NM23-H2, an estrogen receptor β-associated protein, shows diminished expression with progression of atherosclerosis
Bibliographic record
Abstract
While estrogen receptor (ER) profile plays an important role in response to estrogens, receptor coregulators act as critical determinants of signaling. Although the clinical effects of ovarian hormones on various normal and pathological processes are an active area of research, the exact signaling effects on, for example, the vessel wall, are incompletely understood. Hence, we sought to discover proteins that associate with ERbeta, the isoform that shows upregulated mRNA expression after arterial injury. Using a yeast two-hybrid screen we identified NM23-H2, a multifaceted metastasis suppressor candidate protein, as an ERbeta-associated protein. Although NM23-H2 was immunodetected in arteries from young subjects (27 +/- 6 yr, 14 men and 6 women) with benign intimal hyperplasia, expression was diminished in fatty streaks/atheromas and altogether absent in advanced atherosclerotic lesions. Both nm23-H2 mRNA and protein were expressed by vascular cells in vitro. Treatment with 17beta-estradiol and an ERbeta-selective agonist, diarylpropionitrile, increased protein expression of NM23-H2; an effect that was not seen with an ERalpha-selective agonist, propylpyrazole-triol. Estrogen also prompted nuclear localization of NM23-H2 protein in human coronary smooth muscle cells (SMCs). An in vitro mimic of inflammation decreased the expression of NM23-H2 in SMCs, which was restored on addition of estrogen and dependent on the estrogen receptor. In summary, we report the novel association of NM23-H2 with ERbeta and show for the first time its expression in vascular cells and demonstrate regulation of its expression and localization by estrogen. In that the abundance of NM23-H2 diminishes with both the advancement of atherosclerosis and inflammation, this ERbeta-associated protein may play an important role in mediating the vasculoprotective effects of estrogens.
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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 | 0.002 |
| 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".