Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation

Integrated in silico analysis of transcriptomic alterations in nanoparticle toxicity across human and mouse models

Nanoparticles, due to their exceptional physicochemical properties are used in our day-to-day environment. They are currently not regulated which might lead to increased levels in the biological systems causing adverse effects. However, the overall mechanism behind nanotoxicity remains elusive. Previously, we analysed the transcriptome datasets of copper oxide nanoparticles using in silico tools and identified IL-17, chemokine signaling pathway, and cytokine-cytokine receptor interaction as the key pathways altered. Based on the findings, we hypothesized a common pathway could be involved in transition metal oxide nanoparticles toxicity irrespective of the variables. Further, there could be unique transcriptome changes between metal oxide nanoparticles and other nanoparticles. To accomplish this, the overall transcriptome datasets of nanoparticles consisting of microarray and RNA-Seq were obtained. >90 studies for 17 different nanoparticles, performed in humans, rats, and mice were assessed. After initial screening, 24 mouse studies (with 196 datasets) and 34 human studies (with 200 datasets) were used for further analyses. The common genes that are dysregulated upon NPs exposure were identified for human and mouse datasets separately. Further, an overrepresentation functional enrichment analysis was performed. The common genes, their gene ontology, gene-gene, and protein-protein interactions were assessed. The overall results suggest that IL-17 and its related pathways might be commonly altered in nanoparticle exposure with lung as one of the major organs affected.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Expanding adverse outcome pathways towards one health models for nanosafety

The ever-growing production of nano-enabled products has generated the need for dedicated risk assessment strategies that ensure safety for humans and the environment. Transdisciplinary approaches are needed to support the development of new technologies while respecting environmental limits, as also highlighted by the EU Green Deal Chemicals Strategy for Sustainability and its safe and sustainable by design (SSbD) framework. The One Health concept offers a holistic multiscale approach for the assessment of nanosafety. However, toxicology is not yet capable of explaining the interaction between chemicals and biological systems at the multiscale level and in the context of the One Health framework. Furthermore, there is a disconnect between chemical safety assessment, epidemiology, and other fields of biology that, if unified, would enable the adoption of the One Health model. The development of mechanistic toxicology and the generation of omics data has provided important biological knowledge of the response of individual biological systems to nanomaterials (NMs). On the other hand, epigenetic data have the potential to inform on interspecies mechanisms of adaptation. These data types, however, need to be linked to concepts that support their intuitive interpretation. Adverse Outcome Pathways (AOPs) represent an evolving framework to anchor existing knowledge to chemical risk assessment. In this perspective, we discuss the possibility of integrating multi-level toxicogenomics data, including toxicoepigenetic insights, into the AOP framework. We anticipate that this new direction of toxicogenomics can support the development of One Health models applicable to groups of chemicals and to multiple species in the tree of life.

An ancestral molecular response to nanomaterial particulates

The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.

The in vitro immunomodulatory effect of multi-walled carbon nanotubes by multilayer analysis

The study of multi-walled carbon nanotube (MWCNT) induced immunotoxicity is crucial for determining hazards posed to human health. MWCNT exposure most commonly occurs via the airways, where macrophages are first line responders. Here we exploit an in vitro assay, measuring dose-dependent secretion of a wide panel of cytokines, as a measure of immunotoxicity following the non-lethal, multi-dose exposure (IC5, IC10 and IC20) to 7 MWCNTs with different intrinsic properties. We find that a tangled structure, and small aspect ratio are key properties predicting MWCNT induced immunotoxicity, mediated predominantly by IL1B cytokine secretion. To assess the mechanism of action giving rise to MWCNT immunotoxicity, transcriptomics analysis was linked to cytokine secretion in a multilayer model established through correlation analysis across exposure concentrations. This reinforced the finding that tangled MWCNTs have greater immunomodulatory potency, displaying enrichment of immune system, signal transduction and pattern recognition associated pathways. Together our results further elucidate how structure, length and aspect ratio, critical intrinsic properties of MWCNTs, are tied to immunotoxicity.

The Effect of Nanomaterials on DNA Methylation: A Review

DNA methylation is an epigenetic mechanism that involves the addition of a methyl group to a cytosine residue in CpG dinucleotides, which are particularly abundant in gene promoter regions. Several studies have highlighted the role that modifications of DNA methylation may have on the adverse health effects caused by exposure to environmental toxicants. One group of xenobiotics that is increasingly present in our daily lives are nanomaterials, whose unique physicochemical properties make them interesting for a large number of industrial and biomedical applications. Their widespread use has raised concerns about human exposure, and several toxicological studies have been performed, although the studies focusing on nanomaterials' effect on DNA methylation are still limited. The aim of this review is to investigate the possible impact of nanomaterials on DNA methylation. From the 70 studies found eligible for data analysis, the majority were in vitro, with about half using cell models related to the lungs. Among the in vivo studies, several animal models were used, but most were mice models. Only two studies were performed on human exposed populations. Global DNA methylation analyses was the most frequently applied approach. Although no trend towards hypo- or hyper-methylation could be observed, the importance of this epigenetic mechanism in the molecular response to nanomaterials is evident. Furthermore, methylation analysis of target genes and, particularly, the application of comprehensive DNA methylation analysis techniques, such as genome-wide sequencing, allowed identifying differentially methylated genes after nanomaterial exposure and affected molecular pathways, contributing to the understanding of their possible adverse health effects.

Mechanistic Insights into the Biological Effects of Engineered Nanomaterials: A Focus on Gold Nanoparticles

Nanotechnology has great potential to significantly advance the biomedical field for the benefit of human health. However, the limited understanding of nano-bio interactions leading to unknowns about the potential adverse health effects of engineered nanomaterials and to the poor efficacy of nanomedicines has hindered their use and commercialization. This is well evidenced considering gold nanoparticles, one of the most promising nanomaterials for biomedical applications. Thus, a fundamental understanding of nano-bio interactions is of interest to nanotoxicology and nanomedicine, enabling the development of safe-by-design nanomaterials and improving the efficacy of nanomedicines. In this review, we introduce the advanced approaches currently applied in nano-bio interaction studies-omics and systems toxicology-to provide insights into the biological effects of nanomaterials at the molecular level. We highlight the use of omics and systems toxicology studies focusing on the assessment of the mechanisms underlying the in vitro biological responses to gold nanoparticles. First, the great potential of gold-based nanoplatforms to improve healthcare along with the main challenges for their clinical translation are presented. We then discuss the current limitations in the translation of omics data to support risk assessment of engineered nanomaterials.

Gene expression profiling of human macrophages after graphene oxide and graphene nanoplatelets treatment reveals particle-specific regulation of pathways

Graphene and its derivatives are attractive materials envisaged to enable a wealth of novel applications in many fields including energy, electronics, composite materials or health. A comprehensive understanding of the potential adverse effects of graphene-related materials (GRM) in humans is a prerequisite to the safe use of these promising materials. Here, we exploited gene expression profiling to identify transcriptional responses and toxicity pathways induced by graphene oxide (GO) and graphene nanoplatelets (GNP) in human macrophages. Primary human monocyte-derived macrophages (MDM) and a human macrophage cell line, i.e. differentiated THP-1 cells, were exposed to 5 or 20 μg/mL GO and GNP for 6 and 24 h to capture early and more persistent acute responses at realistic or slightly overdose concentrations. GO and GNP induced time-, dose- and macrophage type-specific differential expression of a substantial number of genes with some overlap between the two GRM types (up to 384 genes (9.6%) or 447 genes (20.4%) in THP-1 or MDM, respectively) but also a high number of genes exclusively deregulated from each material type. Furthermore, GRM responses on gene expression were highly different from those induced by inflammogenic material crystalline quartz (maximum of 64 (2.3%) or 318 (11.3%) common genes for MDM treated with 20 μg/mL GO and GNP, respectively). Further bioinformatics analysis revealed that GNP predominantly activated genes controlling inflammatory and apoptotic pathways whereas GO showed only limited inflammatory responses. Interestingly, both GRM affected the expression of genes related to antigen processing and presentation and in addition, GO activated pathways of neutrophil activation, degranulation and immunity in MDM. Overall, this study provides an extensive resource of potential toxicity mechanisms for future safety assessment of GRM in more advanced model systems to verify if the observed changes in gene expression in human macrophages could lead to long-term consequences on human health.

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.

Colloidal stability classification of TiO2 nanoparticles in artificial and in natural waters by cluster analysis and a global stability index: Influence of standard and natural colloidal particles

In the field of exposure-driven risk assessment of engineered nanoparticles (NPs), the highly complex interactions of NPs with natural components in surface waters are considered key factors to understand their fate and behavior in the environment. However, since experimental approaches aiming at imitating environmentally relevant conditions include many parameters and lead to a high number of outcomes, statistical tools can be extremely useful to support the results' interpretation. In this context, a multimethod approach was applied to investigate the colloidal behavior of TiO₂ NPs in both artificial waters and natural brackish water (from the Venice lagoon, Italy), in the presence of standard kaolinite and natural organic matter (NOM), or of the fine fraction of natural colloidal particles (NCPs) from the lagoon sediment. In detail, the experimental data obtained, i.e. hydrodynamic size, surface charge and sedimentation velocity values, were i) statistically treated by hierarchical clustering and ii) merged into a global stability index (I_G). The hierarchical clustering allowed to group the dispersions into three colloidal stability classes, where the main discriminant was the medium composition (i.e. ionic strength and presence of NOM), while the I_G allowed to establish a colloidal stability ranking of the dispersions within each class. Moreover, the comparison among the different dispersions suggested that kaolinite could be considered as a suitable surrogate for NCPs, to estimate the colloidal behavior and environmental fate of TiO₂ NPs in natural aqueous media.

Biomarkers of nanomaterials hazard from multi-layer data

There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.

Genetic instability of lung induced by carbon black nanoparticles is related with Plk1 signals changes

As a possible carcinogen, carbon black has threatened public health. However, the evidences are insufficient and the mechanism of carcinogenesis is still not specified. Thirty rats were randomly divided into 3 groups, namely 0, 5 and 30 mg/m3 Carbon Black nanoparticles (CBNPs) groups, respectively. Rats were treated with CBNPs by nose-only inhalation for 28 days, 6 h/day. The human bronchial epithelial (16HBE) cells were treated with 0, 50, 100 and 200 μg/mL CBNPs for 24 h. Polo-like kinase 1 (PLK1) overexpression cell line was established by pcDNA3.1-PLK1 stable transfection. Our results showed that CBNPs exposure could induce DNA damage and genetic changes as well as apoptosis in vivo and in vitro. The DNA repair ability increased after CBNPs exposure. Cell cycle process was retarded at the G2/M phases in 16HBE cells after CBNPs treatment. The PLK1, ChK2 GADD45α and XRCC1 expression levels changed in rat lung and 16HBE cells after CBNPs treatment. Compared with NC 16HBE cells, DNA damage and repair, numbers of apoptotic cells and micronucleus (MN) rates, as well as the ChK2, GADD45α, XRCC1 expression levels decreased, whereas cytokinesis block proliferation index (CBPI) and replicative index (RI) increase in PLK overexpression (PLK+/+) cells after CBNPs treatment. This study highlighted that PLK1 related with the genetic toxicity of CBNPs in vitro and in vivo. Our results provided evidences supporting reclassification of carbon black as a human possible carcinogen.

Nextcast: A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

Multi-walled carbon nanotubes elicit concordant changes in DNA methylation and gene expression following long-term pulmonary exposure in mice

Pulmonary exposure to multi-walled carbon nanotubes (MWCNTs) causes inflammation and fibrosis. Our previous work has shown that industrially produced MWCNTs trigger specific changes in gene expression in the lungs of exposed animals. To elucidate whether epigenetic effects play a role for these gene expression changes, we performed whole genome bisulphite sequencing to assess DNA methylation patterns in the lungs 56 days after exposure to MWCNTs. Lung tissues were also evaluated with respect to histopathological changes and cytokine profiling of bronchoalveolar lavage (BAL) fluid was conducted using a multi-plex array. Integrated analysis of transcriptomics data and DNA methylation data revealed concordant changes in gene expression. Functional analysis showed that the muscle contraction, immune system/inflammation, and extracellular matrix pathways were the most affected pathways. Taken together, the present study revealed that MWCNTs exert epigenetic effects in the lungs of exposed animals, potentially driving the subsequent gene expression changes.

Machine learning-integrated omics for the risk and safety assessment of nanomaterials

With the advancement in nanotechnology, we are experiencing transformation in world order with deep insemination of nanoproducts from basic necessities to advanced electronics, health care products and medicines. Therefore, nanoproducts, however, can have negative side effects and must be strictly monitored to avoid negative outcomes. Future toxicity and safety challenges regarding nanomaterial incorporation into consumer products, including rapid addition of nanomaterials with diverse functionalities and attributes, highlight the limitations of traditional safety evaluation tools. Currently, artificial intelligence and machine learning algorithms are envisioned for enhancing and improving the nano-bio-interaction simulation and modeling, and they extend to the post-marketing surveillance of nanomaterials in the real world. Thus, hyphenation of machine learning with biology and nanomaterials could provide exclusive insights into the perturbations of delicate biological functions after integration with nanomaterials. In this review, we discuss the potential of combining integrative omics with machine learning in profiling nanomaterial safety and risk assessment and provide guidance for regulatory authorities as well.

Toxicogenomic Profiling of 28 Nanomaterials in Mouse Airways

Toxicogenomics opens novel opportunities for hazard assessment by utilizing computational methods to map molecular events and biological processes. In this study, the transcriptomic and immunopathological changes associated with airway exposure to a total of 28 engineered nanomaterials (ENM) are investigated. The ENM are selected to have different core (Ag, Au, TiO2, CuO, nanodiamond, and multiwalled carbon nanotubes) and surface chemistries (COOH, NH2, or polyethylene glycosylation (PEG)). Additionally, ENM with variations in either size (Au) or shape (TiO2) are included. Mice are exposed to 10 µg of ENM by oropharyngeal aspiration for 4 consecutive days, followed by extensive histological/cytological analyses and transcriptomic characterization of lung tissue. The results demonstrate that transcriptomic alterations are correlated with the inflammatory cell infiltrate in the lungs. Surface modification has varying effects on the airways with amination rendering the strongest inflammatory response, while PEGylation suppresses toxicity. However, toxicological responses are also dependent on ENM core chemistry. In addition to ENM-specific transcriptional changes, a subset of 50 shared differentially expressed genes is also highlighted that cluster these ENM according to their toxicity. This study provides the largest in vivo data set currently available and as such provides valuable information to be utilized in developing predictive models for ENM toxicity.

Profiling of Sub-Lethal in Vitro Effects of Multi-Walled Carbon Nanotubes Reveals Changes in Chemokines and Chemokine Receptors

Engineered nanomaterials are potentially very useful for a variety of applications, but studies are needed to ascertain whether these materials pose a risk to human health. Here, we studied three benchmark nanomaterials (Ag nanoparticles, TiO2 nanoparticles, and multi-walled carbon nanotubes, MWCNTs) procured from the nanomaterial repository at the Joint Research Centre of the European Commission. Having established a sub-lethal concentration of these materials using two human cell lines representative of the immune system and the lungs, respectively, we performed RNA sequencing of the macrophage-like cell line after exposure for 6, 12, and 24 h. Downstream analysis of the transcriptomics data revealed significant effects on chemokine signaling pathways. CCR2 was identified as the most significantly upregulated gene in MWCNT-exposed cells. Using multiplex assays to evaluate cytokine and chemokine secretion, we could show significant effects of MWCNTs on several chemokines, including CCL2, a ligand of CCR2. The results demonstrate the importance of evaluating sub-lethal concentrations of nanomaterials in relevant target cells.

Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials

Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and to develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for ENMs, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo including the physicochemical characteristics of the ENMs used in each study.

Identification of potential circRNA-miRNA-mRNA regulatory networks in response to graphene quantum dots in microglia by microarray analysis

Along with the increasing application of graphene quantum dots (GQDs) in the fields of biomedicine and neuroscience, it is important to assess the probably adverse effects of GQDs in the central nervous system (CNS) but their underlying toxic mechanisms is still unclear. In this study, we evaluate the molecular mechanisms associated with circular RNAs (circRNAs) of nitrogen-doped GQDs (N-GQDs) and amino-functionalized GQDs (A-GQDs) damaging the cell viability and cellular structure in microglia by an integrative analysis of RNA microarray. The differentially expressed circRNA (DEcircRNAs)-miRNA- differentially expressed mRNA (DEmRNAs) regulatory networks were conducted in BV2 microglial cells treated with 25 µg/mL N-GQDs, 100 µg/mL N-GQDs and 100 µg/mL A-GQDs. Based on that, the protein-coding genes in each ceRNA network were collected to do bio-functional analysis to evaluate signaling pathways that were indirectly mediated by circRNAs. Some pathways that could play indispensable roles in the neurotoxicity of N-GQDs or both two kinds of GQDs were found. Low-dosed N-GQDs exposure mainly induced inflammatory action in microglia, while high-dosed N-GQDs and A-GQDs exposure both affect olfactory transduction and GABAergic synapse. Meanwhile, several classical signaling pathways, including mTOR, ErbB and MAPK, could make diverse contributions to the neurotoxicity of both two kinds of GQDs. These circRNAs could be toxic biomarkers or protective targets in neurotoxicity of GQDs. More importantly, they emphasized the necessity of comprehensive analysis of latent molecular mechanisms through epigenetics approaches in biosafety assessment of graphene-based nanomaterials.

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.

Covid-19 acute responses and possible long term consequences: What nanotoxicology can teach us

Long-term effects of Covid-19 disease are still poorly understood. However, similarities between the responses to SARS-CoV-2 and certain nanomaterials suggest fibrotic pulmonary disease as a concern for public health in the next future. Cross-talk between nanotoxicology and other relevant disciplines can help us to deploy more effective Covid-19 therapies and management strategies.

Covid-19 acute responses and possible long term consequences: What nanotoxicology can teach us

Long-term effects of Covid-19 disease are still poorly understood. However, similarities between the responses to SARS-CoV-2 and certain nanomaterials suggest fibrotic pulmonary disease as a concern for public health in the next future. Cross-talk between nanotoxicology and other relevant disciplines can help us to deploy more effective Covid-19 therapies and management strategies.

Network-based analysis implies critical roles of microRNAs in the long-term cellular responses to gold nanoparticles

Since gold nanoparticles (AuNPs) have great potential to bring improvements to the biomedical field, their impact on biological systems should be better understood, particularly over the long term, using realistic doses of exposure. MicroRNAs (miRNAs) are small noncoding RNAs that play key roles in the regulation of biological pathways, from development to cellular stress responses. In this study, we performed genome-wide miRNA expression profiling in primary human dermal fibroblasts 20 weeks after chronic and acute (non-chronic) treatments to four AuNPs with different shapes and surface chemistries at a low dose. The exposure condition and AuNP surface chemistry had a significant impact on the modulation of miRNA levels. In addition, a network-based analysis was employed to provide a more complex, systems-level perspective of the miRNA expression changes. In response to the stress caused by AuNPs, miRNA co-expression networks perturbed in cells under non-chronic exposure to AuNPs were enriched for target genes implicated in the suppression of proliferative pathways, possibly in attempt to restore cell homeostasis, while changes in miRNA co-expression networks enriched for target genes related to activation of proliferative and suppression of apoptotic pathways were observed in cells chronically exposed to one specific type of AuNPs. In this case, miRNA dysregulation might be contributing to enforce a new cell phenotype during stress. Our findings suggest that miRNAs exert critical roles in the cellular responses to the stress provoked by a low dose of NPs in the long term and provide a fertile ground for further targeted experimental studies.

Engineered nanoparticle-induced epigenetic changes: An important consideration in nanomedicine

Engineered nanoparticles (ENPs) are now being applied across a range of disciplines, and as a result numerous studies have now assessed ENP-related bioeffects. Among them, ENP-induced epigenetic changes including DNA methylation, histone modifications, and miRNA-mediated regulation of gene expression have recently attracted attention. In this review, we describe the diversity of ENP-induced epigenetic changes, focusing on their interplay with related functional biological events, especially oxidative stress, MAPK pathway activation, and inflammation. In doing so, we highlight the underlying mechanisms and biological effects of ENP-induced epigenetic changes. We also summarize how high-throughput technologies have helped to uncover ENP-induced epigenetic changes. Finally, we discuss future perspectives and the challenges related to ENP-induced epigenetic changes that still need to be addressed.

Proteomic Analysis Identifies Markers of Exposure to Cadmium Sulphide Quantum Dots (CdS QDs)

The use of cadmium sulphide quantum dot (CdS QD)-enabled products has become increasingly widespread. The prospect of their release in the environment is raising concerns. Here we have used the yeast model Saccharomyces cerevisiae to determine the potential impact of CdS QD nanoparticles on living organisms. Proteomic analyses and cell viability assays performed after 9 h exposure revealed expression of proteins involved in oxidative stress and reduced lethality, respectively, whereas oxidative stress declined, and lethality increased after 24 h incubation in the presence of CdS QDs. Quantitative proteomics using the iTRAQ approach (isobaric tags for relative and absolute quantitation) revealed that key proteins involved in essential biological pathways were differentially regulated over the time course of the experiment. At 9 h, most of the glycolytic functions increased, and the abundance of the number of heat shock proteins increased. This contrasts with the situation at 24 h where glycolytic functions, some heat shock proteins as well as oxidative phosphorylation and ATP synthesis were down-regulated. It can be concluded from our data that cell exposure to CdS QDs provokes a metabolic shift from respiration to fermentation, comparable to the situation reported in some cancer cell lines.

Carbon Nanomaterials Promote M1/M2 Macrophage Activation

Toxic effects of certain carbon nanomaterials (CNM) have been observed in several exposure scenarios both in vivo and in vitro. However, most of the data currently available has been generated in a high-dose/acute exposure setup, limiting the understanding of their immunomodulatory mechanisms. Here, macrophage-like THP-1 cells, exposed to ten different CNM for 48 h in low-cytotoxic concentration of 10 µg mL^-1 , are characterized by secretion of different cytokines and global transcriptional changes. Subsequently, the relationships between cytokine secretion and transcriptional patterns are modeled, highlighting specific pathways related to alternative macrophage activation. Finally, time- and dose-dependent activation of transcription and secretion of M1 marker genes IL-1β and tumor necrosis factor, and M2 marker genes IL-10 and CSF1 is confirmed among the three most responsive CNM, with concentrations of 5, 10, and 20 µg mL^-1 at 24, 48, and 72 h of exposure. These results underline CNM effects on the formation of cell microenvironment and gene expression leading to specific patterns of macrophage polarization. Taken together, these findings imply that, instead of a high and toxic CNM dose, a sub-lethal dose in controlled exposure setup can be utilized to alter the cell microenvironment and program antigen presenting cells, with fascinating implications for novel therapeutic strategies.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

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

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.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Prospects and challenges of multi-omics data integration in toxicology

Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.