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In vitro toxicity evaluation of heavy metals in urban air particulate matter on human lung epithelial cells

Yuan, Yue, Wu, Yun, Ge, Xinlei, Nie, Dongyang, Wang, Mei, Zhou, Haitao, Chen, Mindong
The Science of the total environment 2019 v.678 pp. 301-308
air, arsenic, cadmium, cell viability, chromium, copper, epithelial cells, heavy metals, iron, lead, lungs, manganese, median effective concentration, models, mortality, nickel, particulates, strontium, toxicity, transporters, zinc
Heavy metals are widely recognized as toxic components in urban air particulate matter (PM). However, the major toxic metals and their interactions are poorly understood. In this study, we attempted to explore the toxicity contribution and combined effects of PM-bounded metals in human lung epithelial cells (A549). Real-time cell analysis indicated that the critical toxic concentration (EC50) of PM detected in this study was 107.90 mg/L (r2 = 1.00, p < 0.01). The cell viability of A549 increased significantly (12.3%) after metal removal in PM, demonstrating an important contribution of metal components to PM toxicity. Among eleven elements examined (Zn, Cr, Mn, Fe, Ni, Cu, As, Se, Sr, Cd, and Pb), six heavy metals (Zn, Cr, Mn, Fe, Cu, and Pb) might account for PM toxicity in A549 cells, and their co-exposure led to a high mortality of A549 cells (36.5 ± 7.3%). For combination treatments, cell mortality caused by single or multiple metal mixtures was usually alleviated by Fe addition, while it was often aggravated in the presence of Mn. The varying effects of other metals (Zn, Cu, Pb and Cr) on different metal mixtures might be explained by their interactions (e.g., similar or dissimilar membrane transporters and intracellular targets). Furthermore, the concentration addition model (CA), independent action model (IA), integrated addition model (IAM) and integrated addition and interaction model (IAI) were used to predict mixture toxicity, and the IAI model exhibited the least variation between observed and predicted toxic effects (r2 = 0.87, p < 0.01). Our results highlight the potential contribution from heavy metals and their interactions to PM toxicity, and promote the application of toxicity prediction models on metal components in PM.