Integration of transcriptome analysis with pathophysiological endpoints to evaluate cigarette smoke toxicity in an in vitro human airway tissue model

Abstract

AbstractExposure to cigarette smoke (CS) is a known risk factor in the pathogenesis of smoking-caused diseases, such as chronic obstructive pulmonary diseases (COPD) and lung cancer. To assess the effects of CS on the function and phenotype of airway epithelial cells, we developed a novel repeated treatment protocol and comprehensively evaluated the progression of key molecular, functional, and structural abnormalities induced by CS in a human in vitro air–liquid-interface (ALI) airway tissue model. Cultures were exposed to CS (diluted with 0.5 L/min, 1.0 L/min, and 4.0 L/min clean air) generated from smoking five 3R4F University of Kentucky reference cigarettes under the International Organization for Standardization (ISO) machine smoking regimen, every other day for 4 weeks (3 days per week, 40 min/day). By integrating the transcriptomics-based approach with the in vitro pathophysiological measurements, we demonstrated CS-mediated effects on oxidative stress, pro-inflammatory cytokines and matrix metalloproteinases (MMPs), ciliary function, expression and secretion of mucins, and squamous cell differentiation that are highly consistent with abnormalities observed in airways of smokers. Enrichment analysis on the transcriptomic profiles of the ALI cultures revealed key molecular pathways, such as xenobiotic metabolism, oxidative stress, and inflammatory responses that were perturbed in response to CS exposure. These responses, in turn, may trigger aberrant tissue remodeling, eventually leading to the onset of respiratory diseases. Furthermore, changes of a panel of genes known to be disturbed in smokers with COPD were successfully reproduced in the ALI cultures exposed to CS. In summary, findings from this study suggest that such an integrative approach may be a useful tool for identifying genes and adverse cellular events caused by inhaled toxicants, like CS.