Loss of TIMP3 selectively exacerbates diabetic nephropathy
Bibliographic record
Abstract
Diabetic nephropathy is the most common cause of end-stage renal disease. Polymorphism in the tissue inhibitor of metalloproteinase-3 (TIMP3) gene, and the ECM-bound inhibitor of matrix metalloproteinases (MMPs), has been linked to diabetic nephropathy in humans. To elucidate the mechanism, we generated double mutant mice in which the TIMP3 gene was deleted in the genetic diabetic Akita mouse background. The aggravation of diabetic injury occurred in the absence of worsening of hypertension or hyperglycemia. In fact, myocardial TIMP3 levels were not affected in Akita hearts, and cardiac diastolic and systolic function remained unchanged in the double mutant mice. However, TIMP3 levels increased in Akita kidneys and deletion of TIMP3 exacerbated the diabetic renal injury in the Akita mouse, characterized by increased albuminuria, mesangial matrix expansion, and kidney hypertrophy. The progression of diabetic renal injury was accompanied by the upregulation of fibrotic and inflammatory markers, increased production of reactive oxygen species and NADPH oxidase activity, and elevated activity of TNF-α-converting enzyme (TACE) in the TIMP3(-/-)/Akita kidneys. Moreover, while the elevated phospho-Akt (S473 and T308) and phospho-ERK1/2 in the Akita mice was not detected in the TIMP3(-/-)/Akita kidneys, PKCβ1 (but not PKCα) was markedly elevated in the double mutant kidneys. Our data provide definitive evidence for a critical and selective role of TIMP3 in diabetic renal injury consistent with gene expression findings from human diabetic kidneys.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
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".