Glucocorticoid Ligands Specify Different Interactions with NF-κB by Allosteric Effects on the Glucocorticoid Receptor DNA Binding Domain
Bibliographic record
Abstract
Glucocorticoids inhibit inflammation by acting through the glucocorticoid receptor (GR) and powerfully repressing NF-kappaB function. Ligand binding to the C-terminal of GR promotes the nuclear translocation of the receptor and binding to NF-kappaB through the GR DNA binding domain. We sought how ligand recognition influences the interaction between NF-kappaB and GR. Both dexamethasone (agonist) and RU486 (antagonist) promote efficient nuclear translocation, and we show occupancy of the same intranuclear compartment as NF-kappaB with both ligands. However, unlike dexamethasone, RU486 had negligible activity to inhibit NF-kappaB transactivation. This failure may stem from altered co-factor recruitment or altered interaction with NF-kappaB. Using both glutathione S-transferase pull-down and bioluminescence resonance energy transfer approaches, we identified a major glucocorticoid ligand effect on interaction between the GR and the p65 component of NF-kappaB, with RU486 inhibiting recruitment compared with dexamethasone. Using the bioluminescence resonance energy transfer assay, we found that RU486 efficiently recruited NCoR to the GR, unlike dexamethasone, which recruited SRC1. Therefore, RU486 promotes differential protein recruitment to both the C-terminal and DNA binding domain of the receptor. Importantly, using chromatin immunoprecipitation, we show that impaired interaction between GR and p65 with RU486 leads to reduced recruitment of the GR to the NF-kappaB-responsive region of the interleukin-8 promoter, again in contrast to dexamethasone that significantly increased GR binding. We demonstrate that ligand-induced conformation of the GR C-terminal has profound effects on the functional surface generated by the DNA binding domain of the GR. This has implications for understanding ligand-dependent interdomain communication.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".