Genotoxicity Assessment of Haloacetaldehyde Disinfection Byproducts via a Simplified Yeast-Based Toxicogenomics Assay.

Haloacetaldehydes (HALs) represent the third-largest category of disinfection byproducts (DBPs) in drinking water in terms of weight. As a subset of unregulated DBPs, only a few HALs have undergone assessment, yielding limited information regarding their genotoxicity mechanisms. Herein, we developed a simplified yeast-based toxicogenomics assay to evaluate the genotoxicity of five specific HALs. This assay recorded the protein expression profiles of eight Saccharomyces cerevisiae strains fused with green fluorescent protein, including all known DNA damage and repair pathways. High-resolution real-time pathway activation data and protein expression profiles in conjunction with clustering analysis revealed that the five HALs induced various DNA damage and repair pathways. Among these, chloroacetaldehyde and trichloroacetaldehyde were found to be positively associated with genotoxicity, while dichloroacetaldehyde, bromoacetaldehyde, and tribromoacetaldehyde displayed negative associations. The protein effect level index, which are molecular end points derived from a toxicogenomics assay, exhibited a statistically significant positive correlation with the results of traditional genotoxicity assays, such as the comet assay (rp = 0.830 and p < 0.001) and SOS/umu assay (rp = 0.786 and p = 0.004). This yeast-based toxicogenomics assay, which employs a minimal set of gene biomarkers, can be used for mechanistic genotoxicity screening and assessment of HALs and other chemical compounds. These results contribute to bridging the knowledge gap regarding the molecular mechanisms underlying the genotoxicity of HALs and enable the categorization of HALs based on their distinct DNA damage and repair mechanisms.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction.

BACKGROUND: Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. OBJECTIVES: This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. METHODS: Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. RESULTS: Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. CONCLUSION: TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

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.

Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment?

In vitro toxicogenomics (TGx) has the potential to replace or supplement animal studies. However, TGx studies often suffer from a limited sample size and cell types. Meanwhile, transcriptomic data have been generated for tens of thousands of compounds using cancer cell lines mainly for drug efficacy screening. Here, we asked the question of whether these types of transcriptomic data can be used to support toxicity assessment. We compared transcriptomic profiles from three cancer lines (HL60, MCF7, and PC3) from the CMap data set with those using primary hepatocytes or in vivo repeated dose studies from the Open TG-GATEs database by using our previously reported pair ranking (PRank) method. We observed an encouraging similarity between HL60 and human primary hepatocytes (PRank score = 0.70), suggesting the two cellular assays could be potentially interchangeable. When the analysis was limited to drug-induced liver injury (DILI)-related compounds or genes, the cancer cell lines exhibited promise in DILI assessment in comparison with conventional TGx systems (i.e., human primary hepatocytes or rat in vivo repeated dose). Also, some toxicity-related pathways, such as PPAR signaling pathways and fatty acid-related pathways, were preserved across various assay systems, indicating the assay transferability is biological process-specific. Furthermore, we established a potential application of transcriptomic profiles of cancer cell lines for studying immune-related biological processes involving some specific cell types. Moreover, if PRank analysis was focused on only landmark genes from L1000 or S1500+, the advantage of cancer cell lines over the TGx studies was limited. In conclusion, repurposing of existing cancer-related transcript profiling data has great potential for toxicity assessment, particularly in predicting DILI.

Application of the comparison approach to open TG-GATEs: A useful toxicogenomics tool for detecting modes of action in chemical risk assessment.

Mode of action information is one of the key components for chemical risk assessment as mechanistic insight leads to better understanding of potential adverse health effects of a chemical. This insight greatly facilitates assessment of human relevance and enhances the use of non-animal methods for risk assessment, as it ultimately enables extrapolation from initiating events to adverse effects. Recently, we reported an in vitro toxicogenomics comparison approach to categorize (non-)genotoxic carcinogens according to similarities in their proposed modes of action. The present study aimed to make this comparison approach generally applicable, allowing comparison of outcomes across different studies. The resulting further developed comparison approach was evaluated through application to toxicogenomics data on 18 liver toxicants in human and rat primary hepatocytes from the Open TG-GATEs database. The results showed sensible matches between compounds with (partial) overlap in mode of action, whilst matches for compounds with different modes of action were absent. Comparison of the results across species revealed pronounced and relevant differences between primary rat and human hepatocytes, underpinning that information on mode of action enhances assessment of human relevance. Thus, we demonstrate that the comparison approach now is generally applicable, facilitating its use as tool in mechanism-based risk assessment.

Availability, Quality, and Relevance of Toxicogenomics Data for Human Health Risk Assessment: A Scoping Review of the Literature on Trihalomethanes.

Human health risk assessment (HHRA) must be adapted to the challenges of the 21st century, and the use of toxicogenomics data in HHRA is among the changes that regulatory agencies worldwide are trying to implement. However, the use of toxicogenomics data in HHRA is still limited. The purpose of this study was to explore the availability, quality, and relevance to HHRA of toxicogenomics publications as potential barriers to their use in HHRA. We conducted a scoping review of available toxicogenomics literature, using trihalomethanes as a case study. Four bibliographic databases (including the Comparative Toxicogenomics Database) were assessed. An evaluation table was developed to characterize quality and relevance of studies included on the basis of criteria proposed in the literature. Studies were selected and analyzed by 2 independent reviewers. Only 9 studies, published between 1997 and 2015, were included in the analysis. Based on the selected criteria, critical methodological details were often missing; in fact, only 3 out of 9 studies were considered to be of adequate quality for HHRA. No studies met >3 (out of 7) criteria of relevance to HHRA (eg, adequate number of doses and sample size). This first scoping review of toxicogenomics publications on trihalomethanes shows that low availability, quality, and relevance to HHRA of toxicogenomics publications presents potential barriers to their use in HHRA. Improved reporting of methodological details and study design is needed in the future so that toxicogenomics studies can be appropriately assessed regarding their quality and value for HHRA.

In vitro RNA-seq-based toxicogenomics assessment shows reduced biological effect of tobacco heating products when compared to cigarette smoke.

The battery of regulatory tests used to evaluate the risk of novel tobacco products such as heated tobacco products (THPs) presents some limitations including a bias towards the apical endpoint tested, and limited information on the mode of action. This is driving a paradigm shift to more holistic systems biology approaches. In this study, we used RNA-sequencing to compare the transcriptomic perturbations following acute exposure of a 3D airway tissue to the aerosols from two commercial THPs and a reference 3R4F cigarette. 2809 RNAs were differentially expressed for the 3R4F treatment and 115 and 2 RNAs for the two THPs (pFDR < 0.05, FC > 1.5), respectively. The relationship between the identified RNA features and gene ontologies were mapped showing a strong association with stress response, xenobiotics metabolism, and COPD-related terms for 3R4F. In contrast, fewer ontologies were found enriched for the THPs aerosols. "Response to wounding" was a common COPD-related term over-represented for the two THPs but at a reduced significance. Quantification of a cytokine panel post-exposure confirmed a pro-inflammatory effect of cigarette smoke but not for THPs. In conclusion, THPs have a reduced impact on gene expression compared to 3R4F.

Genetic toxicological assessment and anti-arthritic effects of an ethanolic extract obtained from Salvia lachnostachys Benth leaves in mice.

In the present study, mice were subjected to prolonged treatment with ethanolic extract of Salvia lachnostachys Benth leaves (SLEE), and the inflammatory and arthritic parameters were evaluated using the Complete Freund's Adjuvant (CFA) model. The genotoxicity of SLEE were also assayed using genetic toxicological tests. For the CFA model, 28 male C57BL/6 mice were distributed randomly into four groups (control, 50 mg/kg of SLEE, 100 mg/kg of SLEE and dexamethasone) for the evaluation of hyperalgesia and paw edema for 21 days after injection of CFA into the paw. To conduct the toxicogenetic assessments (comet assay and micronuclei assay), apoptosis and splenic phagocytosis were evaluated in male Swiss mice after the administration of saline (control group), cyclophosphamide (positive control group) and SLEE (10, 100 and 1000 mg/kg). SLEE significantly reduced the mechanical hyperalgesia and edema caused by CFA injection. The results of the toxicogenetic assessment revealed no toxicogenetic potential in the mice, and the evaluation of apoptosis showed an increase in apoptotic cells in the spleen after 72 h of treatment with SLEE (1000 mg/kg). SLEE exhibited anti-arthritic activity with no toxicogenetic damage. These toxicogenic results support the safety of SLEE.

Multiple-endpoints gene alteration-based (MEGA) assay: A toxicogenomics approach for water quality assessment of wastewater effluents.

Wastewater effluents contain a significant number of toxic contaminants, which, even at low concentrations, display a wide variety of toxic actions. In this study, we developed a multiple-endpoints gene alteration-based (MEGA) assay, a real-time PCR-based transcriptomic analysis, to assess the water quality of wastewater effluents for human health risk assessment and management. Twenty-one genes from the human hepatoblastoma cell line (HepG2), covering the basic health-relevant stress responses such as response to xenobiotics, genotoxicity, and cytotoxicity, were selected and incorporated into the MEGA assay. The genes related to the p53-mediated DNA damage response and cytochrome P450 were selected as markers for genotoxicity and response to xenobiotics, respectively. Additionally, the genes that were dose-dependently regulated by exposure to the wastewater effluents were chosen as markers for cytotoxicity. The alterations in the expression of an individual gene, induced by exposure to the wastewater effluents, were evaluated by real-time PCR and the results were validated by genotoxicity (e.g., comet assay) and cell-based cytotoxicity tests. In summary, the MEGA assay is a real-time PCR-based assay that targets cellular responses to contaminants present in wastewater effluents at the transcriptional level; it is rapid, cost-effective, and high-throughput and can thus complement any chemical analysis for water quality assessment and management.

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.

Occupational chemical exposure and diabetes mellitus risk.

Diabetes mellitus (DM) is a group of metabolic diseases that may originate from an interaction between genetic and lifestyle risk factors. However, the possible role of occupational chemical exposures in the disease development and progression remains unclear. Therefore, this review aimed to provide a comprehensive evaluation of the relationship between occupational exposure to specific chemical substances or industrial activities and DM morbidity and mortality outcomes. Although some positive findings may support the diabetogenic role of certain pesticides and dioxins in different workplaces, the variable conditions of exposure, the lack of quantitative environmental or biological monitoring data and the different outcomes evaluated do not allow defining a specific exposure-disease causality. Therefore, further epidemiological studies will be necessary to adequately assess modes of action for different substances, dose-response relationships as well as individual susceptibility factors potentially affecting the exposure-disease continuum. Overall, this appears important to adequately assess, communicate and manage risks in occupational chemical exposure settings with the aim to protect workers and build healthier job conditions for diabetic employees.

Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment.

There is increasing interest in the use of quantitative transcriptomic data to determine benchmark dose (BMD) and estimate a point of departure (POD) for human health risk assessment. Although studies have shown that transcriptional PODs correlate with those derived from apical endpoint changes, there is no consensus on the process used to derive a transcriptional POD. Specifically, the subsets of informative genes that produce BMDs that best approximate the doses at which adverse apical effects occur have not been defined. To determine the best way to select predictive groups of genes, we used published microarray data from dose-response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). The relationship between transcriptional BMDs derived using these 11 approaches and PODs derived from apical data that might be used in chemical risk assessment was examined. Transcriptional BMD values for all 11 approaches were remarkably aligned with corresponding apical PODs, with the vast majority of toxicogenomics PODs being within tenfold of those derived from apical endpoints. We identified at least four approaches that produce BMDs that are effective estimates of apical PODs across multiple sampling time points. Our results support that a variety of approaches can be used to derive reproducible transcriptional PODs that are consistent with PODs produced from traditional methods for chemical risk assessment.

A Quantitative Toxicogenomics Assay for High-throughput and Mechanistic Genotoxicity Assessment and Screening of Environmental Pollutants.

The ecological and health concern of mutagenicity and carcinogenicity potentially associated with an overwhelmingly large and ever-increasing number of chemicals demands for cost-effective and feasible method for genotoxicity screening and risk assessment. This study proposed a genotoxicity assay using GFP-tagged yeast reporter strains, covering 38 selected protein biomarkers indicative of all the seven known DNA damage repair pathways. The assay was applied to assess four model genotoxic chemicals, eight environmental pollutants and four negative controls across six concentrations. Quantitative molecular genotoxicity end points were derived based on dose response modeling of a newly developed integrated molecular effect quantifier, Protein Effect Level Index (PELI). The molecular genotoxicity end points were consistent with multiple conventional in vitro genotoxicity assays, as well as with in vivo carcinogenicity assay results. Further more, the proposed genotoxicity end point PELI values quantitatively correlated with both comet assay in human cell and carcinogenicity potency assay in mice, providing promising evidence for linking the molecular disturbance measurements to adverse outcomes at a biological relevant level. In addition, the high-resolution DNA damaging repair pathway alternated protein expression profiles allowed for chemical clustering and classification. This toxicogenomics-based assay presents a promising alternative for fast, efficient and mechanistic genotoxicity screening and assessment of drugs, foods, and environmental contaminants.

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting.

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.

Development of the adverse outcome pathway "alkylation of DNA in male premeiotic germ cells leading to heritable mutations" using the OECD's users' handbook supplement.

The Organisation for Economic Cooperation and Development's (OECD) Adverse Outcome Pathway (AOP) programme aims to develop a knowledgebase of all known pathways of toxicity that lead to adverse effects in humans and ecosystems. A Users' Handbook was recently released to provide supplementary guidance on AOP development. This article describes one AOP-alkylation of DNA in male premeiotic germ cells leading to heritable mutations. This outcome is an important regulatory endpoint. The AOP describes the biological plausibility and empirical evidence supporting that compounds capable of alkylating DNA cause germ cell mutations and subsequent mutations in the offspring of exposed males. Alkyl adducts are subject to DNA repair; however, at high doses the repair machinery becomes saturated. Lack of repair leads to replication of alkylated DNA and ensuing mutations in male premeiotic germ cells. Mutations that do not impair spermatogenesis persist and eventually are present in mature sperm. Thus, the mutations are transmitted to the offspring. Although there are some gaps in empirical support and evidence for essentiality of the key events for certain aspects of this AOP, the overall AOP is generally accepted as dogma and applies broadly to any species that produces sperm. The AOP was developed and used in an iterative process to test and refine the Users' Handbook, and is one of the first publicly available AOPs. It is our hope that this AOP will be leveraged to develop other AOPs in this field to advance method development, computational models to predict germ cell effects, and integrated testing strategies.

Integrating toxicogenomics into human health risk assessment: lessons learned from the benzo[a]pyrene case study.

The use of short-term toxicogenomic tests to predict cancer (or other health effects) offers considerable advantages relative to traditional toxicity testing methods. The advantages include increased throughput, increased mechanistic data, and significantly reduced costs. However, precisely how toxicogenomics data can be used to support human health risk assessment (RA) is unclear. In a companion paper ( Moffat et al. 2014 ), we present a case study evaluating the utility of toxicogenomics in the RA of benzo[a]pyrene (BaP), a known human carcinogen. The case study is meant as a proof-of-principle exercise using a well-established mode of action (MOA) that impacts multiple tissues, which should provide a best case example. We found that toxicogenomics provided rich mechanistic data applicable to hazard identification, dose-response analysis, and quantitative RA of BaP. Based on this work, here we share some useful lessons for both research and RA, and outline our perspective on how toxicogenomics can benefit RA in the short- and long-term. Specifically, we focus on (1) obtaining biologically relevant data that are readily suitable for establishing an MOA for toxicants, (2) examining the human relevance of an MOA from animal testing, and (3) proposing appropriate quantitative values for RA. We describe our envisioned strategy on how toxicogenomics can become a tool in RA, especially when anchored to other short-term toxicity tests (apical endpoints) to increase confidence in the proposed MOA, and emphasize the need for additional studies on other MOAs to define the best practices in the application of toxicogenomics in RA.

Comparison of toxicogenomics and traditional approaches to inform mode of action and points of departure in human health risk assessment of benzo[a]pyrene in drinking water.

Toxicogenomics is proposed to be a useful tool in human health risk assessment. However, a systematic comparison of traditional risk assessment approaches with those applying toxicogenomics has never been done. We conducted a case study to evaluate the utility of toxicogenomics in the risk assessment of benzo[a]pyrene (BaP), a well-studied carcinogen, for drinking water exposures. Our study was intended to compare methodologies, not to evaluate drinking water safety. We compared traditional (RA1), genomics-informed (RA2) and genomics-only (RA3) approaches. RA2 and RA3 applied toxicogenomics data from human cell cultures and mice exposed to BaP to determine if these data could provide insight into BaP's mode of action (MOA) and derive tissue-specific points of departure (POD). Our global gene expression analysis supported that BaP is genotoxic in mice and allowed the development of a detailed MOA. Toxicogenomics analysis in human lymphoblastoid TK6 cells demonstrated a high degree of consistency in perturbed pathways with animal tissues. Quantitatively, the PODs for traditional and transcriptional approaches were similar (liver 1.2 vs. 1.0 mg/kg-bw/day; lungs 0.8 vs. 3.7 mg/kg-bw/day; forestomach 0.5 vs. 7.4 mg/kg-bw/day). RA3, which applied toxicogenomics in the absence of apical toxicology data, demonstrates that this approach provides useful information in data-poor situations. Overall, our study supports the use of toxicogenomics as a relatively fast and cost-effective tool for hazard identification, preliminary evaluation of potential carcinogens, and carcinogenic potency, in addition to identifying current limitations and practical questions for future work.

RNA-Seq-based toxicogenomic assessment of fresh frozen and formalin-fixed tissues yields similar mechanistic insights.

Formalin-fixed, paraffin-embedded (FFPE) pathology specimens represent a potentially vast resource for transcriptomic-based biomarker discovery. We present here a comparison of results from a whole transcriptome RNA-Seq analysis of RNA extracted from fresh frozen and FFPE livers. The samples were derived from rats exposed to aflatoxin B1 (AFB1 ) and a corresponding set of control animals. Principal components analysis indicated that samples were separated in the two groups representing presence or absence of chemical exposure, both in fresh frozen and FFPE sample types. Sixty-five percent of the differentially expressed transcripts (AFB1 vs. controls) in fresh frozen samples were also differentially expressed in FFPE samples (overlap significance: P < 0.0001). Genomic signature and gene set analysis of AFB1 differentially expressed transcript lists indicated highly similar results between fresh frozen and FFPE at the level of chemogenomic signatures (i.e., single chemical/dose/duration elicited transcriptomic signatures), mechanistic and pathology signatures, biological processes, canonical pathways and transcription factor networks. Overall, our results suggest that similar hypotheses about the biological mechanism of toxicity would be formulated from fresh frozen and FFPE samples. These results indicate that phenotypically anchored archival specimens represent a potentially informative resource for signature-based biomarker discovery and mechanistic characterization of toxicity.

Systematic Omics Analysis Review (SOAR) tool to support risk assessment.

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.