Raloxifene and ICI182,780 Increase Estrogen Receptor-α Association with a Nuclear Compartment via Overlapping Sets of Hydrophobic Amino Acids in Activation Function 2 Helix 12
Bibliographic record
Abstract
The basis for the differential repressive effects of antiestrogens on transactivation by estrogen receptor-alpha (ERalpha) remains incompletely understood. Here, we show that the full antiestrogen ICI182,780 and, to a lesser extent, the selective ER modulator raloxifene (Ral), induce accumulation of exogenous ERalpha in a poorly soluble fraction in transiently transfected HepG2 or stably transfected MDA-MB231 cells and of endogenous receptor in MCF7 cells. ERalpha remained nuclear in HepG2 cells treated with either compound. Replacement of selected hydrophobic residues of ERalpha ligand-binding domain helix 12 (H12) enhanced receptor solubility in the presence of ICI182,780 or Ral. These mutations also increased transcriptional activity with Ral or ICI182,780 on reporter genes or on the endogenous estrogen target gene TFF1 in a manner requiring the integrity of the N-terminal AF-1 domain. The antiestrogen-specific effects of single mutations suggest that they affect receptor function by mechanisms other than a simple decrease in hydrophobicity of H12, possibly due to relief from local steric hindrance between these residues and the antiestrogen side chains. Fluorescence anisotropy experiments indicated an enhanced regional stabilization of mutant ligand-binding domains in the presence of antiestrogens. H12 mutations also prevent the increase in bioluminescence resonance energy transfer between ERalpha monomers induced by Ral or ICI182,780 and increase intranuclear receptor mobility in correlation with transcriptional activity in the presence of these antiestrogens. Our data indicate that ICI182,780 and Ral locally alter the ERalpha ligand binding structure via specific hydrophobic residues of H12 and decrease its transcriptional activity through tighter association with an insoluble nuclear structure.
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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) | 0.000 | 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) | 0.000 | 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".