Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices.

Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.

ErbB2-driven breast cancer cell invasion depends on a complex signaling network activating myeloid zinc finger-1-dependent cathepsin B expression.

Aberrant ErbB2 receptor tyrosine kinase activation in breast cancer is strongly linked to an invasive disease. The molecular basis of ErbB2-driven invasion is largely unknown. We show that cysteine cathepsins B and L are elevated in ErbB2 positive primary human breast cancer and function as effectors of ErbB2-induced invasion in vitro. We identify Cdc42-binding protein kinase beta, extracellular regulated kinase 2, p21-activated protein kinase 4, and protein kinase C alpha as essential mediators of ErbB2-induced cysteine cathepsin expression and breast cancer cell invasiveness. The identified signaling network activates the transcription of cathepsin B gene (CTSB) via myeloid zinc finger-1 transcription factor that binds to an ErbB2-responsive enhancer element in the first intron of CTSB. This work provides a model system for ErbB2-induced breast cancer cell invasiveness, reveals a signaling network that is crucial for invasion in vitro, and defines a specific role and targets for the identified serine-threonine kinases.

toxFlow: A Web-Based Application for Read-Across Toxicity Prediction Using Omics and Physicochemical Data.

We present toxFlow, a web application developed for enrichment analysis of omics data coupled with read-across toxicity prediction. A sequential analysis workflow is suggested where users can filter omics data using enrichment scores and incorporate their findings into a correlation-based read-across technique for predicting the toxicity of a substance based on its analogs. Either embedded or in-house gene signature libraries can be used for enrichment analysis. The suggested approach can be used for toxicity prediction of diverse chemical entities; however, this article focuses on the multiperspective characterization of nanoparticles and selects their neighbors based on both physicochemical and biological similarity criteria. In addition, visualization options are offered to interactively explore correlation patterns in the data, whereas results can be exported for further analysis. toxFlow is accessible at http://147.102.86.129:3838/toxflow .

Androgen receptor-interacting protein HSPBAP1 facilitates growth of prostate cancer cells in androgen-deficient conditions.

Hormonal therapies targeting androgen receptor (AR) are effective in prostate cancer (PCa), but often the cancers progress to fatal castrate-resistant disease. Improved understanding of the cellular events during androgen deprivation would help to identify survival and stress pathways whose inhibition could synergize with androgen deprivation. Toward this aim, we performed an RNAi screen on 2,068 genes, including kinases, phosphatases, epigenetic enzymes and other druggable gene targets. High-content cell spot microarray (CSMA) screen was performed in VCaP cells in the presence and absence of androgens with detection of Ki67 and cleaved ADP-ribose polymerase (cPARP) as assays for cell proliferation and apoptosis. Thirty-nine candidate genes were identified, whose silencing inhibited proliferation or induced apoptosis of VCaP cells exclusively under androgen-deprived conditions. One of the candidates, HSPB (heat shock 27 kDa)-associated protein 1 (HSPBAP1), was confirmed to be highly expressed in tumor samples and its mRNA expression levels increased with the Gleason grade. We found that strong HSPBAP1 immunohistochemical staining (IHC) was associated with shorter disease-specific survival of PCa patients compared with negative to moderate staining. Furthermore, we demonstrate that HSPBAP1 interacts with AR in the nucleus of PCa cells specifically during androgen-deprived conditions, occupies chromatin at PSA/klk3 and TMPRSS2/tmprss2 enhancers and regulates their expression. In conclusion, we suggest that HSPBAP1 aids in sustaining cell viability by maintaining AR signaling during androgen-deprived conditions.

Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures.

This paper outlines the work for which Roland Grafström and Pekka Kohonen were awarded the 2014 Lush Science Prize. The research activities of the Grafström laboratory have, for many years, covered cancer biology studies, as well as the development and application of toxicity-predictive in vitro models to determine chemical safety. Through the integration of in silico analyses of diverse types of genomics data (transcriptomic and proteomic), their efforts have proved to fit well into the recently-developed Adverse Outcome Pathway paradigm. Genomics analysis within state-of-the-art cancer biology research and Toxicology in the 21st Century concepts share many technological tools. A key category within the Three Rs paradigm is the Replacement of animals in toxicity testing with alternative methods, such as bioinformatics-driven analyses of data obtained from human cell cultures exposed to diverse toxicants. This work was recently expanded within the pan-European SEURAT-1 project (Safety Evaluation Ultimately Replacing Animal Testing), to replace repeat-dose toxicity testing with data-rich analyses of sophisticated cell culture models. The aims and objectives of the SEURAT project have been to guide the application, analysis, interpretation and storage of 'omics' technology-derived data within the service-oriented sub-project, ToxBank. Particularly addressing the Lush Science Prize focus on the relevance of toxicity pathways, a 'data warehouse' that is under continuous expansion, coupled with the development of novel data storage and management methods for toxicology, serve to address data integration across multiple 'omics' technologies. The prize winners' guiding principles and concepts for modern knowledge management of toxicological data are summarised. The translation of basic discovery results ranged from chemical-testing and material-testing data, to information relevant to human health and environmental safety.

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.

Meta-Analysis of Integrated Proteomic and Transcriptomic Data Discerns Structure-Activity Relationship of Carbon Materials with Different Morphologies.

The Fiber Pathogenicity Paradigm (FPP) establishes connections between fiber structure, durability, and disease-causing potential observed in materials like asbestos and synthetic fibers. While emerging nanofibers are anticipated to exhibit pathogenic traits according to the FPP, their nanoscale diameter limits rigidity, leading to tangling and loss of fiber characteristics. The absence of validated rigidity measurement methods complicates nanofiber toxicity assessment. By comprehensively analyzing 89 transcriptomics and 37 proteomics studies, this study aims to enhance carbon material toxicity understanding and proposes an alternative strategy to assess morphology-driven toxicity. Carbon materials are categorized as non-fibrous, high aspect ratio with shorter lengths, tangled, and rigid fibers. Mitsui-7 serves as a benchmark for pathogenic fibers. The meta-analysis reveals distinct cellular changes for each category, effectively distinguishing rigid fibers from other carbon materials. Subsequently, a robust random forest model is developed to predict morphology, unveiling the pathogenicity of previously deemed non-pathogenic NM-400 due to its secondary structures. This study fills a crucial gap in nanosafety by linking toxicological effects to material morphology, in particular regarding fibers. It demonstrates the significant impact of morphology on toxicological behavior and the necessity of integrating morphological considerations into regulatory frameworks.

Novel pyrimidine-2,4-diamine derivative suppresses the cell viability and spindle assembly checkpoint activity by targeting Aurora kinases.

Mitosis represents a clinically important determination point in the life cycle of proliferating cells. One potential drug target within the mitotic machinery is the spindle assembly checkpoint (SAC), an evolutionarily conserved signaling pathway that monitors the connections between microtubules (MTs) and chromosomes. Mistakes in SAC signaling may lead to cell division errors that can trigger elimination of cancer cells at M phase or soon after exit from mitosis. In this study, we describe the cellular effects of a novel pyrimidine-2,4-diamine derivative that we discovered to inhibit the activity of SAC. The compound caused rapid escape from the mitotic arrest induced by lack of interkinetochore tension but not by lack of MT-kinetochore attachments. In cycling cells, the compound disrupted the architecture of mitotic spindle that triggered a transient M-phase arrest that was rapidly followed by a forced mitotic exit. The premature termination of M phase was found to be a consequence of precocious inactivation of SAC caused by a direct inhibitory effect of the compound on Aurora B kinase in vitro and in cells. The compound also targets Aurora A kinase and tubulin in vitro and in cells, which can explain the observed spindle anomalies. The reduced activity of Aurora B kinase resulted in polyploidy and suppression of cancer cell viability. Our data suggest that this new pharmacophore possesses interesting anticancer properties that could be exploited in development of mitosis-targeting therapies.

Identification of miR-193b targets in breast cancer cells and systems biological analysis of their functional impact.

Identification of protein targets for microRNAs (miRNAs) is a significant challenge due to the complexity of miRNA-mediated regulation. We have previously demonstrated that miR-193b targets estrogen receptor-α (ERα) and inhibits estrogen-induced growth of breast cancer cells. Here, we applied a high-throughput strategy using quantitative iTRAQ (isobaric tag for relative and absolute quantitation) reagents to identify other target proteins regulated by miR-193b in breast cancer cells. iTRAQ analysis of pre-miR-193b transfected MCF-7 cells resulted in identification of 743 unique proteins, of which 39 were down-regulated and 44 up-regulated as compared with negative control transfected cells. Computationally predicted targets of miR-193b were highly enriched (sevenfold) among the proteins whose level of expression decreased after miR-193b transfection. Only a minority of these (13%) showed similar effect at the mRNA level illustrating the importance of post-transcriptional regulation. The most significantly repressed proteins were selected for validation experiments. These data confirmed 14-3-3ζ (YWHAZ), serine hydroxyl transferase (SHMT2), and aldo-keto reductase family 1, member C2 (AKR1C2) as direct, previously uncharacterized, targets of miR-193b. Functional RNAi assays demonstrated that specific combinations of knockdowns of these target genes by siRNAs inhibited growth of MCF-7 cells, mimicking the effects of the miR-193b overexpression. Interestingly, the data imply that besides targeting ERα, the miR-193b effects include suppression of the local production of estrogens and other steroid hormones mediated by the AKR1C2 gene, thus provoking two separate molecular mechanisms inhibiting steroid-dependent growth of breast cancer cells. In conclusion, we present here a proteomic screen to identify targets of miR-193b, and a systems biological approach to mimic its effects at the level of cellular phenotypes. This led to the identification of multiple genes whose combinatorial knock-down likely mediates the strong anti-cancer effects observed for miR-193b in breast cancer cells.

Integrative genomic, transcriptomic, and RNAi analysis indicates a potential oncogenic role for FAM110B in castration-resistant prostate cancer.

BACKGROUND: Castration-resistant prostate cancer (CRPC) represents a therapeutic challenge for current medications. METHODS: In order to explore the molecular mechanisms involved in CRPC progression and to identify new therapeutic targets, we analyzed a unique sample set of 11 CRPCs and 7 advanced tumors by array-CGH and gene expression microarrays. The genome-wide DNA and RNA data were integrated to identify genes whose overexpression was driven by their amplification. To assess the functional role of these genes, their expression was analyzed in a transcriptional data set of 329 clinical prostate cancers and the corresponding gene products were silenced using RNA interference in prostate cancer cells. RESULTS: Six recurrent genetic targets were identified in the CRPCs; ATP1B1, AR, FAM110B, LAS1L, MYC, and YIPF6. In addition to AR and MYC, FAM110B emerged as a potential key gene involved in CRPC progression in a subset of the tumors. FAM110B was able to regulate AR signaling in prostate cancer cells and FAM110B itself was regulated by androgens. FAM110B siRNA inhibited the growth of prostate cancer cells in vitro, and this effect was substantially enhanced in androgen deficient conditions. Ectopic FAM110B expression in non-cancerous epithelial prostate cells induced aneuploidy and impaired antigen presentation. CONCLUSIONS: The DNA/RNA gene outlier detection combined with siRNA cell proliferation assay identified FAM110B as a potential growth promoting key gene for CRPC. FAM110B appears to have a key role in the androgen signaling and progression of CRPC impacting multiple cancer hallmarks and therefore highlighting a potential therapeutic target.

Arachidonic acid pathway members PLA2G7, HPGD, EPHX2, and CYP4F8 identified as putative novel therapeutic targets in prostate cancer.

The arachidonic acid and prostaglandin pathway has been implicated in prostate carcinogenesis, but comprehensive studies of the individual members in this key pathway are lacking. Here, we first conducted a systematic bioinformatic study of the expression of 36 arachidonic acid pathway genes across 9783 human tissue samples. The results showed that the PLA2G7, HPGD, EPHX2, and CYP4F8 genes are highly expressed in prostate cancer. Functional studies using RNA interference in prostate cancer cells indicated that all four genes are also essential for cell growth and survival. Clinical validation confirmed high PLA2G7 expression, especially in ERG oncogene-positive prostate cancers, and its silencing sensitized ERG-positive prostate cancer cells to oxidative stress. HPGD was highly expressed in androgen receptor (AR)-overexpressing advanced tumors, as well as in metastatic prostate cancers. EPHX2 mRNA correlated with AR in primary prostate cancers, and its inhibition in vitro reduced AR signaling and potentiated the effect of antiandrogen flutamide in cultured prostate cancer cells. In summary, we identified four novel putative therapeutic targets with biomarker potential for different subtypes of prostate cancer. In addition, our results indicate that inhibition of these enzymes may be particularly powerful when combined with other treatments, such as androgen deprivation or induction of oxidative stress.

Androgen regulation of micro-RNAs in prostate cancer.

BACKGROUND: Androgens play a critical role in the growth of both androgen dependent and castration-resistant prostate cancer (CRPC). Only a few micro-RNAs (miRNAs) have been suggested to be androgen regulated. We aim to identify androgen regulated miRNAs. METHODS: We utilized LNCaP derived model, we have established, and which overexpresses the androgen receptor (AR), the VCaP cell line, and 13 intact-castrated prostate cancer (PC) xenograft pairs, as well as clinical specimens of untreated (PC) and CRPC. The expression of miRNAs was analyzed by microarrays and quantitative RT-PCR (Q-RT-PCR). Transfection of pre-miR-141 and anti-miR-141 was also used. RESULTS: Seventeen miRNAs were > 1.5-fold up- or downregulated upon dihydrotestosterone (DHT) treatment in the cell lines, and 42 after castration in the AR-positive xenografts. Only four miRNAs (miR-10a, miR-141, miR-150*, and miR-1225-5p) showed similar androgen regulation in both cell lines and xenografts. Of those, miR-141 was found to be expressed more in PC and CRPC compared to benign prostate hyperplasia. Additionally, the overexpression of miR-141 enhanced growth of parental LNCaP cells while inhibition of miR-141 by anti-miR-141 suppressed the growth of the LNCaP subline overexpressing AR. CONCLUSIONS: Only a few miRNAs were found to be androgen-regulated in both cell lines and xenografts models. Of those, the expression of miR-141 was upregulated in cancer. The ectopic overexpression of miR-141 increased growth of LNCaP cell suggesting it may contribute to the progression of PC.

Concerted action of Helios and Ikaros controls the expression of the inositol 5-phosphatase SHIP.

Ikaros family transcription factors have a key role in lymphoid development, and their aberrant function contributes to a multitude of lymphoid malignancies. Ikaros and Helios bind to similar DNA sequences, and Helios associates with Ikaros-containing chromatin remodeling complexes. Previously, we have shown that loss of Ikaros leads to diminished BCR-signaling strength. In this study, we describe a Helios-deficient chicken DT40 B-cell line with a BCR signaling phenotype that is the opposite to that of Ikaros-deficient cells. In contrast to Ikaros-deficient cells, Helios(-/-) B cells exhibit increased calcium release to the cytoplasm after BCR crosslinking, but diminished BCR-induced phosphorylation of signaling molecules. The inositol 5-phosphatase SHIP, an important regulator in several signaling pathways, is differentially expressed in Ikaros- and Helios-deficient cells. In the absence of Ikaros, SHIP is upregulated, whereas Helios deficiency leads to the downregulation of SHIP expression. We also show with ChIP that Ikaros binds to the promoter of the INPP5D gene-encoding SHIP. Considering the critical role of SHIP in the BCR signaling pathway, our findings provide insight into the mechanism of how both Helios and Ikaros are involved in the regulation of BCR signaling.

Enhanced serine production by bone metastatic breast cancer cells stimulates osteoclastogenesis.

Since bone metastatic breast cancer is an incurable disease, causing significant morbidity and mortality, an understanding of the underlying molecular mechanisms would be highly valuable. Here, we describe in vitro and in vivo evidences for the importance of serine biosynthesis in the metastasis of breast cancer to bone. We first characterized the bone metastatic propensity of the MDA-MB-231(SA) cell line variant as compared to the parental MDA-MB-231 cells by radiographic and histological observations in the inoculated mice. Genome-wide gene expression profiling of this isogenic cell line pair revealed that all the three genes involved in the L: -serine biosynthesis pathway, phosphoglycerate dehydrogenase (PHGDH), phosphoserine aminotransferase 1 (PSAT1), and phosphoserine phosphatase (PSPH) were upregulated in the highly metastatic variant. This pathway is the primary endogenous source for L: -serine in mammalian tissues. Consistently, we observed that the proliferation of MDA-MB-231(SA) cells in serine-free conditions was dependent on PSAT1 expression. In addition, we observed that L: -serine is essential for the formation of bone resorbing human osteoclasts and may thus contribute to the vicious cycle of osteolytic bone metastasis. High expression of PHGDH and PSAT1 in primary breast cancer was significantly associated with decreased relapse-free and overall survival of patients and malignant phenotypic features of breast cancer. In conclusion, high expression of serine biosynthesis genes in metastatic breast cancer cells and the stimulating effect of L: -serine on osteoclastogenesis and cancer cell proliferation indicate a functionally critical role for serine biosynthesis in bone metastatic breast cancer and thereby an opportunity for targeted therapeutic interventions.

Towards FAIR nanosafety data.

Nanotechnology is a key enabling technology with billions of euros in global investment from public funding, which include large collaborative projects that have investigated environmental and health safety aspects of nanomaterials, but the reuse of accumulated data is clearly lagging behind. Here we summarize challenges and provide recommendations for the efficient reuse of nanosafety data, in line with the recently established FAIR (findable, accessible, interoperable and reusable) guiding principles. We describe the FAIR-aligned Nanosafety Data Interface, with an aggregated findability, accessibility and interoperability across physicochemical, bio-nano interaction, human toxicity, omics, ecotoxicological and exposure data. Overall, we illustrate a much-needed path towards standards for the optimized use of existing data, which avoids duplication of efforts, and provides a multitude of options to promote safe and sustainable nanotechnology.

Inhibition of receptor tyrosine kinase signalling by small molecule agonist of T-cell protein tyrosine phosphatase.

BACKGROUND: T-cell protein tyrosine phosphatase (TCPTP/TC45) is a ubiquitously expressed intra-cellular non-receptor protein tyrosine phosphatase involved in the negative regulation of several cancer relevant cellular signalling pathways. We have previously shown that interaction between the alpha-cytoplasmic tail of alpha1beta1 integrin and TCPTP activates TCPTP by disrupting an inhibitory intra-molecular bond in TCPTP. Thus, inhibition of the regulatory interaction in TCPTP is a desirable strategy for TCPTP activation and attenuation of oncogenic RTK signalling. However, this is challenging with low molecular weight compounds. METHODS: We developed a high-throughput compatible assay to analyse activity of recombinant TCPTP in vitro. Using this assay we have screened 64280 small molecules to identify novel agonists for TCPTP. Dose-dependent response to TCPTP agonist was performed using the in vitro assay. Inhibition effects and specificity of TCPTP agonists were evaluated using TCPTP expressing and null mouse embryonic fibroblasts. Western blot analysis was used to evaluate attenuation of PDGFRbeta and EGFR phosphorylation. Inhibition of VEGF signalling was analysed with VEGF-induced endothelial cell sprouting assays. RESULTS: From the screen we identified six TCPTP agonists. Two compounds competed with alpha1-cytoplasmic domain for binding to TCPTP, suggesting that they activate TCPTP similar to alpha1-cyt by disrupting the intra-molecular bond in TCPTP. Importantly, one of the compounds (spermidine) displayed specificity towards TCPTP in cells, since TCPTP -/- cells were 43-fold more resistant to the compound than TCPTP expressing cells. This compound attenuates PDGFRbeta and VEGFR2 signalling in cells in a TCPTP-dependent manner and functions as a negative regulator of EGFR phosphorylation in cancer cells. CONCLUSIONS: In this study we showed that small molecules mimicking TCPTP-alpha1 interaction can be used as TCPTP agonists. These data provide the first proof-of-concept description of the use of high-throughput screening to identify small molecule PTP activators that could function as RTK antagonists in cells.

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 II: Preprocessing and Differential Expression Analysis for High Quality Data.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

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.

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.