Effects of PPAR-γ Knock-down and Hyperglycemia on Insulin Signaling in Vascular Smooth Muscle Cells From Hypertensive Rats
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor (PPAR)-gamma, a target in the treatment of diabetes, improves insulin sensitivity and exerts cardiovascular protective effects by mechanisms that are not completely elucidated. To investigate underlying molecular mechanisms responsible for PPAR-gamma-associated vascular insulin signaling in hypertension, we tested whether PPAR-gamma downregulation in vascular smooth muscle cells (VSMC) from WKY and SHRSP rats would decrease insulin signaling and glucose uptake and whether this response would be worsened by hyperglycemia to a greater extent in VSMC of hypertensive origin. Passaged mesenteric artery VSMC grown in euglycemic (5.5 mmol/L) or hyperglycemic media (25.0 mmol/L) were treated with PPAR-gamma-siRNA (5 nmol/L), PPAR-gamma antagonist (GW-9662, 10 micromol/L), or PPAR-gamma activator (rosiglitazone, 10 micromol/L) in the presence or absence of insulin (100 nmol/L). Immunoblotting revealed that hyperglycemia and PPAR-gamma inhibition significantly (P < 0.001) decreased insulin-stimulated insulin receptor (IR)-beta, Akt, and glycogen synthase kinase (GSK)-3beta phosphorylation, whereas phosphotyrosine phosphatase (PTP)-1B expression was increased in VSMC from both strains. These effects were more pronounced in SHRSP under hyperglycemia. Rosiglitazone tended to increase insulin-mediated IR-beta, Akt, and GSK-3beta phosphorylation in VSMC from both strains, whereas insulin-induced PTP-1B expression was reduced by hyperglycemia. Insulin-stimulated GLUT-4 expression and glucose transport were attenuated by hyperglycemia in both VSMC. These data suggest that PPAR-gamma inhibition results in decreased insulin signaling, particularly in SHR, in an IR-beta phosphorylation-dependent manner.
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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 | 0.001 |
| 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) | 0.002 | 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".