Glucocorticoid‐Driven Transcriptomes in Airway Epithelial Cell Models: Commonalities, Differences and Functional Insights
Notice bibliographique
Résumé
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.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».