Abstract
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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