The Androgen Receptor Can Promote β-Catenin Nuclear Translocation Independently of Adenomatous Polyposis Coli
Bibliographic record
Abstract
We provide evidence that the androgen receptor (AR) can promote nuclear translocation of beta-catenin in LNCaP and PC3 prostate cancer cells. Using AR-expressing cells (LNCaP) and non-AR-expressing cells (PC3) we showed by time course cell fractionation that the AR can shuttle beta-catenin into the nucleus when exposed to exogenous androgen. Cells exposed to the synthetic androgen, R1881, show distinct, punctate, nuclear co-localization of the AR and beta-catenin. We further showed that the AR does not interact with adenomatous polyposis coli or glycogen synthase kinase-3beta and, therefore, conclude that androgen-mediated transport of beta-catenin occurs through a distinct pathway. The minimal necessary components of the AR and beta-catenin required for binding nuclear accumulation of beta-catenin nuclear import appears to be the DNA/ligand binding regions and the Armadillo repeats of beta-catenin. We also employed a novel DNA binding assay to illustrate that beta-catenin has the capacity to bind to the probasin promoter in an AR-dependent manner. The physiological relevance of AR-mediated transport of beta-catenin and binding to an AR promoter appeared to be a substantial increase in AR transcriptional reporter activity. AR-mediated import represents a novel mode of nuclear accumulation of beta-catenin.
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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) | 0.002 | 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".