NK Cells Induce Apoptosis in Tubular Epithelial Cells and Contribute to Renal Ischemia-Reperfusion Injury
Bibliographic record
Abstract
Renal ischemia-reperfusion injury (IRI) can result in acute renal failure with mortality rates of 50% in severe cases. NK cells are important participants in early-stage innate immune responses. However, their role in renal tubular epithelial cell (TEC) injury in IRI is currently unknown. Our data indicate that NK cells can kill syngeneic TEC in vitro. Apoptotic death of TEC in vitro is associated with TEC expression of the NK cell ligand Rae-1, as well as NKG2D on NK cells. In vivo following IRI, there was increased expression of Rae-1 on TEC. FACS analyses of kidney cell preparations indicated a quantitative increase in NKG2D-bearing NK cells within the kidney following IRI. NK cell depletion in wild-type C57BL/6 mice was protective, while adoptive transfer of NK cells worsened injury in NK, T, and B cell-null Rag2(-/-)gamma(c)(-/-) mice with IRI. NK cell-mediated kidney injury was perforin (PFN)-dependent as PFN(-/-) NK cells had minimal capacity to kill TEC in vitro compared with NK cells from wild-type, FasL-deficient (gld), or IFN-gamma(-/-) mice. Taken together, these results demonstrate for the first time that NK cells can directly kill TEC and that NK cells contribute substantially to kidney IRI. NK cell killing may represent an important underrecognized mechanism of kidney injury in diverse forms of inflammation, including transplantation.
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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) | 0.001 | 0.001 |
| 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".