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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 plot showing controls (C), staurosporine treated samples (STS) and camptothecin treated samples (CPT) for the different time points (0 h, 3 h, 6 h, 9 h 12 h, 24 h) and biological replicates shown independently (n = 3, identified with K1,K2, and K3) for the HILIC negative dataset (R2(cum) 0.731 and Q2 (cum) 0.684)
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Fig7: Principal Component Analysis score plot showing controls (C), staurosporine treated samples (STS) and camptothecin treated samples (CPT) for the different time points (0 h, 3 h, 6 h, 9 h 12 h, 24 h) and biological replicates shown independently (n = 3, identified with K1,K2, and K3) for the HILIC negative dataset (R2(cum) 0.731 and Q2 (cum) 0.684)

Mentions: The metabolomics data generation and processing were carried out in accordance to the CuO NP toxicity study, again including data normalized to cell number (Additional file 1: Table S3). The statistical evaluation of the metabolomics data detected similar trends to those observed in the caspase activity assays. Limma as well as PCA identified that differences between control and treated samples occurred from 6 h onwards for STS, while for CPT differences were observed from 12 h onwards. Although the treatment with STS induced metabolic changes more rapidly, after 24 h of exposure more metabolic features were differentially expressed upon incubation with CPT than STS, this is visualized by the grouping in the principal component analysis plots (Fig. 7). Notably, this approach signalled that, although both STS and CPT induced apoptosis in A549 cells, different metabolites were involved.Fig. 7


Copper oxide nanoparticle toxicity profiling using untargeted metabolomics
Principal Component Analysis score plot showing controls (C), staurosporine treated samples (STS) and camptothecin treated samples (CPT) for the different time points (0 h, 3 h, 6 h, 9 h 12 h, 24 h) and biological replicates shown independently (n = 3, identified with K1,K2, and K3) for the HILIC negative dataset (R2(cum) 0.731 and Q2 (cum) 0.684)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5017021&req=5

Fig7: Principal Component Analysis score plot showing controls (C), staurosporine treated samples (STS) and camptothecin treated samples (CPT) for the different time points (0 h, 3 h, 6 h, 9 h 12 h, 24 h) and biological replicates shown independently (n = 3, identified with K1,K2, and K3) for the HILIC negative dataset (R2(cum) 0.731 and Q2 (cum) 0.684)
Mentions: The metabolomics data generation and processing were carried out in accordance to the CuO NP toxicity study, again including data normalized to cell number (Additional file 1: Table S3). The statistical evaluation of the metabolomics data detected similar trends to those observed in the caspase activity assays. Limma as well as PCA identified that differences between control and treated samples occurred from 6 h onwards for STS, while for CPT differences were observed from 12 h onwards. Although the treatment with STS induced metabolic changes more rapidly, after 24 h of exposure more metabolic features were differentially expressed upon incubation with CPT than STS, this is visualized by the grouping in the principal component analysis plots (Fig. 7). Notably, this approach signalled that, although both STS and CPT induced apoptosis in A549 cells, different metabolites were involved.Fig. 7

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.