Glucocorticoid‐Driven Transcriptomes in Human Airway Epithelial Cells: Commonalities, Differences and Functional Insight
Notice bibliographique
Résumé
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 .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».