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Glucocorticoid‐Driven Transcriptomes in Human Airway Epithelial Cells: Insight from Primary Cells and Cell Lines

2018· article· en· W3175298816 on OpenAlexafffundabout
Mahmoud Mostafa, Christopher F. Rider, Robert Newton

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
FundersCanadian Institutes of Health ResearchAstraZeneca
KeywordsTranscriptomeGlucocorticoid receptorGlucocorticoidBiologyGene expressionGene expression profilingCell typeGeneCell biologyCellImmunologyGenetics

Abstract

fetched live from OpenAlex

RATIONALE Glucocorticoids are stress response hormones that act on the glucocorticoid receptor (GR) to execute effects, including repression of inflammatory gene expression. The molecular mechanisms for this process are still elusive, indeed controversial. This is, to a large extent, due the knowledge gap in understanding the functional impacts of the key glucocorticoid‐modulated genes in relevant tissues. Identification of such genes may be problematic because the effects of glucocorticoids on gene expression are context‐dependent and show variable effects between different cell types. This confounds the identification of key gene expression features within the glucocorticoid‐response. The current study addresses this by comparing the glucocorticoid‐driven transcriptomes in 3 variants of human airway epithelial cells, a critical player in airway inflammation and physiological responses to glucocorticoid. These include two airway epithelial cell lines, pulmonary type II A549 and bronchial epithelial BEAS‐2B cells, and primary human bronchial epithelial (HBE) cells. METHODS Gene expression profiling of RNA extracted from A549, BEAS‐2B and HBE cells following budesonide treatment for 6 h 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 types were used for further analyses. Representative genes were validated using qPCR. Gene ontology and Ingenuity Pathway Analysis (IPA) were performed using differentially regulated genes in each cell variant. RESULTS Using stringent (fold ≥ 2 or ≤ 0.5, P ≤ 0.05) cutoff criteria, only 17 and 8 of genes induced or repressed, respectively, by glucocorticoid were common to all 3 epithelial cell variants. While a major fraction of the significantly modulated genes was apparently unique to each cell variant, hierarchical clustering revealed greater commonality than was suggested by the stringent cut‐off. Applying less stringent cut‐offs (fold ≥ 1.25 or ≤ 0.8) within the pool of genes identified by stringent cut‐off revealed 93 induced and 82 repressed genes that were in common among all 3 cell variants. Validation of 52 budesonide‐induced genes using qPCR confirmed, for most of the genes, the grouping scheme determined by applying the less stringent cut‐off. Gene ontology (GO) analysis of the genes induced in common showed enrichment of transcriptional control, proliferation/apoptosis and signaling terms. Equally, the GO term growth factor activity was profoundly enriched with budesonide‐repressed genes across all variants. CONCLUSIONS The current comparative transcriptome analysis revealed a surprisingly large variance among 3 variants of airway epithelial cells in response to glucocorticoids. Despite this, the common gene expression features obtained by such analysis represent unbiased listing of potentially key molecular players in glucocorticoid response. While many of these genes, and the associated pathways, are consistent with anti‐inflammatory effects of glucocorticoids, a significant fraction are associated with developmental, proliferative, and metabolic GO terms. The functional impacts for many other genes are still unclear, and, therefore should be given priority for further functional analysis. Filling such gaps is essential for advancing knowledge of glucocorticoid biology. Support or Funding Information Supported by: The Lung Association Alberta & NWT, AstraZeneca, Canadian Institutes of Health Research. This abstract is from the Experimental Biology 2018 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 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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.243
Teacher spread0.230 · 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
Published2018
Admission routes3
Has abstractyes

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