Estradiol (E2) concentration shapes the chromatin binding landscape of estrogen receptor alpha
Bibliographic record
Abstract
How transcription factors (TFs) selectively occupy a minute subset of their binding sites from a sizeable pool of putative sites in large mammalian genomes remains an important unanswered question. In part, nucleosomes help by creating formidable barriers to TF binding. TF concentration itself plays a crucial role in the competition between TFs and nucleosomes. With nuclear receptors, the ligand adds another layer of complexity. Estrogen receptor alpha (ER) is a classic example where its main ligand estradiol (E2) can modulate ER binding on chromatin. Here, we show a shift in ER binding as a function of E2 concentration. As E2 concentration increases by two orders of magnitude, ER levels decrease, and ER binding localizes to promoter-distal sites with strong ER motifs. At low E2 levels, abundant levels of ER are present in the nucleus, and ER binding occurs mostly at sites without a canonical ER binding motif, in cooperation with other TFs like STAT1. We propose that E2's effect on ER activity plays a major role in defining genome-wide ER binding profiles. Thus, variations in E2 concentrations in ER-positive breast tumors could be a significant factor driving heterogeneity in tumor phenotype, treatment response, and potentially drug resistance.
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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".