Natural Killer Cells Mediate Long-term Kidney Allograft Injury
Bibliographic record
Abstract
BACKGROUND: Chronic allograft injury remains the leading cause of late kidney graft loss despite improvements in immunosuppressive drugs and a reduction in acute T cell-mediated rejection. We have recently demonstrated that natural killer (NK) cells are cytotoxic to tubular epithelial cells and contribute to acute kidney ischemia-reperfusion injury. The role of NK cells in kidney allograft rejection has not been studied. METHODS: A "parent to F1" kidney transplant model was used to study NK cell-mediated transplant rejection. RESULTS: The C57BL/6 kidneys were transplanted into fully nephrectomized CB6F1 (C57BL/6 x BALB/c) mice. Serum creatinine levels increased from baseline (18.8 ± 5.0 μmol/L to 37.2 ± 5.9 μmol/L, P < 0.001) at 60 days after transplantation. B6Rag-to-CB6F1Rag (B6RagxBALB/cRag) recipients, which lack T and B cells but retain NK cells, showed similar levels of kidney dysfunction 65 days after transplantation (creatinine, 33.8 ± 7.9 μmol/L vs 17.5 ± 5.1 μmol/L in nontransplant Rag mice, P < 0.05). Importantly, depletion of NK cells in Rag1 recipients inhibited kidney injury (24.6 ± 5.5 μmol/L, P < 0.05). Osteopontin, which can activate NK cells to mediate tubular epithelial cell death in vitro, was highly expressed in 60 days kidney grafts. Osteopontin null kidney grafts had reduced injury after transplantation into CB6F1 mice (17.7 ± 3.1 μmol/L, P < 0.001). CONCLUSIONS: Collectively, these data demonstrate for the first time that independent of T and B cells, NK cells have a critical role in mediating long-term transplant kidney injury. Specific therapeutic strategies that target NK cells in addition to conventional immunosuppression may be required to attenuate chronic kidney transplant injury.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".