OPN expression by epithelial tubular cells regulates NK cell-mediated kidney ischemia reperfusion injury (98.30)
Bibliographic record
Abstract
Abstract Renal ischemia reperfusion injury (IRI) is a major cause of acute injury in both native and transplanted kidneys. This type of kidney injury is considered an antigen-independent inflammatory condition that involves multiple factors leading to tubular and endothelial dysfunction. We have recently found that NK cells induce apoptosis in tubular epithelial cells (TEC) and contribute to renal IRI. Kidney can express osteopontin (OPN) during injury. Therefore we examined the role OPN in NK cell function and kidney IRI. We have found that TEC expressed high levels of OPN in vitro and in vivo after injury. Kidneys in OPN-/- mice had less severity of IRI compared to wild-type mice (P<0.05). Interestingly, recombinant OPN could activate NK cells that express high levels of perforin and granzyme and could mediate TEC apoptotic death. Importantly, we have found that NK cell migrate towards OPN protein as well as OPN-producing TEC in the transwell assay. Whereas, less NK cell migration was seen towards OPN-/- TEC (P<0.01). Further more, Kidneys in OPN-/- mice had less NK cell infiltration after IRI compared to WT mice (P<0.02). Taken together, our study results support a previously unrecognized role for TEC expression of OPN in NK cell-mediated kidney injury. TEC expression of OPN promotes early kidney inflammatory and IRI, and limiting OPN expression may improve kidney function and graft survival.
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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 | 0.001 |
| 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".