RanBPM, a Nuclear Protein That Interacts with and Regulates Transcriptional Activity of Androgen Receptor and Glucocorticoid Receptor
Bibliographic record
Abstract
The androgen receptor (AR) is a ligand-dependent transcription factor that has an essential role in the normal growth, development, and maintenance of the prostate gland. The AR is part of a large family of steroid receptors that also includes the glucocorticoid, progesterone, and mineralocorticoid receptors. Steroid receptor family members share significant homology at their DNA and ligand-binding domains. However, these receptors exhibit a high degree of sequence variability at their NH(2)-terminal domain, which suggests the possibility of receptor-specific interactions with co-regulator proteins. Transcriptional co-regulators that interact with the AR may have a role in defining AR activity and may be involved in directing AR-specific responses. Here we have identified Ran-binding protein in the microtubule-organizing center (RanBPM) to be a novel AR-interacting protein by yeast two-hybrid assay and have confirmed this interaction by glutathione S-transferase- and His-tagged pull-down assays. In addition, transient overexpression of RanBPM in prostate cancer cell lines resulted in enhanced AR activity in a ligand-dependent fashion. Glucocorticoid receptor activity was also enhanced when RanBPM was overexpressed, whereas estrogen receptor activity remained unchanged. These data demonstrate that RanBPM interacts with steroid receptors to selectively modify their activity.
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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".