Inhibition of NK cell cytotoxicity by tubular epithelial cell expression of Clr-b and Clr-f
Bibliographic record
Abstract
NK cells participate in ischemia reperfusion injury (IRI) and transplant rejection. Endogenous regulatory systems may exist to attenuate NK cell activation and cytotoxicity in IRI associated with kidney transplantation. A greater understanding of NK regulation will provide insights in transplant outcomes and could direct new therapeutic strategies. Kidney tubular epithelial cells (TECs) may negatively regulate NK cell activation by their surface expression of a complex family of C-type lectin-related proteins (Clrs). We have found that Clr-b and Clr-f were expressed by TECs. Clr-b was upregulated by inflammatory cytokines TNFα and IFNγ in vitro. Silencing of both Clr-b and Clr-f expression using siRNA resulted in increased NK cell killing of TECs compared to silencing of either Clr-b or Clr-f alone (p < 0.01) and when compared to control TECs (p < 0.001). NK cells treated in vitro with soluble Clr-b and Clr-f proteins reduced their capacity to kill TECs (p < 0.05). Hence, NK cell cytotoxicity can be inhibited by Clr proteins on the surface of TECs. Our study suggests a synergistic effect of Clr molecules in regulating NK cell function in renal cells and this may represent an important endogenous regulatory system to limit NK cell-mediated organ injury during inflammation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".