Journal
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 45, Issue 4, Pages 1695-1702Publisher
AMER CHEMICAL SOC
DOI: 10.1021/es103606x
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Funding
- National Science Foundation
- Environmental Protection Agency [DBI 0830117]
- US Public Health Service [U19 ES019528, RO1 ES016746, RC2 ES018766]
- CICYT [CTQ2009-14627]
- Generalitat de Catalunya [2009SGR-01529]
- EU Commission [037017]
- Div Of Biological Infrastructure
- Direct For Biological Sciences [0830117] Funding Source: National Science Foundation
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The response of a murine macrophage cell line exposed to a library of seven metal and metal oxide nanoparticles was evaluated via High Throughput Screening (HTS) assay employing luciferase-reporters for ten independent toxicity-related signaling pathways. Similarities of toxicity response among the nanoparticles were identified via Self-Organizing Map (SOM) analysis. This analysis, applied to the HTS data, quantified the significance of the signaling pathway responses (SPRs) of the cell population exposed to nanomaterials relative to a population of untreated cells, using the Strictly Standardized Mean Difference (SSMD). Given the high dimensionality of the data and relatively small data set, the validity of the SOM clusters was established via a consensus clustering technique. Analysis of the SPR signatures revealed two cluster groups corresponding to (i) sublethal pro-inflammatory responses to Al2O3, Au, Ag, SiO2 nanoparticles possibly related to ROS generation, and (ii) lethal genotoxic responses due to exposure to ZnO and Pt nanoparticles at a concentration range of 25-100 mu g/mL at 12 h exposure. In addition to identifying and visualizing clusters and quantifying similarity measures, the SOM approach can aid in developing predictive quantitative-structure relations; however, this would require significantly larger data sets generated from combinatorial libraries of engineered nanoparticles.
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