4-Hydroxytamoxifen Is an Isoform-Specific Inhibitor of Orphan Estrogen-Receptor-Related (ERR) Nuclear Receptors β and γ
Bibliographic record
Abstract
Selective estrogen receptor modulators (SERMs) are synthetic molecules that exhibit tissue-specific activities. 4-hydroxytamoxifen (OHT) is a first generation SERM that functions as an antagonist in breast cancer cells but displays estrogen-like activities in the uterus and bone. The estrogen-receptor-related receptors (ERR) alpha, beta and gamma are orphan members of the superfamily of nuclear receptors. While the ERRs do not respond to natural estrogens, these receptors recognize the estrogen response element and have been shown to activate and repress gene expression in the absence of exogenously added ligand. Here we show that OHT disrupts the interaction between the orphan estrogen-receptor-related (ERR) receptors beta and gamma and a coregulator protein and abolishes the constitutive transcriptional activity of these receptors in transient transfection assays. In contrast, OHT has no effect on coregulator/ERR alpha interaction or its transcriptional activity. These results demonstrate the existence of a novel nuclear receptor-based pharmacological pathway that may contribute to the tissue-specific activities of OHT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".