Transcriptomic Characterization of the Long‐term Dihydrotestosterone Effects in Adipose Tissue
Bibliographic record
Abstract
OBJECTIVE: To study the long-term transcriptomic effects of dihydrotestosterone (DHT) in adipose tissue. Fat distribution is regulated by sexual hormones. It is still unclear if androgens are promoting or reducing intra-abdominal fat accumulation. RESEARCH METHODS AND PROCEDURES: Retroperitoneal adipose tissue were isolated from each group of gonadectomized (GDX) C57BL6 male mice treated with vehicle or DHT for 21 days. Serial analysis of gene expression (SAGE) was performed to generate approximately 150,000 SAGE tags from each sample. RESULTS: Among the numerous genes regulated by DHT, transcripts involved in glycolysis, such as aldolase 1 A isoform and pyruvate kinase muscle as well as lipogenic transcripts, such as malic enzyme supernatant and ELOVL family member 6 elongation of long chain fatty acids were down-regulated by androgen supplementation. In contrast, transcripts involved in lipolysis and fatty acid oxidation, such as carboxylesterase 3, acetyl-coenzyme A acyltransferase 1, 3-ketoacyl-CoA thiolase B and enoyl-coenzyme A hydratase/3-hydroxyacyl coenzyme A dehydrogenase were up-regulated by DHT. Pro-apoptotic transcripts such as cell death-inducing DFFA-like effector c, BCL2/adenovirus E1B 19 kDa-interacting protein 1 NIP3 and -interacting protein 3-like were up-regulated by DHT, whereas transcripts involved in promotion of cell cycle such as cyclin D2 were down-regulated by DHT. DISCUSSION: These results suggest that chronic androgen treatment may help to improve metabolic profile by regulating various critical pathways involved in adipose tissue physiology. In addition, several genes associated with a healthier metabolic profile, such as adiponectin and CD36 antigen, were up-regulated by 21 days of DHT treatment.
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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 | 0.001 |
| 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".