Loss of TIMP3 Enhances Interstitial Nephritis and Fibrosis
Bibliographic record
Abstract
The balance of matrix metalloproteinases (MMPs) and tissue inhibitors of matrix metalloproteinases (TIMPs) determines the integrity of the extracellular matrix. TIMP3 is the most highly expressed tissue inhibitor of metalloproteinase (TIMP) in the kidney, but its function in renal disease is incompletely understood. In this study, TIMP3-/- mice demonstrated an age-dependent chronic tubulointerstitial fibrosis. After unilateral ureteral obstruction (UUO), young TIMP3-/- mice exhibited increased renal injury (tubular atrophy, cortical and medullary thinning, and vascular damage) compared with wild-type mice. In addition, TIMP3-/- mice had greater interstitial fibrosis; increased synthesis and deposition of type I collagen; increased activation of fibroblasts; enhanced apoptosis; and greater activation of MMP2, but not MMP9, after UUO. TIMP3 deficiency also led to accelerated processing of TNFalpha, demonstrated by significantly higher TACE activity and greater soluble TNFalpha levels by 3 d after UUO. The additional deletion of TNFalpha markedly reduced inflammation, apoptosis, and induction of a number of MMPs. Moreover, inhibition of MMPs in TIMP3-/-/TNFalpha-/- mice further abrogated postobstructive injury and prevented tubulointerestitial fibrosis. In humans, TIMP3 expression increased in the renal arteries and proximal tubules of subjects with diabetic nephropathy or chronic allograft nephropathy. Taken together, these results provide evidence that TIMP3 is an important mediator of kidney injury, and regulating its activity may have therapeutic benefit for patients with kidney disease.
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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 | 0.001 |
| 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".