Leptin Increases Expression and Activity of Matrix Metalloproteinase-2 and Does Not Alter Collagen Production in Rat Glomerular Mesangial Cells
Bibliographic record
Abstract
Obesity is a leading risk factor for the development of nephropathy. In nephropathy, one of the major structural alterations found in the kidney is the increase in, or altered profile of, extracellular matrix (ECM) proteins such as collagen. Excessive synthesis and decreased degradation of matrix proteins by proteases such as matrix metalloproteinases (MMPs) may contribute to this process. We hypothesized that alterations observed in nephropathy may be due to alterations in direct effects of leptin, the product of the obesity gene. Here, we investigate the effect of leptin on collagen synthesis and MMP-2 production in rat glomerular mesangial cells. Using quantitative real-time PCR we showed that leptin does not alter the expression of collagen type I and IV mRNA. In keeping with this observation, proline incorporation was not altered by leptin. We also demonstrate that leptin induces MMP-2 expression in glomerular mesangial cells, assessed by quantitative real-time PCR. Analysis of conditioned media by gelatin zymography indicated increased activity at a molecular weight corresponding with that of MMP-2 in leptin-treated samples. In summary, our results indicate that leptin induces MMP-2 expression and activity without altering collagen synthesis, suggesting that normal leptin function has the potential to prevent ECM accumulation.
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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".