Hyperglycemia-Induced Protein Kinase C β2 Activation Induces Diastolic Cardiac Dysfunction in Diabetic Rats by Impairing Caveolin-3 Expression and Akt/eNOS Signaling
Bibliographic record
Abstract
Protein kinase C (PKC)β2 is preferably overexpressed in the diabetic myocardium, which induces cardiomyocyte hypertrophy and contributes to diabetic cardiomyopathy, but the underlying mechanisms are incompletely understood. Caveolae are critical in signal transduction of PKC isoforms in cardiomyocytes. Caveolin (Cav)-3, the cardiomyocyte-specific caveolar structural protein isoform, is decreased in the diabetic heart. The current study determined whether PKCβ2 activation affects caveolae and Cav-3 expression. Immunoprecipitation and immunofluorescence analysis revealed that high glucose (HG) increased the association and colocalization of PKCβ2 and Cav-3 in isolated cardiomyocytes. Disruption of caveolae by methyl-β-cyclodextrin or Cav-3 small interfering (si)RNA transfection prevented HG-induced PKCβ2 phosphorylation. Inhibition of PKCβ2 activation by compound CGP53353 or knockdown of PKCβ2 expression via siRNA attenuated the reductions of Cav-3 expression and Akt/endothelial nitric oxide synthase (eNOS) phosphorylation in cardiomyocytes exposed to HG. LY333531 treatment (for a duration of 4 weeks) prevented excessive PKCβ2 activation and attenuated cardiac diastolic dysfunction in rats with streptozotocin-induced diabetes. LY333531 suppressed the decreased expression of myocardial NO, Cav-3, phosphorylated (p)-Akt, and p-eNOS and also mitigated the augmentation of O2(-), nitrotyrosine, Cav-1, and iNOS expression. In conclusion, hyperglycemia-induced PKCβ2 activation requires caveolae and is associated with reduced Cav-3 expression in the diabetic heart. Prevention of excessive PKCβ2 activation attenuated cardiac diastolic dysfunction by restoring Cav-3 expression and subsequently rescuing Akt/eNOS/NO signaling.
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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 | 0.000 |
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".