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In this section, you can access to the latest technical information related to the FUTURE project topic.
The development of a cell-based model for the assessment of carcinogenic potential upon long-term PM2.5 exposure
To assess the carcinogenic potential of PM2.5 exposure, we developed a cell-based experimental protocol to examine the cell transformation activity of PM2.5 samples from different regions in China. The seasonal ambient PM2.5 samples were collected from three megacities, Beijing (BJ), Wuhan (WH), and Guangzhou (GZ), from November 2016 to October 2017. The mean concentrations of PM2.5 were much higher in the winter season (BJ: 109.64??g/m3, WH: 79.99??g/m3, GZ: 49.99??g/m3) than that in summer season (BJ: 42.40??g/m3, WH: 25.82??g/m3, GZ: 19.82??g/m3). The organic extracts (OE) of PM2.5 samples from combined summer (S) (June, July, August) or winter (W) (November, December, January) seasons were subjected to characterization of chemical components. We treated human bronchial epithelial (HBE) cells expressing CYP1A1 (HBE-1A1) with PM2.5 samples at doses ranging from 0 to 100??g/mL (0, 1.563, 3.125, 6.25, 12.5, 25, 50, 100??g/mL) and determined the phenotype of malignant cell transformation. A dose-response relationship was analyzed by benchmark dose (BMD) modeling, and the potential were indicated by BMDL10. The order of the carcinogenic risk of seasonal PM2.5 samples from high to low was BJ-W, WH-W, GZ-W, WH-S, BJ-S, and GZ-S. Notably, we found that the alteration in the lung cancer-related biomarkers, KRAS, PTEN, p53, c-Myc, PCNA, pAKT/AKT, and pERK/ERK was congruent with the activity of cell transformation and the content of specific components of polycyclic aromatic hydrocarbon (PAHs) bound to PM2.5. Taken together, we have successfully developed a cell-based alternative model for the evaluation of potent carcinogenicity upon long-term PM2.5 exposure.
Graphical abstract
» Author: Shen Chen, Daochuan Li, Haiyan Zhang, Dianke Yu, Rui Chen, Bin Zhang, Yafei Tan, Yong Niu, Huawei Duan, Bixian Mai, Shejun Chen, Jianzhen Yu, Tiangang Luan, Liping Chen, Xiumei Xing, Qiong Li, Yongmei Xiao, Guanghui Dong, Yujie Niu, Michael Aschner
» Publication Date: 01/10/2019
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