PKCβ Promotes Vascular Inflammation and Acceleration of Atherosclerosis in Diabetic ApoE Null Mice
Bibliographic record
Abstract
OBJECTIVE: Subjects with diabetes mellitus are at high risk for developing atherosclerosis through a variety of mechanisms. Because the metabolism of glucose results in production of activators of protein kinase C (PKC)β, it was logical to investigate the role of PKCβ in modulation of atherosclerosis in diabetes mellitus. APPROACH AND RESULTS: ApoE(-/-) and PKCβ(-/-)/ApoE(-/-) mice were rendered diabetic with streptozotocin. Quantification of atherosclerosis, gene expression profiling, or analysis of signaling molecules was performed on aortic sinus or aortas from diabetic mice. Diabetes mellitus-accelerated atherosclerosis increased the level of phosphorylated extracellular signal-regulated kinase 1/2 and Jun-N-terminus kinase mitogen-activated protein kinases and augmented vascular expression of inflammatory mediators, as well as increased monocyte/macrophage infiltration and CD11c(+) cells accumulation in diabetic ApoE(-/-) mice, processes that were diminished in diabetic PKCβ(-/-)/ApoE(-/-) mice. In addition, pharmacological inhibition of PKCβ reduced atherosclerotic lesion size in diabetic ApoE(-/-) mice. In vitro, the inhibitors of PKCβ and extracellular signal-regulated kinase 1/2, as well as small interfering RNA to Egr-1, significantly decreased high-glucose-induced expression of CD11c (integrin, alpha X 9 complement component 3 receptor 4 subunit]), chemokine (C-C motif) ligand 2, and interleukin-1β in U937 macrophages. CONCLUSIONS: These data link enhanced activation of PKCβ to accelerated diabetic atherosclerosis via a mechanism that includes modulation of gene transcription and signal transduction in the vascular wall, processes that contribute to acceleration of vascular inflammation and atherosclerosis in diabetes mellitus. Our results uncover a novel role for PKCβ in modulating CD11c expression and inflammatory response of macrophages in the development of diabetic atherosclerosis. These findings support PKCβ activation as a potential therapeutic target for prevention and treatment of diabetic atherosclerosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".