Isolation and identification of l-dopa decarboxylase as a protein that binds to and enhances transcriptional activity of the androgen receptor using the repressed transactivator yeast two-hybrid system
Bibliographic record
Abstract
The AR (androgen receptor) is a ligand-regulated transcription factor, which belongs to the steroid receptor family and plays an essential role in growth and development of the prostate. Transcriptional activity of steroid receptors is modulated by interaction with co-regulator proteins and yeast two-hybrid analysis is commonly used to identify these steroid receptor-interacting proteins. However, a limitation of conventional two-hybrid systems for detecting AR protein partners has been that they only allow for analysis of the ligand- and DNA-binding domains of the receptor, as its NTD (N-terminal domain) possesses intrinsic transactivation activity. To identify AR N-terminus-interacting proteins, its NTD was used in the RTA (repressed transactivator) system, which is specifically designed for transactivator bait proteins and was shown to be suitable for two-hybrid analysis with the AR NTD. DDC (L-dopa decarboxylase) was detected multiple times as a novel AR-interacting protein, which was subsequently confirmed in vitro and in vivo. Furthermore, transient transfection of DDC in prostate cancer cells strongly enhanced ligand-dependent AR transcriptional activity, an effect that was antagonized using high concentrations of the anti-androgen bicalutamide. Glucocorticoid receptor activity was also strongly enhanced with DDC co-transfection, while oestrogen receptor activity was only mildly affected. Together, our data demonstrate that DDC interacts with AR to enhance steroid receptor transactivation, which may have important implications in prostate cancer progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".