Glucocorticoid Receptor Orchestrates Interactions Between Glucocorticoid ‐Responsive and ‐Unresponsive Enhancers to Modulate Gene Expression
Bibliographic record
Abstract
RATIONALE Glucocorticoids (GCs) are stress hormones that act on the GC receptor (GR; NR3C1) to elicit effects, including repression of inflammatory gene expression. The mechanisms by which GR modulate gene expression are not completely understood. GR binds to thousands of genomic loci, but only regulates the expression of hundreds of genes. Genome‐wide chromatin interaction studies revealed that the proximal promotor of various GC‐regulated genes interacts with multiple distal enhancer elements that harbour GC‐dependent GR binding. The significance of such interactions and the contribution of each locus to the achieved gene expression are yet to be investigated. The current work provides a focused perspective on genomic events that lead to the induction of KLF9 by GCs, a gene expression feature that is highly conserved in multiple cell types. RESULTS GC‐mediated induction of KLF9 was confirmed in the airway epithelial cell lines (A549 and BEAS‐2B), as well as in primary culture of various airway structural cells. This induction was abolished in A549 cells either by pretreating cells with the GR antagonist, Org34517, or by knocking down GR with siRNA. Chromatin immunoprecipitation of GR following GC treatment in A549, BEAS‐2B, and primary human bronchial epithelial (HBE) cells showed GR binding to sites 5.9, 6.7, 25, and 65 kb upstream of the KLF9 gene. Global nascent transcript analysis of BEAS‐2B cells following GC treatment showed marked induction of enhancer RNA (eRNA) from each of these sites. This observation was validated using qPCR in A549 and primary HBE cells. GR binding regions were then cloned upstream to a luciferase reporter to test their enhancer activity due to GCs in isolation from their genomic context. There was a clear distinction between proximal regions (i.e. 5.9 and 6.7 kb from KLF9 promotor) and the distal regions (25 and 65 kb) in terms of basal activity and response to GCs. Proximal regions showed very strong basal reporter activity that was not affected by GC treatment. Distal regions, however, showed minimal basal activity but produced dose‐dependent increase in reporter activity by GCs, which was abolished by mutating the GC response elements (GRE) from these regions. Kinetics of long‐range genomic interactions identified a significant interaction between the distal and the proximal regions in a slightly delayed fashion (4h onward), this was temporally associated with enhanced binding of EP300 (histone acetylase associated with gene activation) to the proximal region. CONCLUSIONS Our data suggests that each GR binding site upstream to KLF9 responds differently to GCs, but all sites may collectively contribute to the basal transcriptional activity, induction, and maintenance of KLF9 expression. Co‐localization of other transcriptional regulators may explain the distinction between different sites. Our data are essential in establishing a model to explain GR genomic binding behaviours and how they correlate with gene expression outcomes, thus providing insights on how to modulate GC actions to achieve clinical benefits. Support or Funding Information Funding: Canadian Institutes of Health Research (CIHR) and Natural Sciences and Engineering Research Council of Canada (NSERC)
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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) | 0.002 | 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".