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Comparative proteomic analysis of silver nanoparticle effects in human liver and intestinal cells
Author(s) -
Braeuning Albert,
Oberemm Axel,
Görte Josephine,
Böhmert Linda,
Juling Sabine,
Lampen Alfonso
Publication year - 2018
Publication title -
journal of applied toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.784
H-Index - 87
eISSN - 1099-1263
pISSN - 0260-437X
DOI - 10.1002/jat.3568
Subject(s) - silver nanoparticle , toxicity , oxidative stress , in vivo , proteomics , chemistry , nanoparticle , in vitro , cell culture , cytosol , biochemistry , biophysics , biology , microbiology and biotechnology , nanotechnology , materials science , enzyme , genetics , organic chemistry , gene
Consumers are orally exposed to nanoparticulate or soluble species of the non‐essential element silver due to its use in food contact materials or as a food additive. Potential toxicity of silver nanoparticles has gained special scientific attention. A fraction of ingested ionic or particulate silver is taken up in the intestine and transported to the liver, where it may induce oxidative stress and elicit subsequent adverse responses. Here, we present a comprehensive analysis of global proteomic changes induced in human Hep G2 hepatocarcinoma cells by different concentrations of AgPURE silver nanoparticles or by corresponding concentrations of ionic silver. Bioinformatic analysis of proteomic data confirms and substantiates previous findings on silver‐induced alterations related to redox stress, mitochondrial dysfunction, intermediary metabolism, inflammatory responses, posttranslational protein modification and other cellular parameters. Similarities between the effects exerted by the two silver species are in line with the assumption that silver ions released from nanoparticles substantially contribute to their toxicity. Moreover, a comparative bioinformatic evaluation of proteomic effects in hepatic and intestinal cells exerted either by silver nanoparticles or bionic silver is presented. Our results show that, despite remarkable differences at the level of affected proteins in the different cell lines, highly similar biological consequences, corresponding to previous in vivo findings, can be deduced by applying appropriate bioinformatic data mining.