Cross-experimental analysis of microarray gene expression datasets for in silico risk assessment of TiO₂ nano-particles

As the production and usage of nanomaterials increase, there are growing concerns on the unidentified detrimental effect of nanoparticles on human health and environmental safety. Systematic assessments of the risks associated with exposure to nanoparticles are needed. DNA microarrays have emerged as a powerful tool for toxicology research. Microarraybased toxicogenomics research provides valuable information for understanding underling mechanisms of toxicological behavior of non-classic contaminants, including ultrafine nanoparticles. In this work, we investigated the effect of nano-titanium oxide exposure on human cells by analyzing the change in transcription levels of cellular DNA. Cross-experimental analysis of heterogeneous gene expression datasets was performed using the RankProd algorithm. Multiple gene expression omnibus series obtained from various experimental conditions were combined and used for risk assessment. Several commonly regulated genes were identified as being unaffected by the laboratory specific conditions. Pathway analysis revealed the genes as being associated with six major pathways: arachidonic acid metabolism, purine metabolism, pentose phosphate pathway, mitogen-activated protein kinase signaling pathway, synthesis and degradation of ketone bodies, and methionine metabolism. The identified differently expressed genes provide a robust set of markers for exposure analysis and risk assessment of titanium oxide nanoparticles.