Reactive oxygen species, PKC-β<sub>1</sub>, and PKC-ζ mediate high-glucose-induced vascular endothelial growth factor expression in mesangial cells
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) is implicated in the development of proteinuria in diabetic nephropathy. High ambient glucose present in diabetes stimulates VEGF expression in several cell types, but the molecular mechanisms are incompletely understood. Here primary cultured rat mesangial cells served as a model to investigate the signal transduction pathways involved in high-glucose-induced VEGF expression. Exposure to high glucose (25 mM) significantly increased VEGF mRNA evaluated by real-time PCR by 3 h, VEGF cellular protein content assessed by immunoblotting or immunofluorescence within 24 h, and VEGF secretion by 24 h. High-glucose-induced VEGF expression was blocked by an antioxidant, Tempol, and antisense oligonucleotides directed against p22(phox), a NADPH oxidase subunit. Inhibition of protein kinase C (PKC)-beta(1) with the specific pharmacological inhibitor LY-333531 or inhibition of PKC-zeta with a cell permeable specific pseudosubstrate peptide also prevented enhanced VEGF expression in high glucose. Enhanced VEGF secretion in high glucose was prevented by Tempol, PKC-beta(1), or PKC-zeta inhibition. In normal glucose (5.6 mM), overexpression of p22(phox) or constitutively active PKC-zeta enhanced VEGF expression. Hypoxia inducible factor-1alpha protein was significantly increased in high glucose only by 24 h, suggesting a possible contribution to high-glucose-stimulated VEGF expression at later time points. Thus reactive oxygen species generated by NADPH oxidase, and both PKC-beta(1) and -zeta, play important roles in high-glucose-stimulated VEGF expression and secretion by mesangial cells.
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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) | 0.000 | 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 | 0.001 |
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".