Ischemia–reperfusion injury stimulates gelatinase expression and activity in kidney glomeruli
Bibliographic record
Abstract
Although ischemia remains the leading cause of acute renal failure in humans, there is little information on the expression and activities of gelatinases of kidney glomeruli during ischemia-reperfusion injury. In this study, we used a unilateral ischemia-reperfusion model to investigate the activity and expression of gelatinases in glomeruli during acute ischemia. Unilateral ischemia was induced in rats by vascular clamping (30 min) followed by reperfusion (60 min) and isolation of glomeruli. The activity and expression of gelatinase proteins were determined by gelatin zymography and Western blotting. Gelatinase mRNA levels were evaluated by reverse transcriptase-PCR. Ischemia and reperfusion increased serum creatinine levels, hallmark of acute renal failure. Ischemia induced mRNA and protein MMP-2 expression. There was strong stimulation of MMP-9 mRNA, both forms of dimeric MMP-9, and active monomeric MMP-9. In contrast to TIMP-1 decreasing, TIMP-2 protein and mRNA increased during ischemia. During reperfusion, there was a gradual reversal of the MMP-2 and MMP-9 levels and a strong inhibition of TIMP-1 and TIMP-2 at the protein and mRNA levels. Endocytic receptor LRP was increased during ischemia and returned to normal during reperfusion. Expression of MMP-9 docking receptor CD-44 was increased during reperfusion. Finally, ZO-1, an in vivo MMP-9 substrate, was degraded during ischemia, revealing that MMP-9 upregulated during ischemia was functional. Our data suggest that stimulation of gelatinase activity during ischemia could contribute to glomeruli injury, providing new therapeutic targets for acute renal failure in humans. In contrast, elevated monomeric MMP-9 activity due to TIMP-1 decrease during reperfusion may participate to glomerular recovery.
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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".