Glucocorticoids and Inflammatory Cytokines Synergize to Maintain TLR2 Expression in Airway Epithelial Cells
Bibliographic record
Abstract
Rationale Glucocorticoids (GCs) act on the GC receptor (GR; NR3C1) to downregulate expression of numerous inflammatory genes and reduce inflammation. However, some inflammatory genes, including many involved in innate immune responses, are not repressed by GCs. These effects are poorly understood, yet may be relevant in severe asthma, patients who smoke, or during viral and bacterial exacerbations, which all respond poorly to GC therapy. Transcriptional regulation of the toll‐like receptor, TLR2, is examined as an inflammatory gene that escapes GC repression. Methods Pulmonary epithelial (A549 and BEAS‐2B) cells were used to model TLR2 expression induced by inflammatory stimuli and GCs. TLR2 expression was tested by qPCR and western blotting. Results In A549 cells, IL1B, TNF and dexamethasone (Dex) induced TLR2 mRNA and protein. IL1B, or TNF, co‐treated with Dex produced a delayed synergy on TLR2 expression. Analysis of unspliced RNA, a surrogate of transcription rate, suggested that this effect was transcriptional. ChIP‐seq data from BEAS‐2B cells showed 1 h of TNF or/and Dex to recruit RELA or/and GR, respectively, upstream of the TLR2 gene. In A549 cells, ChIP‐PCR confirmed that 1 h of IL1B or/and GC also induced RELA or/and GR binding, respectively, to the TLR2 promoter. Overexpression of a dominant inhibitor of NF‐κB (IκBαΔN) prevented TLR2 expression induced by IL1B or IL1B + Dex. Silencing of GR significantly reduced TLR2 expression induced by IL1B + Dex. In A549 cells, Dex modestly repressed, IL1B‐ or TNF‐induced NF‐κB‐dependent transcription. Likewise, IL1B and TNF modestly reduced 2×GRE‐dependent transcription induced by Dex. Thus, generic effects on each pathway do not explain TLR2 synergy. Rather NF‐κB and GR are both necessary and synergy occurs following recruitment of each factor at 1 h. Since synergistic increases in TLR2 mRNA following IL1B + Dex co‐treatment are delayed (from ~4 h), a role for additional factors is suggested. One early phase gene induced by GCs is DUSP1. This inhibits MAPKs and promotes repression of multiple inflammatory genes. However, in A549 cells, DUSP1 overexpression enhanced IL1B‐induced TLR2 expression. Similarly, the p38 MAPK inhibitor, SB203580, increased IL1B‐induced TLR2 mRNA. Thus, IL1B‐induced p38 activity acts to reduce TLR2 expression. As Dex inhibits IL1B‐activated p38 from ~1 h, a role for DUSP1 is suggested in the synergistic increase in TLR2 expression produced by IL1B + Dex. Conclusion Dual mechanisms by which GCs maintain inflammatory cytokine‐induced TLR2 expression in airway epithelial cells are demonstrated: i) GC‐activated GR and NF‐κB are necessary for TLR2 synergy; and ii) GC‐driven inhibition of p38 MAPK increases TLR2 expression. These regulatory events represent key mechanisms by which innate immune, or other, genes are hardwired to avoid, or escape, the repressive effects of GCs. Support or Funding Information Supported by Canadian Institutes of Health Research (CIHR) This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".