Protective Effects of GLP-1 on Glomerular Endothelium and Its Inhibition by PKCβ Activation in Diabetes
Bibliographic record
Abstract
To characterize glucagon-like peptide (GLP)-1 signaling and its effect on renal endothelial dysfunction and glomerulopathy. We studied the expression and signaling of GLP-1 receptor (GLP-1R) on glomerular endothelial cells and the novel finding of protein kinase A-dependent phosphorylation of c-Raf at Ser259 and its inhibition of angiotensin II (Ang II) phospho-c-Raf(Ser338) and Erk1/2 phosphorylation. Mice overexpressing protein kinase C (PKC)β2 in endothelial cells (EC-PKCβ2Tg) were established. Ang II and GLP-1 actions in glomerular endothelial cells were analyzed with small interfering RNA of GLP-1R. PKCβ isoform activation induced by diabetes decreased GLP-1R expression and protective action on the renal endothelium by increasing its degradation via ubiquitination and enhancing phospho-c-Raf(Ser338) and Ang II activation of phospho-Erk1/2. EC-PKCβ2Tg mice exhibited decreased GLP-1R expression and increased phospho-c-Raf(Ser338), leading to enhanced effects of Ang II. Diabetic EC-PKCβ2Tg mice exhibited greater loss of endothelial GLP-1R expression and exendin-4-protective actions and exhibited more albuminuria and mesangial expansion than diabetic controls. These results showed that the renal protective effects of GLP-1 were mediated via the inhibition of Ang II actions on cRaf(Ser259) and diminished by diabetes because of PKCβ activation and the increased degradation of GLP-1R in the glomerular endothelial 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) | 0.000 | 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".