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

Bannuscher, Anne; Karkossa, Isabel; Buhs, Sophia; Nollau, Peter; Kettler, Katja; Balaș, Mihaela; Dinischiotu, Anca; Hellack, Bryan; Wiemann, Martin; Luch, Andreas; Bergen, Martin von; Haase, Andrea; Schubert, Kristin

doi:10.1080/17435390.2019.1684592

Cited by 34 publications

(37 citation statements)

References 46 publications

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“…In contrast, almost no signi cantly altered proteins were observable in type II cells, while dose-dependent increases of the portion of signi cantly altered proteins were noticeable for SiO2_40 and SiO2_7 after treatment of macrophages. Thus, in accordance to our previous studies, SiO2_40, SiO2_7, and TiO2_NM105 were considered "active" NMs [11,12], while SiO2_15_Unmod and SiO2_15_Amino were classi ed as "passive".…”

Section: 1supporting

confidence: 73%

“…Thus, nominal doses assuming complete sedimentation were 10, 5, and 2.5 µg/cm 2 . Selected results obtained with this experimental setup were previously published [12]. Type II cells were treated with 1, 10, or 50 µg/cm 2 NMs for 24 h and 48 h, respectively.…”

Section: Cell Culturementioning

confidence: 99%

“…For the in vitro samples, the preparation was conducted as described before [11,12]. Metabolites and proteins were extracted from individual samples.…”

Section: Sample Preparation For Omics Studiesmentioning

confidence: 99%

“…The here presented metabolomics data were published before [80] but re-evaluated in analogy to the proteomics data. In brief, the AbsoluteIDQ p180 Kit (Biocrates, Austria) was used as described previously [11,12,80,81], and samples were analyzed with an API 5500 triple quadrupole mass spectrometer (ABSciex, Germany), coupled to an Agilent 1260 In nity HPLC system (Agilent, USA). Analyst® software and MetIDQ were used for data analysis.…”

Section: Targeted Metabolomics and Untargeted Proteomicsmentioning

confidence: 99%

“…Effects on macrophages were investigated as described previously [12]. In brief, paramagnetic beads were used, which lead to an improved sample quality and allow for fractionation [84,85].…”

Section: Targeted Metabolomics and Untargeted Proteomicsmentioning

confidence: 99%

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Nanomaterials induce different levels of oxidative stress, depending on the used model system: Comparison of in vitro and in vivo effects

Karkossa

Bannuscher

Hellack³

et al. 2020

Preprint

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Background The immense variety and constant development of nanomaterials (NMs) raise the demand for a facilitated risk assessment, for which knowledge on NMs mode of actions (MoAs) is required. For this purpose, a comprehensive data basis is of paramountcy that can be obtained using omics. Furthermore, the establishment of suitable in vitro test systems is indispensable to follow the 3R concept and to master the high number of NMs. In the present study, we aimed at comparing NM effects in vitro and in vivo using a multi-omics approach. We applied an integrated data evaluation strategy based on proteomics and metabolomics to four silica NMs and one titanium dioxide-based NM. For in vitro investigations, alveolar epithelial cells and alveolar macrophages were treated with different doses of NMs, and the results were compared to effects on rat lungs after short-term inhalations and instillations at varying doses with and without a recovery period.Results Since the production of reactive oxygen species (ROS) is described to be a critical biological effect of NMs, and enrichment analyses confirmed oxidative stress as a significant effect upon NM treatment in vitro in the present study, we focused on different levels of oxidative stress. Thus, we found opposite changes for proteins and metabolites that are related to the production of reduced glutathione in alveolar epithelial cells and alveolar macrophages, illustrating that NMs MoAs depend on the used model system. Interestingly, in vivo, pathways related to inflammation were affected to a greater extent than oxidative stress responses. Hence, the assignment of the observed effects to the levels of oxidative stress was different in vitro and in vivo as well. However, the overall classification of “active” and “passive” NMs was consistent in vitro and in vivo.Conclusions The consistent classification indicates both tested cell lines to be suitable for NM toxicity assessment even though the induced levels of oxidative stress strongly depend on the used model systems. Thus, the here presented results highlight that model systems need to be carefully revised to decipher the extent to which they can replace in vivo testing.

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Section: 1supporting

confidence: 73%

Section: Cell Culturementioning

confidence: 99%

“…For the in vitro samples, the preparation was conducted as described before [11,12]. Metabolites and proteins were extracted from individual samples.…”

Section: Sample Preparation For Omics Studiesmentioning

confidence: 99%

Section: Targeted Metabolomics and Untargeted Proteomicsmentioning

confidence: 99%

Section: Targeted Metabolomics and Untargeted Proteomicsmentioning

confidence: 99%

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Nanomaterials induce different levels of oxidative stress, depending on the used model system: Comparison of in vitro and in vivo effects

Karkossa

Bannuscher

Hellack³

et al. 2020

Preprint

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Lipidomics in Multi‐Omics Studies

Titz¹,

Lavrynenko²,

Ivanov³

2023

Mass Spectrometry for Lipidomics

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Meta‐Analysis of Integrated Proteomic and Transcriptomic Data Discerns Structure–Activity Relationship of Carbon Materials with Different Morphologies

Dumit,

Liu,

Bahl

et al. 2023

Advanced Science

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The Fiber Pathogenicity Paradigm (FPP) establishes connections between fiber structure, durability, and disease‐causing potential observed in materials like asbestos and synthetic fibers. While emerging nanofibers are anticipated to exhibit pathogenic traits according to the FPP, their nanoscale diameter limits rigidity, leading to tangling and loss of fiber characteristics. The absence of validated rigidity measurement methods complicates nanofiber toxicity assessment. By comprehensively analyzing 89 transcriptomics and 37 proteomics studies, this study aims to enhance carbon material toxicity understanding and proposes an alternative strategy to assess morphology‐driven toxicity. Carbon materials are categorized as non‐fibrous, high aspect ratio with shorter lengths, tangled, and rigid fibers. Mitsui‐7 serves as a benchmark for pathogenic fibers. The meta‐analysis reveals distinct cellular changes for each category, effectively distinguishing rigid fibers from other carbon materials. Subsequently, a robust random forest model is developed to predict morphology, unveiling the pathogenicity of previously deemed non‐pathogenic NM‐400 due to its secondary structures. This study fills a crucial gap in nanosafety by linking toxicological effects to material morphology, in particular regarding fibers. It demonstrates the significant impact of morphology on toxicological behavior and the necessity of integrating morphological considerations into regulatory frameworks.

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

Cited by 34 publications

References 46 publications

Nanomaterials induce different levels of oxidative stress, depending on the used model system: Comparison of in vitro and in vivo effects

Nanomaterials induce different levels of oxidative stress, depending on the used model system: Comparison of in vitro and in vivo effects

Lipidomics in Multi‐Omics Studies

Meta‐Analysis of Integrated Proteomic and Transcriptomic Data Discerns Structure–Activity Relationship of Carbon Materials with Different Morphologies

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