Growth Hormone-induced STAT5B Regulates<i>Star</i>Gene Expression Through a Cooperation With cJUN in Mouse MA-10 Leydig Cells
Bibliographic record
Abstract
Leydig cells produce androgens that are essential for male sex differentiation and reproductive function. Leydig cell function is regulated by several hormones and signaling molecules, including growth hormone (GH). Although GH is known to upregulate Star gene expression in Leydig cells, its molecular mechanism of action remains unknown. The STAT5B transcription factor is a downstream effector of GH signaling in other systems. While STAT5B is present in both primary and Leydig cell lines, its function in these cells has yet to be ascertained. Here we report that treatment of MA-10 Leydig cells with GH or overexpression of STAT5B induces Star messenger RNA levels and increases steroid hormone output. The mouse Star promoter contains a consensus STAT5B element (TTCnnnGAA) at -756 bp to which STAT5B binds in vitro (electrophoretic mobility shift assay and supershift) and in vivo (chromatin immunoprecipitation) in a GH-induced manner. In functional promoter assays, STAT5B was found to activate a -980 bp mouse Star reporter. Mutating the -756 bp element prevented STAT5B binding but did not abrogate STAT5B-responsiveness. STAT5B was found to functionally cooperate with DNA-bound cJUN. The STAT5B/cJUN cooperation was only observed in Leydig cells and not in Sertoli or fibroblast cells, indicating that additional Leydig cell-enriched transcription factors are required. The STAT5B/cJUN cooperation was lost only when both STAT5B and cJUN elements were mutated. In addition to identifying the Star gene as a novel target for STAT5B in Leydig cells, our data provide important new insights into the mechanism of GH and STAT5B action in the regulation of Leydig cell function.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".