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Copper oxide nanoparticle toxicity profiling using untargeted metabolomics

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Principal Component Analysis Score plots showing controls (C) and treated samples (T) for the different time points (0 h, 1 h, 3 h, 6 h, 12 h, 24 h) and the biological replicates independently (n = 3) as datasets 1, 2, and 3. a reversed-phase HPLC - & positive ionization (RP_pos): R2cum (0.591),Q2cum (0.481); b reversed-phase HPLC & negative ionization (RP_neg): R2cum (0.575),Q2cum (0.459); c HILIC & positive ionization (pos) (without T.1 h.1, T.24 h.1): R2cum (0.732),Q2cum (0.586); d HILIC & negative ionization (neg) (without T.24 h.1): R2cum (0.618),Q2cum (0.542)
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Fig3: Principal Component Analysis Score plots showing controls (C) and treated samples (T) for the different time points (0 h, 1 h, 3 h, 6 h, 12 h, 24 h) and the biological replicates independently (n = 3) as datasets 1, 2, and 3. a reversed-phase HPLC - & positive ionization (RP_pos): R2cum (0.591),Q2cum (0.481); b reversed-phase HPLC & negative ionization (RP_neg): R2cum (0.575),Q2cum (0.459); c HILIC & positive ionization (pos) (without T.1 h.1, T.24 h.1): R2cum (0.732),Q2cum (0.586); d HILIC & negative ionization (neg) (without T.24 h.1): R2cum (0.618),Q2cum (0.542)

Mentions: In order to profile the time-dependent responses of metabolites upon exposure of A549 cells with 10 μg/ml CuO NPs, six time points (0, 1, 3, 6, 12 and 24 h) were tested and the cells’ metabolome was assessed in CuO NP treated cells and cells incubated for the same time period in CCM only. Principle component analysis (PCA) is a statistical tool that can be used to visually explore the grouping pattern of all samples of the study, where R2 and Q2 are used for evaluating the performance of the PCA model. The PCA score plots for the CuO NP study demonstrated that in all four methods used, chromatographic separation with reversed-phase high-performance liquid chromatography (RP-HPLC)/hydrophilic interaction liquid chromatography (HILIC) and negative (neg)/positive (pos) electrospray ionization followed by mass spectrometric detection, CuO NP exposure induced a separation in the grouping of regulated metabolites from the untreated cells after 6, 12 and 24 h (Fig. 3). Taking into consideration the comprehensiveness of the features detected by all methods together (Additional file 1: Figure S1), the holistic view of metabolic processes influenced by CuO NP treatment is well supported.Fig. 3


Copper oxide nanoparticle toxicity profiling using untargeted metabolomics
Principal Component Analysis Score plots showing controls (C) and treated samples (T) for the different time points (0 h, 1 h, 3 h, 6 h, 12 h, 24 h) and the biological replicates independently (n = 3) as datasets 1, 2, and 3. a reversed-phase HPLC - & positive ionization (RP_pos): R2cum (0.591),Q2cum (0.481); b reversed-phase HPLC & negative ionization (RP_neg): R2cum (0.575),Q2cum (0.459); c HILIC & positive ionization (pos) (without T.1 h.1, T.24 h.1): R2cum (0.732),Q2cum (0.586); d HILIC & negative ionization (neg) (without T.24 h.1): R2cum (0.618),Q2cum (0.542)
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Fig3: Principal Component Analysis Score plots showing controls (C) and treated samples (T) for the different time points (0 h, 1 h, 3 h, 6 h, 12 h, 24 h) and the biological replicates independently (n = 3) as datasets 1, 2, and 3. a reversed-phase HPLC - & positive ionization (RP_pos): R2cum (0.591),Q2cum (0.481); b reversed-phase HPLC & negative ionization (RP_neg): R2cum (0.575),Q2cum (0.459); c HILIC & positive ionization (pos) (without T.1 h.1, T.24 h.1): R2cum (0.732),Q2cum (0.586); d HILIC & negative ionization (neg) (without T.24 h.1): R2cum (0.618),Q2cum (0.542)
Mentions: In order to profile the time-dependent responses of metabolites upon exposure of A549 cells with 10 μg/ml CuO NPs, six time points (0, 1, 3, 6, 12 and 24 h) were tested and the cells’ metabolome was assessed in CuO NP treated cells and cells incubated for the same time period in CCM only. Principle component analysis (PCA) is a statistical tool that can be used to visually explore the grouping pattern of all samples of the study, where R2 and Q2 are used for evaluating the performance of the PCA model. The PCA score plots for the CuO NP study demonstrated that in all four methods used, chromatographic separation with reversed-phase high-performance liquid chromatography (RP-HPLC)/hydrophilic interaction liquid chromatography (HILIC) and negative (neg)/positive (pos) electrospray ionization followed by mass spectrometric detection, CuO NP exposure induced a separation in the grouping of regulated metabolites from the untreated cells after 6, 12 and 24 h (Fig. 3). Taking into consideration the comprehensiveness of the features detected by all methods together (Additional file 1: Figure S1), the holistic view of metabolic processes influenced by CuO NP treatment is well supported.Fig. 3

View Article: PubMed Central - PubMed

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.

No MeSH data available.