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An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization

BACKGROUND: Nanomaterials (NMs) can be fine-tuned in their properties resulting in a high number of variants, each requiring a thorough safety assessment. Grouping and categorization approaches that would reduce the amount of testing are in principle existing for NMs but are still mostly conceptual....

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Autores principales: Karkossa, Isabel, Bannuscher, Anne, Hellack, Bryan, Bahl, Aileen, Buhs, Sophia, Nollau, Peter, Luch, Andreas, Schubert, Kristin, von Bergen, Martin, Haase, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814995/
https://www.ncbi.nlm.nih.gov/pubmed/31653258
http://dx.doi.org/10.1186/s12989-019-0321-5
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author Karkossa, Isabel
Bannuscher, Anne
Hellack, Bryan
Bahl, Aileen
Buhs, Sophia
Nollau, Peter
Luch, Andreas
Schubert, Kristin
von Bergen, Martin
Haase, Andrea
author_facet Karkossa, Isabel
Bannuscher, Anne
Hellack, Bryan
Bahl, Aileen
Buhs, Sophia
Nollau, Peter
Luch, Andreas
Schubert, Kristin
von Bergen, Martin
Haase, Andrea
author_sort Karkossa, Isabel
collection PubMed
description BACKGROUND: Nanomaterials (NMs) can be fine-tuned in their properties resulting in a high number of variants, each requiring a thorough safety assessment. Grouping and categorization approaches that would reduce the amount of testing are in principle existing for NMs but are still mostly conceptual. One drawback is the limited mechanistic understanding of NM toxicity. Thus, we conducted a multi-omics in vitro study in RLE-6TN rat alveolar epithelial cells involving 12 NMs covering different materials and including a systematic variation of particle size, surface charge and hydrophobicity for SiO(2) NMs. Cellular responses were analyzed by global proteomics, targeted metabolomics and SH2 profiling. Results were integrated using Weighted Gene Correlation Network Analysis (WGCNA). RESULTS: Cluster analyses involving all data sets separated Graphene Oxide, TiO2_NM105, SiO2_40 and Phthalocyanine Blue from the other NMs as their cellular responses showed a high degree of similarities, although apical in vivo results may differ. SiO2_7 behaved differently but still induced significant changes. In contrast, the remaining NMs were more similar to untreated controls. WGCNA revealed correlations of specific physico-chemical properties such as agglomerate size and redox potential to cellular responses. A key driver analysis could identify biomolecules being highly correlated to the observed effects, which might be representative biomarker candidates. Key drivers in our study were mainly related to oxidative stress responses and apoptosis. CONCLUSIONS: Our multi-omics approach involving proteomics, metabolomics and SH2 profiling proved useful to obtain insights into NMs Mode of Actions. Integrating results allowed for a more robust NM categorization. Moreover, key physico-chemical properties strongly correlating with NM toxicity were identified. Finally, we suggest several key drivers of toxicity that bear the potential to improve future testing and assessment approaches.
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spelling pubmed-68149952019-10-31 An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization Karkossa, Isabel Bannuscher, Anne Hellack, Bryan Bahl, Aileen Buhs, Sophia Nollau, Peter Luch, Andreas Schubert, Kristin von Bergen, Martin Haase, Andrea Part Fibre Toxicol Research BACKGROUND: Nanomaterials (NMs) can be fine-tuned in their properties resulting in a high number of variants, each requiring a thorough safety assessment. Grouping and categorization approaches that would reduce the amount of testing are in principle existing for NMs but are still mostly conceptual. One drawback is the limited mechanistic understanding of NM toxicity. Thus, we conducted a multi-omics in vitro study in RLE-6TN rat alveolar epithelial cells involving 12 NMs covering different materials and including a systematic variation of particle size, surface charge and hydrophobicity for SiO(2) NMs. Cellular responses were analyzed by global proteomics, targeted metabolomics and SH2 profiling. Results were integrated using Weighted Gene Correlation Network Analysis (WGCNA). RESULTS: Cluster analyses involving all data sets separated Graphene Oxide, TiO2_NM105, SiO2_40 and Phthalocyanine Blue from the other NMs as their cellular responses showed a high degree of similarities, although apical in vivo results may differ. SiO2_7 behaved differently but still induced significant changes. In contrast, the remaining NMs were more similar to untreated controls. WGCNA revealed correlations of specific physico-chemical properties such as agglomerate size and redox potential to cellular responses. A key driver analysis could identify biomolecules being highly correlated to the observed effects, which might be representative biomarker candidates. Key drivers in our study were mainly related to oxidative stress responses and apoptosis. CONCLUSIONS: Our multi-omics approach involving proteomics, metabolomics and SH2 profiling proved useful to obtain insights into NMs Mode of Actions. Integrating results allowed for a more robust NM categorization. Moreover, key physico-chemical properties strongly correlating with NM toxicity were identified. Finally, we suggest several key drivers of toxicity that bear the potential to improve future testing and assessment approaches. BioMed Central 2019-10-25 /pmc/articles/PMC6814995/ /pubmed/31653258 http://dx.doi.org/10.1186/s12989-019-0321-5 Text en © The Author(s). 2019 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.
spellingShingle Research
Karkossa, Isabel
Bannuscher, Anne
Hellack, Bryan
Bahl, Aileen
Buhs, Sophia
Nollau, Peter
Luch, Andreas
Schubert, Kristin
von Bergen, Martin
Haase, Andrea
An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization
title An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization
title_full An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization
title_fullStr An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization
title_full_unstemmed An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization
title_short An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization
title_sort in-depth multi-omics analysis in rle-6tn rat alveolar epithelial cells allows for nanomaterial categorization
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814995/
https://www.ncbi.nlm.nih.gov/pubmed/31653258
http://dx.doi.org/10.1186/s12989-019-0321-5
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