Nuclear Receptor Dax-1 Represses the Transcriptional Cooperation Between GATA-4 and SF-1 in Sertoli Cells1
Bibliographic record
Abstract
A crucial step in mammalian sex differentiation is the regression of the Müllerian ducts in males. This is achieved through the action of Müllerian inhibiting substance (MIS), a key hormone produced by fetal Sertoli cells. Proper spatiotemporal expression of the MIS gene requires the concerted action of several transcription factors that include Sox9, SF-1, WT-1, GATA-4, and Dax-1. Indeed, SF-1 contributes to MIS gene expression by transcriptionally cooperating with other factors such as GATA-4 and WT-1. Dax-1 is coexpressed with SF-1 in many tissues, including the gonads, where it acts as a negative modulator of SF-1-dependent transcription. We now report that Dax-1 can repress MIS transcription in Sertoli cells by disrupting transcriptional synergism between GATA-4 and SF-1. Dax-1-mediated repression of GATA-4/SF-1 synergism did not involve direct repression of GATA-dependent transactivation, but rather, it occurred through a direct protein-protein interaction with DNA-bound SF-1. It is interesting that SF-1, Dax-1, and GATA factors are coexpressed in several tissues such as the pituitary, the adrenals, and the gonads. Because we have shown that other GATA family members also have the ability to synergize with SF-1, Dax-1 repression of GATA/SF-1 synergism may represent an important mechanism for fine-tuning the regulation of SF-1-dependent genes in multiple target tissues.
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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) | 0.001 | 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 | 0.001 |
| 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".