Hyperhomocysteinemia Activates Nuclear Factor-κB in Endothelial Cells via Oxidative Stress
Bibliographic record
Abstract
Hyperhomocysteinemia is an independent risk factor for cardiovascular diseases. Our previous studies demonstrated an important interaction between nuclear factor-kappaB (NF-kappaB) activation and homocysteine (Hcy)-induced chemokine expression in vascular smooth muscle cells and macrophages. The objective of the present study was to investigate the in vivo effect of hyperhomocysteinemia on NF-kappaB activation and the underlying mechanism of Hcy-induced NF-kappaB activation in endothelial cells. Hyperhomocysteinemia was induced in Sprague-Dawley rats after 4 weeks of a high-methionine diet. The activated form of NF-kappaB and increased level of superoxide anions were detected in the endothelium of aortas isolated from hyperhomocysteinemic rats. The underlying mechanism of Hcy-induced NF-kappaB activation was investigated in human umbilical cord vein endothelial cells and in human aortic endothelial cells. Incubation of cells with Hcy (100 micromol/L) activated IkappaB kinases (IKKalpha and IKKbeta), leading to phosphorylation and subsequent degradation of IkappaBalpha. As a consequence, NF-kappaB nuclear translocation, enhanced NF-kappaB/DNA binding activity, and increased transcriptional activity occurred. Additional analysis revealed a marked elevation of superoxide anion levels in Hcy-treated cells. Treatment of cells with a superoxide anion scavenger (polyethylene glycol-superoxide dismutase) or IkappaB kinase inhibitor (prostaglandin A(1)) could prevent Hcy-induced activation of IKK kinases and NF-kappaB in endothelial cells. In conclusion, these results suggest that Hcy-induced superoxide anion production may play a potential role for NF-kappaB activation in the early stages of atherosclerosis in the vascular wall via activation of IkappaB kinases.
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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) | 0.002 | 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".