A Mutated Form of Steroidogenic Factor 1 (SF-1 G35E) That Causes Sex Reversal in Humans Fails to Synergize with Transcription Factor GATA-4
Bibliographic record
Abstract
Steroidogenic factor 1 (SF-1) is a transcription factor belonging to the nuclear receptor superfamily. SF-1 regulates the expression of many genes involved in reproduction, steroidogenesis, and sexual differentiation. An important SF-1 target for male sexual differentiation is the gene encoding the Müllerian-inhibiting substance hormone that induces regression of the Müllerian ducts in the developing male embryo. Not long ago, a mutation (G35E) in the human SF-1 gene was identified as the cause of sex reversal and adrenal failure in a phenotypically female but genotypically XY individual. This suggested that the mutated SF-1 protein might interfere with the expression of SF-1 target gene(s) involved in the male sexual differentiation pathway, such as MIS. Surprisingly, the initial biochemical characterization of the SF-1 G35E mutant revealed that it could bind and activate the MIS promoter as efficiently as wild-type SF-1. MIS expression, however, does not rely solely on SF-1 but rather requires the concerted action of several transcription factors including GATA-4. We have previously reported that GATA-4 and SF-1 transcriptionally cooperate to synergistically activate the MIS promoter. Thus, we hypothesized that the phenotype observed with the SF-1 G35E mutation could be explained, at least in part, by a failure and/or a disruption of GATA-4/SF-1 synergism. We found that the SF-1 G35E mutant failed to synergize with GATA-4 despite a direct physical interaction between the two proteins. Interestingly, the SF-1 G35E mutant also disrupted transcriptional synergism between wild-type SF-1 and GATA-4, indicating that it could act as a dominant negative competitor. Thus, our results strengthen the importance of a GATA-4/SF-1 cooperation for MIS transcription and reveal that disruption of this synergism might be responsible for some cases of abnormal sex differentiation in humans.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".