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Interplay between the Glucocorticoid Receptor and Inflammatory Signaling leads to Enhanced TLR2 Expression in Pulmonary Epithelial Cells

2020· article· en· W3016975310 on OpenAlexaffabout
Akanksha Bansal, Mahmoud Mostafa, Cora Kooi, Suharsh Shah, Richard Leigh, Anthony N. Gerber, Robert Newton

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChromatin immunoprecipitationTLR2A549 cellGlucocorticoid receptorTranscription factorReceptorRepressorBiologyMolecular biologyInnate immune systemChemistryCell biologyPromoterCancer researchGlucocorticoidGene expressionApoptosisImmunologyInternal medicineGeneMedicine

Abstract

fetched live from OpenAlex

Glucocorticoids (GCs) acting on the GC receptor (GR; NR3C1) repress the expression of numerous inflammatory genes. Consequently, GCs are mainstay therapy for asthma. However, many genes, for example some involved in innate immune responses, “escape” repression by GCs. Mechanisms for this and their functional consequences are poorly understood, but may be relevant in severe asthma, patients who smoke, or during exacerbations, where GCs show reduced efficacy. We use pulmonary epithelial cells to explore mechanisms that allow TLR2 to escape the repressive effects of GCs. Methods Pulmonary epithelial A549 and primary human bronchial epithelial (pHBE) cells were used to model TLR2 expression upon stimulation with inflammatory cytokines (IL1B and TNF) and GCs (dexamethasone, budesonide) using qPCR and western blotting. Chromatin immunoprecipitation (ChIP) was used to test binding of transcription factors to DNA. Results In A549 and pHBE cells, IL1B/TNF and GCs induced TLR2 mRNA and protein in a manner that produced delayed synergy upon co‐treatment. IL1B+GC also increased unspliced TLR2 RNA (unRNA), a proxy of transcription rate, suggesting this synergy to be transcriptional. IL1B+GC‐induced TLR2 expression was dependent on NF‐κB and GR, as assessed using the dominant inhibitor of NF‐κB, IκBαΔN, and siRNA targeting GR. ChIP‐PCR in A549 and pHBE cells confirmed binding of RELA and GR to regions upstream of the TLR2 locus following IL1B and GC treatment, respectively. A maximally effective ( E max ) concentration of IL1B did not affect GRE‐dependent transcription and E max GC concentrations modestly decreased NF‐κB dependent transcription. Thus, possible effects of IL1B on simple GRE‐dependent transcription, or, of GCs on NF‐κB‐dependent transcription do not appear to explain TLR2 synergy. Nevertheless, both NF‐κB and GR are necessary for TLR2 expression and synergy occurs after they are recruited to the TLR2 promoter at 1h. Since GR and RELA were enriched at the TLR2 promoter as early as 1 h but synergy in TLR2 mRNA expression upon IL1B+GC co‐treatment occurred from ~4 h, roles for additional processes are apparent. One candidate for this is the GC‐dependent reduction in IL1B‐induced p38 MAPK activity, which occurred from 1h onwards. Thus, the p38 MAPK inhibitors SB203580, BIRB796 and VX745 dose‐dependently increased IL1B‐induced TLR2 mRNA expression. Compound selectivity of these inhibitors suggests a negative role for p38α/β MAPK on TLR2 expression. Indeed, siRNAs targeting p38α increased IL1B‐induced TLR2 expression, while p38β‐targeting siRNAs had no effect. Conclusion Inflammatory stimuli (IL1B/TNF) and GCs act together to synergistically increase TLR2 expression in pulmonary epithelial cells via mechanisms that require NF‐κB and GR. GCs also inhibit p38 MAPK. This reduces the inhibitory effect of p38α on TLR2 levels and facilitates the increase in TLR2 expression in the context of co‐treatment. Interplay between GR and inflammatory pathways (NF‐κB and MAPK) may therefore represent key mechanisms by which some inflammatory genes are hard‐wired to escape GC‐repression. Support or Funding Information Canadian Institutes of Health Research (CIHR)

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.011
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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