Increasing Resistance of Tubular Epithelial Cells to Apoptosis by shRNA Therapy Ameliorates Renal Ischemia-Reperfusion Injury
Bibliographic record
Abstract
Renal tubular epithelial cells (TEC) die by apoptosis or necrosis in renal ischemia-reperfusion injury (IRI). Fas/Fas ligand-dependent fratricide is critical in TEC apoptosis, and Fas promotes renal IRI. Therefore, targeting Fas or caspase-8 may have therapeutic potential for renal injury in kidney transplant or failure. RNA silencing by short hairpin RNA (shRNA) is a novel strategy to down-regulate protein expression. Using this approach, silencing of Fas or caspase-8 by shRNA to prevent TEC apoptosis and IRI was evaluated. IRI was induced by renal artery clamping for 45 or 60 min at 32 degrees C in uninephrectomized C57BL/6 mice. Here, we showed that Fas or pro-caspase-8 expression was significantly knocked down in TEC by stable expression of shRNA, resulting in resistance to apoptosis induced by superoxide, IFN-gamma/TNF-alpha and anti-Fas antibody. Inferior vena cava delivery of pHEX-small interfering RNA targeting Fas or pro-caspase-8 resulted in protection of kidney from IRI, indicated by reduction of renal tubular injury (necrosis and apoptosis) and serum creatinine or blood urea nitrogen. Our data suggest that shRNA-based therapy targeting Fas and caspase-8 in renal cells can lead to protection of kidney from IRI. Attenuation of pro-apoptotic proteins using genetic manipulation strategies such as shRNA might represent a novel strategy to promote kidney allograft survival from rejection or failure.
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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 | 0.000 |
| 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) | 0.002 | 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".