The bradykinin-cGMP-PKG pathway augments insulin sensitivity via upregulation of MAPK phosphatase-5 and inhibition of JNK
Bibliographic record
Abstract
Bradykinin (BK) promotes insulin sensitivity and glucose uptake in adipocytes and other cell types. We demonstrated that in rat adipocytes BK enhances insulin-stimulated glucose transport via endothelial nitric oxide synthase, nitric oxide (NO) generation, and decreased activity of the mitogen-activated protein kinase (MAPK) JNK (c-Jun NH2-terminal kinase). In endothelial cells, NO increases soluble guanylate cyclase (sGC) activity, which, in turn, activates protein kinase G (PKG) by increasing cGMP levels. In this study, we investigated whether BK acts via the sGC-cGMP-PKG pathway to inhibit the negative effects of JNK on insulin signaling and glucose uptake in rat adipocytes. BK augmented cGMP concentrations. The BK-induced enhancement of insulin-stimulated glucose uptake was mimicked by the sGC activator YC-1 and a cell-permeable cGMP analog, CPT-cGMP, and inhibited by the sGC inhibitor ODQ and the PKG inhibitor KT 5823. Transfection of dominant-negative PKG reduced the BK augmentation of insulin-induced Akt phosphorylation. The activation of JNK and ERK1/2 by insulin was attenuated by BK, which was mediated by the sGC-cGMP-PKG pathway. Whereas insulin-stimulated phosphorylation of upstream activators of JNK and ERK, i.e., MKK4 and MEK1/2, was unaffected, BK augmented insulin-mediated induction of MKP-5 mRNA and protein levels. Furthermore, zaprinast, a phosphodiesterase inhibitor, enhanced cGMP and MKP-5 and prolonged the action of BK. These data indicate that BK enhances insulin action by inhibition of negative feedback by JNK and ERK via upregulation of MKP-5, mediated by the sGC-cGMP-PKG signaling pathway.
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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".