Genome-wide analysis of androgen receptor binding and transcriptomic analysis in mesenchymal subsets during prostate development
Bibliographic record
Abstract
ABSTRACT Prostate development is controlled by androgens, the androgen receptor (AR) and mesenchymal–epithelial signalling. We used chromatin immunoprecipitation sequencing (ChIP-seq) to define AR genomic binding in the male and female mesenchyme. Tissue- and single-cell-based transcriptional profiling was used to define mesenchymal AR target genes. We observed significant AR genomic binding in females and a strong enrichment at proximal promoters in both sexes. In males, there was greater AR binding to introns and intergenic regions as well as to classical AR binding motifs. In females, there was increased proximal promoter binding and involvement of cofactors. Comparison of AR-bound genes with transcriptomic data enabled the identification of novel sexually dimorphic AR target genes. We validated the dimorphic expression of AR target genes using published datasets and confirmed regulation by androgens using ex vivo organ cultures. AR targets showed variable expression in patients with androgen insensitivity syndrome. We examined AR function at single-cell resolution using single-cell RNA sequencing (scRNA-seq) in male and female mesenchyme. Surprisingly, both AR and target genes were distributed throughout cell subsets, with few positive cells within each subset. AR binding was weakly correlated with target gene expression.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".