Functional Genomics Identifies a Mechanism for Estrogen Activation of the Retinoic Acid Receptor α1 Gene in Breast Cancer Cells
Bibliographic record
Abstract
The identification of estrogen receptor (ERalpha) target genes is crucial to our understanding of its predominant role in breast cancer. In this study, we used a chromatin immunoprecipitation (ChIP)-cloning strategy to identify ERalpha-regulatory modules and associated target genes in the human breast cancer cell line MCF-7. We isolated 12 transcriptionally active genomic modules that recruit ERalpha and the coactivator steroid receptor coactivator (SRC)-3 to different intensities in vivo. One of the ERalpha-regulatory modules identified is located 3.7 kb downstream of the first transcriptional start site of the RARA locus, which encodes retinoic acid receptor alpha1 (RARalpha1). This module, which includes an estrogen response element (ERE), is conserved between the human and mouse genomes. Direct binding of ERalpha to the ERE was shown using EMSAs, and transient transfections in MCF-7 cells demonstrated that endogenous ERalpha can induce estrogen-dependent transcriptional activation from the module or the ERE linked to a heterologous promoter. Furthermore, ChIP assays showed that the coregulators SRC-1, SRC-3, and receptor-interacting protein 140 are recruited to this intronic module in an estrogen-dependent manner. As expected from previous studies, the transcription factor Sp1 can be detected at the RARA alpha1 promoter by ChIP. However, treatment with estradiol did not influence Sp1 recruitment nor help recruit ERalpha to the promoter. Finally, ablation of the intronic ERE was sufficient to abrogate the up-regulation of RARA alpha1 promoter activity by estradiol. Thus, this study uncovered a mechanism by which ERalpha significantly activates RARalpha1 expression in breast cancer cells and exemplifies the utility of functional genomics strategies in identifying long-distance regulatory modules for nuclear receptors.
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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 | 0.001 |
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".