Innovative graph analysis method to assess gene expression modulation after fine particles exposures of 3D human airway epithelia.

ABSTRACT

Environmental particles have dramatic consequences for health, especially for the most vulnerable people, such as asthmatics. To better understand the impact on gene expression modulation of fine particles (PM2.5-0.3) from different emission sources, a 3D-airway model, a human bronchial epithelium (MucilAir-HF™) reconstructed from primary cells from healthy (EpiH) or asthmatic (EpiA) donors, was used. Repeated air-liquid exposures were performed, and epithelia were sacrificed to extract RNAs and assess gene expression. Data were analyzed according to the emission sources, physiological status, and exposure doses using a recent model consisting in a graph analysis on pairwise expression ratio. The results were compared with those from the classical ΔΔCt method. The graph analysis method proved to have better statistical properties than the classical ΔΔCt method and demonstrated that repeated PM2.5-0.3 exposures induced a dose-dependent up-regulation of the metabolic gene (CYP1B1) and a down-regulation of the inflammation gene (CXCL10). These modulations were greater for "industrial" than for "urban traffic" fine particles, and the effects were found to be greater after exposure of EpiA than EpiH, thus emphasizing the importance of the epithelium's physiological status in sensitivity to particles. Our study is original in terms of the experimental conditions and the graphical statistical analysis model established. The results highlight the importance of particle chemistry on the modulation of cellular and molecular responses, which may vary according to the individual's vulnerability.