Network Analysis Reveals Similar Transcriptomic Responses to Intrinsic Properties of Carbon Nanomaterials in Vitro and in Vivo

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.

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.

A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.

Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction

Assessing chemical safety is essential to evaluate the potential risks of chemical exposure to human health and the environment. Traditional methods relying on animal testing are being replaced by 3R (reduction, refinement, and replacement) principle-based alternatives, mainly depending on in vitro test methods and the Adverse Outcome Pathway framework. However, these approaches often focus on the properties of the compound, missing the broader chemical-biological interaction perspective. Currently, the lack of comprehensive molecular characterization of the in vitro test system results in limited real-world representation and contextualization of the toxicological effect under study. Leveraging omics data strengthens the understanding of the responses of different biological systems, emphasizing holistic chemical-biological interactions when developing in vitro methods. Here, we discuss the relevance of meticulous test system characterization on two safety assessment relevant scenarios and how omics-based, data-driven approaches can improve the future generation of alternative methods.

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.

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.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.

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.

In vitro to in vivo extrapolation to support the development of the next generation risk assessment (NGRA) strategy for nanomaterials

There is growing interest in developing novel strategies to support assessment of human health risks due to chemicals. Regulatory and decision-making agencies have recommended that non-animal-based alternatives should be applied whenever possible instead of experimentation on living animals. These alternative methods are beneficial because they are ethical, inexpensive, and rapid. Herein, we review recent activities aimed at developing in vitro to in vivo extrapolation (IVIVE) models as a part of the Next Generation Risk Assessment (NGRA) of nanomaterials. In this context, we show the adverse outcome pathway (AOP)-based methodology for the identification of mechanistically relevant events serving as biomarkers for the targeted selection of in vitro assays. Considered events need to be biologically plausible, regulatory relevant, and crucial for the examination of occurrence of adverse outcomes. The promising advantages of using high-throughout-based omics data are highlighted. Furthermore, the application of 3D in vitro models and nano genome atlases to study nanoparticle toxicity is briefly summarized. Additionally, the challenges related to the extrapolation of in vitro doses into in vivo-relevant responses are presented. We also discuss the limitations of models applied thus far to study the fate of chemicals in the human body, which exist due to the lack of available knowledge regarding transformations of nanomaterials occurring in biological systems.

Nextcast: a software suite to analyse and model toxicogenomics data

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Toxicogenomics is emerging as a valid approach to characterise the mechanism of action of chemicals.

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Structured pipelines for toxicogenomics increase standardisation and regulatory acceptance.

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We developed the Nextcast software suite for robust analysis and modelling of toxicogenomic data.

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Nextcast offers customisable modular pipelines to tackle multiple biological questions.

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).

RNA m6A Alterations Induced by Biomineralization Nanoparticles: A Proof-of-Concept Study of Epitranscriptomics for Nanotoxicity Evaluation

Although various strategies have been included in nanotoxicity evaluation, epitranscriptomics has rarely been integrated into this field. In this proof-of-concept study, N6-methyladenosine (m⁶A) changes of mRNA in HEK293T cells induced by three bovine serum albumin (BSA)-templated Au, CuS and Gd₂O₃ nanoparticles are systematically explored, and their possible biological mechanisms are preliminarily investigated. It has been found that all the three BSA-templated nanoparticles can reduce m⁶A levels, and the genes with reduced m⁶A are enriched for TGF-beta signaling, which is critical for cell proliferation, differentiation and apoptosis. Further results indicate that abnormal aggregation of m⁶A-related enzymes at least partly account for the nanoparticle-induced epitranscriptomic changes. These findings demonstrate that epitranscriptomics analysis can provide an unprecedented landscape of the biological effect induced by nanomaterials, which should be involved in the nanotoxicity evaluation to promote the potential clinical translation of nanomaterials.

Network Analysis of Microarray Data

DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.

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.

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.

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

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.

Mechanistic Similarities between 3D Human Bronchial Epithelium and Mice Lung, Exposed to Copper Oxide Nanoparticles, Support Non-Animal Methods for Hazard Assessment

The diversity and increasing prevalence of products derived from engineered nanomaterials (ENM), warrants implementation of non-animal approaches to health hazard assessment for ethical and practical reasons. Although non-animal approaches are becoming increasingly popular, there are almost no studies of side-by-side comparisons with traditional in vivo assays. Here, transcriptomics is used to investigate mechanistic similarities between healthy/asthmatic models of 3D air-liquid interface (ALI) cultures of donor-derived human bronchial epithelia cells, and mouse lung tissue, following exposure to copper oxide ENM. Only 19% of mouse lung genes with human orthologues are not expressed in the human 3D ALI model. Despite differences in taxonomy and cellular complexity between the systems, a core subset of matching genes cluster mouse and human samples strictly based on ENM dose (exposure severity). Overlapping gene orthologue pairs are highly enriched for innate immune functions, suggesting an important and maybe underestimated role of epithelial cells. In conclusion, 3D ALI models based on epithelial cells, are primed to bridge the gap between traditional 2D in vitro assays and animal models of airway exposure, and transcriptomics appears to be a unifying dose metric that links in vivo and in vitro test systems.

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.

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

Cellular Response of Escherichia coli to Photocatalysis: Flagellar Assembly Variation and Beyond

Bacterial cells can be inactivated by external reactive oxygen species (ROS) produced by semiconductor photocatalysis. However, little is known about cellular responses to photocatalysis. For a better understanding of this issue, one strain of Escherichia coli ( E. coli, hereafter named as MT), which has an increased ability to metabolize carbon sources, was screened out from the wild-type (WT) E. coli K12 by repeated exposure to photocatalysis with palladium oxide modified nitrogen-doped titanium dioxide. In this study, transcriptome sequencing of the WT and MT strains that were exposed or unexposed to photocatalysis were carried out. Cellular responses to photocatalysis were inferred from the functions of genes whose transcripts were either increased or decreased. Upregulation of expression of bacterial flagellar assembly genes used for chemotaxis was detected in cells exposed to semilethal photocatalytic conditions of the WT E. coli. Increased capability to degrade superoxide radicals and decreased bacterial flagellar assembly and chemotaxis were observed in MT E. coli compared to WT cells. We conclude that the differences in motility and intracellular ROS between MT and WT are directly related to survivability of E. coli during exposure to photodisinfection.

eUTOPIA: solUTion for Omics data PreprocessIng and Analysis

Background

Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results.

Results

We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application.

Conclusions

eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods.

Electronic supplementary material

The online version of this article (10.1186/s13029-019-0071-7) contains supplementary material, which is available to authorized users.

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

Engineered nanomaterials (ENMs) are widely present in our daily lives. Despite the efforts to characterize their mechanism of action in multiple species, their possible implications in human pathologies are still not fully understood. Here we performed an integrated analysis of the effects of ENMs on human health by contextualizing their transcriptional mechanism-of-action with respect to drugs, chemicals and diseases. We built a network of interactions of over 3,000 biological entities and developed a novel computational tool, INSIdE NANO, to infer new knowledge about ENM behavior. We highlight striking association of metal and metal-oxide nanoparticles and major neurodegenerative disorders. Our novel strategy opens possibilities to achieve fast and accurate read-across evaluation of ENMs and other chemicals based on their biosignatures.

Multi-hierarchical profiling the structure-activity relationships of engineered nanomaterials at nano-bio interfaces

Increasing concerns over the possible risks of nanotechnology necessitates breakthroughs in structure-activity relationship (SAR) analyses of engineered nanomaterials (ENMs) at nano-bio interfaces. However, current nano-SARs are often based on univariate assessments and fail to provide tiered views on ENM-induced bio-effects. Here we report a multi-hierarchical nano-SAR assessment for a representative ENM, Fe2O3, by metabolomics and proteomics analyses. The established nano-SAR profile allows the visualizing of the contributions of seven basic properties of Fe2O3 to its diverse bio-effects. For instance, although surface reactivity is responsible for Fe2O3-induced cell migration, the inflammatory effects of Fe2O3 are determined by aspect ratio (nanorods) or surface reactivity (nanoplates). These nano-SARs are examined in THP-1 cells and animal lungs, which allow us to decipher the detailed mechanisms including NLRP3 inflammasome pathway and monocyte chemoattractant protein-1-dependent signaling. This study provides more insights for nano-SARs, and may facilitate the tailored design of ENMs to render them desired bio-effects.

Advanced tools for the safety assessment of nanomaterials

Engineered nanomaterials (ENMs) have tremendous potential to produce beneficial technological impact in numerous sectors in society. Safety assessment is, of course, of paramount importance. However, the myriad variations of ENM properties makes the identification of specific features driving toxicity challenging. At the same time, reducing animal tests by introducing alternative and/or predictive in vitro and in silico methods has become a priority. It is important to embrace these new advances in the safety assessment of ENMs. Indeed, remarkable progress has been made in recent years with respect to mechanism-based hazard assessment of ENMs, including systems biology approaches as well as high-throughput screening platforms, and new tools are also emerging in risk assessment and risk management for humans and the environment across the whole life-cycle of nano-enabled products. Here, we highlight some of the key advances in the hazard and risk assessment of ENMs.

Nasal mucosa and blood cell transcriptome profiles do not reflect respiratory symptoms associated with moisture damage

Upper and lower respiratory symptoms and asthma are adverse health effects associated with moisture-damaged buildings. Quantitative measures to detect adverse health effects related to exposure to dampness and mold are needed. Here, we investigate differences in gene expression between occupants of moisture-damaged and reference buildings. Moisture-damaged (N = 11) and control (N = 5) buildings were evaluated for dampness and mold by trained inspectors. The transcriptomics cohort consisted of nasal brushings and peripheral blood mononuclear cells (PBMCs) from 86 teachers, with/without self-perceived respiratory symptoms. Subject categories comprised reference (R) and damaged (D) buildings with (S) or without (NS) symptoms, that is, R-S, R-NS, DS, and D-NS. Component analyses and k-means clustering of transcriptome profiles did not distinguish building status (R/D) or presence of respiratory symptoms (S/NS). Only one nasal mucosa gene (YBX3P1) exhibited a significant change in expression between D-S and D-NS. Nine other nasal mucosa genes were differentially expressed between R-S and D-S teachers. No differentially expressed genes were identified in PBMCs. We conclude that the observed mRNA differences provide very weak biological evidence for adverse health effects associated with subject occupancy of the specified moisture-damaged buildings. This emphasizes the need to evaluate all potential factors (including those not related to toxicity) influencing perceived/self-reported ill health in moisture-damaged buildings.

RNA-sequencing reveals long-term effects of silver nanoparticles on human lung cells

Despite a considerable focus on the adverse effects of silver nanoparticles (AgNPs) in recent years, studies on the potential long-term effects of AgNPs are scarce. The aim of this study was to explore the effects of AgNPs following repeated low-dose, long-term exposure of human bronchial epithelial cells. To this end, the human BEAS-2B cell line was exposed to 1 µg/mL AgNPs (10 nm) for 6 weeks followed by RNA-sequencing (RNA-Seq) as well as genome-wide DNA methylation analysis. The transcriptomics analysis showed that a substantial number of genes (1717) were differentially expressed following AgNP exposure whereas only marginal effects on DNA methylation were observed. Downstream analysis of the transcriptomics data identified several affected pathways including the ‘fibrosis’ and ‘epithelial-mesenchymal transition’ (EMT) pathway. Subsequently, functional validation studies were performed using AgNPs of two different sizes (10 nm and 75 nm). Both NPs increased collagen deposition, indicative of fibrosis, and induced EMT, as evidenced by an increased invasion index, anchorage independent cell growth, as well as cadherin switching. In conclusion, using a combination of RNA-Seq and functional assays, our study revealed that repeated low-dose, long-term exposure of human BEAS-2B cells to AgNPs is pro-fibrotic, induces EMT and cell transformation.