Candidate Genes and Mechanisms for 2-Methoxyestradiol–Mediated Vasoprotection
Bibliographic record
Abstract
2-Methoxyestradiol (2-ME; estradiol metabolite) inhibits vascular smooth muscle cell (VSMC) growth and protects against atherosclerosis and vascular injury; however, the mechanisms by which 2-ME induces these actions remain obscure. To assess the impact of 2-ME on biochemical pathways regulating VSMC biology, we used high-density oligonucleotide microarrays to identify differentially expressed genes in cultured human female aortic VSMCs treated with 2-ME acutely (4 hours) or long term (30 hours). Both single gene analysis and Gene Set Enrichment Analysis revealed 2-ME-induced downregulation of genes involved in mitotic spindle assembly and function in VSMCs. Also, Gene Set Enrichment Analysis identified effects of 2-ME on genes regulating cell-cycle progression, cell migration/adhesion, vasorelaxation, inflammation, and cholesterol metabolism. Transcriptional changes were associated with changes in protein expression, including inhibition of cyclin D1, cyclin B1, cyclin-dependent kinase 6, cyclin-dependent kinase 4, tubulin polymerization, cholesterol and steroid synthesis, and upregulation of cyclooxygenase 2 and matrix metalloproteinase 1. Microarray data suggested that 2-ME may activate peroxisome proliferator-activated receptors (PPARs) in VSMCs, and 2-ME has structural similarities with rosiglitazone (PPARγ agonist). However, our finding of weak activation and lack of binding of 2-ME to PPARs suggests that 2-ME may modulate PPAR-associated genes via indirect mechanisms, potentially involving cyclooxygenase 2. Indeed, the antimitogenic effects of 2-ME at concentrations that do not inhibit tubulin polymerization were blocked by the PPAR antagonist GW9662 and the cyclooxygenase 2 inhibitor NS398. Finally, we demonstrated that 2-ME inhibited hypoxia-inducible factor 1α. Identification of candidate genes that are positively or negatively regulated by 2-ME provides important leads to investigate and better understand the mechanisms by which 2-ME induces its vasoprotective actions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".