Glucocorticoid‐Driven Transcriptomes in Airway Epithelial Cell Models: Commonalities, Differences and Functional Insights
Bibliographic record
Abstract
RATIONALE Glucocorticoids, typically in an inhaled form, represent the main pharmacotherapeutic option for the treatment of asthma. Acting on the glucocorticoid receptor (GR, NR3C1), glucocorticoids exert anti‐inflammatory effects on target tissues by reducing the expression of numerous inflammatory genes. It is well established that transcriptional activation of multiple “anti‐inflammatory” genes by GR plays a role reducing inflammatory gene expression and/or function at transcriptional, post‐transcriptional, translational and also post‐translational levels. Despite this, a major fraction of those genes induced by glucocorticoids have unknown or unclear functional consequences. Furthermore, consideration of the cell type‐ or line‐specific nature of glucocorticoid is needed to promote understanding of glucocorticoid‐driven transcriptional networks. AIMS To provide a descriptive summary of glucocorticoid‐driven gene expression profiles in pulmonary type II A549 and bronchial epithelial BEAS‐2B cells, two commonly‐used epithelial cell lines, and primary human bronchial epithelial (HBE) cells. Both, induction and repression of gene expression are essential components in mediating glucocorticoid function, however, for the purpose of this study, only induced genes are described. METHODS Gene expression profiling of RNA extracted from HBE, A549 and BEAS‐2B cells following 6 h of budesonide (300 nM) was performed using Affymetrix Prime View microarrays. Genes that show induction (fold ≥ 2, ANOVA P ≤ 0.05), when compared to untreated, in any of the cell types were used for further comparisons (391 genes). The genes in this pool were also grouped based on a less stringent cutoff (fold ≥ 1.25). Representative genes from each group were validated by qPCR. Gene ontology and Ingenuity Pathway Analysis (IPA) were performed using genes induced in each cell type independently, as well as for groups of commonly regulated genes. RESULTS Using the stringent cutoff criteria (fold ≥ 2, ANOVA P ≤ 0.05) for genes induced by glucocorticoid, only 19 genes (~ 5%) were common to all three epithelial cell models and a major fraction of the induced genes was apparently unique to each cell type. However, applying a less stringent cut‐off (fold ≥ 1.25) to this same pool of 391 genes revealed 91 genes (23%) that were commonly induced in all three cell models. Likewise, increased numbers of mutually upregulated genes were observed between any two models. A correspondingly lower percentage of genes were uniquely upregulated in a single cell model (34% of the genes in the pool). Representative genes from each group were validated by qPCR. Gene ontology analysis of commonly upregulated genes showed a significant enrichment of transcriptional control genes and genes involved in signaling. CONCLUSTIONS Induction of gene expression is an essential component of glucocorticoid function. While the profile of gene induction differs between cell types, genes with conserved inducibility may represent the key players in shaping glucocorticoid responses. Many such genes are consistent with the anti‐inflammatory effects of glucocorticoids. Support or Funding Information Supported by: The Lung Association ‐ Alberta & NWT, AstraZeneca, Canadian Institutes of Health Research.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".