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BackgroundThe rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity.ResultsWe have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis.ConclusionsOur findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12989-016-0160-6) contains supplementary material, which is available to authorized users.

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Background: Drug toxicity testing calls for in vitro assays as alternatives to animal models. Results: OpenMS and KNIME are applicable for processing of HPLC-MS data sets to reveal metabolic changes upon chloroacetaldehyde treatment of kidney cells. Conclusion: Most significant changes are related to oxidative stress. Significance: Comprehensive multiomics studies support the risk assessment at an early stage of drug development.

The stress metabolome provides a thorough insight into the signals and hence mechanisms of response of organisms. This is an excellent tool to advance the understanding of interactions, especially for substances like nanomaterials (NMs), for which there is an urgent need for alternative methods for hazard assessment. The metabolome of Enchytraeus crypticus was studied for the first time. The case study, CuO NM (and CuCl) covered exposure along a time frame [0-7-14 days (d)] and two reproduction effect concentrations (EC10 and EC50). High-performance liquid chromatography-mass spectrometry based method (HPLC-MS) was used, with reversed phase (RP) separation and mass spectrometric detection in positive and negative modes. Metabolite profiling of Cu materials yielded 155 and 382 metabolite features in positive and negative modes, respectively, showing an expression related with time, material, and ECx. The number of differentially expressed metabolites (DEMs) decreased with exposure time (14 d) for CuO NM, whereas for CuCl EC50 it increased. Overall, almost all DEMs are down-regulated for CuO NM and up-regulated for CuCl (both modes). Early effects were mainly related to amino acids and later to lysophospholipids (down-regulation). Furthermore, the underlying mechanisms of CuO NM toxicity (e.g. neurotransmission, nucleic acids generation, cellular energy, and immune defense) differ from CuCl, where later metabolomic responses are mostly linked to the metabolism of lipids and fewer to amino acids. This study reports a large scale metabolome profiling for E. crypticus and identifies potential markers of Cu materials, which can help to align intelligent testing strategies and safer-by-design materials.