Glucocorticoid‐Driven Transcriptomes in Human Airway Epithelial Cells: Commonalities, Differences and Functional Insight
Bibliographic record
Abstract
RATIONALE Glucocorticoids act on the glucocorticoid receptor (GR; NR3C1) to resolve inflammation and, as inhaled corticosteroids (ICS), are the cornerstone of treatment for asthma. However, reduced efficacy in severe disease and during exacerbations indicates a need to improve ICS actions. Since ICS target the epithelium, glucocorticoid‐driven transcriptomes were compared between primary human bronchial epithelial (HBE) cells and common cell lines, pulmonary type II A549 and bronchial epithelial BEAS‐2B cells. METHODS Gene expression profiling of RNA extracted from A549, BEAS‐2B and HBE cells following budesonide treatment was performed using Affymetrix PrimeView microarrays. Genes showing significant induction (fold ≥2, P ≤0.05) or repression (fold ≤0.5, P ≤0.05) compared to untreated cells, in any of the cell variants were used for further analyses. RESULTS In BEAS‐2B cells, budesonide induced or, in a delayed fashion, repressed the expression of 63, 133, 240, and 257 or 15, 56, 236, and 344 mRNAs at 1, 2, 6, and 18 h, respectively. Within the early‐induced mRNAs were multiple transcriptional activators and repressors, thereby providing mechanisms for the subsequent modulation of gene expression. Using the above criteria, 17 (BCL6, BIRC3, CEBPD, ERRFI1, FBXL16, FKBP5, GADD45B, IRS2, KLF9, PDK4, PER1, RGCC, RGS2, SEC14L2, SLC16A12, TFCP2L1, TSC22D3) induced and 8 (ARL4C, FLRT2, IER3, IL11, PLAUR, SEMA3A, SLC4A7, SOX9) repressed mRNAs were common between A549, BEAS‐2B and HBE cells at 6 h. As absolute gene expression change showed greater commonality, lowering the cut‐off (≥1.25 or ≤0.8‐fold) within these groups produced 93 and 82 genes induced or repressed in common. Since large changes in few mRNAs and/or small changes in many mRNAs may drive function, gene ontology (GO)/pathway analyses were performed using both stringency criteria. Budesonide‐induced genes showed GO term enrichment for positive and negative regulation of transcription, signaling, proliferation, apoptosis, and movement, as well as FOXO and PI3K‐Akt signaling pathways. Repressed genes were enriched for inflammatory signaling pathways (TNF, NF‐κB) and GO terms for cytokine activity, chemotaxis and cell signaling. Reduced growth factor expression and effects on proliferation and apoptosis were highlighted. CONCLUSIONS While glucocorticoids repress mRNAs associated with inflammation, prior induction of transcriptional activators and repressors may explain longer‐term responses to these agents. Positive and negative effects on signaling, proliferation, migration and apoptosis were revealed. Since many such gene expression changes occurred in human airways post‐ICS inhalation, effects in cell lines and primary HBE cells in vitro may be relevant to ICS in vivo . Support or Funding Information This work was supported by: RN grants: Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant, and AstraZeneca; MMM studentships: NSERC Postgraduate Scholarship – Doctoral, Queen Elizabeth II Doctoral scholarship, and The Lung Association – Alberta & NWT studentship award. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".