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      Copper oxide nanoparticle toxicity profiling using untargeted metabolomics

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          Abstract

          Background

          The 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.

          Results

          We 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.

          Conclusions

          Our 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 material

          The 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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          Most cited references106

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          Innovation: Metabolomics: the apogee of the omics trilogy.

          Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.
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            Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment.

            Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk-effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework.
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              METLIN: a metabolite mass spectral database.

              Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLINincludes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.
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                Author and article information

                Contributors
                matthew.boyles@sbg.ac.at
                christina.ranninger@sbg.ac.at
                roland.reischl@sbg.ac.at
                rurik@informatik.uni-tuebingen.de
                Richard.Tessadri@uibk.ac.at
                oliver.kohlbacher@uni-tuebingen.de
                +43 662 8044 5730 , albert.duschl@sbg.ac.at
                +43 662 8044 5738 , c.huber@sbg.ac.at
                Journal
                Part Fibre Toxicol
                Part Fibre Toxicol
                Particle and Fibre Toxicology
                BioMed Central (London )
                1743-8977
                8 September 2016
                8 September 2016
                2015
                : 13
                : 1
                : 49
                Affiliations
                [1 ]Department of Molecular Biology, Division of Allergy and Immunology, University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria
                [2 ]Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria
                [3 ]Center for Bioinformatics, University of Tübingen, Tübingen, Germany
                [4 ]Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
                [5 ]Faculty of Geo- and Atmospheric Science, Institute of Mineralogy and Petrography, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria
                [6 ]Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
                [7 ]Faculty of Medicine, University of Tübingen, Geissweg 3, 72076 Tübingen, Germany
                [8 ]Max Planck Institute for Developmental Biology, Spemannstraße 35, 72076 Tübingen, Germany
                Article
                160
                10.1186/s12989-016-0160-6
                5017021
                27609141
                6dd7539b-cc28-4769-8d11-6ff41cc2d0ce
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 18 April 2016
                : 26 August 2016
                Funding
                Funded by: EU
                Award ID: 263147
                Award ID: 263215
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Toxicology
                untargeted metabolomics,copper oxide nanoparticles,apoptosis,oxidative stress,toxicity profiling,adverse outcome pathways

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