Comparative proteomic analysis reveals the different hepatotoxic mechanisms of human hepatocytes exposed to silver nanoparticles

蛋白质组学 化学 银纳米粒子 纳米毒理学 细胞毒性 金属硫蛋白 细胞生物学 分子生物学 生物化学 纳米颗粒 生物 纳米技术 毒性 体外 有机化学 基因 材料科学
作者
Tin Yan Wong,Neng Yan,Kenneth Kin Leung Kwan,Yanrong Pan,Jingjing Liu,Yao Xiao,Long Wu,Henry Lam
出处
期刊:Journal of Hazardous Materials [Elsevier BV]
卷期号:445: 130599-130599 被引量:3
标识
DOI:10.1016/j.jhazmat.2022.130599
摘要

Silver nanoparticles (AgNPs), which have been used extensively in consuming products and eventually released into the natural environment, have aroused concerns recently because of their potentially harmful effects on human beings following various routes of exposure. As the liver is one of the largest accumulation and deposition sites of circulatory AgNPs, it is important to evaluate the hepatotoxicity induced by AgNPs. However, the acting mechanisms of AgNPs-induced hepatotoxicity are still elusive to a great extent. Herein, we investigated the hepatotoxic effects of AgNPs using a comparative proteomics approach. First, we evaluated the cytotoxicity of different-sized AgNPs and found that the cancerous liver cells were generally more sensitive than the normal liver cells. Next, proteomics results suggested that HepG2 and L02 cells showed distinct adaptive responses upon AgNPs exposure. HepG2 cells respond to stresses by adapting energy metabolism, upregulating metallothionein expression and increasing the expression of antioxidants, while L02 cells protect themselves by increasing DNA repair and macro-autophagy. Besides, mitochondrial ROS has been identified as one of the causes of AgNPs-induced hepatotoxicity. Collectively, our results revealed that hepatic cancer cells and normal cells cope with AgNPs in notably different pathways, providing new insights into mechanisms underlying AgNPs-induced hepatotoxicity. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (Deutsch et al. (2020)) via the PRIDE (Perez-Riverol et al. (2019)) partner repository with the dataset identifier PXD029511.

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