Elevated Glucose Levels Promote Contractile and Cytoskeletal Gene Expression in Vascular Smooth Muscle via Rho/Protein Kinase C and Actin Polymerization
Bibliographic record
Abstract
Both type 1 and type 2 diabetes are associated with increased risk of cardiovascular disease. This is in part attributed to the effects of hyperglycemia on vascular endothelial and smooth muscle cells, but the underlying mechanisms are not fully understood. In diabetic animal models, hyperglycemia results in hypercontractility of vascular smooth muscle possibly due to increased activation of Rho-kinase. The aim of the present study was to investigate the regulation of contractile smooth muscle markers by glucose and to determine the signaling pathways that are activated by hyperglycemia in smooth muscle cells. Microarray, quantitative PCR, and Western blot analyses revealed that both mRNA and protein expression of contractile smooth muscle markers were increased in isolated smooth muscle cells cultured under high compared with low glucose conditions. This effect was also observed in hyperglycemic Akita mice and in diabetic patients. Elevated glucose activated the protein kinase C and Rho/Rho-kinase signaling pathways and stimulated actin polymerization. Glucose-induced expression of contractile smooth muscle markers in cultured cells could be partially or completely repressed by inhibitors of advanced glycation end products, L-type calcium channels, protein kinase C, Rho-kinase, actin polymerization, and myocardin-related transcription factors. Furthermore, genetic ablation of the miR-143/145 cluster prevented the effects of glucose on smooth muscle marker expression. In conclusion, these data demonstrate a possible link between hyperglycemia and vascular disease states associated with smooth muscle contractility.
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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 | 0.000 |
| 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".