Insulin increases gelatinase activity in rat glomerular mesangial cells via ERK‐ and PI‐3 kinase‐dependent signalling
Bibliographic record
Abstract
Diabetic nephropathy is associated with increased accumulation of the extracellular matrix (ECM) in the kidney, which ultimately leads to kidney failure. This may occur due to excessive synthesis of ECM components or reduced degradation, a process primarily mediated by matrix metalloproteinases (MMPs). The direct effect of insulin on ECM synthesis and degradation in glomerular mesangial cells (GMCs) is unclear. Here, we show an increased gelatinase activity in conditioned media from insulin-treated rat GMCs, determined by gelatin zymography. Furthermore, we show using the specific inhibitors LY294002 and PD98059 that insulin induced increased gelatinase activity via an intracellular signalling mechanism involving phosphatidylinositol-3 kinase (PI-3K) and the extracellular signal-regulated kinase 1/2 (ERK1/2) mitogen-activated protein kinases (MAPKs) respectively. In addition, we demonstrate that PI-3 kinase and ERK1/2 MAPK are activated by insulin in GMCs. The appearance of protease activity at approximately 72 kDa suggested that MMP-2 activity may be induced by insulin, however, we did not detect an increase in MMP-2 expression by Western blotting. In summary, our results suggest that insulin can induce gelatinase activity in GMCs, and it is possible that loss of this input in insulin-resistant type 2 diabetic individuals may contribute to ECM accumulation and the development of nephropathy.
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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) | 0.001 | 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".