Integrative meta-analysis of publically available microarray datasets of several epithelial cell lines identifies biological processes affected by silver nanoparticles exposure

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Transcriptomic Integration Analyses Uncover Common Bisphenol A Effects Across Species and Tissues Primarily Mediated by Disruption of JUN/FOS, EGFR, ER, PPARG, and P53 Pathways

Bisphenol A (BPA) is a common endocrine disruptor widely used in the production of electronic, sports, and medical equipment, as well as consumer products like milk bottles, dental sealants, and thermal paper. Despite its widespread use, current assessments of BPA exposure risks remain limited due to the lack of comprehensive cross-species comparative analyses. To address this gap, we conducted a study aimed at identifying genes and fundamental molecular processes consistently affected by BPA in various species and tissues, employing an effective data integration method and bioinformatic analyses. Our findings revealed that exposure to BPA led to significant changes in processes like lipid metabolism, proliferation, and apoptosis in the tissues/cells of mammals, fish, and nematodes. These processes were found to be commonly affected in adipose, liver, mammary, uterus, testes, and ovary tissues. Additionally, through an in-depth analysis of signaling pathways influenced by BPA in different species and tissues, we observed that the JUN/FOS, EGFR, ER, PPARG, and P53 pathways, along with their downstream key transcription factors and kinases, were all impacted by BPA. Our study provides compelling evidence that BPA indeed induces similar toxic effects across different species and tissues. Furthermore, our investigation sheds light on the underlying molecular mechanisms responsible for these toxic effects. By uncovering these mechanisms, we gain valuable insights into the potential health implications associated with BPA exposure, highlighting the importance of comprehensive assessments and awareness of this widespread endocrine disruptor.

Importance of Metal Biotransformation in Cell Response to Metallic Nanoparticles: A Transcriptomic Meta-analysis Study

Metallic nanoparticles are increasingly present in our environment, raising concerns on their interactions with living organisms and potential toxicity. Indeed, metallic nanoparticles release metal ions that can be toxic, bioessential, therapeutically active, or combine several of these features. However, human cell responses to different metallic nanoparticles and ions have rarely been compared so far. We propose here a meta-analysis of the transcriptomic responses of human cells to nanoparticles and ions of various metals (titanium, iron, copper, zinc, silver, cadmium, platinum, gold), in order to identify the commonalities and differences between cell responses to these compounds. This analysis revealed that the chemical properties of metals are more important than their known biological functions (i.e., essential metals, toxicity) in governing the cell transcriptome. Particularly, we evidence that the response to nanoparticles is dominated by the response to the ions they contain, and depend on the nanoparticles' solubility. The formulation as nanoparticles impacts the cell response at lower intensity than the released ions, by altering genes related to vesicle intracellular transport and the cytoskeleton. Moreover, we put into light that several metals (i.e., copper, zinc, silver, cadmium, and gold) trigger a common cell response governed by metallothioneins, which coexist with singular signatures that are specific to a given element.

Effect of silver nanoparticles on gene transcription of land snail Helix aspersa

Silver nanoparticles (Ag-NPs) are extremely useful in a diverse range of consumer goods. However, their impact on the environment is still under research, especially regarding the mechanisms involved in their effect. Aiming to provide some insight, the present work analyzes the transcriptional activity of six genes (Hsp83, Hsp17.2, Hsp19.8, SOD Cu-Zn, Mn-SOD, and BPI) in the terrestrial snail Helix aspersa in the presence of different concentrations of Ag-NPs. The animals were exposed for seven days to Lactuca sativa soaked for one hour in different concentrations of Ag-NPs (20, 50, 100 mg/L). The results revealed that the highest concentration tested of Ag-NPs (100 mg/L) led to a statistically significant induction of the Hsp83 and BPI expression in the digestive gland compared to the control group. However, a trend to upregulation with no statistical significance was observed for all the genes in the digestive gland and the foot, while in the hemolymph, the trend was to downregulation. Ag-NPs affected the stress response and immunity under the tested conditions, although the impact was weak. It is necessary to explore longer exposure times to confirm that the effect can be maintained and impact on health. Our results highlight the usefulness of the terrestrial snail Helix aspersa as a bioindicator organism for silver nanoparticle pollution biomonitoring and, in particular, the use of molecular biomarkers of pollutant effect as candidates to be included in a multi-biomarker strategy.

Concentration-dependent toxicogenomic changes of silver nanoparticles in hepatocyte-like cells derived from human induced pluripotent stem cells

The application of silver nanoparticles (AgNPs) in consumer products has been increasing rapidly over the past decades. Therefore, in vitro models capable of accurately predicting the toxicity of AgNPs are much needed. Hepatocyte-like cells (HLCs) derived from human induced pluripotent stem cells (iPSCs) represent an attractive alternative in vitro hepatotoxicity model. Yet, the use of iPSC-derived HLCs (iPSC-HLCs) for the study of nanoparticle toxicity has not been reported so far. In the present study, transcriptomic changes induced by varying concentrations (5-25 μg/ml) of AgNPs were characterized in iPSC-HLCs after 24-h exposure. AgNPs caused concentration-dependent gene expression changes in iPSC-HLCs. At all the concentrations, members of the metallothionein (MT) and the heat shock protein (HSP) families were the dominating upregulated genes, suggesting that exposure to AgNPs induced oxidative stresses in iPSC-HLCs and as a result elicited cellular protective responses in the cells. Functional analysis showed that the differentially expressed genes (DEGs) were majorly involved in the biological processes of metabolism, response to stress, and cell organization and biogenesis. Ingenuity Pathway Analysis revealed that cancer was at the top of diseases and disorders associated with the DEGs at all concentrations. These results were in accordance with those reported previously on hepatoma cell lines and primary hepatocytes. Considering the advantages iPSC-HLCs have over other liver cell models in terms of unlimited supply, consistency in quality, sustainability of function in long-term culture, and, more importantly, affordability of donor specificity, the results of the current study suggest that iPSC-HLCs may serve as a better in vitro model for liver nanotoxicology.