Toxicogenomics in Risk Assessment: Applications and Needs

Thyroid under siege: Unravelling the toxic impact of real-life metal mixture exposures in Wistar rats

This study explored the effect of a toxic metal(oid) mixture (cadmium, lead, arsenic, mercury, chromium, and nickel) on thyroid function in Wistar rats exposed for 28 or 90 days. Dose levels were determined based on prior human-biomonitoring investigation. The experiment included control (male/female rats, 28 and 90 days) and treated groups, reflecting the lower confidence limit of the Benchmark Dose (BMDL) for hormone levels (M1/F1, 28 and 90 days), median concentrations (M2/F2, 28 and 90 days), 95th percentile concentrations (M3/F3, 28 and 90 days) measured in a human study, and reference values for individual metals extracted from the literature (M4/F4, 28 days only). Blood and thyroid gland samples were collected at the experimental termination. Serum TSH, fT3, fT4, T3, and T4 levels were measured, and SPINA-GT and SPINA-GD parameters were calculated. In silico analysis, employing the Comparative Toxicogenomic Database and ToppGene Suite portal, aimed to reveal molecular mechanisms underlying the observed effects. Results showed greater sensitivity in the female rats, with significant effects observed at lower doses. Subacute exposure increased TSH, fT3, and T3 levels in females, while subchronic exposure in males decreased TSH and fT3 levels and increased fT4. Subacute exposure induced changes even at allegedly safe doses, emphasizing potential health risks. Histological abnormalities were observed in all the treated groups. In silico findings suggested that toxic metal exposure contributes to thyroid disorders via oxidative stress, disruption of micronutrients, interference with hormone synthesis, and gene expression dysregulation. These results indicate that seemingly safe doses in single-substance research can adversely affect thyroid structure and function when administered as a mixture. These findings highlight the complex impact of toxic metal exposure on thyroid health, emphasizing that adhering to accepted safety limits for single-substance research fails to account for adverse effects on thyroid structure and function upon exposures to metal mixtures.

Endocrine disruptors in e-waste dismantling dust: In silico prediction of mixture-induced reproductive toxicity mechanisms

The constant exposure of humans to a mixture of low doses of toxic substances, emerging from the daily emission of toxic dust containing various metals and organic compounds in electrical and electronic waste (e-waste) recycling areas, poses potential harmful effects on health and the environment. While individually recognized as endocrine disruptors affecting hormonal balance, the combined impact of these toxic substances in a mixture remains insufficiently explored, particularly in relation to reproductive health. Thus, the aim of this in silico analysis was to: (i) assess the relationship between the exposure to a mixture of DBDE, DBDPE, TBBPA, Pb, Cd and Ni and development of male and female reproductive system disorders; and (ii) demonstrate the ability of in silico toxicogenomic tools in revealing the potential molecular mechanisms involved in the mixture toxicity. As the main data-mining tool, Comparative Toxicogenomics Database (CTD) was used, along with the ToppGene Suite portal and GeneMANIA online server. Our analysis identified 5 genes common to all the investigated substances and linked to reproductive system disorders. Notably, the most prominent interactions among these genes were physical interactions (77.64 %). Pathway enrichment analysis identified oxidative stress response as the central disrupted molecular pathway linked to reproductive pathology in the investigated mixture, while our chemical-phenotype CTD analysis uncovered additional affected pathways - apoptosis, hormonal regulation, and developmental functions. These findings highlight an increased risk of reproductive system disorders associated with the exposure to the investigated mixture of toxic substances in electronic waste recycling areas, emphasizing the urgent need for attention to address this environmental health concern. Hence, future laboratory studies should prioritize investigating the specific genes and common mechanisms identified in this study.

Abnormality of mussel in the early developmental stages induced by graphene and triphenyl phosphate: In silico toxicogenomic data-mining, in vivo, and toxicity pathway-oriented approach

Increasing number of complex mixtures of organic pollutants in coastal area (especially for nanomaterials and micro/nanoplastics associated chemicals) threaten aquatic ecosystems and their joint hazards are complex and demanding tasks. Mussels are the most sensitive marine faunal groups in the world, and their early developmental stages (embryo and larvae) are particularly susceptible to environmental contaminants, which can distinguish the probable mechanisms of mixture-induced growth toxicity. In this study, the potential critical target and biological processes affected by graphene and triphenyl phosphate (TPP) were developed by mining public toxicogenomic data. And their combined toxic effects were verified by toxicological assay at early developmental stages in filter-feeding mussels (embryo and larvae). It showed that interactions among graphene/TPP with 111 genes (ABCB1, TP53, SOD, CAT, HSP, etc.) affected phenotypes along conceptual framework linking these chemicals to developmental abnormality endpoints. The PPAR signaling pathway, monocarboxylic acid metabolic process, regulation of lipid metabolic process, response to oxidative stress, and gonad development were noted as the key molecular pathways that contributed to the developmental abnormality. Enriched phenotype analysis revealed biological processes (cell proliferation, cell apoptosis, inflammatory response, response to oxidative stress, and lipid metabolism) affected by the investigated mixture. Combined, our results supported that adverse effects induced by contaminants/ mixture could not only be mediated by single receptor signaling or be predicted by the simple additive effect of contaminants. The results offer a framework for better comprehending the developmental toxicity of environmental contaminants in mussels and other invertebrate species, which have considerable potential for hazard assessment of coastal mixture.

Testing sulforaphane as a strategy against toxic chemicals of public health concern by toxicogenomic data analysis: Friend or foe at the gene level - Colorectal carcinoma case study

Toxic metals (cadmium (Cd), lead (Pb), mercury (Hg) and arsenic (As)) and plastificators (bis (2 - ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP)) and bisphenol A (BPA)) have been suggested to aid in colorectal carcinoma (CRC) advancement. Sulforaphane (SFN), isothiocyanate from cruciferous vegetables, diminishes chemical carcinogenesis susceptibility, but has been shown to act as a friend or a foe depending on various factors. By conducting the mechanistic toxicogenomic data mining approach, this research aimed to determine if SFN can alleviate toxic-metal and/or phthalate/BPA mixture-induced CRC at the gene level. Comparative Toxicogenomics Database, ToppGene Suite portal, Cytoscape software, InteractiVenn and Gene Expression Omnibus (GEO) database (GEO2R tool) was used. Among the mutual genes for all the investigated substances, SFN had a protective impact only through PTGS2. Other proposed protective SFN-targets included ABCA1, ALDH2, BMP2, DPYD, MYC, SLCO2A1, and SOD2, only in the case of phthalates/BPA exposure. The only additional gene relevant for SFN protection against the toxic metal mixture-induced CRC was ABCB1. Additionally, the majority of the top 15 molecular pathways extracted for SFN impact on phthalate and BPA mixture-linked CRC development were directly linked with cancer development, which was not the case with the toxic metal mixture. The current research has indicated that SFN is a more effective chemoprotective agent against CRC induced by phthalates/BPA mixture than by toxic-metal mixture. It has also presented the value of computational methods as a simple tool for directing further research, selecting appropriate biomarkers and exploring the mechanisms of toxicity.

Toxicogenomic analysis of physiologically important metals: An integrated in silico approach

Biologically important metals regulate cellular homeostasis in living systems. Anthropogenic exposure to these metals can cause adverse effects, including an increased incidence of diseases in humans such as cancer, lung, and cardiovascular defects. However, the effects of metals and the common genes/signaling pathways involved in metal toxicity have not been elucidated. Hence, the present study used toxicogenomic data mining with the comparative toxicogenomics database to explore the impact of these metals. The metals were categorized into transition, alkali, and alkali earth. The common genes were identified and subjected to functional enrichment analysis. Further, gene-gene and protein-protein interactions were assessed. Also, the top ten transcription factors and miRNAs that regulate the genes were identified. The phenotypes and diseases that have increased incidence upon alterations of these genes were detected. Overall, we were able to identify IL1B and SOD2 as the common genes, along with the AGE-RAGE signaling pathway in diabetic complications as the common pathway altered. Enriched genes and pathways specific to each metal category were also found. Further, we identified heart failure as the major diseases that have increased the incidence of these metals' exposure. In conclusion, exposure to essential metals might cause adverse effects via inflammation and oxidative stress.

Comprehensive investigation of hepatotoxicity of the mixture containing phthalates and bisphenol A

Connections between the mixture containing bis(2- ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP) and bisphenol A (BPA) and liver injury were explored through in silico investigation and 2 in vivo models. Comparative Toxicogenomics Database (CTD), ShinyGO, ToppCluster and Cytoscape were used for bioinformatic analysis. In vivo subacute study was performed on rats - five groups (n = 6): (1) Control: corn oil, (2) DEHP: 50 mg/kg b.w./day, (3) DBP: 50 mg/kg b.w./day, (4) BPA: 25 mg/kg b.w./day, (5) MIX: DEHP + DBP + BPA. Zebrafish embryos were exposed to the investigated substances in different doses, singularly and combined (binary and ternary mixtures). Liver injury was linked to 75 DEHP, DBP, and BPA genes, mostly connected to inflammation/oxidative stress. In rats, significant alterations in redox status/bioelements and pathohistology were most notable or exclusively present in MIX (probable additive effects). BPA decreased liver area (LA) index in dose-dependent manner. DEHP (< 2 µg/mL) and DBP (≤ 5 µg/mL) reduced LA values, while their higher doses increased LA index. The effect of DBP in binary mixtures led to a lethal outcome at the two highest concentrations, while the hepatotoxicity of DEHP/DBP/BPA mixture was dictated by BPA (confirmed by the benchmark dose analysis).

Pilot testing and optimization of a larval fathead minnow high throughput transcriptomics assay

Concentrations at which global gene expression profiles in cells or animals exposed to a test substance start to differ significantly from those of controls have been proposed as an alternative point of departure for use in screening level hazard assessment. The present study describes pilot testing of a high throughput compatible transcriptomics assay with larval fathead minnows. One day post hatch fathead minnows were exposed to eleven different concentrations of three metals, three selective serotonin reuptake inhibitors, and four neonicotinoid-like compounds for 24 h and concentration response modeling was applied to whole body gene expression data. Transcriptomics-based points of departure (tPODs) were consistently lower than effect concentrations reported in apical endpoint studies in fish. However, larval fathead minnow-based tPODs were not always lower than concentrations reported to elicit apical toxicity in other aquatic organisms like crustaceans or insects. Random in silico subsampling of data from the pilot assays was used to evaluate various assay design and acceptance considerations such as transcriptome coverage, number of replicate individuals to sequence per treatment, and minimum number of differentially expressed genes to produce a reliable tPOD estimate. Results showed a strong association between the total number of genes for which a concentration response relationship could be derived and the overall variability in the resulting tPOD estimates. We conclude that, for our current assay design and analysis pipeline, tPODs based on fewer than 15 differentially expressed genes are likely to be unreliable for screening and that interindividual variability in gene expression profiles appears to be a more significant driver of tPOD variability than sample size alone. Results represent initial steps toward developing high throughput transcriptomics assays for use in ecological hazard screening.

Joint impact of key air pollutants on COVID-19 severity: prediction based on toxicogenomic data analysis

Considering that some researchers point to a possible influence of air pollution on COVID-19 transmission, severity, and death rate, the aim of our in silico study was to determine the relationship between the key air pollutants [sulphur dioxide (SO), carbon monoxide (CO), ₂particulate matter (PM_x), nitrogen dioxide (NO₂), and ozone (O₃)] and COVID-19 complications using the publicly available toxicogenomic analytical and prediction tools: (i) Comparative Toxicogenomic Database (CTD) to identify genes common to air pollutants and COVID-19 complications; (ii) GeneMANIA to construct a network of these common and related genes; (iii) ToppGene Suite to extract the most important biological processes and molecular pathways; and (iv) DisGeNET to search for the top gene-disease pairs. SO₂, CO, PM_x, NO₂, and O₃ interacted with 6, 6, 18, 9, and 12 COVID-19-related genes, respectively. Four of these are common for all pollutants (IL10, IL6, IL1B, and TNF) and participate in most (77.64 %) physical interactions. Further analysis pointed to cytokine binding and cytokine-mediated signalling pathway as the most important molecular function and biological process, respectively. Other molecular functions and biological processes are mostly related to cytokine activity and inflammation, which might be connected to the cytokine storm and resulting COVID-19 complications. The final step singled out the link between the CEBPA gene and acute myelocytic leukaemia and between TNFRSF1A and TNF receptor-associated periodic fever syndrome. This indicates possible complications in COVID-19 patients suffering from these diseases, especially those living in urban areas with poor air quality.

'Omics in environmental epidemiological studies of chemical exposures: A systematic evidence map

BACKGROUND

Systematic evidence maps are increasingly used to develop chemical risk assessments. These maps can provide an overview of available studies and relevant study information to be used for various research objectives and applications. Environmental epidemiological studies that examine the impact of chemical exposures on various 'omic profiles in human populations provide relevant mechanistic information and can be used for benchmark dose modeling to derive potential human health reference values.

OBJECTIVES

To create a systematic evidence map of environmental epidemiological studies examining environmental contaminant exposures with 'omics in order to characterize the extent of available studies for future research needs.

METHODS

Systematic review methods were used to search and screen the literature and included the use of machine learning methods to facilitate screening studies. The Populations, Exposures, Comparators and Outcomes (PECO) criteria were developed to identify and screen relevant studies. Studies that met the PECO criteria after full-text review were summarized with information such as study population, study design, sample size, exposure measurement, and 'omics analysis.

RESULTS

Over 10,000 studies were identified from scientific databases. Screening processes were used to identify 84 studies considered PECO-relevant after full-text review. Various contaminants (e.g. phthalate, benzene, arsenic, etc.) were investigated in epidemiological studies that used one or more of the four 'omics of interest: epigenomics, transcriptomics, proteomics, and metabolomics . The epidemiological study designs that were used to explore single or integrated 'omic research questions with contaminant exposures were cohort studies, controlled trials, cross-sectional, and case-control studies. An interactive web-based systematic evidence map was created to display more study-related information.

CONCLUSIONS

This systematic evidence map is a novel tool to visually characterize the available environmental epidemiological studies investigating contaminants and biological effects using 'omics technology and serves as a resource for investigators and allows for a range of applications in chemical research and risk assessment needs.

Integrated Genomic and Bioinformatics Approaches to Identify Molecular Links between Endocrine Disruptors and Adverse Outcomes

Exposure to Endocrine Disrupting Chemicals (EDC) has been linked with several adverse outcomes. In this review, we examine EDCs that are pervasive in the environment and are of concern in the context of human, animal, and environmental health. We explore the consequences of EDC exposure on aquatic life, terrestrial animals, and humans. We focus on the exploitation of genomics technologies and in particular whole transcriptome sequencing. Genome-wide analyses using RNAseq provides snap shots of cellular, tissue and whole organism transcriptomes under normal physiological and EDC perturbed conditions. A global view of gene expression provides highly valuable information as it uncovers gene families or more specifically, pathways that are affected by EDC exposures, but also reveals those that are unaffected. Hypotheses about genes with unknown functions can also be formed by comparison of their expression levels with genes of known function. Risk assessment strategies leveraging genomic technologies and the development of toxicology databases are explored. Finally, we review how the Adverse Outcome Pathway (AOP) has exploited this high throughput data to provide a framework for toxicology studies.

Probiotic reduced the impact of phthalates and bisphenol A mixture on type 2 diabetes mellitus development: Merging bioinformatics with in vivo analysis

Linkage between bis(2-ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), and bisphenol A (BPA) co-exposure and type 2 diabetes mellitus (T2DM), as well as ability of multi-strained probiotic to reduce DEHP, DBP and BPA mixture-induced oxidative damage in rat pancreas were investigated. The Comparative Toxicogenomics Database, Cytoscape software and ToppGene Suite were used for data-mining. Animals were sorted into seven groups (n = 6): (1) Control group: corn oil, (2) P: probiotic: Saccharomyces boulardii + Lactobacillus rhamnosus + Lactobacillus plantarum LP 6595 + Lactobacillus plantarum HEAL9; (3) DEHP: 50 mg/kg b.w./day, (4) DBP: 50 mg/kg b.w./day, (5) BPA: 25 mg/kg b.w./day, and (6) MIX: 50 mg/kg b.w./day DEHP + 50 mg/kg b.w/day DBP + 25 mg/kg b.w./day BPA; (7) MIX + P. Rats were sacrificed after 28 days of oral exposure. In silico investigation highlighted 44 DEHP, DBP and BPA mutual genes linked to the T2DM, while apoptosis and oxidative stress were highlighted as the main mechanisms of DEHP, DBP and BPA mixture-linked T2DM. In vivo experiment confirmed the presence of significant changes in redox status parameters (TOS, SOD and SH groups) only in the MIX group, indicating possible additive effects, while probiotic ameliorated mixture-induced redox status changes in rat pancreatic tissue.

Combining in vivo pathohistological and redox status analysis with in silico toxicogenomic study to explore the phthalates and bisphenol A mixture-induced testicular toxicity

The aim of this study was to: (i) determine and compare the capacity of bis (2 -ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), bisphenol A (BPA), and their mixture to produce testicular toxicity after the subacute exposure; (ii) explore the mechanisms behind the observed changes using in silico toxicogenomic approach. Male rats were randomly split into groups (n = 6): (1) Control (corn oil); (2) DEHP (50 mg/kg b.w./day); (3) DBP (50 mg/kg b.w./day); (4) BPA (25 mg/kg b.w./day); and (5) MIX (50 mg/kg b.w./day DEHP + 50 mg/kg b.w/day DBP + 25 mg/kg b.w./day BPA). Animals were sacrificed after 28 days of oral exposure, testes were extracted and prepared for histological assessments under the light microscope (haematoxylin and eosin staining) and redox status analysis. The Comparative Toxicogenomics Database (CTD; http://CTD.mdibl.org), Cytoscape software (https://cytoscape.org) and ToppGene Suite (https://toppgene.cchmc.org) were used for data-mining. Present pathohistological study has demonstrated more pronounced testicular toxicity of the MIX group (desquamated germinal epithelium cells, enlarged cells with hyperchromatic nuclei, multinucleated cell forms and intracytoplasmic vacuoles) in comparison with the single substances, while effects on redox status parameters were either more prominent, or present only in the MIX group. In silico investigation revealed 20 genes linked to male reproductive disorders, affected by all three investigated substances. Effects on metabolism, AhR pathway, apoptosis and oxidative stress could be singled out as the most probable mechanisms involved in the subacute DEHP, DBP and BPA mixture testicular toxicity, while the effect on oxidative stress parameters was confirmed by in vivo experiment.

Elucidating the influence of environmentally relevant toxic metal mixture on molecular mechanisms involved in the development of neurodegenerative diseases: In silico toxicogenomic data-mining

This in silico toxicogenomic analysis aims to: (i) testify the hypothesis about the influence of the environmentally relevant toxic metals (lead, methylmercury (organic form of mercury), cadmium and arsenic) on molecular mechanisms involved in amyotrophic lateral sclerosis (ALS), Parkinson's Disease (PD) and Alzheimer's disease (AD) development; and (ii) demonstrate the capability of in silico toxicogenomic data-mining for distinguishing the probable mechanisms of mixture-induced toxic effects. The Comparative Toxicogenomics Database (CTD; http://ctd. mdibl.org) and Cytoscape software were used as the main data-mining tools in this analysis. The results have shown that there were 7, 13 and 14 common genes for all the metals present in the mixture for each of the selected neurodegenerative disease (ND), respectively: ALS, PD and AD. Physical interactions (68.18%) were the most prominent interactions between the genes extracted for ALS, co-expression (60.85%) for PD and interactions predicted by the server (44.30%) for AD. SOD2 gene was noted as the mutual gene for all the selected ND. Oxidative stress, folate metabolism, vitamin B12, AGE-RAGE, apoptosis were noted as the key disrupted molecular pathways that contribute to the neurodegenerative disease's development. Gene ontology analysis revealed biological processes affected by the investigated mixture (glutathione metabolic process was listed as the most important for ALS, cellular response to toxic substance for PD, and neuron death for AD). Our results emphasize the role of oxidative stress, particularly SOD2, in neurodegeneration triggered by environmental toxic metal mixture and give a new insight into common molecular mechanisms involved in ALS, PD and AD pathology.

Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach

Improvement of COVID-19 clinical condition was seen in studies where combination of antiretroviral drugs, lopinavir and ritonavir, as well as immunomodulant antimalaric, chloroquine/hydroxychloroquine together with the macrolide-type antibiotic, azithromycin, was used for patient's treatment. Although these drugs are "old", their pharmacological and toxicological profile in SARS-CoV-2 - infected patients are still unknown. Thus, by using in silico toxicogenomic data-mining approach, we aimed to assess both risks and benefits of the COVID-19 treatment with the most promising candidate drugs combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine + azithromycin. The Comparative Toxicogenomics Database (CTD; http://CTD.mdibl.org), Cytoscape software (https://cytoscape.org) and ToppGene Suite portal (https://toppgene.cchmc.org) served as a foundation in our research. Our results have demonstrated that lopinavir/ritonavir increased the expression of the genes involved in immune response and lipid metabolism (IL6, ICAM1, CCL2, TNF, APOA1, etc.). Chloroquine/hydroxychloroquine + azithromycin interacted with 6 genes (CCL2, CTSB, CXCL8, IL1B, IL6 and TNF), whereas chloroquine and azithromycin affected two additional genes (BCL2L1 and CYP3A4), which might be a reason behind a greater number of consequential diseases. In contrast to lopinavir/ritonavir, chloroquine/hydroxychloroquine + azithromycin downregulated the expression of TNF and IL6. As expected, inflammation, cardiotoxicity, and dyslipidaemias were revealed as the main risks of lopinavir/ritonavir treatment, while chloroquine/hydroxychloroquine + azithromycin therapy was additionally linked to gastrointestinal and skin diseases. According to our results, these drug combinations should be administrated with caution to patients suffering from cardiovascular problems, autoimmune diseases, or acquired and hereditary lipid disorders.

Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives

BACKGROUND

Some 20 y ago, scientific and regulatory communities identified the potential of omics sciences (genomics, transcriptomics, proteomics, metabolomics) to improve chemical risk assessment through development of toxicogenomics. Recognizing that regulators adopt new scientific methods cautiously given accountability to diverse stakeholders, the scope and pace of adoption of toxicogenomics tools and data have nonetheless not met the ambitious, early expectations of omics proponents.

OBJECTIVE

Our objective was, therefore, to inventory, investigate, and derive insights into drivers of and obstacles to adoption of toxicogenomics in chemical risk assessment. By invoking established social science frameworks conceptualizing innovation adoption, we also aimed to develop recommendations for proponents of toxicogenomics and other new approach methodologies (NAMs).

METHODS

We report findings from an analysis of 56 scientific and regulatory publications from 1998 through 2017 that address the adoption of toxicogenomics for chemical risk assessment. From this purposeful sample of toxicogenomics discourse, we identified major categories of drivers of and obstacles to adoption of toxicogenomics tools and data sets. We then mapped these categories onto social science frameworks for conceptualizing innovation adoption to generate actionable insights for proponents of toxicogenomics.

DISCUSSION

We identify the most salient drivers and obstacles. From 1998 through 2017, adoption of toxicogenomics was understood to be helped by drivers such as those we labeled Superior scientific understanding, New applications, and Reduced cost & increased efficiency but hindered by obstacles such as those we labeled Insufficient validation, Complexity of interpretation, and Lack of standardization. Leveraging social science frameworks, we find that arguments for adoption that draw on the most salient drivers, which emphasize superior and novel functionality of omics as rationales, overlook potential adopters' key concerns: simplicity of use and compatibility with existing practices. We also identify two perspectives-innovation-centric and adopter-centric-on omics adoption and explain how overreliance on the former may be undermining efforts to promote toxicogenomics. https://doi.org/10.1289/EHP6500.

Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method

Assessment of reproductive toxicity is one of the important safety considerations in drug development. Thus, in the present research, the naïve Bayes (NB)-classifier method was applied to develop binary classification models. Six important molecular descriptors for reproductive toxicity were selected by the genetic algorithm. Then, 110 classification models were developed using six molecular descriptors and10 types of fingerprints with 11 different maximum diameters. Among these established models, the model based on six molecular descriptors and the SciTegic extended-connectivity fingerprints with 20 maximum diameters (LCFC_20) displayed the best prediction performance for reproductive toxicity (NB-1), which gave a 0.884 receiver operating characteristic (ROC) score and 91.8% overall prediction accuracy for the Training Set, and produced a 0.888 ROC score and 83.0% overall accuracy for the external Test Set I. In addition, for the external rat multi-generation reproductive toxicity dataset (Test Set II), the NB-1 model generated a 0.806 ROC score and 85.1% concordance. The generated prediction results indicated that the NB-1 model could give robust and reliable predictions for a reproductive toxicity potential of chemicals. Thus, the established model could be applied to filter early-stage molecules for potential reproductive adverse effects. In addition, six important molecular descriptors and new structural alerts for reproductive toxicity were identified, which could help medicinal chemists rationally guide the optimization of lead compounds and select chemicals with the best prospects of being safe and effective.

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post-modified non-negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

Determination of chemical-disease risk values to prioritize connections between environmental factors, genetic variants, and human diseases

Traditional methods for chemical risk assessment are too time-consuming and resource-intensive to characterize either the diversity of chemicals to which humans are exposed or how that diversity may manifest in population susceptibility differences. The advent of novel toxicological data sources and their integration with bioinformatic databases affords opportunities for modern approaches that consider gene-environment (GxE) interactions in population risk assessment. Here, we present an approach that systematically links multiple data sources to relate chemical risk values to diseases and gene-disease variants. These data sources include high-throughput screening (HTS) results from Tox21/ToxCast, chemical-disease relationships from the Comparative Toxicogenomics Database (CTD), hazard data from resources like the Integrated Risk Information System, exposure data from the ExpoCast initiative, and gene-variant-disease information from the DisGeNET database. We use these integrated data to identify variants implicated in chemical-disease enrichments and develop a new value that estimates the risk of these associations toward differential population responses. Finally, we use this value to prioritize chemical-disease associations by exploring the genomic distribution of variants implicated in high-risk diseases. We offer this modular approach, termed DisQGOS (Disease Quotient Genetic Overview Score), for relating overall chemical-disease risk to potential for population variable responses, as a complement to methods aiming to modernize aspects of risk assessment.

Ecogenetics of lead toxicity and its influence on risk assessment

Lead (Pb) toxicity is a public health problem affecting millions worldwide. Advances in 'omic' technology have paved the way to toxico-genomics which is currently revolutionizing the understanding of interindividual variations in susceptibility to Pb toxicity and its functional consequences to exposure. Our objective was to identify, comprehensively analyze, and curate all the potential genetic and epigenetic biomarkers studied to date in relation to Pb toxicity and its association with diseases. We screened a volume of research articles that focused on Pb toxicity and its association with genetic and epigenetic signatures in the perspective of occupational and environmental Pb exposure. Due to wide variations in population size, ethnicity, age-groups, and source of exposure in different studies, researchers continue to be skeptical on the topic of the influence of genetic variations in Pb toxicity. However, surface knowledge of the underlying genetic factors will aid in elucidating the mechanism of action of Pb. Moreover, in recent years, the application of epigenetics in Pb toxicity has become a promising area in toxicology to understand the influence of epigenetic mechanisms such as DNA methylation, chromatin remodeling, and small RNAs for the regulation of genes in response to Pb exposure during early life. Growing evidences of ecogenetic understanding (both genetic and epigenetic processes) in a dose-dependent manner may help uncover the mechanism of action of Pb and in the identification of susceptible groups. Such studies will further help in refining uncertainty factors and in addressing risk assessment of Pb poisoning.

Differences among Unique Nanoparticle Protein Corona Constructs: A Case Study Using Data Analytics and Multi-Variant Visualization to Describe Physicochemical Characteristics

Gold nanoparticles (AuNPs) used in pharmaceutical treatments have been shown to effectively deliver a payload, such as an active pharmaceutical ingredient or image contrast agent, to targeted tissues in need of therapy or diagnostics while minimizing exposure, availability, and accumulation to surrounding biological compartments. Data sets collected in this field of study include some toxico- and pharmacodynamic properties (e.g., distribution and metabolism) but many studies lack information about adsorption of biological molecules or absorption into cells. When nanoparticles are suspended in blood serum, a protein corona cloud forms around its surface. The extent of the applications and implications of this formed cloud are unknown. Some researchers have speculated that the successful use of nanoparticles in pharmaceutical treatments relies on a comprehensive understanding of the protein corona composition. The work presented in this paper uses a suite of data analytics and multi-variant visualization techniques to elucidate particle-to-protein interactions at the molecular level. Through mass spectrometry analyses, corona proteins were identified through large and complex datasets. With such high-output analyses, complex datasets pose a challenge when visualizing and communicating nanoparticle-protein interactions. Thus, the creation of a streamlined visualization method is necessary. A series of user-friendly data informatics techniques were used to demonstrate the data flow of protein corona characteristics. Multi-variant heat maps, pie charts, tables, and three-dimensional regression analyses were used to improve results interpretation, facilitate an iterative data transfer process, and emphasize features of the nanoparticle-protein corona system that might be controllable. Data informatics successfully highlights the differences between protein corona compositions and how they relate to nanoparticle surface charge.

Translational regulation is a key determinant of the cellular response to benzo[a]pyrene

Translational control is a cellular response mechanism which initiates adaptation during various stress situations. Here, we investigated the role of translational control after benzo[a]pyrene (BaP) exposure in primary mouse hepatocytes. Translated mRNAs were separated and captured based on the number of associated ribosomes using sucrose gradients and subjected to RNA sequencing (RNAseq) to investigate translational changes. Furthermore, unseparated RNA (total RNA) was used for RNAseq to determine the transcriptional alterations. We showed that, after 24 h of exposure to 10 μM BaP, the number of genes altered by changes in mRNA translation was substantially higher compared with the number of genes altered by transcription. Although part of the BaP regulated genes were regulated by both transcription and translation, we identified genes that were uniquely regulated by mRNA translation. These mRNA transcripts encode proteins that are involved in biological processes that are not affected by transcriptional regulation. Al together this work identified a new layer of gene expression regulation that might contribute to BaP-induced carcinogenesis.

Mechanism-based risk assessment strategy for drug-induced cholestasis using the transcriptional benchmark dose derived by toxicogenomics

Cholestasis is one of the major causes of drug-induced liver injury (DILI), which can result in withdrawal of approved drugs from the market. Early identification of cholestatic drugs is difficult due to the complex mechanisms involved. In order to develop a strategy for mechanism-based risk assessment of cholestatic drugs, we analyzed gene expression data obtained from the livers of rats that had been orally administered with 12 known cholestatic compounds repeatedly for 28 days at three dose levels. Qualitative analyses were performed using two statistical approaches (hierarchical clustering and principle component analysis), in addition to pathway analysis. The transcriptional benchmark dose (tBMD) and tBMD 95% lower limit (tBMDL) were used for quantitative analyses, which revealed three compound sub-groups that produced different types of differential gene expression; these groups of genes were mainly involved in inflammation, cholesterol biosynthesis, and oxidative stress. Furthermore, the tBMDL values for each test compound were in good agreement with the relevant no observed adverse effect level. These results indicate that our novel strategy for drug safety evaluation using mechanism-based classification and tBMDL would facilitate the application of toxicogenomics for risk assessment of cholestatic DILI.

A Retrospective Analysis of Toxicogenomics in the Safety Assessment of Drug Candidates

Toxicogenomics is considered a valuable tool for reducing pharmaceutical candidate attrition by facilitating earlier identification, prediction and understanding of toxicities. A retrospective evaluation of 3 years of routine transcriptional profiling in non-clinical safety studies was undertaken to assess the utility of toxicogenomics in drug safety assessment. Based on the analysis of studies with 33 compounds, marked global transcriptional changes (>4% transcripts at p < 0.01) were shown to be a robust biomarker for dosages considered to be toxic. In general, there was an inconsistent correlation between transcription and histopathology, most likely due to differences in sensitivity to focal microscopic lesions, to secondary effects, and to events that precede structural tissue changes. For 60% of toxicities investigated with multiple time-point data, transcriptional changes were observed prior to changes in traditional study endpoints. Candidate transcriptional markers of pharmacologic effects were detected in 40% of targets profiled. Mechanistic classification of toxicity was obtained for 30% of targets. Furthermore, data comparison to compendia of transcriptional changes provided assessments of the specificity of transcriptional responses. Overall, our experience suggests that toxicogenomics has contributed to a greater understanding of mechanisms of toxicity and to reducing drug attrition by empiric analysis where safety assessment combines toxicogenomic and traditional evaluations.

Identification of novel signature genes attesting arsenic-induced immune alterations in adult zebrafish (Danio rerio)

Arsenic poisoning is a serious global issue. Apart from causing developmental and systemic toxicity, arsenic has recently been reported for its ability to hinder immune responses. The present study is designed to identify the global expression profile associated with arsenic-induced immune alterations at the organismic level. Adult zebrafish (Danio rerio) were exposed to 20, 40 and 80ppb of arsenic trioxide for 30days, sacrificed and global gene expression profile studied. Microarray data suggested 65 immune related genes were commonly affected in the three treatment regimens. The expression profile of key immune related genes (tlr1, nitr1f, nitr1c, crfb8, socs7, socs3b, abcb3/1, mch1uja, ifnγ1-2, cxcl12b and crlf1a) was validated by qPCR. Pathway analysis suggested the major involvement of JAK-STAT circuit in the process. The expression of these marker genes was also studied in arsenic exposed and bacteria (Aeromonas hydrophila) challenged zebrafish. Increase in bacterial colony forming units (CFU) coupled with gross histopathology of kidney in arsenic exposed-bacteria challenged fish suggested profound immuno-compromised condition. We propose that chronic arsenic exposure leads to hyperactivation of the immune system as a consequence when exposed to further stress (microbial) it induces immuno-suppression with pathological implications. The study provides a molecular snap shot for predicting arsenic immuno-toxicity.

Comparative analysis of gene expression between renal cortex and papilla in nedaplatin-induced nephrotoxicity in rats

To elucidate the mechanism of nephrotoxicity caused by anti-neoplastic platinum complex, nedaplatin (NDP), treatment with a particular focus on the renal papillary toxicity, we analysed the gene expression profiles of two renal regions, the cortex (RC) and the papilla (RP) in rat kidneys. Male Wistar rats received a single administration of 10 mg/kg intravenous NDP or vehicle alone (5% xylitol solution) and were sacrificed six days later. The kidneys were dissected into the RC and RP and used for histopathological and microarray analyses. Histopathologically, NDP caused characteristic renal lesions, such as necrosis, single cell necrosis (with TUNEL TdT-mediated dUTP-biotin nick end labelling-positive) and regeneration/hyperplasia of the epithelial cells in both renal regions. Global gene expression analysis revealed that several genes involved in various functional categories were commonly deregulated in both renal regions, such as apoptosis, cell cycle regulation, DNA metabolism, cell migration/adhesion and cytoskeleton organization or genes induced as a perturbation of oxidative status and calcium homeostasis. Comparative analysis of gene expression between RC and RP revealed that genes encoding several subtypes of cytokeratins were identified as being specifically overexpressed in RP by the NDP treatment. Differential expression patterns of these selected genes observed by microarray analysis were further confirmed by quantitative real time RT-PCR and immunohistochemistry, which demonstrated increased expression of cytokeratins (CKs) 14 and 19 at the epithelium covering RP and/or collecting duct epithelium. Overall, the results contribute to understanding the renal molecular events of NDP-induced nephrotoxicity including novel potential biomarker genes encoding CKs 14 and 19 that may serve as indicators of renal papillary toxicity. Human & Experimental Toxicology (2007) 26, 767—780

Integrative functional transcriptomic analyses implicate specific molecular pathways in pulmonary toxicity from exposure to aluminum oxide nanoparticles

Gene expression profiling has developed rapidly in recent years and it can predict and define mechanisms underlying chemical toxicity. Here, RNA microarray and computational technology were used to show that aluminum oxide nanoparticles (Al2O3 NPs) were capable of triggering up-regulation of genes related to the cell cycle and cell death in a human A549 lung adenocarcinoma cell line. Gene expression levels were validated in Al2O3 NPs exposed A549 cells and mice lung tissues, most of which showed consistent trends in regulation. Gene-transcription factor network analysis coupled with cell- and animal-based assays demonstrated that the genes encoding PTPN6, RTN4, BAX and IER play a role in the biological responses induced by the nanoparticle exposure, which caused cell death and cell cycle arrest in the G2/S phase. Further, down-regulated PTPN6 expression demonstrated a core role in the network, thus expression level of PTPN6 was rescued by plasmid transfection, which showed ameliorative effects of A549 cells against cell death and cell cycle arrest. These results demonstrate the feasibility of using gene expression profiling to predict cellular responses induced by nanomaterials, which could be used to develop a comprehensive knowledge of nanotoxicity.

Defining the role of omics in assessing ecosystem health: Perspectives from the Canadian environmental monitoring program

Scientific reviews and studies continue to describe omics technologies as the next generation of tools for environmental monitoring, while cautioning that there are limitations and obstacles to overcome. However, omics has not yet transitioned into national environmental monitoring programs designed to assess ecosystem health. Using the example of the Canadian Environmental Effects Monitoring (EEM) program, the authors describe the steps that would be required for omics technologies to be included in such an established program. These steps include baseline collection of omics endpoints across different species and sites to generate a range of what is biologically normal within a particular ecosystem. Natural individual variability in the omes is not adequately characterized and is often not measured in the field, but is a key component to an environmental monitoring program, to determine the critical effect size or action threshold for management. Omics endpoints must develop a level of standardization, consistency, and rigor that will allow interpretation of the relevance of changes across broader scales. To date, population-level consequences of routinely measured endpoints such as reduced gonad size or intersex in fish is not entirely clear, and the significance of genome-wide molecular, proteome, or metabolic changes on organism or population health is further removed from the levels of ecological change traditionally managed. The present review is not intended to dismiss the idea that omics will play a future role in large-scale environmental monitoring studies, but rather outlines the necessary actions for its inclusion in regulatory monitoring programs focused on assessing ecosystem health.

Cardiotoxicity screening: a review of rapid-throughput in vitro approaches

Cardiac toxicity represents one of the leading causes of drug failure along different stages of drug development. Multiple very successful pharmaceuticals had to be pulled from the market or labeled with strict usage warnings due to adverse cardiac effects. In order to protect clinical trial participants and patients, the International Conference on Harmonization published guidelines to recommend that all new drugs to be tested preclinically for hERG (Kv11.1) channel sensitivity before submitting for regulatory reviews. However, extensive studies have demonstrated that measurement of hERG activity has limitations due to the multiple molecular targets of drug compound through which it may mitigate or abolish a potential arrhythmia, and therefore, a model measuring multiple ion channel effects is likely to be more predictive. Several phenotypic rapid-throughput methods have been developed to predict the potential cardiac toxic compounds in the early stages of drug development using embryonic stem cells- or human induced pluripotent stem cell-derived cardiomyocytes. These rapid-throughput methods include microelectrode array-based field potential assay, impedance-based or Ca(2+) dynamics-based cardiomyocytes contractility assays. This review aims to discuss advantages and limitations of these phenotypic assays for cardiac toxicity assessment.

Toxicogenomic Screening of Replacements for Di(2-Ethylhexyl) Phthalate (DEHP) Using the Immortalized TM4 Sertoli Cell Line

Phthalate plasticizers such as di(2-ethylhexyl) phthalate (DEHP) are being phased out of many consumer products because of their endocrine disrupting properties and their ubiquitous presence in the environment. The concerns raised from the use of phthalates have prompted consumers, government, and industry to find alternative plasticizers that are safe, biodegradable, and have the versatility for multiple commercial applications. We examined the toxicogenomic profile of mono(2-ethylhexyl) phthalate (MEHP, the active metabolite of DEHP), the commercial plasticizer diisononyl cyclohexane-1,2-dicarboxylate (DINCH), and three recently proposed plasticizers: 1,4-butanediol dibenzoate (BDB), dioctyl succinate (DOS), and dioctyl maleate (DOM), using the immortalized TM4 Sertoli cell line. Results of gene expression studies revealed that DOS and BDB clustered with control samples while MEHP, DINCH and DOM were distributed far away from the control-DOS-BDB cluster, as determined by principle component analysis. While no significant changes in gene expression were found after treatment with BDB and DOS, treatment with MEHP, DINCH and DOM resulted in many differentially expressed genes. MEHP upregulated genes downstream of PPAR and targeted pathways of cholesterol biosynthesis without modulating the expression of PPAR’s themselves. DOM upregulated genes involved in glutathione stress response, DNA repair, and cholesterol biosynthesis. Treatment with DINCH resulted in altered expression of a large number of genes involved in major signal transduction pathways including ERK/MAPK and Rho signalling. These data suggest DOS and BDB may be safer alternatives to DEHP/MEHP than DOM or the commercial alternative DINCH.

Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO

Variable selection is of crucial significance in QSAR modeling since it increases the model predictive ability and reduces noise. The selection of the right variables is far more complicated than the development of predictive models. In this study, eight continuous and categorical data sets were employed to explore the applicability of two distinct variable selection methods random forests (RF) and least absolute shrinkage and selection operator (LASSO). Variable selection was performed: (1) by using recursive random forests to rule out a quarter of the least important descriptors at each iteration and (2) by using LASSO modeling with 10-fold inner cross-validation to tune its penalty λ for each data set. Along with regular statistical parameters of model performance, we proposed the highest pairwise correlation rate, average pairwise Pearson's correlation coefficient, and Tanimoto coefficient to evaluate the optimal by RF and LASSO in an extensive way. Results showed that variable selection could allow a tremendous reduction of noisy descriptors (at most 96% with RF method in this study) and apparently enhance model's predictive performance as well. Furthermore, random forests showed property of gathering important predictors without restricting their pairwise correlation, which is contrary to LASSO. The mutual exclusion of highly correlated variables in LASSO modeling tends to skip important variables that are highly related to response endpoints and thus undermine the model's predictive performance. The optimal variables selected by RF share low similarity with those by LASSO (e.g., the Tanimoto coefficients were smaller than 0.20 in seven out of eight data sets). We found that the differences between RF and LASSO predictive performances mainly resulted from the variables selected by different strategies rather than the learning algorithms. Our study showed that the right selection of variables is more important than the learning algorithm for modeling. We hope that a standard procedure could be developed based on these proposed statistical metrics to select the truly important variables for model interpretation, as well as for further use to facilitate drug discovery and environmental toxicity assessment.

Stressor-induced proteome alterations in zebrafish: a meta-analysis of response patterns

Proteomics approaches are being increasingly applied in ecotoxicology on the premise that the identification of specific protein expression changes in response to a particular chemical would allow elucidation of the underlying molecular pathways leading to an adverse effect. This in turn is expected to promote the development of focused testing strategies for specific groups of toxicants. Although both gel-based and gel-free global characterization techniques provide limited proteome coverage, the conclusions regarding the cellular processes affected are still being drawn based on the few changes detected. To investigate how specific the detected responses are, we analyzed a set of studies that characterized proteome alterations induced by various physiological, chemical and biological stressors in zebrafish, a popular model organism. Our analysis highlights several proteins and protein groups, including heat shock and oxidative stress defense proteins, energy metabolism enzymes and cytoskeletal proteins, to be most frequently identified as responding to diverse stressors. In contrast, other potentially more specifically responding protein groups are detected much less frequently. Thus, zebrafish proteome responses to stress reported by different studies appear to depend mostly on the level of stress rather than on the specific stressor itself. This suggests that the most broadly used current proteomics technologies do not provide sufficient proteome coverage to allow in-depth investigation of specific mechanisms of toxicant action. We suggest that the results of any differential proteomics experiment performed with zebrafish should be interpreted keeping in mind the list of the most frequent responders that we have identified. Similar reservations should apply to any other species where proteome responses are analyzed by global proteomics methods. Careful consideration of the reliability and significance of observed changes is necessary in order not to over-interpret the experimental results and to prevent the proliferation of false positive linkages between the chemical and the cellular functions it perturbs. We further discuss the implications of the identified "top lists" of frequently responding proteins and protein families, and suggest further directions for proteomics research in ecotoxicology. Apart from improving the proteome coverage, further research should focus on defining the significance of the observed stress response patterns for organism phenotypes and on searching for common upstream regulators that can be targeted by specific assays.

Zebrafish models for assessing developmental and reproductive toxicity

The zebrafish is increasingly used as a vertebrate animal model for in vivo drug discovery and for assessing chemical toxicity and safety. Numerous studies have confirmed that zebrafish and mammals are similar in their physiology, development, metabolism and pathways, and that zebrafish responses to toxic substances are highly predictive of mammalian responses. Developmental and reproductive toxicity assessments are an important part of new drug safety profiling. A significant number of drug candidates have failed in preclinical tests due to their adverse effect on development and reproductivity. Compared to conventional mammal testing, zebrafish testing for assessing developmental and reproductive toxicity offers several compelling experimental advantages, including transparency of embryo and larva, higher throughput, shorter test period, lower cost, smaller amount of compound required, easier manipulation and direct compound delivery. Toxicity and safety assessments using zebrafish have also been accepted by the FDA and EMEA for investigative new drug (IND) approval.

Profiling the molecular mechanism of fullerene cytotoxicity on tumor cells by RNA-seq

The interest on functionalized fullerenes in the field of nanomedicine has seen a significant increase in the past decade. However, the different methods employed to increase C60 solubility profoundly influence the physicochemical properties and the toxicological effects of these compounds, thus complicating the evaluation of their toxicity and potential therapeutic use. Here we report a whole-transcriptome RNA-seq analysis assessing the effect of two fullerenes (1 and 2) on gene expression in the human MCF7 cell line. Although these two compounds had previously been characterized by in vitro studies as having a cytotoxic and null effect respectively, to date the mechanisms at the basis of this different behavior and, more in general, at the basis of the effect of most fullerene derivatives in living cells are still completely unknown. Our data evidence that: (a) fullerene 2 caused a significant, time-dependent alteration of gene expression, whereas 1 only had a negligible effect; (b) the biological processes mostly influenced over the 48h experimental time course were transcription, protein synthesis, cell cycle progression and cell adhesion; (c) the gene expression signature of 2-treated cells was strikingly similar to those induced by selective inhibitors of mTOR signaling, thus suggesting an effect on this pathway for fullerene 2. Our work represents the first approach toward the application of RNA-seq to the study of the molecular mechanisms underlying the interaction of fullerenes with cellular systems and provides an objective view of the feasibility and the safety of these nanomaterials for a medical application.

Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure

New approaches are urgently needed to evaluate potential hazards posed by exposure to nanomaterials. Gene expression profiling provides information on potential modes of action and human relevance, and tools have recently become available for pathway-based quantitative risk assessment. The objective of this study was to use toxicogenomics in the context of human health risk assessment. We explore the utility of toxicogenomics in risk assessment, using published gene expression data from C57BL/6 mice exposed to 18, 54 and 162 μg Printex 90 carbon black nanoparticles (CBNP). Analysis of CBNP-perturbed pathways, networks and transcription factors revealed concomitant changes in predicted phenotypes (e.g., pulmonary inflammation and genotoxicity), that correlated with dose and time. Benchmark doses (BMDs) for apical endpoints were comparable to minimum BMDs for relevant pathway-specific expression changes. Comparison to inflammatory lung disease models (i.e., allergic airway inflammation, bacterial infection and tissue injury and fibrosis) and human disease profiles revealed that induced gene expression changes in Printex 90 exposed mice were similar to those typical for pulmonary injury and fibrosis. Very similar fibrotic pathways were perturbed in CBNP-exposed mice and human fibrosis disease models. Our synthesis demonstrates how toxicogenomic profiles may be used in human health risk assessment of nanoparticles and constitutes an important step forward in the ultimate recognition of toxicogenomic endpoints in human health risk. As our knowledge of molecular pathways, dose-response characteristics and relevance to human disease continues to grow, we anticipate that toxicogenomics will become increasingly useful in assessing chemical toxicities and in human health risk assessment.

Molecular and genomic approach for understanding the gene-environment interaction between Nrf2 deficiency and carcinogenic nickel-induced DNA damage

Nickel (II) is a toxic and carcinogenic metal which induces a redox imbalance following oxidative stress. Nuclear factor erythroid-2 related factor 2 (Nrf2) is a redox factor that regulates oxidation/reduction status and consequently mediates cytoprotective responses against exposure to environmental toxicants. In this study, we investigated the protective roles of the Nrf2 gene against oxidative stress and DNA damage induced by nickel at sub-lethal doses. Under nickel exposure conditions, we detected significantly increased intracellular ROS generation, in addition to higher amounts of DNA damage using comet assay and γ-H2AX immunofluorescence staining in Nrf2 lacking cells, as compared to Nrf2 wild-type cells. In addition, we attempted to identify potential nickel and Nrf2-responsive targets and the relevant pathway. The genomic expression data were analyzed using microarray for the selection of synergistic effect-related genes by Nrf2 knockdown under nickel treatment. In particular, altered expressions of 6 upregulated genes (CAV1, FOSL2, MICA, PIM2, RUNX1 and SLC7A6) and 4 downregulated genes (APLP1, CLSPN, PCAF and PRAME) were confirmed by qRT-PCR. Additionally, using bioinformatics tool, we found that these genes functioned principally in a variety of molecular processes, including oxidative stress response, necrosis, DNA repair and cell survival. Thus, we describe the potential biomarkers regarded as molecular candidates for Nrf2-related cellular protection against nickel exposure. In conclusion, these findings indicate that Nrf2 is an important factor with a protective role in the suppression of mutagenicity and carcinogenicity by environmental nickel exposure in terms of gene-environment interaction.

The impact of network biology in pharmacology and toxicology

With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical researchers are urged to implement new types of analyses that can lead to new therapeutic interventions with increased safety and efficacy compared with existing medications.

Nuclear magnetic resonance-based metabolomics for prediction of gastric damage induced by indomethacin in rats

Non-steroidal anti-inflammatory drugs (NSAIDs) have side effects including gastric erosions, ulceration and bleeding. In this study, pattern recognition analysis of the (1)H-nuclear magnetic resonance (NMR) spectra of urine was performed to develop surrogate biomarkers related to the gastrointestinal (GI) damage induced by indomethacin in rats. Urine was collected for 5 h after oral administration of indomethacin (25 mg kg(-1)) or co-administration with cimetidine (100 mg kg(-1)), which protects against GI damage. The (1)H-NMR urine spectra were divided into spectral bins (0.04 ppm) for global profiling, and 36 endogenous metabolites were assigned for targeted profiling. The level of gastric damage in each animal was also determined. Indomethacin caused severe gastric damage; however, indomethacin administered with cimetidine did not. Simultaneously, the patterns of changes in their endogenous metabolites were different. Multivariate data analyses were carried out to recognize the spectral pattern of endogenous metabolites related to indomethacin using partial least square-discrimination analysis. In targeted profiling, a few endogenous metabolites, 2-oxoglutarate, acetate, taurine and hippurate, were selected as putative biomarkers for the gastric damage induced by indomethacin. These metabolites changed depending on the degree of GI damage, although the same dose of indomethacin (10 mg kg(-1)) was administered to rats. The results of global and targeted profiling suggest that the gastric damage induced by NSAIDs can be screened in the preclinical stage of drug development using a NMR based metabolomics approach.

Toward toxicity testing of nanomaterials in the 21st century: a paradigm for moving forward

A challenge-facing hazard identification and safety evaluation of engineered nanomaterials being introduced to market is the diversity and complexity of the types of materials with varying physicochemical properties, many of which can affect their toxicity by different mechanisms. In general, in vitro test systems have limited usefulness for hazard identification of nanoparticles due to various issues. Meanwhile, conducting chronic toxicity/carcinogenicity studies in rodents for every new nanomaterial introduced into the commerce is impractical if not impossible. New toxicity testing systems which rely on predictive, high-throughput technologies may be the ultimate goal of evaluating the potential hazard of nanomaterials. However, at present, this approach alone is unlikely to succeed in evaluating the toxicity of the wide array of nanomaterials and requires validation from in vivo studies. This article proposes a paradigm for toxicity testing and elucidation of the molecular mechanisms of reference materials for specific nanomaterial classes/subclasses using short-term in vivo animal studies in conjunction with high-throughput screenings and mechanism-based short-term in vitro assays. The hazard potential of a particular nanomaterial can be evaluated by conducting only in vitro high-throughput assays and mechanistic studies and comparing the data with those of the reference materials in the specific class/subclass-an approach in line with the vision for 'Toxicity Testing in the 21st Century' of chemicals. With well-designed experiments, testing nanomaterials of varying/selected physicochemical parameters may be able to identify the physicochemical parameters contributing to toxicity. The data so derived could be used for the development of computer model systems to predict the hazard potential of specific nanoparticles based on property-activity relationships.

Evaluation of research activities and research needs to increase the impact and applicability of alternative testing strategies in risk assessment practice

The present paper aims at identifying strategies to increase the impact and applicability of alternative testing strategies in risk assessment. To this end, a quantitative and qualitative literature evaluation was performed on (a) current research efforts in the development of in vitro methods aiming for alternatives to animal testing, (b) the possibilities and limitations of in vitro methods for regulatory purposes and (c) the potential of physiologically-based kinetic (PBK) modeling to improve the impact and applicability of in vitro methods in risk assessment practice. Overall, the evaluation showed that the focus of state-of-the-art research activities does not seem to be optimally directed at developing in vitro alternatives for those endpoints that are most animal-demanding, such as reproductive and developmental toxicity, and carcinogenicity. A key limitation in the application of in vitro alternatives to such systemic endpoints is that in vitro methods do not provide so-called points of departure, necessary for regulators to set safe exposure limits. PBK-modeling could contribute to overcoming this limitation by providing a method that allows extrapolation of in vitro concentration-response curves to in vivo dose-response curves. However, more proofs of principle are required.