Protein kinase C-β mediates lipoprotein-induced generation of PAI-1 from vascular endothelial cells
Bibliographic record
Abstract
Elevated levels of low-density lipoproteins (LDL) and lipoprotein(a) [Lp(a)] have been considered strong risk factors for atherosclerotic cardiovascular disease. Increased production of plasminogen activator inhibitor-1 (PAI-1) has been implicated in the development of thrombosis and atherosclerosis. Previous studies by our group and others demonstrated that oxidation enhances LDL- and Lp(a)-induced production of PAI-1 in human umbilical vein endothelial cells (HUVEC). The present study examined the involvement of protein kinase C (PKC) and its isoform in vascular endothelial cells (EC) induced by native or oxidized LDL and Lp(a). Treatment with Lp(a) or LDL transiently increased PKC activity at 15 min and 5.5 h after the start of lipoprotein treatment in EC. Copper-oxidized LDL and Lp(a) induced greater PKC activation in EC compared with comparable forms of those lipoproteins. Additions of 1 microM calphostin C, a PKC-specific inhibitor, at the beginning or > or =5 h, but not > or = 9 h, after the initiation of lipoprotein treatment, blocked native and oxidized LDL- or Lp(a)-induced increases in PKC activity and PAI-1 production. Treatment of LDL, Lp(a), or their oxidized forms was induced in translocation of PKC-beta1 from cytosol to membrane in HUVEC. Treatments with 60 nM 379196, a PKC-beta-specific inhibitor, effectively prevented PAI-1 production induced by LDL, Lp(a), or their oxidized forms in HUVEC and human coronary artery EC. The results suggest that activation of PKC-beta may mediate the production of PAI-1 in cultured arterial and venous EC induced by LDL, Lp(a), or their oxidized forms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".