Integration of metabolomics and transcriptomics in nanotoxicity studies

Abstract

Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The '-omics' analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The 'omics' approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the 'omics' approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed "metabotranscriptomics". [BMB Reports 2018; 51(1): 14-20].

Fig. 1

Summary diagram comparing conventional methods and metabotranscriptomics approach for the assessment of MNPs@SiO₂(RITC)-induced nanotoxicity (, –31). MNPs: MNPs@SiO₂(RITC), IP: Intraperitoneal, TEM: transmission electron microscopy.

Fig. 2

Bioinformatics of ROS generation using ingenuity pathway analysis (IPA), (A) metabolomics, (B) transcriptomics, and (C) metabotranscriptomics based on a previous report (27). Red and green areas indicate up- and downregulated metabolites, respectively, in cells treated with MNPs@SiO₂(RITC) compared with control cells. Differentially regulated metabolites obtained from the metabolic profile (more than a ± 20% change) and microarray data (genes with a > 3-fold change) are shown. In the representation of the genetic networks, the red and green colors indicate up- and down-regulated genes, respectively. Network shape indicates categorization of molecules and function. Information pertaining to the corresponding genes can be found in NCBI (https://www.ncbi.nlm.nih.gov/).