MEF2 Cooperates With Forskolin/cAMP and GATA4 to Regulate Star Gene Expression in Mouse MA-10 Leydig Cells
Bibliographic record
Abstract
In Leydig cells, steroidogenic acute regulatory protein (STAR) participates in cholesterol shuttling from the outer to the inner mitochondrial membrane, the rate-limiting step in steroidogenesis. Steroid hormone biosynthesis and steroidogenic gene expression are regulated by LH, which activates various signaling pathways and transcription factors, including cAMP/Ca(2+)/CAMK (Ca(2+)/calmodulin-dependent kinase)-myocyte enhancer factor 2 (MEF2). The 4 MEF2 transcription factors are essential regulators of cell differentiation and organogenesis in numerous tissues. Recently, MEF2 was identified in Sertoli and Leydig cells of the testis. Here, we report that MEF2 regulates steroidogenesis in mouse MA-10 Leydig cells by acting on the Star gene. In MA-10 cells depleted of MEF2 using siRNAs (small interfering RNAs), STAR protein levels, Star mRNA levels, and promoter activity were significantly decreased. On its own, MEF2 did not activate the mouse Star promoter but was found to cooperate with forskolin/cAMP. By chromatin immunoprecipitation and DNA precipitation assays, we confirmed MEF2 binding to a consensus element located at -232 bp of the Star promoter. Mutation or deletion of the MEF2 element reduced but did not abrogate the MEF2/cAMP cooperation, indicating that MEF2 cooperates with other DNA-bound transcription factor(s). We identified GATA4 (GATA binding protein 4) as a partner for MEF2 in Leydig cells, because mutation of the GATA element abrogated the MEF2/cAMP cooperation on a reporter lacking a MEF2 element. MEF2 and GATA4 interact as revealed by coimmunoprecipitation, and MEF2 and GATA4 transcriptionally cooperate on the Star promoter. Altogether, our results define MEF2 as a novel regulator of steroidogenesis and Star transcription in Leydig cells and identify GATA4 as a key partner for MEF2-mediated action.
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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".