The transfer of host MHC class I protein protects donor cells from NK cell and macrophage-mediated rejection during hematopoietic stem cell transplantation and engraftment in mice
Bibliographic record
Abstract
Human hematopoietic stem cell engraftment has been studied extensively using xenograft transplant models with immunocompromised mice. It is standard practice to incorporate mouse models, such as the limiting dilution assay, to accurately assess the number of repopulating stem cells in bone marrow or umbilical cord blood collections or to confirm the long-term repopulating ability of cultured hematopoietic stem cells. In a previous study using a standard NOD/SCID mouse model to assess human hematopoietic stem cell engraftment we observed that all human cells had mouse MHC class I protein on their surface, suggesting that this is a mechanism adopted by the cells to evade host immune surveillance. To determine whether this was a xenograft phenomenon we studied host MHC transfer in an intraspecies mouse model and observed similar results. The transfer of MHC class I proteins has implications for antigen presentation and immune modulation. In this report, we used a standard mouse model of bone marrow transplantation to demonstrate that surface protein transfer between cells plays an important role in protecting donor hematopoietic cells from NK cell and macrophage-mediated rejection. The transfer of intact MHC class I antigens from host cells to transplanted donor cells confers a self identity on these otherwise foreign cells. This gives them the ability to evade detection by the host NK cells and macrophages. Once full donor chimerism is established, transplanted cells no longer require host MHC class I protein transfer to survive.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".