Regulation of GREB1 Transcription by Estrogen Receptor α through a Multipartite Enhancer Spread Over 20 kb of Upstream Flanking Sequences
Bibliographic record
Abstract
Estrogen receptors activate transcription in part through direct interactions with specific DNA motifs, called estrogen response elements (EREs). Here we show that the strong and sustained induction of the gene regulated in breast cancer 1 (GREB1), a gene of unknown function that has been previously suggested to play a role in the effects of estradiol on breast cancer cell proliferation (Rae, J. M., Johnson, M. D., Scheys, J. O., Cordero, K. E., Larios, J. M., and Lippman, M. E. (2005) Breast Cancer Res. Treat 92, 141-149), is mediated by binding of estrogen receptor alpha (ERalpha) to three consensus EREs spread over approximately 20 kb of upstream flanking sequences. In addition to ERalpha, coactivator SRC-3, acetylated histones and phosphorylated RNA polymerase II (P-polII) were detected on all three EREs in the presence of estrogen, while basal recruitment of ERalpha and P-polII was observed only on the proximal element. Chromatin loops were formed between each ERE and the GREB1 transcriptional start site in the presence of estrogen but not of a total antiestrogen. Furthermore, estradiol induced physical association between EREs, suggesting that these elements function as a potent multipartite enhancer to regulate GREB1 transcription.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".