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Articles

A multi-omics approach reveals mechanisms of nanomaterial toxicity and structure–activity relationships in alveolar macrophages

, ORCID Icon, , , , , , , , , ORCID Icon, & ORCID Icon show all
Pages 181-195 | Received 19 Jul 2019, Accepted 21 Oct 2019, Published online: 27 Nov 2019
 

Abstract

In respect to the high number of released nanomaterials and their highly variable properties, novel grouping approaches are required based on the effects of nanomaterials. Proper grouping calls for a combination of an experimental setup with a higher number of structurally similar nanomaterials and for employing integrated omics approaches to identify the mode of action. Here, we analyzed the effects of seven well-characterized NMs comprising different chemical compositions, sizes and chemical surface modifications on the rat alveolar macrophage cell line NR8383. The NMs were investigated at three doses ranging from 2.5 to 10 µg/cm2 after 24 h incubation using an integrated multi-omics approach involving untargeted proteomics, targeted metabolomics, and src homology 2 (SH2) profiling. By using Weighted Gene Correlation Network Analysis (WGCNA) for the integrative data, we identified correlations of molecular pathways with physico-chemical properties and toxicological endpoints. The three investigated SiO2 variants induced strong alterations in all three omics approaches and were, therefore, be classified as “active.” Two organic phthalocyanines showed minor responses and Mn2O3 induced a different molecular response pattern than the other NMs. WGCNA revealed that agglomerate size and surface area as well as LDH release are among the most important parameters correlating with nanotoxicology. Moreover, we identified key drivers that can serve as representative biomarker candidates, supporting the value of multi-omics approaches to establish integrated approaches to testing and assessment (IATAs).

Acknowledgments

The authors would like to take this opportunity to thank all institutions for their support of this project. In addition, the authors want to thank Antje Bergert and Doreen Wittke for excellent technical assistance.

Disclosure statement

The authors report no conflict of interest.

Data availability statement

The proteomics and metabolomics datasets generated in this study are available at Zenodo under following DOI: https://doi.org/10.5281/zenodo.3514213.

Additional information

Funding

This project is part of the SIINN ERA-NET and is funded under the ERA-NET scheme of the Seventh Framework Program of the European Commission, BMBF Grant agreement no. 03XP0008.

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