Protein-Protein Interactions and Transcriptional Antagonism between the Subfamily of NGFI-B/Nur77 Orphan Nuclear Receptors and Glucocorticoid Receptor
Bibliographic record
Abstract
Glucocorticoids (Gc) act through the glucocorticoid receptor (GR) to enhance or repress transcription of glucocorticoid-responsive genes depending on the promoter and cellular context. Repression of proopiomelanocortin (POMC) gene expression by Gc was proposed to use different mechanisms. We described the POMC promoter Nur response element (NurRE) as a target for Gc repression. NGFI-B (Nur77), an orphan nuclear receptor, and two related factors, Nurr1 and NOR1, bind the NurRE as homo- or heterodimers to enhance POMC gene expression in response to CRH. Gc antagonize CRH-stimulated as well as NGFI-B-dependent transcription. We now show that GR antagonizes NurRE-dependent transcription induced by all members of the Nur77 subfamily and that these nuclear receptors can all interact directly with GR. Transcriptional antagonism as well as direct protein-protein interaction between NGFI-B and GR take place primarily via their respective DNA binding domains, although DNA binding itself and the GR homodimerization interface are not involved. In vivo, GR and Nur factors can be coimmunoprecipitated whereas GR is recruited to the POMC promoter upon glucocorticoid action. Thus, our data suggest a mechanism for transrepression between two nuclear receptors, GR and NGFI-B, that is unique, although quite similar to that proposed for transrepression between GR and activator protein 1 (AP-1) or nuclear factor-kappaB (NFkappaB).
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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 | 0.000 |
| 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) | 0.001 | 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".