Disruption of endothelial adherens junctions by high glucose is mediated by protein kinase C-β–dependent vascular endothelial cadherin tyrosine phosphorylation
Bibliographic record
Abstract
BACKGROUND: Hyperglycemia has been recognized as a primary factor in endothelial barrier dysfunction and in the development of micro- and macrovascular diseases associated with diabetes, but the underlying biochemical mechanisms remain elusive. Tyrosine phosphorylation of vascular endothelial cadherin (VE-cad) leads to the disruption of endothelial adherens junctions and increases the transendothelial migration (TEM) of leukocytes. METHODS: VE-cad tyrosine phosphorylation, adherens junction integrity and TEM of monocytes in human umbilical vein endothelial cells (HUVECs) treated with high-concentration glucose were evaluated. The role of protein kinase C (PKC) in induction of endothelial cells adherence junction disruption by exposure of HUVECs to high concentration of glucose was explored. RESULTS: The treatment of HUVEC with high-concentration glucose increased VE-cad tyrosine phosphorylation, whereas mannitol or 3-O-methyl-D-glucose had no effect. In addition, high-concentration glucose increased the dissociation of the VE-cad-β-catenin complex, activation of the Wnt/β-catenin pathway, and the TEM of monocytes. These alterations were accompanied by the activation of endothelial PKC and increased phosphorylation of ERK and myosin light chain (MLC). High-concentration glucose-induced tyrosine phosphorylation of VE-cad was attenuated by: 1- the inhibition of PKC-β by overexpression of dominant-negative PKC-β 2- inhibition of MLC phosphorylation by overexpression of a nonphosphorylatable dominant-negative form of MLC, 3- the inhibition of actin polymerization by cytochalasin D and 4- the treatment of HUVECs with forskolin (an activator of adenylate cyclase). CONCLUSIONS: Our findings show that the high-concentration glucose-induced disruption of endothelial adherens junctions is mediated by tyrosine phosphorylation of VE-cad through PKC-β and MLC phosphorylation.
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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 | 0.001 |
| 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".