Diabetes-induced increased oxidative stress in cardiomyocytes is sustained by a positive feedback loop involving Rho kinase and PKCβ<sub>2</sub>
Bibliographic record
Abstract
We previously reported that acute inhibition of the RhoA/Rho kinase (ROCK) pathway normalized contractile function of diabetic rat hearts, but the underlying mechanism is unclear. Protein kinase C (PKC) β(2) has been proposed to play a major role in diabetic cardiomyopathy at least in part by increasing oxidative stress. Further evidence suggests that PKC positively regulates RhoA expression through induction of inducible nitric oxide synthase (iNOS) in diabetes. However, in preliminary studies, we found that inhibition of ROCK itself reduced RhoA expression in diabetic hearts. We hypothesized that there is an interaction between RhoA/ROCK and PKCβ(2) in the form of a positive feedback loop that sustains their activation and the production of reactive oxygen species (ROS). This was investigated in cardiomyocytes isolated from diabetic and control rat hearts, incubated with or without cytochalasin D or inhibitors of ROCK, RhoA, PKCβ(2), or iNOS. Inhibition of RhoA and ROCK markedly attenuated the diabetes-induced increases in PKCβ(2) activity and iNOS and RhoA expression in diabetic cardiomyocytes, while having no effect in control cells. Inhibition of PKCβ(2) and iNOS also normalized RhoA expression and ROCK overactivation, whereas iNOS inhibition reversed the increase in PKCβ(2) activity. Each of these treatments also normalized the diabetes-induced increase in production of ROS. Actin cytoskeleton disruption attenuated the increased expression and/or activity of all of these targets in diabetic cardiomyocytes. These data suggest that, in the diabetic heart, the RhoA/ROCK pathway contributes to contractile dysfunction at least in part by sustaining PKCβ(2) activation and ROS production via a positive feedback loop that requires an intact cytoskeleton.
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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".