells are unresponsive to MHC I-deficient cells and hyporesponsive when stimulated through activating receptors, suggesting that hyporesponsiveness is responsible for self-tolerance. We generated irradiation chimeras, or carried out adoptive transfers, with wild-type (WT) and/or MHC I-deficient hematopoietic cells in WT or MHC I-deficient C57BL/6 host mice. Unexpectedly, in WT hosts, donor MHC I-deficient hematopoietic cells failed to induce hyporesponsiveness to activating receptor stimulation, but did induce tolerance to MHC I-deficient grafts. Therefore, these two properties of NK cells are separable. Both tolerance and hyporesponsiveness occurred when the host was MHC I deficient. Interestingly, infections of mice or exposure to inflammatory cytokines reversed the tolerance of NK cells that was induced by MHC I-deficient hematopoietic cells, but not the tolerance induced by MHC I-deficient nonhematopoietic cells. These data have implications for successful bone marrow transplantation, and suggest that tolerance induced by hematopoietic cells versus nonhematopoietic cells may be imposed by distinct mechanisms.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".