Isoform-Selective Interactions between Estrogen Receptors and Steroid Receptor Coactivators Promoted by Estradiol and ErbB-2 Signaling in Living Cells
Bibliographic record
Abstract
Estrogen receptor (ER)alpha and -beta interact with a variety of coactivator proteins, most notably members of the steroid receptor coactivator (SRC) family, and these interactions have been shown to be regulated by estrogenic ligands and growth factor signaling. Here, using fluorescence resonance energy transfer (FRET), the selectivity of different stimulants on ERalpha and -beta interactions with coactivator receptor interaction domains (RIDs) were examined in living cells. We first show that ERalpha and ERbeta homo- and heterodimers form in vivo independently of the presence of 17beta-estradiol (E2) or antiestrogens. We then demonstrate that E2 enhances interactions between ERalpha and the RIDs of SRC-1 and SRC-3, whereas the interaction between ERalpha with the SRC-2 RID is ligand independent. The transcriptionally inactive mutant ERalphaL539A showed no interaction with all three SRC RIDs. Similarly, treatment with the antagonists 4-hydroxytamoxifen and EM-652 abolished all interactions between ERalpha and the SRC RIDs. FRET data also demonstrate that, in contrast to ERalpha, ERbeta interacts with all three SRC RIDs in a ligand-independent manner. However, these interactions were further enhanced or stabilized by E2, whereas the antiestrogen EM-652 abolished all interactions. In the presence of both ERalpha and ERbeta, E2 treatment led to the recruitment of SRC RIDs to the nuclei. Finally, expression of the oncogenic activated ErbB-2/Neu protein specifically enhanced ERalpha but not ERbeta interactions with SRC RIDs to an extent similar to E2-stimulated interactions. In summary, using FRET, we demonstrated preferential interactions between ER isoforms and coactivators upon hormonal treatment and activation of a growth factor signal transduction pathway in living cells.
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".