Role of SUMOylation in Full Antiestrogenicity
Bibliographic record
Abstract
The selective estrogen receptor downregulator (SERD) fulvestrant can be used as second-line treatment for patients relapsing after treatment with tamoxifen, a selective estrogen receptor modulator (SERM). Unlike tamoxifen, SERDs are devoid of partial agonist activity. While the full antiestrogenicity of SERDs may result in part from their capacity to downregulate levels of estrogen receptor alpha (ERα) through proteasome-mediated degradation, SERDs are also fully antiestrogenic in the absence of increased receptor turnover in HepG2 cells. Here we report that SERDs induce the rapid and strong SUMOylation of ERα in ERα-positive and -negative cell lines, including HepG2 cells. Four sites of SUMOylation were identified by mass spectrometry analysis. In derivatives of the SERD ICI164,384, SUMOylation was dependent on the length of the side chain and correlated with full antiestrogenicity. Preventing SUMOylation by the overexpression of a SUMO-specific protease (SENP) deSUMOylase partially derepressed transcription in the presence of full antiestrogens in HepG2 cells without a corresponding increase in activity in the presence of agonists or of the SERM tamoxifen. Mutations increasing transcriptional activity in the presence of full antiestrogens reduced SUMOylation levels and suppressed stimulation by SENP1. Our results indicate that ERα SUMOylation contributes to full antiestrogenicity in the absence of accelerated receptor turnover.
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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".