NK Cells Mediate Chronic Kidney Allograft Injury.
Bibliographic record
Abstract
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 NK cells are cytotoxic to tubular epithelial cells (TECs) and contribute to acute kidney ischemia-reperfusion injury (IRI). The role of NK cells in kidney allograft rejection has not been studied. A parent to F1 transplant model in which T cell tolerance is present, was utilized for these studies. C57BL/6 kidneys were transplanted into fully nephrectomized F1 (C57BL/6 x BALB/c, CB6F1) mice. Serum creatinine levels increased from baseline (18.8+5.0 umol/l to 37.2±5.9 umol/l, P<0.001) at 60 days post transplantation. In further support of this, in B6Rag-/--to- CB6F1Rag-/- (B6Rag-/-xBALB/cRag-/-) F1 kidney transplants which lack T and B cells but retain NK cells, similar levels of kidney dysfunction were observed 65 days post transplant (creatinine: 33.8+7.9 umol/l vs. 17.5+5.1 umol/l in non-transplant Rag-/- mice, P<0.05). Depletion of NK cells in Rag1-/- mice prevented kidney injury (24.6±5.5 umol/l, p<0.05). Interestingly, osteopontin (OPN) was highly expressed in 60 days kidney grafts, and OPN deficiency in kidney graft had reduced chronic injury after transplantation (17.7+3.1 umol/l, P<0.001). OPN activated NK cells can mediate TEC death in vitro. In conclusion, these data demonstrate for the first time a critical role for NK cells in mediating chronic kidney injury in transplantation, which is independent of T and B cells. It may require specific therapeutic strategies to attenuate NK cells' contribution to chronic kidney 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".