Adiponectin attenuates angiotensin II-induced oxidative stress in renal tubular cells through AMPK and cAMP-Epac signal transduction pathways
Bibliographic record
Abstract
Obesity is a risk factor for chronic kidney disease (CKD) progression. Circulating levels of adiponectin, an adipokine, decrease with obesity and play a protective role in the cardiovascular system. We hypothesized that adiponectin might also protect the kidney. Because activation of the renin-angiotensin system (RAS) is a contributor to CKD progression, we tested our hypothesis by studying the interactions between adiponectin and angiotensin II (ANG II) in renal tubular cells. Primary human renal proximal tubule cells expressed both adiponectin receptor 1 and 2 (adipoR1 and R2). ANG II-induced NADPH oxidase activation and oxidative stress were attenuated by adiponectin and dependent on adipoR1. Activation of AMPK with 5-aminoimidazole-4-carboxamide-1-β-d-ribofuranoside (AICAR) mimicked, while inhibition of AMPK with compound C abrogated, the effect of adiponectin on ANG II-induced activation of NADPH oxidase. Similarly, the effect of adiponectin was recapitulated by the stable cAMP analogs 4-chlorophenylthio (pCPT)-cAMP and dibutyryl (db)-cAMP and blocked by the adenylate cyclase inhibitor SQ22536. Adiponectin did not activate PKA in renal tubular cells, and the specific PKA inhibitor myristoylated PKI (14-22) amide failed to block the inhibitory effect of adiponectin on ANG II-induced NADPH oxidase activation. In contrast, the specific Epac activator 8-(4-chlorophenylthio)-2'-O-methyl (8-CPT-2-O-Me)-cAMP blocked ANG II-induced activation of NADPH oxidase, an effect that was reversed by coincubation with the AMPK inhibitor compound C. Finally, adiponectin attenuated ANG II-induced NF-κB activation and fibronectin protein expression. These in vitro findings support the hypothesis that adiponectin may attenuate the deleterious effects of ANG II in the kidney and play a protective role in CKD.
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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) | 0.001 | 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".