INSIdE NANO: a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials

Serra, Angela; Letunić, Ivica; Fortino, Vittorio; Handy, Richard D.; Fadeel, Bengt; Tagliaferri, Roberto; Greco, Dario

doi:10.1038/s41598-018-37411-y

Cited by 32 publications

(27 citation statements)

References 46 publications

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“…The current dataset spanning 31 different ENMs is the largest transcriptomics dataset thus far, and our results provide a useful resource for in silico exploration of nano–bio interactions, as exemplified in previous publications. [ 43,44 ] The most striking finding in this regard was that the small, ammonium modified Ag and Au ENMs which were found to be strongly cytotoxic in our model were also clustered together based on significant changes in gene expression (at EC 10 ). Furthermore, with regard to the six different metal (Ag, Au, CdTe) ENMs that were found to be the most transcriptionally “active” among the 31 ENMs, we found a remarkably consistent pattern in terms of affected cellular pathways.…”

Section: Discussionmentioning

confidence: 68%

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

et al. 2020

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Despite considerable efforts, the properties that drive the cytotoxicity of engineered nanomaterials (ENMs) remain poorly understood. Here, the authors inverstigate a panel of 31 ENMs with different core chemistries and a variety of surface modifications using conventional in vitro assays coupled with omics-based approaches. Cytotoxicity screening and multiplex-based cytokine profiling reveals a good concordance between primary human monocyte-derived macrophages and the human monocyte-like cell line THP-1. Proteomics analysis following a low-dose exposure of cells suggests a nonspecific stress response to ENMs, while microarray-based profiling reveals significant changes in gene expression as a function of both surface modification and core chemistry. Pathway analysis highlights that the ENMs with cationic surfaces that are shown to elicit cytotoxicity downregulated DNA replication and cell cycle responses, while inflammatory responses are upregulated. These findings are validated using cell-based assays. Notably, certain small, PEGylated ENMs are found to be noncytotoxic yet they induce transcriptional responses reminiscent of viruses. In sum, using a multiparametric approach, it is shown that surface chemistry is a key determinant of cellular responses to ENMs. The data also reveal that cytotoxicity, determined by conventional in vitro assays, does not necessarily correlate with transcriptional effects of ENMs.

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Section: Discussionmentioning

confidence: 68%

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

et al. 2020

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“…To explore potential associations between NPs and human diseases, we employed insideNANO (“Integrated Network of Systems Biology Effects of Nanomaterials”), a web‐based tool (publicly available at http://inano.biobyte.de) that highlights connections between phenotypic entities based on their effects on gene expression . The tool comprises gene expression data corresponding to four different entities (ie, NPs, drugs, chemical substances, and human diseases), derived from scientific databases as described in . Gene expression data for NPs were retrieved from NanoMiner, a transcriptomics database encompassing in vitro transcriptomics profiles .…”

Section: Methodsmentioning

confidence: 99%

Cobalt nanoparticles trigger ferroptosis‐like cell death (oxytosis) in neuronal cells: Potential implications for neurodegenerative disease

et al. 2020

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The neurotoxicity of hard metal‐based nanoparticles (NPs) remains poorly understood. Here, we deployed the human neuroblastoma cell line SH‐SY5Y differentiated or not into dopaminergic‐ and cholinergic‐like neurons to study the impact of tungsten carbide (WC) NPs, WC NPs sintered with cobalt (Co), or Co NPs versus soluble CoCl2. Co NPs and Co salt triggered a dose‐dependent cytotoxicity with an increase in cytosolic calcium, lipid peroxidation, and depletion of glutathione (GSH). Co NPs and Co salt also suppressed glutathione peroxidase 4 (GPX4) mRNA and protein expression. Co‐exposed cells were rescued by N‐acetylcysteine (NAC), a precursor of GSH, and partially by liproxstatin‐1, an inhibitor of lipid peroxidation. Furthermore, in silico analyses predicted a significant correlation, based on similarities in gene expression profiles, between Co‐containing NPs and Parkinson's disease, and changes in the expression of selected genes were validated by RT‐PCR. Finally, experiments using primary human dopaminergic neurons demonstrated cytotoxicity and GSH depletion in response to Co NPs and CoCl2 with loss of axonal integrity. Overall, these data point to a marked neurotoxic potential of Co‐based but not WC NPs and show that neuronal cell death may occur through a ferroptosis‐like mechanism.

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“…For example, Zhu et al developed a read-across method based on a consensus similarity approach starting from different biological data, to assess acute toxicity in the form of estrogenic endocrine disruption [68]. Moreover, Serra et al proposed a network-based integrative methodology to perform read-across of nanomaterials exposure with respect to other phenotypic entities such as human diseases, drug treatments and chemical exposures [20]. In particular, they integrated gene expression data from microarray experiments for 29 nanomaterials, with other gene expression data for drug treatments and data available from the literature that relates differentially expressed genes to chemical exposures and human diseases.…”

Section: Read-acrossmentioning

confidence: 99%

“…Notably, TGx approaches have been used to analyze quantitative transcriptomic data, to determine the BMD and estimate the critical point of departure for human health risk assessment [16][17][18][19]. These approaches are applied in the framework of read-across analysis, with the aim of predicting the behaviour of uncharacterized compounds by comparing them to other substances whose molecular effects are known [20,21].…”

Section: Introductionmentioning

confidence: 99%

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

et al. 2020

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Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

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INSIdE NANO: a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials

Cited by 32 publications

References 46 publications

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

Cobalt nanoparticles trigger ferroptosis‐like cell death (oxytosis) in neuronal cells: Potential implications for neurodegenerative disease

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

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