The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Insights into the Cellular Response Triggered by Silver Nanoparticles Using Quantitative Proteomics
Abstract
The use of nanoparticles in foods, materials, and clinical treatments has increased dramatically in the past decade. Because of the possibility of human exposure to nanoparticles, there is an urgent need to investigate the molecular mechanisms underlying the cellular responses that might be triggered. Such information is necessary to assess potential health risks arising from the use of nanoparticles, and for developing new formulations of next generation nanoparticles for clinical treatments. Using mass spectrometry-based proteomic technologies and complementary techniques (e.g., Western blotting and confocal laser scanning microscopy), we present insights into the silver nanoparticle-protein interaction in the human LoVo cell line. Our data indicate that some unique cellular processes are driven by the size. The 100 nm nanoparticles exerted indirect effects via serine/threonine protein kinase (PAK), mitogen-activated protein kinase (MAPK), and phosphatase 2A pathways, and the 20 nm nanoparticles induced direct effects on cellular stress, including generation of reactive oxygen species and protein carbonylation. In addition, we report that proteins involved in SUMOylation were up-regulated after exposure to 20 nm silver nanoparticles. These results were further substantiated by the observation of silver nanoparticles entering the cells; however, data indicate that this was determined by the size of the nanoparticles, since 20 nm particles entered the cells while 100 nm particles did not.
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Cites
Universal sample preparation method for proteome analysis
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009) · Nature Methods · doi:10.1038/nmeth.1322 · Cited by 8,352
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424
Kittler, S., Greulich, C., Diendorf, J., Köller, M., & Epple, M. (2010) · Chemistry of Materials · doi:10.1021/cm100023p · Cited by 1,126
You, C., Han, C., Wang, X., Zheng, Y., Li, Q., Hu, X., & Sun, H. (2012) · Molecular Biology Reports · doi:10.1007/s11033-012-1792-8 · Cited by 447
Cited by
Wang, L., Zhang, T., Li, P., Huang, W., Tang, J., Wang, P., Liu, J., Yuan, Q., Bai, R., Li, B., et al. (2015) · ACS Nano · doi:10.1021/acsnano.5b02483 · Cited by 290
Singh, R., Swanner, J., Mims, J., Akman, S., Furdui, C., Torti, S., & Carroll, D. (2015) · International Journal of Nanomedicine · doi:10.2147/ijn.s80349 · Cited by 130
Metadata
- Authors
- Verano-Braga, T., Miethling-Graff, R., Wojdyla, K., Rogowska-Wrzesinska, A., Brewer, J. R., Erdmann, H., & Kjeldsen, F.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-02-20
- Volume
- 8
- Issue
- 3
- Pages
- 2161-2175
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2057017636
- Cited by
- 221
- References
- 5
- FWCI
- 10.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Verano-Braga, T., Miethling-Graff, R., Wojdyla, K., Rogowska-Wrzesinska, A., Brewer, J. R., Erdmann, H., & Kjeldsen, F. (2014). Insights into the Cellular Response Triggered by Silver Nanoparticles Using Quantitative Proteomics. ACS Nano. https://doi.org/10.1021/nn4050744
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