Gene Expression Profiling of Immune-Competent Human Cells Exposed to Engineered Zinc Oxide or Titanium Dioxide Nanoparticles

posted on 22.07.2013 by Soile Tuomela, Reija Autio, Tina Buerki-Thurnherr, Osman Arslan, Andrea Kunzmann, Britta Andersson-Willman, Peter Wick, Sanjay Mathur, Annika Scheynius, Harald F. Krug, Bengt Fadeel, Riitta Lahesmaa

A comprehensive in vitro assessment of two commercial metal oxide nanoparticles, TiO2 and ZnO, was performed using human monocyte-derived macrophages (HMDM), monocyte-derived dendritic cells (MDDC), and Jurkat T cell leukemia-derived cell line. TiO2 nanoparticles were found to be non-toxic whereas ZnO nanoparticles caused dose-dependent cell death. Subsequently, global gene expression profiling was performed to identify transcriptional response underlying the cytotoxicity caused by ZnO nanoparticles. Analysis was done with doses 1 µg/ml and 10 µg/ml after 6 and 24 h of exposure. Interestingly, 2703 genes were significantly differentially expressed in HMDM upon exposure to 10 µg/ml ZnO nanoparticles, while in MDDCs only 12 genes were affected. In Jurkat cells, 980 genes were differentially expressed. It is noteworthy that only the gene expression of metallothioneins was upregulated in all the three cell types and a notable proportion of the genes were regulated in a cell type-specific manner. Gene ontology analysis revealed that the top biological processes disturbed in HMDM and Jurkat cells were regulating cell death and growth. In addition, genes controlling immune system development were affected. Using a panel of modified ZnO nanoparticles, we obtained an additional support that the cellular response to ZnO nanoparticles is largely dependent on particle dissolution and show that the ligand used to modify ZnO nanoparticles modulates Zn2+ leaching. Overall, the study provides an extensive resource of transcriptional markers for mediating ZnO nanoparticle-induced toxicity for further mechanistic studies, and demonstrates the value of assessing nanoparticle responses through a combined transcriptomics and bioinformatics approach.