The AF-2 cofactor binding region is key for the selective SUMOylation of estrogen receptor alpha by antiestrogens
Bibliographic record
Abstract
Antiestrogens (AEs) are used to treat all stages of estrogen receptor (ER)-positive breast cancer. Selective estrogen receptor modulators such as tamoxifen have tissue-specific partial agonist activity, while selective estrogen receptor downregulators such as fulvestrant (ICI182,780) display a more complete antiestrogenic profile. We have previously observed that fulvestrant-induced ERα SUMOylation contributes to transcriptional suppression, but whether this effect is seen with other AEs and is specific to ERα is unclear. Here we show that several AEs induce SUMOylation of ERα, but not ERβ, at different levels. Swapping domains between ERα and ERβ indicates that the ERα identity of the ligand-binding domain helices 3 and 4 (H3-H4 region), which contribute to the static part of the activation function-2 (AF-2) cofactor binding groove, is sufficient to confer fulvestrant-induced SUMOylation to ERβ. This region does not contain lysine residues unique to ERα, suggesting that ERα-specific residues in H3-H4 determine the capacity of the AE-bound ERα ligand-binding domain to recruit the SUMOylation machinery. We also show that the SUMO E3 ligase protein inhibitor of activated STAT 1 increases SUMOylation of ERα and of ERβ containing the H3-H4 region of ERα, but not of ERβ. Together, these results shed new light on the molecular basis for the differential capacity of selective estrogen receptor modulators and selective estrogen receptor downregulators to suppress transcription by ERα.
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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 | 0.001 |
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".