Selective Estrogen Receptor-β Agonists Repress Transcription of Proinflammatory Genes
Bibliographic record
Abstract
In addition to their role in the development and function of the reproductive system, estrogens have significant anti-inflammatory properties. Although both estrogen receptors (ERs) can mediate anti-inflammatory actions, ERbeta is a more desirable therapeutic target because ERalpha mediates the proliferative effects of estrogens on the mammary gland and uterus. In fact, selective ERbeta agonists have beneficial effects in preclinical models involving inflammation without causing growth-promoting effects on the uterus or mammary gland. However, their mechanism of action is unclear. The purpose of this study was to use microarray analysis to determine whether ERbeta-selective compounds produce their anti-inflammatory effects by repressing transcription of proinflammatory genes. We identified 49 genes that were activated by TNF-alpha in human osteosarcoma U2OS cells expressing ERbeta. Estradiol treatment significantly reduced the activation by TNF-alpha on 18 genes via ERbeta or ERalpha. Most repressed genes were inflammatory genes, such as TNF-alpha, IL-6, and CSF2. Three ERbeta-selective compounds, ERB-041, WAY-202196, and WAY-214156, repressed the expression of these and other inflammatory genes. ERB-041 was the most ERbeta-selective compound, whereas WAY-202196 and WAY-214156 were the most potent. The ERbeta-selective compounds repressed inflammatory genes by recruiting the coactivator, SRC-2. ERB-041 also repressed cytokine genes in PBMCs, demonstrating that ERbeta-selective estrogens have anti-inflammatory properties in immune cells. Our study suggests that the anti-inflammatory effects of ERB-041 and other ERbeta-selective estrogens in animal models are due to transcriptional repression of proinflammatory genes. These compounds might represent a new class of drugs to treat inflammatory disorders.
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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 | 0.000 |
| 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".