Interplay between nuclear factor-κB, p38 MAPK, and glucocorticoid receptor signaling synergistically induces functional TLR2 in lung epithelial cells
Bibliographic record
Abstract
While glucocorticoids act via the glucocorticoid receptor (GR; NR3C1) to reduce the expression of many inflammatory genes, repression is not an invariable outcome. Here, we explore synergy occurring between synthetic glucocorticoids (dexamethasone and budesonide) and proinflammatory cytokines (IL1B and TNF) on the expression of the toll-like receptor 2 (TLR2). This effect is observed in epithelial cell lines and both undifferentiated and differentiated primary human bronchial epithelial cells (pHBECs). In A549 cells, IL1B-plus-glucocorticoid-induced TLR2 expression required nuclear factor (NF)-κB and GR. Likewise, in A549 cells, BEAS-2B cells, and pHBECs, chromatin immunoprecipitation identified GR- and NF-κB/p65-binding regions ∼32 kb (R1) and ∼7.3 kb (R2) upstream of the TLR2 gene. Treatment of BEAS-2B cells with TNF or/and dexamethasone followed by global run-on sequencing confirmed transcriptional activity at these regions. Furthermore, cloning R1 or R2 into luciferase reporters revealed transcriptional activation by budesonide or IL1B, respectively, while R1+R2 juxtaposition enabled synergistic activation by IL1B and budesonide. In addition, small-molecule inhibitors and siRNA knockdown showed p38α MAPK to negatively regulate both IL1B-induced TLR2 expression and R1+R2 reporter activity. Finally, agonism of IL1B-plus-dexamethasone-induced TLR2 in A549 cells and pHBECs stimulated NF-κB- and interferon regulatory factor-dependent reporter activity and chemokine release. We conclude that glucocorticoid-plus-cytokine-driven synergy at TLR2 involves GR and NF-κB acting via specific enhancer regions, which combined with the inhibition of p38α MAPK promotes TLR2 expression. Subsequent inflammatory effects that occur following TLR2 agonism may be pertinent in severe neutrophilic asthma or chronic obstructive pulmonary disease, where glucocorticoid-based therapies are less efficacious.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".