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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 present work, the genotoxicity of Hg2+, Ag+, Cr6+, Ni2+, Pb2+, Co2+, Mn2+, Zn2+, and Cr3+ was investigated via a quantitative toxicogenomics assay, to understand the toxic mechanism of heavy metals with greater depth. Under the experimental conditions, Hg2+, Ag+, and Cr6+ showed a more serious toxic impact on the expression of functional genes (eg., oxyR, katG, grxA, osmE, emrE, dinG) than Ni2+, Pb2+, Co2+, Mn2+, Zn2+, and Cr3+, while the protein, oxidative, and membrane stress response pathways were more sensitive to the toxicity of Hg2+, Ag+, and Cr6+ than the DNA and general stress response pathways. Compared with the other kinds of heavy metals, Ni2+, Pb2+, Co2+, and Mn2+ altered the expression of functional genes (uvrY, recX, mutY, and sbmC) related to the DNA stress response pathways more seriously, while Zn2+ and Cr3+ changed the expression of the functional genes (yfjG, ydgL, ssrA, and osmC) associated with the general stress response pathway more significantly. Meanwhile, the toxicity of Ni2+, Pb2+, Co2+, and Mn2+ was slightly higher than that of Zn2+ and Cr3+ in terms of the total value of transcriptional effect level Index (TELI) by detecting the promoter activities of different functional genes. In addition, to survive the toxicity of heavy metals, the expression of multidrug efflux genes (ydgL, cyoA, emrA, and emrE) and toxicity-resistant genes (Ion, dnaJ, clpB, mutY, dnaK, rpoD, sbmC) mainly functioned. [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]

Dietary supplements containing usnic acid have been increasingly marketed for weight loss over the past decades, even though incidences of severe hepatotoxicity and acute liver failure due to their overuse have been reported. To date, the toxic mechanism of usnic acid‐induced liver injury at the molecular level still remains to be fully elucidated. Here, we conducted a transcriptomic study on usnic acid using a novel in vitro hepatotoxicity model employing human induced pluripotent stem cell (iPSC)‐derived hepatocytes. Treatment with 20 μM usnic acid for 24 h caused 4272 differentially expressed genes (DEGs) in the cells. Ingenuity Pathway Analysis (IPA) based on the DEGs and gene set enrichment analysis (GSEA) using the whole transcriptome expression data concordantly revealed several signaling pathways and biological processes that, when taken together, suggest that usnic acid caused oxidative stress and DNA damage in the cells, which further led to cell cycle arrest and eventually resulted in cell death through apoptosis. These transcriptomic findings were subsequently corroborated by a variety of cellular assays, including reactive oxygen species (ROS) generation and glutathione (GSH) depletion, DNA damage (pH2AX detection and 8‐hydroxy‐2′‐deoxyguanosine [8‐OH‐dg] assay), cell cycle analysis, and caspase 3/7 activity. Collectively, the results of the current study accord with previous in vivo and in vitro findings, provide further evidence that oxidative stress‐caused DNA damage contributes to usnic acid‐induced hepatotoxicity, shed new light on molecular mechanisms of usnic acid‐induced hepatotoxicity, and demonstrate the usefulness of iPSC‐derived hepatocytes as an in vitro model for hepatotoxicity testing and prediction. Treatment with 20 μM usnic acid for 24 h caused 4272 differentially expressed genes (DEGs) in human iPSC‐derived hepatocytes. Functional analysis of the transcriptomic data revealed signaling pathways and biological processes suggesting that usnic acid caused cellular oxidative stress and DNA damage, which further led to cell cycle arrest and eventually resulted in apoptosis. Results of a variety of cellular assays corroborated these transcriptomic findings, including ROS generation, GSH depletion, DNA damage, cell cycle arrest, and increased caspase 3/7 activity. Treatment with 20 μM usnic acid for 24 h caused 4272 differentially expressed genes (DEGs) in human iPSC‐derived hepatocytes. Functional analysis of the transcriptomic data revealed signaling pathways and biological processes suggesting that usnic acid caused cellular oxidative stress and DNA damage, which further led to cell cycle arrest and eventually resulted in apoptosis. Results of a variety of cellular assays corroborated these transcriptomic findings, including ROS generation, GSH depletion, DNA damage, cell cycle arrest, and increased caspase 3/7 activity. [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]

Di(ethylhexyl) phthalate (DEHP) is a ubiquitous environmental contaminant to which humans are exposed via multiple routes. Human health risk assessments for this substance have recently been updated, focusing on reproductive toxicity, including DEHP, in the list of chemicals classified as carcinogenic, mutagenic, or toxic to reproduction (CMR). Moreover, DEHP has also been defined as probably and possibly carcinogenic to humans based on its carcinogenicity in rodents. However, the mechanism of action of DEHP and its relevance in humans remain unclear. Rodent data suggests that DEHP induces cancer through non-genotoxic mechanisms related to multiple molecular signals, including PPARa activation, perturbation of fatty acid metabolism, induction of cell proliferation, decreased apoptosis, production of reactive oxygen species, and oxidative stress. According to the DEHP toxicological dataset, several in vitro cell transformation assays have been performed using different protocols and cellular models to produce different results. This study aimed to evaluate the carcinogenic potential of DEHP by using the A31-1-1 BALB/c-3T3 cell line in a standard cell transformation assay. Additionally, transcriptomic analysis was performed to explore the molecular responses and identify the affected toxicological pathways. Although DEHP treatment did not induce transformation in BALB/c-3T3 cells, the transcriptomic results revealed significant modulation of several pathways associated with DEHP metabolism, tissue-specific functions related to systemic metabolism, and basal cellular signaling with pleiotropic outcomes. Among these signaling pathways, modulation of cell-regulating signaling pathways, such as Notch, Wnt, and TGF-ß, can be highlighted. More specific modulation of such genes and pathways with double functions in metabolism and neurophysiology underlies the well-known crosstalk that may be crucial for the mechanism of action of DEHP. Our findings offer evidence to support the notion that these models are effective in minimizing the use of animal testing for toxicity assessment. [ABSTRACT FROM AUTHOR]

In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as "CGPD-tetramers," wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum. [ABSTRACT FROM AUTHOR]

Abstract 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.

Immune signalling is a crucial component in the progression of fibrosis. However, approaches for the safety assessment of potentially profibrotic substances, that provide information on mechanistic immune responses, are underdeveloped. This study aimed to develop a novel framework for assessing the immunotoxicity of fibrotic compounds. We exposed macrophages in vitro to multiple sublethal concentrations of the profibrotic agent bleomycin, over multiple timepoints, and generated RNA sequencing data. Using a toxicogenomic approach, we performed dose-dependent analysis to discover genes dysregulated by bleomycin exposure in a dose-responsive manner. A subset of immune genes displayed a sustained dose-dependent and differential expression response to profibrotic challenge. An immunoassay revealed cytokines and proteinases responding to bleomycin exposure that closely correlated to transcriptomic alterations, underscoring the integration between transcriptional immune response and external immune signalling activity. This study not only increases our understanding of the immunological mechanisms of fibrosis, but also offers an innovative framework for the toxicological evaluation of substances with potential fibrogenic effects on macrophage signalling. Our work brings a new immunotoxicogenomic direction for hazard assessment of fibrotic compounds, through the implementation of a time and resource efficient in vitro methodology.

With the advent of new alternative methods for rapid toxicity screening of chemicals comes the need for new statistical methodologies which appropriately synthesize the large amount of data collected. For example, transcriptomic assays can be used to assess the impact of a chemical on thousands of genes, but current approaches to analyzing the data treat each gene separately and do not allow sharing of information among genes within pathways. Furthermore, the methods employed are fully parametric and do not account for changes in distribution shape that may occur at high exposure levels. To address the limitations of these methods, we propose Constrained Logistic Density Regression (COLDER) to model expression data from different genes simultaneously. Under COLDER, the dose‐response function for each gene is assigned a prior via a discrete logistic stick‐breaking process (LSBP) whose weights depend on gene‐level characteristics (e.g., pathway membership) and atoms consist of different dose‐response functions subject to a shape constraint that ensures biological plausibility. The posterior distribution for the benchmark dose among genes within the same pathways can be estimated directly from the model, which is another advantage over current methods. The ability of COLDER to predict gene‐level dose‐response is evaluated in a simulation study and the method is illustrated with data from a National Toxicology Program study of Aflatoxin B1. [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 key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene setbased method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets. [ABSTRACT FROM AUTHOR]

In this study, the toxicogenomic effects of five cytostatics (tamoxifen, methotrexate, capecitabine, cyclophosphamide, and ifosfamide) on fathead minnow (Pimephales promelas) larvae were evaluated. Post-fertilization eggs were exposed to increasing concentrations of the drugs for six days. The expression levels of two genetic biomarkers for toxicity and four thyroid hormone-related gene pathways were measured. Interestingly, the results showed that all concentrations of the five cytostatics affect the transcription levels of both toxicity biomarker genes. Additionally, the thyroid hormone-related genes had different expression levels than the control, with the most significant changes observed in those larvae exposed to cyclophosphamide and ifosfamide. While a previous study found no effects on fish morphology, this study suggests that the five cytostatics modify subtle molecular responses of P. promelas, highlighting the importance of assessing multibiological level endpoints throughout the lifecycle of animals to understand the full portrait of potential effects of cytostatics and other contaminants. [ABSTRACT FROM AUTHOR]

Abstract 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.

SCIENTIFIC RELEVANCE. The Tox21 (Toxicology in the 21st Century) programme was developed by the US Tox21 Consortium with the aim to replace animal-based toxicity assessments of chemicals with a wide range of in vitro and in silico testing approaches and has since been successfully applied in practice.AIM. The study aimed to review information on alternative in vitro models developed as part of the Tox21 programme for testing the toxicity of chemical compounds.DISCUSSION. According to the information provided by the National Toxicology Program, Environmental Protection Agency, National Center for Advancing Translational Sciences, and other Tox21 Consortium members on their official websites and in the literature, the Tox21 Consortium has developed a quantitative high-throughput screening technology for testing the safety of chemicals and created the Tox21 10K library of chemical compounds using this screening technology. The library has been successfully used to create models that predict the toxicity of chemicals prior to preclinical studies. Researchers have proposed new approaches to studying the safety of chemical compounds in human cell lines to replace in vivo studies. Innovative organ-on-chip, multi-organ-on-chip, and organoid models are free from the drawbacks and limitations of cell-line models and offer more accurate representations of complex cell–matrix and organ–organ interactions. Developed under the Tox21 programme to search for new chemical toxicity biomarkers and gene signatures, novel transcriptomics (toxicogenomics) technologies can be used to classify toxicants according to their health risks and to identify potential side effects long before discovering any pathological changes in the body. The Interagency Coordinating Committee on the Validation of Alternative Methods conducts technical evaluation of alternative testing methods and promotes their implementation into regulatory practice.CONCLUSIONS. Thus, new tools and technologies provide an opportunity for switching from in vivo toxicity testing of candidate medicinal products to in silico and in vitro methods.

The 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.

Compounds of natural or synthetic origin present in personal care products, food additives, and packaging may interfere with hormonal regulation and are called endocrine-disrupting chemicals (EDCs). The thyroid gland is an important target of these compounds. The objective of this study was to analyze public data on the human thyroid transcriptome and investigate potential new targets of EDCs in the embryonic and adult thyroid glands. We compared the public transcriptome data of adult and embryonic human thyroid glands and selected 100 up- or downregulated genes that were subsequently subjected to functional enrichment analysis. In the embryonic thyroid, the most highly expressed gene was PRMT6, which methylates arginine-4 of histone H2A (86.21%), and the downregulated clusters included plasma lipoprotein particles (39.24%) and endopeptidase inhibitory activity (24.05%). For the adult thyroid gland, the most highly expressed genes were related to the following categories: metallothionein-binding metals (56.67%), steroid hormone biosynthetic process (16.67%), and cellular response to vascular endothelial growth factor stimulus (6.67%). Several compounds ranging from antihypertensive drugs to enzyme inhibitors were identified as potentially harmful to thyroid gland development and adult function. [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]

Di(ethylhexyl) phthalate (DEHP) is a ubiquitous environmental contaminant to which humans are exposed via multiple routes. Human health risk assessments for this substance have recently been updated, focusing on reproductive toxicity, including DEHP, in the list of chemicals classified as carcinogenic, mutagenic, or toxic to reproduction (CMR). Moreover, DEHP has also been defined as probably and possibly carcinogenic to humans based on its carcinogenicity in rodents. However, the mechanism of action of DEHP and its relevance in humans remain unclear. Rodent data suggests that DEHP induces cancer through non-genotoxic mechanisms related to multiple molecular signals, including PPARα activation, perturbation of fatty acid metabolism, induction of cell proliferation, decreased apoptosis, production of reactive oxygen species, and oxidative stress. According to the DEHP toxicological dataset, several in vitro cell transformation assays have been performed using different protocols and cellular models to produce different results. This study aimed to evaluate the carcinogenic potential of DEHP by using the A31-1-1 BALB/c-3T3 cell line in a standard cell transformation assay. Additionally, transcriptomic analysis was performed to explore the molecular responses and identify the affected toxicological pathways. Although DEHP treatment did not induce transformation in BALB/c-3T3 cells, the transcriptomic results revealed significant modulation of several pathways associated with DEHP metabolism, tissue-specific functions related to systemic metabolism, and basal cellular signaling with pleiotropic outcomes. Among these signaling pathways, modulation of cell-regulating signaling pathways, such as Notch, Wnt, and TGF-β, can be highlighted. More specific modulation of such genes and pathways with double functions in metabolism and neurophysiology underlies the well-known crosstalk that may be crucial for the mechanism of action of DEHP. Our findings offer evidence to support the notion that these models are effective in minimizing the use of animal testing for toxicity assessment.

The impact of emerging pollutants such as ibuprofen and dibutyl phthalate on aquatic species is a growing concern and the need for proper assessment and evaluation of these toxicants is imperative. The objective of this study was to examine the toxicogenomic impacts of ibuprofen and dibutyl phthalate on Clarias gariepinus, a widely distributed African catfish species. Results showed that exposure to the test compounds caused significant changes in gene expression, including upregulation of growth hormone, interleukin, melatonin receptors, 17β-Hydroxysteroid Dehydrogenase, heat shock protein, doublesex, and mab-3 related transcription factor. On the other hand, expression of forkhead Box Protein L2 and cytochrome P450 was downregulated, revealing a potential to induce female to male sex reversal. The binding affinities and hydrophobic interactions of the test compounds with the reference genes were also studied, showing that ibuprofen had the lowest binding energy and the highest affinity for the docked genes. Both compounds revealed a mutual molecular interaction with amino acids residues within the catalytic cavity of the docked genes. These results provide new insights into the toxic effects of ibuprofen and dibutyl phthalate on Clarias gariepinus, contributing to a better understanding of the environmental impact of these pollutants.

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3–1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

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]

Per- and poly-fluoroalkyl substances (PFAS) are a large class of fluorinated carbon chains that include legacy PFAS, such as perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS). These compounds induce adverse health effects, including hepatotoxicity. Potential alternatives to the legacy PFAS (HFPO-DA (GenX), HFPO4, HFPO-TA, F-53B, 6:2 FTSA, and 6:2 FTCA), as well as a byproduct of PFAS manufacturing (Nafion BP2), are increasingly being found in the environment. The potential hazards of these new alternatives are less well known. To better understand the diversity of molecular targets of the PFAS, we performed a comparative toxicogenomics analysis of the gene expression changes in the livers of mice exposed to these PFAS, and compared these to five activators of PPARα, a common target of many PFAS. Using hierarchical clustering, pathway analysis, and predictive biomarkers, we found that most of the alternative PFAS modulate molecular targets that overlap with legacy PFAS. Only three of the 11 PFAS tested did not appreciably activate PPARα (Nafion BP2, 6:2 FTSA, and 6:2 FTCA). Predictive biomarkers showed that most PFAS (PFHxS, PFOA, PFOS, PFNA, HFPO-TA, F-53B, HFPO4, Nafion BP2) activated CAR. PFNA, PFHxS, PFOA, PFOS, HFPO4, HFPO-TA, F-53B, Nafion BP2, and 6:2 FTSA suppressed STAT5b, activated NRF2, and activated SREBP. There was no apparent relationship between the length of the carbon chain, type of head group, or number of ether linkages and the transcriptomic changes. This work highlights the similarities in molecular targets between the legacy and alternative PFAS. [ABSTRACT FROM AUTHOR]

Halobenzoquinones (HBQs) were recently discovered as an emerging class of drinking water disinfection byproducts with carcinogenic concern. However, the molecular mechanism underlying HBQs-induced DNA damage is not clear. In this study, we integrated in vitro genotoxicity, computational toxicology, and the quantitative toxicogenomic analysis of HBQs on DNA damage/repair pathways in human bladder epithelial cells SV-HUC-1. The results showed that HBQs could induce cytotoxicity with the descending order as 2,6-DIBQ > 2,6-DCBQ ≈ 2,6-DBBQ. Also, HBQs can increase DNA damage in SV-HUC-1 cells and thus generate genotoxicity. However, there is no significant difference in genotoxicity among the three HBQs. The results of molecular docking and molecular dynamics simulation further confirmed that HBQs had high binding fractions and stability to DNA. Toxicogenomic analysis indicated that HBQs interfered with DNA repair pathways, mainly affecting base excision repair, nucleotide excision repair and homologous recombination repair. These results have provided new insights into the underlying molecular mechanisms of HBQs-induced DNA damage, and contributed to the understanding of the relationship between exposure to DBPs and risks of developing bladder cancer.

Flupyradifurone (FPF), a novel butenolide insecticide binding to nicotinic acetylcholine receptors (nAChRs), has been shown to be less acutely toxic to western honey bees (Apis mellifera) than other insecticides such as neonicotinoids sharing the same target-site. A previous study revealed that this is due to enhanced oxidative metabolism of FPF, mediated by three cytochrome P450 monooxygenases (P450s), including CYP6AQ1. Therefore, we followed a toxicogenomics approach and investigated the potential role of functional CYP6AQ1 orthologs in FPF metabolism from eight different bee species, including stingless bees (Tribe: Meliponini). We conducted a phylogenetic analysis on four stingless bee species, including Frieseomelitta varia, Heterotrigona itama, Melipona quadrifasciata and Tetragonula carbonaria to identify CYP6AQ1-like functional orthologs. Three non-Meliponini, but tropical bee species, i.e., Ammobates syriacus, Euglossa dilemma and Megalopta genalis were analyzed as well. We identified candidate P450s in all (neo)tropical species with greater than 61% and 67% predicted protein sequence identities when compared to A. mellifera CYP6AQ1 and Bombus terrestris CYP6AQ26, respectively. Heterologous expression in High Five insect cells of these functional orthologs revealed a common coumarin substrate profile and a preference for the O-debenzylation of bulkier substrates. Competition assays using the fluorescent probe substrate 7-benzyloxymethoxy-4-trifluoromethylcoumarin (BOMFC) with these enzymes indicated inhibition of BOMFC metabolism by increasing concentrations of FPF. Furthermore, UPLC-MS/MS analysis revealed the capacity of all CYP6AQ1-like orthologs to metabolize FPF by hydroxylation in vitro at various levels, indicating a conserved FPF detoxification potential in different (neo)tropical bee species including Meliponini. This research, employing a toxicogenomics approach, provides important insights into the potential of stingless and other tropical bee species to detoxify FPF, and highlights the significance of investigating the detoxification mechanisms of insecticides in non-Apis bee species by molecular tools to inform risk assessment and conservation efforts.

New approach methodologies (NAM), including omics and in vitro approaches, are contributing to the implementation of 3R (reduction, refinement and replacement) strategies in regulatory science and risk assessment. In this study, we present an integrative transcriptomics and proteomics analysis workflow for the validation and revision of complex fish genomes and demonstrate how proteogenomics expression matrices can be used to support multi-level omics data integration in non-model species in vivo and in vitro. Using Atlantic salmon as an example, we constructed proteogenomic databases from publicly available transcriptomic data and in-house generated RNA-Seq and LC-MS/MS data. Our analysis identified ∼80,000 peptides, providing direct evidence of translation for over 40,000 RefSeq structures. The data also highlighted 183 co-located peptide groups that supported a single transcript each, and in each case, either corrected a previous annotation, supported Ensembl annotations not present in RefSeq, or identified novel previously unannotated genes. Proteogenomics data-derived expression matrices revealed distinct profiles for the different tissue types analyzed. Focusing on proteins involved in defense against xenobiotics, we detected distinct expression patterns across different salmon tissues and observed homology in the expression of chemical defense proteins between in vivo and in vitro liver systems. Our study demonstrates the potential of proteogenomic analyses in extending our understanding of complex fish genomes and provides an advanced bioinformatic toolkit to support the further development of NAMs and their application in regulatory science and (eco)toxicological studies of non-model species.

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

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]

Compounds of natural or synthetic origin present in personal care products, food additives, and packaging may interfere with hormonal regulation and are called endocrine-disrupting chemicals (EDCs). The thyroid gland is an important target of these compounds. The objective of this study was to analyze public data on the human thyroid transcriptome and investigate potential new targets of EDCs in the embryonic and adult thyroid glands. We compared the public transcriptome data of adult and embryonic human thyroid glands and selected 100 up- or downregulated genes that were subsequently subjected to functional enrichment analysis. In the embryonic thyroid, the most highly expressed gene was PRMT6, which methylates arginine-4 of histone H2A (86.21%), and the downregulated clusters included plasma lipoprotein particles (39.24%) and endopeptidase inhibitory activity (24.05%). For the adult thyroid gland, the most highly expressed genes were related to the following categories: metallothionein-binding metals (56.67%), steroid hormone biosynthetic process (16.67%), and cellular response to vascular endothelial growth factor stimulus (6.67%). Several compounds ranging from antihypertensive drugs to enzyme inhibitors were identified as potentially harmful to thyroid gland development and adult function.

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]

Adverse outcome pathways (AOPs) are emerging as a central framework in modern toxicology and other fields in biomedicine. They serve as an extension of pathway-based concepts by depicting biological mechanisms as causally linked sequences of key events (KEs) from a molecular initiating event (MIE) to an adverse outcome. AOPs guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation. While AOPs model the systemic mechanisms at various levels of biological organisation, toxicogenomics provides the means to study the molecular mechanisms of chemical exposures. Systematic integration of these two concepts would improve the application of AOP-based knowledge while also supporting the interpretation of complex omics data. Hence, we established this link through rigorous curation of molecular annotations for the KEs of human relevant AOPs. We further expanded and consolidated the annotations of the biological context of KEs. These curated annotations pave the way to embed AOPs in molecular data interpretation, facilitating the emergence of new knowledge in biomedicine. [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]

Background: Identification of molecular signatures from omics studies is widely applied in toxicological studies, and the evaluation of potential toxic effects provides novel insights into molecular resolution. Objective: The prediction of toxic effects and drug tolerance provides important clues regarding the mode of action of target compounds. However, heterogeneity within samples makes toxicology studies challenging because the purity of the target cell in the samples remains unknown until their actual utilization. Result: Single-cell resolution studies have been suggested in toxicogenomics, and several studies have explained toxic effects and drug tolerance using heterogeneous cells in both in vivo and in vitro conditions. In this review, we presented an understanding of single-cell transcriptomes and their applications in toxicogenomics. Conclusion: The most toxicological mechanism in organisms occurs through intramolecular combinations, and heterogeneity issues have reached a surmountable level. We hope this review provides insights to successfully conduct future studies on toxicology. Purpose of the review: Toxicogenomics is an interdisciplinary field between toxicology and genomics that was successfully applied to construct molecular profiles in a broad spectrum of toxicology. However, heterogeneity within samples makes toxicology studies challenging because the purity of target cell in the samples remains unknown until their actual utilisation. In this review, we presented an understanding of single-cell transcriptomes and their applications in toxicogenomics. Recent findings: A high-throughput techniques have been used to understand cellular heterogeneity and molecular mechanisms at toxicogenomics. Single-cell resolution analysis is required to identify biomarkers of explain toxic effect and in order to understand drug tolerance. [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]