Genome-Wide Identification of High-Affinity Estrogen Response Elements in Human and Mouse
Bibliographic record
Abstract
Although estrogen receptors (ERs) recognize 15-bp palindromic estrogen response elements (EREs) with maximal affinity in vitro, few near-consensus sequences have been characterized in estrogen target genes. Here we report the design of a genome-wide screen for high-affinity EREs and the identification of approximately 70000 motifs in the human and mouse genomes. EREs are enriched in regions proximal to the transcriptional start sites, and approximately 1% of elements appear conserved in the flanking regions (-10 kb to +5 kb) of orthologous human and mouse genes. Conserved and nonconserved elements were also found, often in multiple occurrences, in more than 230 estrogen-stimulated human genes previously identified from expression studies. In genes containing known EREs, we also identified additional distal elements, sometimes with higher in vitro binding affinity and/or better conservation between the species considered. Chromatin immunoprecipitation experiments in breast cancer cell lines indicate that most novel elements present in responsive genes bind ERalpha in vivo, including some EREs located up to approximately 10 kb from transcriptional start sites. Our results demonstrate that near-consensus EREs occur frequently in both genomes and that whereas chromatin structure likely modulates access to binding sites, far upstream elements can be evolutionarily conserved and bind ERs in vivo.
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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 | 0.001 | 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) | 0.002 | 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".