Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression
Bibliographic record
Abstract
Abstract Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by superenhancers (SEs). The transcription of genes at SEs is enabled by the formation of phase-separated condensates by transcription factors and co-activators with intrinsically disordered regions. The androgen receptor (AR), main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the co-activator MED1 to SEs to promote oncogenic programs. In this work, we show that dynamic AR-rich, liquid-like foci form in PCa models upon androgen stimulation and correlate with AR transcriptional activity. The co-activator MED1 plays an essential role in the formation of AR foci while AR antagonists hinder their formation. These results suggest that enhanced compartmentalization of AR and co-activators at SEs may play an important role in the activation of oncogenic transcription programs in PCa. A better understanding of the assembly and the regulation of these AR-rich compartments may provide novel therapeutic options.
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) | 0.002 | 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".