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Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are intentionally added to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP–Wiki. Based on these plastic additive–AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into ten priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives–AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/. Finally, we showed the utility of the constructed plastic additives–AOP network by identifying highly relevant AOPs associated with benzo[a]pyrene (B[a]P), bisphenol A (BPA), and bis(2-ethylhexyl) phthalate (DEHP) and thereafter, explored the associated toxicity pathways in humans and aquatic species. Overall, the constructed plastic additives–AOP network will assist regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics. [ABSTRACT FROM AUTHOR]

In silico toxicogenomics methods are resource- and time-efficient approaches for inferring chemical–protein–disease associations with potential mechanism information for exploring toxicological effects. However, current in silico toxicogenomics systems make inferences based on only chemical–protein interactions without considering tissue-specific gene/protein expressions. As a result, inferred diseases could be overpredicted with false positives. In this work, six tissue-specific expression datasets of genes and proteins were collected from the Expression Atlas. Genes were then categorized into high, medium, and low expression levels in a tissue- and dataset-specific manner. Subsequently, the tissue-specific expression datasets were incorporated into the chemical–protein–disease inference process of our ChemDIS system by filtering out relatively low-expressed genes. By incorporating tissue-specific gene/protein expression data, the enrichment rate for chemical–disease inference was largely improved with up to 62.26% improvement. A case study of melamine showed the ability of the proposed method to identify more specific disease terms that are consistent with the literature. A user-friendly user interface was implemented in the ChemDIS system. The methodology is expected to be useful for chemical–disease inference and can be implemented for other in silico toxicogenomics tools. [ABSTRACT FROM AUTHOR]

Drug-induced liver injury (DILI) is the most common trigger for acute liver failure and the leading cause of attrition in drug development. In this study, we developed an in silico framework to screen drug-induced hepatocellular toxicity (INSIGHT) by integrating the post-treatment transcriptomic data from both rodent models and primary human hepatocytes. We first built an early prediction model using logistic regression with elastic net regularization for 123 compounds and established the INSIGHT framework that can screen for drug-induced hepatotoxicity. The 235 signature genes identified by INSIGHT were involved in metabolism, bile acid synthesis, and stress response pathways. Applying the INSIGHT to an independent transcriptomic dataset treated by 185 compounds predicted that 27 compounds show a high DILI risk, including zoxazolamine and emetine. Further integration with cell image data revealed that predicted compounds with high DILI risk can induce abnormal morphological changes in the endoplasmic reticulum and mitochondrion. Clustering analysis of the treatment-induced transcriptomic changes delineated distinct DILI mechanisms induced by these compounds. Our study presents a computational framework for a mechanistic understanding of long-term liver injury and the prospective prediction of DILI risk. [ABSTRACT FROM AUTHOR]

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant that disrupts hepatic function leading to steatotic liver disease (SLD)-like pathologies, such as steatosis, steatohepatitis, and fibrosis. These effects are mediated by the aryl hydrocarbon receptor following changes in gene expression. Although diverse cell types are involved, initial cell-specific changes in gene expression have not been reported. In this study, differential gene expression in hepatic cell types was examined in male C57BL/6 mice gavaged with 30 µg/kg of TCDD using single-nuclei RNA-sequencing. Ten liver cell types were identified with the proportions of most cell types remaining unchanged, except for neutrophils which increased at 72 h. Gene expression suggests TCDD induced genes related to oxidative stress in hepatocytes as early as 2 h. Lipid homeostasis was disrupted in hepatocytes, macrophages, B cells, and T cells, characterized by the induction of genes associated with lipid transport, steroid hormone biosynthesis, and the suppression of β-oxidation, while linoleic acid metabolism was altered in hepatic stellate cells (HSCs), B cells, portal fibroblasts, and plasmacytoid dendritic cells. Pro-fibrogenic processes were also enriched, including the induction retinol metabolism genes in HSCs and the early induction of anti-fibrolysis genes in hepatocytes, endothelial cells, HSCs, and macrophages. Hepatocytes also had gene expression changes consistent with hepatocellular carcinoma. Collectively, these findings underscore the effects of TCDD in initiating SLD-like phenotypes and identified cell-specific gene expression changes related to oxidative stress, steatosis, fibrosis, cell proliferation and the development of HCC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over the last decade, nanoparticles have found great interest among scientists and researchers working in various fields within the realm of biomedicine including drug delivery, gene delivery, diagnostics, targeted therapy and biomarker mapping. While their physical and chemical properties are impressive, there is growing concern about the toxicological potential of nanoparticles and possible adverse health effects as enhanced exposure of biological systems to nanoparticles may result in toxic effects leading to serious contraindications. Toxicity associated with nanoparticles (nanotoxicity) may include the undesired response of several physiological mechanisms including the distressing of cells by external and internal interaction with nanoparticles. However, comprehensive knowledge of nanotoxicity mechanisms and mitigation strategies may be useful to overcome the hazardous situation while treating diseases with therapeutic nanoparticles. With the same objectives, this review discusses various mechanisms of nanotoxicity and provides an overview of the current state of knowledge on the impact of nanotoxicity on biological control systems and organs including liver, brain, kidneys and lungs. An attempt also been made to present various approaches of scientific research and strategies that could be useful to overcome the effect of nanotoxicity during the development of nanoparticle-based systems including coating, doping, grafting, ligation and addition of antioxidants. [ABSTRACT FROM AUTHOR]

A toxicogenomic approach was used for toxicity evaluation of arsenic in the aquatic environment, and differential gene expression was investigated from 24 h and 96 h water-only acute toxicity tests with the aquatic oligochaete, Tubifex tubifex (Annelida, Clitellata). Several toxicological endpoints (survival and autotomy) of the oligochaete and tissue residues were measured, and dose-response modelling of gene expression data was studied. A reference transcriptome of the aquatic oligochaete, T. tubifex, was reconstructed for the first time, and genes related to cell stress response (Hsc70, Hsp10, Hsp60, and Hsp83), energy metabolism (COX1), oxidative stress (Cat, GSR, and MnSOD), and the genes involved in the homeostasis of organisms (CaM, RpS13, and UBE2) were identified and characterised. The potential use of the genes identified for risk assessment in freshwater ecosystems as early biomarkers of arsenic toxicity is discussed. [ABSTRACT FROM AUTHOR]

Animal studies are typically utilized to understand the complex mechanisms associated with toxicant-induced hepatotoxicity. Among the alternative approaches to animal studies, in vitro pooled human hepatocytes have the potential to capture population variability. Here, we examined the effect of the hepatotoxicant thioacetamide on pooled human hepatocytes, divided into five lots, obtained from forty diverse donors. For 24 h, pooled human hepatocytes were exposed to vehicle, 1.33 mM (low dose), and 12 mM (high dose) thioacetamide, followed by RNA-seq analysis. We assessed gene expression variability using heat maps, correlation plots, and statistical variance. We used KEGG pathways and co-expression modules to identify underlying physiological processes/pathways. The co-expression module analysis showed that the majority of the lots exhibited activation for the bile duct proliferation module. Despite lot-to-lot variability, we identified a set of common differentially expressed genes across the lots with similarities in their response to amino acid, lipid, and carbohydrate metabolism. We also examined efflux transporters and found larger lot-to-lot variability in their expression patterns, indicating a potential for alteration in toxicant bioavailability within the cells, which could in turn affect the gene expression patterns between the lots. Overall, our analysis highlights the challenges in using pooled hepatocytes to understand mechanisms of toxicity. [ABSTRACT FROM AUTHOR]

Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene coregulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions and applied weighted gene coregulated network analysis to the transcriptomics data followed by the identification of gene coregulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene coregulated networks. We correlated gene coregulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with the onset of cell death was evaluated using RNA interference screening. We identified GTPBP2 , HSPA1B , IRF1 , SIRT1 , and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI. [ABSTRACT FROM AUTHOR]

With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach. Using the Percellome toxicogenomics database, instead of utilizing the numerical gene expression values of the transcripts (gene probes of the microarray) for dose-time combinations, DTox learned the image representation of 3D surface plots of distinct time and dosage data points to train the classifier on the experts' labels of gene probe significance. DTox outperformed statistical threshold-based bioinformatics and machine learning approaches based on numerical expression values. This result shows the ability of image-driven neural networks to overcome the limitations of classical numeric value-based approaches. Further, by augmenting the model with explainability modules, our study showed the potential to reveal the visual analysis process of human experts in toxicogenomics through the model weights. While the current work demonstrates the application of the DTox model in toxicogenomic studies, it can be further generalized as an in visio approach for multi-dimensional numeric data with applications in various fields in medical data sciences. [ABSTRACT FROM AUTHOR]

Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are 3-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences in deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in adverse outcome pathway. [ABSTRACT FROM AUTHOR]

The impact of solubility on the toxicity of metal oxide nanoparticles (MONPs) requires further exploration to ascertain the impact of the dissolved and particulate species on response. In this study, FE1 mouse lung epithelial cells were exposed for 2–48 h to 4 MONPs of varying solubility: zinc oxide, nickel oxide, aluminum oxide, and titanium dioxide, in addition to microparticle analogues and metal chloride equivalents. Previously published data from FE1 cells exposed for 2–48 h to copper oxide and copper chloride were examined in the context of exposures in the present study. Viability was assessed using Trypan Blue staining and transcriptomic responses via microarray analysis. Results indicate material solubility is not the sole property governing MONP toxicity. Transcriptional signaling through the 'HIF-1α Signaling' pathway describes the response to hypoxia, which also includes genes associated with processes such as oxidative stress and unfolded protein responses and represents a conserved response across all MONPs tested. The number of differentially expressed genes (DEGs) in this pathway correlated with apical toxicity, and a panel of the top ten ranked DEGs was constructed (Hmox1, Hspa1a, Hspa1b, Mmp10, Adm, Serpine1, Slc2a1, Egln1, Rasd1, Hk2), highlighting mechanistic differences among tested MONPs. The HIF-1α pathway is proposed as a biomarker of MONP exposure and toxicity that can help prioritize MONPs for further evaluation and guide specific testing strategies. [ABSTRACT FROM AUTHOR]

Biological clock technologies are designed to assess the acceleration of biological age (B-age) in diverse cell types, offering a distinctive opportunity in toxicogenomic research to explore the impact of environmental stressors, social challenges, and unhealthy lifestyles on health impairment. These clocks also play a role in identifying factors that can hinder aging and promote a healthy lifestyle. Over the past decade, researchers in epigenetics have developed testing methods that predict the chronological and biological age of organisms. These methods rely on assessing DNA methylation (DNAm) levels at specific CpG sites, RNA levels, and various biomolecules across multiple cell types, tissues, and entire organisms. Commonly known as 'biological clocks' (B-clocks), these estimators hold promise for gaining deeper insights into the pathways contributing to the development of age-related disorders. They also provide a foundation for devising biomedical or social interventions to prevent, reverse, or mitigate these disorders. This review article provides a concise overview of various epigenetic clocks and explores their susceptibility to environmental stressors. [ABSTRACT FROM AUTHOR]

Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research. [ABSTRACT FROM AUTHOR]

Toxicogenomics is a critical area of inquiry for hazard identification and to identify both mechanisms of action and potential markers of exposure to toxic compounds. However, data generated by these experiments are highly dimensional and present challenges to standard statistical approaches, requiring strict correction for multiple comparisons. This stringency often fails to detect meaningful changes to low expression genes and/or eliminate genes with small but consistent changes particularly in tissues where slight changes in expression can have important functional differences, such as brain. Machine learning offers an alternative analytical approach for "omics" data that effectively sidesteps the challenges of analyzing highly dimensional data. Using 3 rat RNA transcriptome sets, we utilized an ensemble machine learning approach to predict developmental exposure to a mixture of organophosphate esters (OPEs) in brain (newborn cortex and day 10 hippocampus) and late gestation placenta of male and female rats, and identified genes that informed predictor performance. OPE exposure had sex specific effects on hippocampal transcriptome, and significantly impacted genes associated with mitochondrial transcriptional regulation and cation transport in females, including voltage-gated potassium and calcium channels and subunits. To establish if this holds for other tissues, RNAseq data from cortex and placenta, both previously published and analyzed via a more traditional pipeline, were reanalyzed with the ensemble machine learning methodology. Significant enrichment for pathways of oxidative phosphorylation and electron transport chain was found, suggesting a transcriptomic signature of OPE exposure impacting mitochondrial metabolism across tissue types and developmental epoch. Here we show how machine learning can complement more traditional analytical approaches to identify vulnerable "signature" pathways disrupted by chemical exposures and biomarkers of exposure. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and is one of the deadliest gastrointestinal malignancies. Despite numerous transcriptomics studies to understand its molecular basis, the impact of population-specific differences on this disease remains unexplored. Aims: This study aimed to investigate the population-specific differences in gene expression patterns among ESCC samples obtained from six distinct global populations, identify differentially expressed genes (DEGs) and their associated pathways, and identify potential biomarkers for ESCC diagnosis and prognosis. In addition, this study deciphers population specific microbial and chemical risk factors in ESCC. Methods: We compared the gene expression patterns of ESCC samples from six different global populations by analyzing microarray datasets. To identify DEGs, we conducted stringent quality control and employed linear modeling. We cross-compared the resulting DEG lists of each populations along with ESCC ATLAS to identify known and novel DEGs. We performed a survival analysis using The Cancer Genome Atlas Program (TCGA) data to identify potential biomarkers for ESCC diagnosis and prognosis among the novel DEGs. Finally, we performed comparative functional enrichment and toxicogenomic analysis. Results: Here we report 19 genes with distinct expression patterns among populations, indicating population-specific variations in ESCC. Additionally, we discovered 166 novel DEGs, such as ENDOU, SLCO1B3, KCNS3, IFI35, among others. The survival analysis identified three novel genes (CHRM3, CREG2, H2AC6) critical for ESCC survival. Notably, our findings showed that ECM-related gene ontology terms and pathways were significantly enriched among the DEGs in ESCC. We also found population-specific variations in immune response and microbial infection-related pathways which included genes enriched for HPV, Ameobiosis, Leishmaniosis, and Human Cytomegaloviruses. Our toxicogenomic analysis identified tobacco smoking as the primary risk factor and cisplatin as the main drug chemical interacting with the maximum number of DEGs across populations. Conclusion: This study provides new insights into population-specific differences in gene expression patterns and their associated pathways in ESCC. Our findings suggest that changes in extracellular matrix (ECM) organization may be crucial to the development and progression of this cancer, and that environmental and genetic factors play important roles in the disease. The novel DEGs identified may serve as potential biomarkers for diagnosis, prognosis and treatment. [ABSTRACT FROM AUTHOR]

In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached. [ABSTRACT FROM AUTHOR]

New approach methods (NAMs) are increasingly important to help accelerate the pace of ecological risk assessment and offer more ethical, affordable, and efficient alternatives to traditional toxicity tests. In the present study, we describe the development, technical characterization, and initial testing of a toxicogenomics tool, EcoToxChip (384‐well quantitative polymerase chain reaction [qPCR] array), to support chemical management and environmental monitoring for three laboratory model species—fathead minnow (Pimephales promelas), African clawed frog (Xenopus laevis), and Japanese quail (Coturnix japonica). Chip design, including gene selection, was informed by a diverse end‐user group and quality control metrics (e.g., primer assay, reverse transcription, and PCR efficiency) performed well based on a priori established criteria. Correlation with RNA sequencing (seq) data provided additional confidence in this novel toxicogenomics tool. Although the present study represents an initial testing of only 24 EcoToxChips for each of the model species, the results provide increased confidence in the robustness/reproducibility of EcoToxChips for evaluating perturbations in gene expression associated with chemical exposure and thus, this NAM, combined with early‐life stage toxicity testing, could augment current efforts for chemical prioritization and environmental management. Environ Toxicol Chem 2023;42:1763–1771. © 2023 SETAC [ABSTRACT FROM AUTHOR]

The evaluation of chemical and pharmaceutical safety for humans is moving from animal studies to New Approach Methodologies (NAM), reducing animal use and focusing on mechanism of action, whilst enhancing human relevance. In developmental toxicology, the mechanistic approach is facilitated by the assessment of predictive biomarkers, which allow mechanistic pathways perturbation monitoring at the basis of human hazard assessment. In our search for biomarkers of maldevelopment, we focused on chemically-induced perturbation of the retinoic acid signaling pathway (RA-SP), a major pathway implicated in a plethora of developmental processes. A genome-wide expression screening was performed on zebrafish embryos treated with two teratogens, all-trans retinoic acid (ATRA) and valproic acid (VPA), and a non-teratogen reference compound, folic acid (FA). Each compound was found to have a specific mRNA expression profile with 248 genes commonly dysregulated by both teratogenic compounds but not by FA. These genes were implicated in several developmental processes (e.g., the circulatory and nervous system). Given the prominent response of neurodevelopmental gene sets, and the crucial need to better understand developmental neurotoxicity, our study then focused on nervous system development. We found 62 genes that are potential early neurodevelopmental toxicity biomarker candidates. These results advance NAM-based safety assessment evaluation by highlighting the usefulness of the RA-SP in providing early toxicity biomarker candidates. • ATRA and VPA both induce perturbation of the retinoic acid signaling pathway. • ATRA and VPA commonly regulate 248 genes not regulated by the negative control FA. • These genes are implicated in diverse developmental processes. • The nervous system development is prominently influenced. • 62 potential neurodevelopmental toxicity gene expression biomarkers were found. [ABSTRACT FROM AUTHOR]

Background & Objectives: Accurate identification of molecular and toxicological functions of potential drug candidates is crucial for drug discovery and development. This may aid in the evaluation of the risks of genotoxicity and carcinogenesis. In addition, in silico characterization of existing and new drugs might offer clues for future investigations and aid in the development of anticancer treatments. Using next-generation knowledge discovery (NGKD) methodology, we endeavored to establish a risk assessment of anticancer drugs for their molecular mechanism(s) and genotoxicity. Methods: This study was performed at the Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia, in November 2022. Using innovative in silico model systems, we assessed the molecular mechanism of action and toxicity of around 20 distinct substances such as Deguelin, Etoposide, Camptothecin, Cytarabine (Ara-C), Cisplatin, Hydroxyurea, Trichostain A, Antimycin, Colchicine, 2-deoxyglucose, Tunicamycin, Thapsigargin, Vinblastin, Docetaxel, Oxaliplatin, Methotrexate, 5-flurouracil, Bleomycin, Taxol (Paclitaxel), and Apicidin. Using the Ingenuity Pathway Analysis (IPA) knowledge base, the number of targets for each compound was determined in silico. Subsequently, they were examined using Fisher's exact test and Benjamini Hochberg Multiple Testing Correction (P<0.05) and submitted to core analysis with IPA to decode the biological and toxicological activities differently controlled by these drugs. In addition, a multiple comparison module in IPA was used to compare the core analyses of each molecule. In addition, we obtained the top 100 protein targets of Etoposide, Camptothecin, and Ara-C using SwissTargetPrediction, as well as the key pathways and gene ontologies affected by these drugs and disease associations using the WebGestalt tool. Results: We identified distinct toxicological signatures and canonical signaling pathways in tumor cell lines regulated by these 20 anticancer drugs. These signaling pathways included cell death and apoptosis in addition to molecular processes, p53 signaling, and aryl hydrocarbon receptor signaling. The TP53 signaling pathway is utilized by these agents to effectively trigger cell death and apoptosis, and p53 functions as a master regulator in a variety of cellular stress responses, including genotoxic stress. Conclusion: Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and effectiveness of treatment. Our mechanism based "NGKD" tools have more relevance for the identification of safer therapies and has the potential to lead to the rational screening of drug candidates targeting specific molecular networks and canonical pathways implicated in cancer and genotoxicity. In addition, the combination of protein, microRNA and metabolome profiles may be essential for the development of translatable biomarkers for the safety and efficacy of pharmacotherapeutic agents. Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and the effectiveness of a treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: The toxicogenomic approach has gained increased attention for understanding key molecular events within organisms to deal with exposure conditions. However, finding relevant biomarkers to predict toxicity still remains a challenging task. In this study, we investigated machine learning applications as a tool to aid toxicity prediction and gene ranking method by training eight different standard supervised machine learning classifiers with qPCR gene expression data. We have shown that the toxicogenomic approach can benefit greatly from machine learning to identify unique expression profiles and predict toxicity, as well as visualization of genes by using Shapley values. Zinc (Zn) is an essential element that influences many cellular functions. Depending on bioavailability, Zn can cause both deficiency and toxicity. Zn bioavailability is influenced by water hardness. Therefore, water quality analysis for health-risk assessment should consider both Zn concentration and water hardness. However, exposure media selection for traditional toxicology tests are set to defined hardness levels and do not represent the diverse water chemistry compositions observed in nature. Moreover, these tests commonly use whole organism endpoints, such as survival and reproduction, which require high numbers of test animals and are labor intensive. Gene expression stands out as a promising alternative to provide insight into molecular events that can be used for risk assessment. In this work, we apply machine learning techniques to classify the Zn concentrations and water hardness from Daphnia magna gene expression by using quantitative PCR. A method for gene ranking was explored using techniques from game theory, namely, Shapley values. The results show that standard machine learning classifiers can classify both Zn concentration and water hardness simultaneously, and that Shapley values are a versatile and useful alternative for gene ranking that can provide insight about the importance of individual genes. [ABSTRACT FROM AUTHOR]

NASA is currently planning return missions to the Moon for further exploration and research. The Moon is covered by a layer of potentially reactive fine dust, which could pose a toxicological risk of exposure to explorers. To assess this risk, we exposed rats to lunar dust (LD) that was collected during the Apollo14 mission. Rats were exposed to respirable sizes of LD at concentrations of 0, 2.1, 6.8, 20.8, or 60.6 mg/m3 for 4 weeks. At thirteen weeks after exposure, we assessed 44,000 gene transcripts and found the expression of 614 genes with known functions were significantly altered in the rats exposed to the 2 higher concentrations of LD, whereas few changes in gene expression were detected in the group exposed to the lowest concentration of LD. Many of the significant changes in gene expression involved genes known to be associated with inflammation or fibrosis. Four genes encoding pro-inflammatory chemokines were analyzed further for all the sampling points at 1 day, and 1, 4, and 13 weeks after the 4-week dust exposure, using real-time polymerase chain reaction. The expression of these genes was altered in a dose- and time-dependent manner and persistently changed in the lungs of the rats exposed to the two higher concentrations of LD. Their expressions are consistent with changes we detected in pulmonary toxicity biomarkers and pathology in these animals during a previous study. Because Apollo-14 LD contains common mineral oxides similar to an Arizona volcanic ash, besides revealing the toxicity of LD, our findings could help elucidate the genomic and molecular mechanisms involved in pulmonary toxicity induced by terrestrial mineral dusts. [ABSTRACT FROM AUTHOR]

AIM: Esophageal Squamous Cell Carcinoma (ESCC) is a histological subtype of esophageal cancer that begins in the squamous cells in the esophagus. In only 19% of the ESCC-diagnosed patients, a five-year survival rate has been seen. This necessitates the identification of high-confidence biomarkers for early diagnosis, prognosis, and potential therapeutic targets for the mitigation of ESCC. METHOD: We performed a meta-analysis of 10 mRNA datasets and identified consistently perturbed genes across the studies. Then, integrated with ESCC ATLAS to segregate 'core' genes to identify consequences of primary gene perturbation events leading to gene-gene interactions and dysregulated molecular signaling pathways. Further, by integrating with toxicogenomics data, inferences were drawn for gene interaction with environmental exposures, trace elements, chemical carcinogens, and drug chemicals. We also deduce the clinical outcomes of candidate genes based on survival analysis using the ESCC related dataset in The Cancer Genome Atlas. RESULT: We identified 237 known and 18 novel perturbed candidate genes. Desmoglein 1 (DSG1) is one such gene that we found significantly downregulated (Fold Change = - 1.89, p -value = 8.2e-06) in ESCC across six different datasets. Further, we identified 31 'core' genes (that either harbor genetic variants or are regulated by epigenetic modifications) and found regulating key biological pathways via adjoining genes in gene-gene interaction networks. Functional enrichment analysis showed dysregulated biological processes and pathways including "Extracellular matrix", "Collagen trimmer" and "HPV infection" are significantly overrepresented in our candidate genes. Based on the toxicogenomic inferences from Comparative Toxicogenomics Database we report the key genes that interacted with risk factors such as tobacco smoking, zinc, nitroso benzylmethylamine, and drug chemicals such as cisplatin, Fluorouracil, and Mitomycin in relation to ESCC. We also point to the STC2 gene that shows a high risk for mortality in ESCC patients. CONCLUSION: We identified novel perturbed genes in relation to ESCC and explored their interaction network. DSG1 is one such gene, its association with microbiota and a clinical presentation seen commonly with ESCC hints that it is a good candidate for early diagnostic marker. Besides, in this study we highlight candidate genes and their molecular connections to risk factors, biological pathways, drug chemicals, and the survival probability of ESCC patients. [ABSTRACT FROM AUTHOR]

Single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) are nanomaterials with one or multiple layers of carbon sheets. While it is suggested that various properties influence their toxicity, the specific mechanisms are not completely known. This study was aimed to determine if single or multi-walled structures and surface functionalization influence pulmonary toxicity and to identify the underlying mechanisms of toxicity. Female C57BL/6J BomTac mice were exposed to a single dose of 6, 18, or 54 μg/mouse of twelve SWCNTs or MWCNTs of different properties. Neutrophil influx and DNA damage were assessed on days 1 and 28 post-exposure. Genome microarrays and various bioinformatics and statistical methods were used to identify the biological processes, pathways and functions altered post-exposure to CNTs. All CNTs were ranked for their potency to induce transcriptional perturbation using benchmark dose modelling. All CNTs induced tissue inflammation. MWCNTs were more genotoxic than SWCNTs. Transcriptomics analysis showed similar responses across CNTs at the pathway level at the high dose, which included the perturbation of inflammatory, cellular stress, metabolism, and DNA damage responses. Of all CNTs, one pristine SWCNT was found to be the most potent and potentially fibrogenic, so it should be prioritized for further toxicity testing. [ABSTRACT FROM AUTHOR]

Objectives: Accurately identifying the cellular, biomolecular, and toxicological functions of anticancer drugs help to decipher the potential risk of genotoxicity and other side effects. Here, we examined bleomycin for cellular, molecular and toxicological mechanisms using next-generation knowledge discovery (NGKD) tools. Methods: This study was conducted at the Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia in October 2022. We first analyzed the raw Toxicogenomic and DNA damage-inducing (TGx-DDI) gene expression data from Gene Expression Omnibus (GEO) (GSE196373) of TK6 cells treated with 10 µM bleomycin and TK6 cells treated with DMSO for four hours using the GEO2R tool based on the Linear Models for Microarray Analysis (limma) R packages to derive the differentially expressed genes (DEGs). Then, iPathwayGuide was used to determine differentially regulated signaling pathways, biological processes, cellular, molecular functions and upstream regulators (genes and miRNAs). Results: Bleomycin differently regulates the p53 pathway, transcriptional dysregulation in cancer, FOXO pathway, viral carcinogenesis, and cancer pathways. The biological processes such as p53 class mediator signaling, intrinsic apoptotic signaling, DNA damage response, and DNA damage-induced intrinsic apoptotic signaling and molecular functions like ubiquitin protein transferase and p53 binding were differentially regulated by bleomycin. iPathwayGuide analysis showed that the p53 and its regulatory gene and microRNA networks induced by bleomycin. Conclusion: Analysis of TGx-DDI data of bleomycin using NGKD tools provided information about toxicogenomics and other mechanisms. Integration of all "omics" based approaches is crucial for the development of translatable biomarkers for evaluating anticancer drugs for safety and efficacy. [ABSTRACT FROM AUTHOR]

Since initial regulatory action in 2010 in Canada, bisphenol A (BPA) has been progressively replaced by structurally related alternative chemicals. Unfortunately, many of these chemicals are data-poor, limiting toxicological risk assessment. We used high-throughput transcriptomics to evaluate potential hazards and compare potencies of BPA and 15 BPA alternative chemicals in cultured breast cancer cells. MCF-7 cells were exposed to BPA and 15 alternative chemicals (0.0005–100 µM) for 48 h. TempO-Seq (BioSpyder Inc) was used to examine global transcriptomic changes and estrogen receptor alpha (ERα)-associated transcriptional changes. Benchmark concentration (BMC) analysis was conducted to identify 2 global transcriptomic points of departure: (1) the lowest pathway median gene BMC and (2) the 25th lowest rank-ordered gene BMC. ERα activation was evaluated using a published transcriptomic biomarker and an ERα-specific transcriptomic point of departure was derived. Genes fitting BMC models were subjected to upstream regulator and canonical pathway analysis in Ingenuity Pathway Analysis. Biomarker analysis identified BPA and 8 alternative chemicals as ERα active. Global and ERα transcriptomic points of departure produced highly similar potency rankings with bisphenol AF as the most potent chemical tested, followed by BPA and bisphenol C. Further, BPA and transcriptionally active alternative chemicals enriched similar gene sets associated with increased cell division and cancer-related processes. These data provide support for future read-across applications of transcriptomic profiling for risk assessment of data-poor chemicals and suggest that several BPA alternative chemicals may cause hazards at similar concentrations to BPA. [ABSTRACT FROM AUTHOR]

Dilated cardiomyopathy (DCM) is characterized by the left ventricular dilatation and impaired myocardial systolic dysfunction with high mortality and morbidity. However, the underlying mechanisms remain elusive. We first identified the differentially expressed genes (DEGs) between the DCM and control group using two expression profiles from GSE3585 and GSE84796. Enrichment analysis was conducted to explore the potential mechanisms underlying DCM. A total of four algorithms, including key module of MCODE, degree, maximum neighborhood component (MNC), and maximal clique centrality (MCC), were used to identify the hub genes within Cytoscape. The correlation between hub genes and infiltrated immune cells was evaluated to determine potential immune-related genes. The expression analysis and diagnosis value analysis of potential immune-related genes were performed. Finally, the expression analysis with GSE57338 and relationship analysis with the comparative toxicogenomics database (CTD) were performed to identify the key immune-related genes in DCM. A total of 80 DEGs were screened for DCM. Enrichment analysis revealed that DEGs were involved in the immune-related pathological process. Immune infiltration analysis indicated a potentially abnormal immune response in DCM. Four up-regulated genes (COL1A2, COL3A1, CD53, and POSTN) were identified as potential immune-related genes. Finally, three genes (COL1A2, COL3A1, and POSTN) were determined as the key immune-related genes in DCM via expression analysis with a validation set (GSE57338) and relationship analysis with CTD. Our study suggested that the upregulated COL1A2, COL3A1, and POSTN might be the key immune-related genes for DCM. Further studies are needed to validate the underlying mechanisms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Use of molecular data in human and ecological health risk assessments of industrial chemicals and agrochemicals has been anticipated by the scientific community for many years; however, these data are rarely used for risk assessment. Here, a logic framework is proposed to explore the feasibility and future development of transcriptomic methods to refine and replace the current apical endpoint-based regulatory toxicity testing paradigm. Four foundational principles are outlined and discussed that would need to be accepted by stakeholders prior to this transformative vision being realized. Well-supported by current knowledge, the first principle is that transcriptomics is a reliable tool for detecting alterations in gene expression that result from endogenous or exogenous influences on the test organism. The second principle states that alterations in gene expression are indicators of adverse or adaptive biological responses to stressors in an organism. Principle 3 is that transcriptomics can be employed to establish a benchmark dose-based point of departure (POD) from short-term, in vivo studies at a dose level below which a concerted molecular change (CMC) is not expected. Finally, Principle 4 states that the use of a transcriptomic POD (set at the CMC dose level) will support a human health-protective risk assessment. If all four principles are substantiated, this vision is expected to transform aspects of the industrial chemical and agrochemical risk assessment process that are focused on establishing safe exposure levels for mammals across numerous toxicological contexts resulting in a significant reduction in animal use while providing equal or greater protection of human health. Importantly, these principles and approaches are also generally applicable for ecological safety assessment. [ABSTRACT FROM AUTHOR]

Butylated hydroxytoluene (BHT) is a synthetic antioxidant widely used in many industrial sectors. BHT is a well-studied compound for which there are many favorable regulatory decisions. However, a recent opinion by the French Agency for Food, Environmental and Occupational Health and Safety (ANSES) hypothesizes a role for BHT in endocrine disruption (ANSES (2021). This opinion is based on observations in mostly rat studies where changes to thyroid physiology are observed. Enzymatic induction of Cytochrome P450-mediated thyroid hormone catabolism has been proposed as a mechanism for these observations, however, a causal relationship has not been proven. Other evidence proposed in the document includes a read across argument to butylated hydroxyanisole (BHA), another Community Rolling Action Plan (CoRAP)-listed substance with endocrine disruption concerns. We tested the hypothesis that BHT is an endocrine disruptor by using a Next Generation Risk Assessment (NGRA) method. Four different cell lines: A549, HCC1428, HepG2, and MCF7 were treated with BHT and a series of BHT analogs at 5 different concentrations, RNA was isolated from cell extracts and run on the L1000 gene array platform. A toxicogenomics-based assessment was performed by comparing BHT's unique genomic signature to a large external database containing signatures of other compounds (including many known endocrine disruptors) to identify if any endocrine disruption-related modes of action (MoAs) are prevalent among BHT and other compounds with similar genomic signatures. In addition, we performed a toxicogenomics-based structure activity relationship (SAR) assessment of BHT and a series of structurally similar analogs to understand if endocrine disruption is a relevant MoA for chemicals that are considered suitable analogs to BHT using the P&G read across framework (Wu et al. , 2010). Neither BHT nor any of its analogs connected to compounds that had endocrine activity for estrogens, androgens, thyroid, or steroidogenesis. [ABSTRACT FROM AUTHOR]

Nanobiotechnology influences many different areas, including the medical, food, energy, clothing, and cosmetics industries. Considering the wide usage of nanomaterials, it is necessary to investigate the toxicity potentials of specific nanosized molecules. Boron-containing nanoparticles (NPs) are attracting much interest from scientists due to their unique physicochemical properties. However, there is limited information concerning the toxicity of boron-containing NPs, including cobalt boride (Co2B) NPs. Therefore, in this study, Co2B NPs were characterized using X-ray crystallography (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM), and energy-dispersive X-ray spectroscopy (EDX) techniques. Then, we performed 3-(4,5-dimethyl-thiazol-2-yl) 2,5-diphenyltetrazolium bromide (MTT), lactate dehydrogenase (LDH) release, and neutral red (NR) assays for assessing cell viability against Co2B NP exposure on cultured human pulmonary alveolar epithelial cells (HPAEpiC). In addition, whole-genome microarray analysis was carried out to reveal the global gene expression differentiation of HPAEpiC cells after Co2B NP application. The cell viability tests unveiled an IC50 value for Co2B NPs of 310.353 mg/L. The results of our microarray analysis displayed 719 gene expression differentiations (FC ≥ 2) among the analyzed 40,000 genes. The performed visualization and integrated discovery (DAVID) analysis revealed that there were interactions between various gene pathways and administration of the NPs. Based on gene ontology biological processes analysis, we found that the P53 signaling pathway, cell cycle, and cancer-affecting genes were mostly affected by the Co2B NPs. In conclusion, we suggested that Co2B NPs would be a safe and effective nanomolecule for industrial applications, particularly for medical purposes. [ABSTRACT FROM AUTHOR]

The animal‐centric approach so far predominantly employed in risk assessment has been questioned in recent years due to a number of shortcomings regarding performance, consistency, transferability of results, sustainability, costs and ethical reasons. Alternatives to animal testing, collectively termed NAMs, may have the potential to deliver sound, cost‐effective, prompt and reliable information, but their regulatory acceptance has not been established yet. The main reasons behind this are mostly related to actual methodological obstacles, with particular reference to addressing complex endpoints such as repeated‐dose toxicity, the issue of translating the concept of adversity to NAMs, and doubts of stakeholders about the level of chemical safety ensured by NAMs. With the aim of providing an updated view on major conceptual and methodological developments in the field of toxicology, a symposium and a workshop were organised by the German Federal Institute for Risk Assessment (Bundesinstitut für Risikobewertung, BfR) and Helmholtz Centre for Environmental Research on 15–17 November 2021 in Berlin. The conference, entitled 'Challenges in Public Health Protection in the 21st Century: New Methods, Omics and Novel Concepts in Toxicology' brought together eminent scientists with representatives from industry and regulatory authorities. The organisation, day‐to‐day operations and the reporting of the event main outcomes in a position paper were the main focus of the present EFSA EU‐FORA work programme. Tasks pertaining to 'The use of NAMs and omics data in risk assessment' were implemented under the shared supervision of units 'Testing and Assessment Strategies Pesticides' and 'Effect‐based Analytics and Toxicogenomics' of the German Federal Institute for Risk Assessment. [ABSTRACT FROM AUTHOR]

The animal‐centric approach so far predominantly employed in risk assessment has been questioned in recent years due to a number of shortcomings regarding performance, consistency, transferability of results, sustainability, costs and ethical reasons. Alternatives to animal testing, collectively termed NAMs, may have the potential to deliver sound, cost‐effective, prompt and reliable information, but their regulatory acceptance has not been established yet. The main reasons behind this are mostly related to actual methodological obstacles, with particular reference to addressing complex endpoints such as repeated‐dose toxicity, the issue of translating the concept of adversity to NAMs, and doubts of stakeholders about the level of chemical safety ensured by NAMs. With the aim of providing an updated view on major conceptual and methodological developments in the field of toxicology, a symposium and a workshop were organised by the German Federal Institute for Risk Assessment (Bundesinstitut für Risikobewertung, BfR) and Helmholtz Centre for Environmental Research on 15–17 November 2021 in Berlin. The conference, entitled 'Challenges in Public Health Protection in the 21st Century: New Methods, Omics and Novel Concepts in Toxicology' brought together eminent scientists with representatives from industry and regulatory authorities. The organisation, day‐to‐day operations and the reporting of the event main outcomes in a position paper were the main focus of the present EFSA EU‐FORA work programme. Tasks pertaining to 'The use of NAMs and omics data in risk assessment' were implemented under the shared supervision of units 'Testing and Assessment Strategies Pesticides' and 'Effect‐based Analytics and Toxicogenomics' of the German Federal Institute for Risk Assessment. [ABSTRACT FROM AUTHOR]

Human-relevant tests to predict developmental toxicity are urgently needed. A currently intensively studied approach makes use of differentiating human stem cells to measure chemically-induced deviations of the normal developmental program, as in a recent study based on cardiac differentiation (UKK2). Here, we (i) tested the performance of an assay modeling neuroepithelial differentiation (UKN1), and (ii) explored the benefit of combining assays (UKN1 and UKK2) that model different germ layers. Substance-induced cytotoxicity and genome-wide expression profiles of 23 teratogens and 16 non-teratogens at human-relevant concentrations were generated and used for statistical classification, resulting in accuracies of the UKN1 assay of 87–90%. A comparison to the UKK2 assay (accuracies of 90–92%) showed, in general, a high congruence in compound classification that may be explained by the fact that there was a high overlap of signaling pathways. Finally, the combination of both assays improved the prediction compared to each test alone, and reached accuracies of 92–95%. Although some compounds were misclassified by the individual tests, we conclude that UKN1 and UKK2 can be used for a reliable detection of teratogens in vitro, and that a combined analysis of tests that differentiate hiPSCs into different germ layers and cell types can even further improve the prediction of developmental toxicants. [ABSTRACT FROM AUTHOR]

To identify pathway perturbations and examine biological modes of action (MOAs) for various perfluoroalkyl substances, we re-analyzed published in vitro gene expression studies from human primary liver spheroids. With treatment times ranging from 10 to 14 days, shorter-chain PFAS (those with 6 or fewer fluorinated carbon atoms in the alkyl chain) showed enrichment for pathways of fatty acid metabolism and fatty acid beta-oxidation with upregulated genes. Longer-chain PFAS compounds, specifically PFOS (perfluorooctane sulfonate), PFDS (perfluorodecane sulfonate), and higher doses of PFOA (perfluorooctanoic acid), had enrichment for pathways involved in steroid metabolism, fatty acid metabolism, and biological oxidation for downregulated genes. Although PFNA (perfluorononanoic acid), PFDA (perfluorodecanoic acid), and PFUnDA (perfluoroundecanoic acid) were more toxic and could only be examined after a 1-day treatment, all three had enrichment patterns similar to those observed with PFOS. With PFOA there were dose-dependent changes in pathway enrichment, shifting from upregulation of fatty acid metabolism and downregulation of steroid metabolism to downregulation of both at higher doses. The response to PFHpS (perfluoroheptanesulfonic acid) was similar to the PFOA pattern at the lower treatment dose. Based on results of transcription factor binding sites analyses, we propose that downregulation of pathways of lipid metabolism by longer chain PFAS may be due to inhibitory interactions of PPARD on genes controlled by PPARA and PPARG. In conclusion, our transcriptomic analysis indicates that the biological MOAs of PFAS compounds differ according to chain length and dose, and that risk assessments for PFAS should consider these differences in biological MOAs when evaluating mixtures of these compounds. • Transcriptomic analysis of human liver cells treated with PFAS • Shorter chain PFAS upregulated pathways of fatty acid metabolism. • Longer chain PFAS downregulated pathways of fatty acid and lipid metabolism • Risk Assessment for PFAS mixtures should consider these chain length dependent MOAs • Wasting in vivo caused by long-chain PFAS correlates with profound downregulation of lipogenesis. [ABSTRACT FROM AUTHOR]

Summary Biological data repositories are an invaluable source of publicly available research evidence. Unfortunately, the lack of convergence of the scientific community on a common metadata annotation strategy has resulted in large amounts of data with low FAIRness (Findable, Accessible, Interoperable and Reusable). The possibility of generating high-quality insights from their integration relies on data curation, which is typically an error-prone process while also being expensive in terms of time and human labour. Here, we present ESPERANTO, an innovative framework that enables a standardized semi-supervised harmonization and integration of toxicogenomics metadata and increases their FAIRness in a Good Laboratory Practice-compliant fashion. The harmonization across metadata is guaranteed with the definition of an ad hoc vocabulary. The tool interface is designed to support the user in metadata harmonization in a user-friendly manner, regardless of the background and the type of expertise. Availability and implementation ESPERANTO and its user manual are freely available for academic purposes at https://github.com/fhaive/esperanto. The input and the results showcased in Supplementary File S1 are available at the same link. [ABSTRACT FROM AUTHOR]

The co-occurrence and accumulation of mycotoxin in food and feed constitutes a major issue to food safety, food security, and public health. Accurate and sensitive mycotoxins analysis can avoid toxin contamination as well as reduce food wastage caused by false positive results. This mini review focuses on the recent advance in detection methods for multiple mycotoxins, which mainly depends on immunoassay technologies. Advance immunoassay technologies integrated in mycotoxin analysis enable simultaneous detection of multiple mycotoxins and enhance the outcomes' quality. It highlights toxicogenomic as novel approach for hazard assessment by utilizing computational methods to map molecular events and biological processes. Indeed, toxicogenomic is a powerful tool to understand health effects from mycotoxin exposure as it offers insight on the mechanisms by which mycotoxins exposures cause diseases. [ABSTRACT FROM AUTHOR]

Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists. [ABSTRACT FROM AUTHOR]

Drug-induced liver injury (DILI) is the main cause of failure in drug development and postapproval withdrawal. Although toxicogenomic techniques provide an unprecedented opportunity for mechanistic assessment and biomarker discovery, they are not suitable for the screening of large numbers of exploratory compounds in early drug discovery. Using a comprehensive analysis of toxicogenomics (TGx) data, we aimed to find DILI-relevant transcription factors (TFs) that could be incorporated into a reporter gene assay system. Gene set enrichment analysis (GSEA) of the Open TG-GATEs dataset highlighted 4 DILI-relevant TFs, including CREB, NRF2, ELK-1, and E2F. Using ten drugs with already assigned idiosyncratic toxicity (IDT) risks, reporter gene assays were conducted in HepG2 cells in the presence of the S9 mix. There were weak correlations between NRF2 activity and IDT risk, whereas strong correlations were observed between CREB activity and IDT risk. In addition, CREB activation associated with 3 Withdrawn/Black box Warning drugs was reversed by pretreatment with a PKA inhibitor. Collectively, we suggest that CREB might be a sensitive biomarker for DILI prediction, and its response to stress induced by high-risk drugs might be primarily regulated by the PKA/CREB signaling pathway. [ABSTRACT FROM AUTHOR]

Interestingly, the results showed that GO toxicity depends on particle size and composition and that UV-treatment of GO increases its toxicity, especially for proteins. These authors performed a three-dimensional high-throughput toxicogenomic-based analysis (exposure time, specific biomarker and expression alteration magnitude) employing a GFP-fused yeast reporter library to rapidly and effectively assess GO toxicity. 33669601 3 Glazer L., Brennan C.H. Developmental Exposure to Low Concentrations of Methylmercury Causes Increase in Anxiety-Related Behaviour and Locomotor Impairments in Zebrafish. Pollution is defined as the presence in or introduction of a substance into the environment that has harmful or poisonous effects. [Extracted from the article]

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making. [ABSTRACT FROM AUTHOR]

Preterm birth occurs disproportionately in the USA non-Hispanic Black population. Black women also face disproportionate exposure to certain environmental chemicals. The goal of this study was to use publicly available toxicogenomic data to identify chemical exposures that may contribute to preterm birth disparities. We tested 19 chemicals observed at higher levels in the blood or urine of non-Hispanic Black women compared to non-Hispanic White women. We obtained chemical-gene interactions from the Comparative Toxicogenomics Database and a list of genes involved in preterm birth from the Preterm Birth Database. We tested chemicals for enrichment with preterm birth genes using chi-squared tests. We then conducted pathway enrichment analysis for the preterm birth genes using DAVID software and identified chemical impacts on genes involved in these pathways. Genes annotated to all 19 chemicals were enriched with preterm birth genes (FDR-adjusted p value < 0.05). Preterm birth enriched chemicals that were detected at the highest levels in non-Hispanic Black women included methyl mercury, methylparaben, propylparaben, diethyl phthalate, dichlorodiphenyldichloroethylene, and bisphenol S. The preterm birth genes were enriched for pathways including "inflammatory response" (FDR-adjusted p value = 3 × 10–19), "aging" (FDR-adjusted p value = 4 × 10–8) and "response to estradiol" (FDR-adjusted p value = 2 × 10–4). Chemicals enriched with preterm birth genes impacted genes in all three pathways. This study adds to the body of knowledge suggesting that exposures to environmental chemicals contribute to racial disparities in preterm birth and that multiple chemicals drive these effects. These chemicals affect genes involved in biological processes relevant to preterm birth such as inflammation, aging, and estradiol pathways. [ABSTRACT FROM AUTHOR]

The co-occurrence and accumulation of mycotoxin in food and feed constitutes a major issue to food safety, food security, and public health. Accurate and sensitive mycotoxins analysis can avoid toxin contamination as well as reduce food wastage caused by false positive results. This mini review focuses on the recent advance in detection methods for multiple mycotoxins, which mainly depends on immunoassay technologies. Advance immunoassay technologies integrated in mycotoxin analysis enable simultaneous detection of multiple mycotoxins and enhance the outcomes' quality. It highlights toxicogenomic as novel approach for hazard assessment by utilizing computational methods to map molecular events and biological processes. Indeed, toxicogenomic is a powerful tool to understand health effects from mycotoxin exposure as it offers insight on the mechanisms by which mycotoxins exposures cause diseases. [ABSTRACT FROM AUTHOR]

The molecular effects of exposures to engineered nanomaterials (ENMs) are still largely unknown. In classical inhalation toxicology, cell composition of bronchoalveolar lavage (BAL) is a toxicity indicator at the lung tissue level that can aid in interpreting pulmonary histological changes. Toxicogenomic approaches help characterize the mechanism of action (MOA) of ENMs by investigating the differentially expressed genes (DEG). However, dissecting which molecular mechanisms and events are directly induced by the exposure is not straightforward. It is now generally accepted that direct effects follow a monotonic dose-dependent pattern. Here, we applied an integrated modeling approach to study the MOA of four ENMs by retrieving the DEGs that also show a dynamic dose-dependent profile (dddtMOA). We further combined the information of the dddtMOA with the dose dependency of four immune cell populations derived from BAL counts. The dddtMOA analysis highlighted the specific adaptation pattern to each ENM. Furthermore, it revealed the distinct effect of the ENM physicochemical properties on the induced immune response. Finally, we report three genes dose-dependent in all the exposures and correlated with immune deregulation in the lung. The characterization of dddtMOA for ENM exposures, both for apical endpoints and molecular responses, can further promote toxicogenomic approaches in a regulatory context. [ABSTRACT FROM AUTHOR]

Target modulation of the AhR for inflammatory gastrointestinal (GI) conditions holds great promise but also the potential for safety liabilities both within and beyond the GI tract. The ubiquitous expression of the AhR across mammalian tissues coupled with its role in diverse signaling pathways makes development of a "clean" AhR therapeutically challenging. Ligand promiscuity and diversity in context-specific AhR activation further complicates targeting the AhR for drug development due to limitations surrounding clinical translatability. Despite these concerns, several approaches to target the AhR have been explored such as small molecules, microbials, PROTACs, and oligonucleotide-based approaches. These various chemical modalities are not without safety liabilities and require unique de-risking strategies to parse out toxicities. Collectively, these programs can benefit from in silico and in vitro methodologies that investigate specific AhR pathway activation and have the potential to implement thresholding parameters to categorize AhR ligands as "high" or "low" risk for sustained AhR activation. Exploration into transcriptomic signatures for AhR safety assessment, incorporation of physiologically-relevant in vitro model systems, and investigation into chronic activation of the AhR by structurally diverse ligands will help address gaps in our understanding regarding AhR-dependent toxicities. Here, we review the role of the AhR within the GI tract, novel therapeutic modality approaches to target the AhR, key AhR-dependent safety liabilities, and relevant strategies that can be implemented to address drug safety concerns. Together, this review discusses the emerging therapeutic landscape of modalities targeting the AhR for inflammatory GI indications and offers a safety roadmap for AhR drug development. [ABSTRACT FROM AUTHOR]

\nImpact StatementThe impact of contaminants on Copepod sp. and its molecular response is least explored, despite their abundance and dominance among invertebrates in aquatic environments. In the present investigation, Dioithona rigida, a cyclopoid zooplankton, was treated with selenium nanoparticles (SeNPs) to highlight the associated biochemical changes, and the chronic exposure effects were recorded using transcriptomic analysis. It was found that, SeNPs were acutely toxic with a lethal dose 50% of 140.9 mg/L. The de novo assembled transcriptome of the copepod comprised of 81,814 transcripts, which underwent subsequent annotations to biological processes (23,378), cellular components (21,414), and molecular functions (31,015). Comparison of the expressed transcripts against the treated sample showed that a total of 186 transcript genes were differentially expressed among the D. rigida treatments (control and SeNPs). The significant downregulated genes are coding for DNA repair, DNA-templated DNA replication, DNA integration, oxidoreductase activity and transmembrane transport. Similarly, significant upregulations were observed in protein phosphatase binding and regulation of membrane repolarization. Understanding the impact of SeNPs on copepods is crucial not only for aquatic ecosystem health but also for human health, as these organisms play a key role in marine food webs, ultimately affecting the fish consumed by humans. By elucidating the molecular responses and potential toxicological effects of SeNPs, explored in this provides key insights on risk assessments and regulatory policies, ensuring the safety of seafood and protecting human health from the unintended consequences of nanoparticle pollution.The toxicity analysis in the Dioithona rigida, is the first of its kind as an animal model for analysis on dietary fixation of metal toxicity at tropic level. Since, this copepod is a major zooplankton fed by fish and crustacean larvae in marine ecosystems, the toxicity analysis on this copepod will give us more insights of the tropic level food transfer. As far as our knowledge, this is the first study that opted to construct the de novo transcriptomic pipeline for this copepod, treated with selenium nanoparticle. The effectiveness of this work may further extrapolate to assess the other metal nanoparticles in this model organism. Although the selenium toxicity in marine ecosystem is an established sector, through our combined approach of biochemical analysis and omics approach, the solid framework and comprehensive insight of the selenium toxicity in reproductive fitness and molecular changes has been studied. This study chose to seek a reliable alternative in the sense of new copepod model and omics approach to analyse the relevant metal nanoparticle toxicity in the marine ecosystem. [ABSTRACT FROM AUTHOR]

Single, high doses of TCDD in rats are known to cause wasting, a progressive loss of 30 to 50% body weight and death within several weeks. To identify pathway perturbations at or near doses causing wasting, we examined differentially gene expression (DGE) and pathway enrichment in centrilobular (CL) and periportal (PP) regions of female rat livers following 6 dose levels of TCDD – 0, 3, 22, 100, 300, and 1000 ng/kg/day, 5 days/week for 4 weeks. At the higher doses, rats lost weight, had increased liver/body weight ratios and nearly complete cessation of liver cell proliferation, signs consistent with wasting. DGE curves were left shifted for the CL versus the PP regions. Canonical Phase I and Phase II genes were maximally increased at lower doses and remained elevated at all doses. At lower doses, ≤ 22 ng/kg/day in the CL and ≤ 100 ng/kg/day, upregulated genes showed transcription factor (TF) enrichment for AHR and ARNT. At the mid- and high-dose doses, there was a large number of downregulated genes and pathway enrichment for DEGs which showed downregulation of many cellular metabolism processes including those for steroids, fatty acid metabolism, pyruvate metabolism and citric acid cycle. There was significant TF enrichment of the hi-dose downregulated genes for RXR, ESR1, LXR, PPARalpha. At the highest dose, there was also pathway enrichment with upregulated genes for extracellular matrix organization, collagen formation, hemostasis and innate immune system. TCDD demonstrates most of its effects through binding the aryl hydrocarbon receptor (AHR) while the downregulation of metabolism genes at higher TCDD doses is known to be independent of AHR binding to DREs. Based on our results with DEG, we provide a hypothesis for wasting in which high doses of TCDD shift circadian processes away from the resting state, leading to greatly reduced synthesis of steroids and complex lipids needed for cell growth, and producing gene expression signals consistent with an epithelial-to-mesenchymal transition in hepatocytes. • In life studies in female rats with TCDD produce wasting-like responses within 4 weeks • Low doses upregulate genes regulated by AHR and ARNT; higher doses downregulate genes for cellular metabolism. • Downregulated genes at high doses are enriched for TF-binding sites for RXR, LXR, PPARA and ESR1. • Very high doses downregulated metabolic processes and upregulated those for extracellular matrix. • Wasting is most likely associated with sequestration of AHR and ARNT that removes them from roles in circadian cycling. [ABSTRACT FROM AUTHOR]