Anti-IL-2 receptor antibody decreases cytokine-induced apoptosis of human renal tubular epithelial cells (TEC)
Bibliographic record
Abstract
BACKGROUND: Transplant rejection is mediated by T-cell activation which is modulated by interleukin-2 (IL-2) binding to IL-2R (CD25). Monoclonal anti-IL-2 receptor antibody is used in renal transplantation to reduce rejection. Interestingly, proximal tubular epithelial cells (TEC) express CD25, similar to T cells. We have demonstrated that IL-2 induces murine TEC apoptosis through down-regulation of the caspase-8 inhibitor protein c-FLIP. Anti-CD25 antibody may be useful clinically to limit renal injury, but this has not been tested in human TEC. METHODS: Human PT-2 TEC were isolated and cloned from the urine of transplant patients. Apoptosis was determined by FACS with Annexin-V FITC. Protein expression was studied using western blot, and mRNA levels by quantitative real-time (PR-PCR). RESULTS: We demonstrated that the morphology of a human kidney cell line (PT-2) cloned from urine was consistent with proximal TEC and expresses alkaline phosphatase, cytokeratin, vimentin, CD13, CD26, and low levels of E-cadherin. Basal IL-2 receptor (CD25) was up-regulated by IL-2/IFN-γ stimulation, and cytokine exposure induced apoptosis in a dose-dependent manner. Apoptosis with IL-2/IFN-γ was associated with increased caspase-8 activity and decreased endogenous caspase-8 inhibitor c-FLIP mRNA and protein expression. IL-2/IFN-γ-induced apoptosis could be blocked by pre-treatment of PT-2 with anti IL-2R antibody (basiliximab) but not control IgG antibody. CONCLUSIONS: These data demonstrate for the first time in human TEC that IL-2 and IFN-γ can induce TEC apoptosis which can be blocked by CD25 blockade antibody. These data suggest that anti-CD25 mAb might similarly attenuate inflammation-induced TEC injury in vivo. Kidney-expressed CD25 may represent a clinically important new target for attenuating early inflammatory injury in donor kidneys and preserving renal function during anti-rejection therapy.
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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) | 0.002 | 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".