Publication
Global assessment of the integrated stress response in CF patient-derived airway and intestinal tissues
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- Last modified
- 08/19/2025
- Type of Material
- Authors
- Language
- English
- Date
- 2020-11-01
- Publisher
- ELSEVIER
- Publication Version
- Copyright Statement
- © 2020 European Cystic Fibrosis Society. Published by Elsevier B.V.
- License
- Final Published Version (URL)
- Title of Journal or Parent Work
- Volume
- 19
- Issue
- 6
- Start Page
- 1021
- End Page
- 1026
- Grant/Funding Information
- Funding for this research was provided by the Cystic Fibrosis Foundation (OLIVER17F0 to KEO; IGNATO17XXO to ZI; SORSCH13XXO and SORSCH14XXO to EJS), National Institutes of Health (R01HL136414 to EJS), Burroughs Wellcome Fund (Collaborative Research Travel Grant to KEO), German Cystic Fibrosis Foundation muko e.V. (1603 to ZI).
- Supplemental Material (URL)
- Abstract
- Background: Chronic inflammation is a hallmark among patients with cystic fibrosis (CF). We explored whether mutation-induced (F508del) misfolding of the cystic fibrosis transmembrane conductance regulator (CFTR), and/or secondary colonization with opportunistic pathogens, activate tissue remodeling and innate immune response drivers. Methods: Using RNA-seq to interrogate global gene expression profiles, we analyzed stress response signaling cascades in primary human bronchial epithelia (HBE) and intestinal organoids. Results: Primary HBE acquired from CF patients with advanced disease and prolonged exposure to pathogenic microorganisms display a clear molecular signature of activated tissue remodeling pathways, unfolded protein response (UPR), and chronic inflammation. Furthermore, CFTR misfolding induces inflammatory signaling cascades in F508del patient-derived organoids from both the distal small intestine and colon. Conclusion: Despite the small patient cohort size, this proof-of-principle study supports the use of RNA-seq as a means to both identify CF-specific signaling profiles in various tissues and evaluate disease heterogeneity. Our global transcriptomic data is a useful resource for the CF research community for analyzing other gene expression sets influencing CF disease signature but also transcriptionally contributing to CF heterogeneity.
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