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 SiO2 NMs. Cellular responses were analyzed by global proteomics, targeted metabolomics and SH2 profiling. Results were integrated using Weighted Gene Correlation Network Analysis (WGCNA). 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. 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.

中文翻译：

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在RLE-6TN大鼠肺泡上皮细胞中进行的深入多组学分析可实现纳米材料的分类。

可以对纳米材料（NMs）的性能进行微调，从而产生大量的变体，每种变体都需要进行彻底的安全评估。NM原则上存在会减少测试量的分组和分类方法，但仍主要是概念性的。缺点之一是对NM毒性的机理了解有限。因此，我们在RLE-6TN大鼠肺泡上皮细胞中进行了多组学体外研究，涉及12个NMs，覆盖不同的材料，包括SiO2 NMs的粒径，表面电荷和疏水性的系统变化。通过整体蛋白质组学，靶向代谢组学和SH2分析对细胞反应进行了分析。使用加权基因相关网络分析（WGCNA）对结果进行积分。聚类分析涉及所有数据集，其中氧化石墨烯，TiO2_NM105，来自其他NM的SiO2_40和酞菁蓝的细胞反应显示出高度的相似性，尽管顶端的体内结果可能有所不同。SiO2_7的行为不同，但仍引起显着变化。相比之下，其余的NM与未经处理的对照组更为相似。WGCNA揭示了特定物理化学性质（如团聚体大小和氧化还原电位与细胞反应）的相关性。关键驱动因素分析可以确定与观察到的效应高度相关的生物分子，这可能是代表性的生物标志物候选物。我们研究的主要驱动力主要与氧化应激反应和细胞凋亡有关。我们的涉及蛋白质组学，代谢组学和SH2分析的多组学方法被证明有助于深入了解NMs的作用模式。积分结果可实现更可靠的NM分类。此外，确定了与NM毒性强烈相关的关键理化特性。最后，我们提出了毒性的几个主要驱动因素，这些潜在驱动因素有可能改善未来的测试和评估方法。