NF-κB-dependent GR cistrome redistribution recruits GR to inflammatory genes but correlates with lesser glucocorticoid-mediated repression
Bibliographic record
Abstract
While ligand-activated glucocorticoid receptor (GR) binds DNA to activate transcription, glucocorticoids, including budesonide, reduce inflammatory gene expression, yet recruit GR to many such gene loci. In epithelial cells, the inflammatory cytokine, interleukin-1β (IL1B), activates nuclear factor (NF)-κB to induce gene expression, and co-treatment with budesonide produces nanoscale GR-RELA nuclear co-localization. Such co-stimulation orchestrated reciprocal genome-wide redistribution of GR- and RELA-binding regions (GBRs and RBRs, respectively) relative to each mono-treatment to produce widespread GBR-RBR overlap. This correlated with increased RNA polymerase-2 presence and required NF-κB for GR cistrome remodeling. Mapping transcription start sites to the nearest GBR or RBR each revealed associations with upregulated, but not repressed, genes. Importantly, RBR proximity to budesonide-upregulated genes and GBR proximity to IL1B-upregulated genes correlated with attenuated repression on co-treatment. As this occurred on a background of glucocorticoid-induced repression, GR presence at specific IL1B-induced gene loci may reduce/protect from an otherwise more prevalent glucocorticoid-induced repression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".