Time-course cross-species transcriptomics reveals conserved hepatotoxicity pathways induced by repeated administration of cyclosporine A

Cyclosporine A (CsA) has shown efficacy against immunity-related diseases despite its toxicity in various organs, including the liver, emphasizing the need to elucidate its underlying hepatotoxicity mechanism. This study aimed to capture the alterations in genome-wide expression over time and the subsequent perturbations of corresponding pathways across species. Six data from humans, mice, and rats, including animal liver tissue, human liver microtissues, and two liver cell lines exposed to CsA toxic dose, were used. The microtissue exposed to CsA for 10 d was analyzed to obtain dynamically differentially expressed genes (DEGs). Single-time points data at 1, 3, 5, 7, and 28 d of different species were used to provide additional evidence. Using liver microtissue-based longitudinal design, DEGs that were consistently up- or down-regulated over time were captured, and the well-known mechanism involved in CsA toxicity was elucidated. Thirty DEGs that consistently changed in longitudinal data were also altered in 28-d rat in-house data with concordant expression. Some genes (e.g. TUBB2A, PLIN2, APOB) showed good concordance with identified DEGs in 1-d and 7-d mouse data. Pathway analysis revealed up-regulations of protein processing, asparagine N-linked glycosylation, and cargo concentration in the endoplasmic reticulum. Furthermore, the down-regulations of pathways related to biological oxidations and metabolite and lipid metabolism were elucidated. These pathways were also enriched in single-time-point data and conserved across species, implying their biological significance and generalizability. Overall, the human organoids-based longitudinal design coupled with cross-species validation provides temporal molecular change tracking, aiding mechanistic elucidation and biologically relevant biomarker discovery.

Using available in vitro metabolite identification and time course kinetics for β-chloroprene and its metabolite, (1-chloroethenyl) oxirane, to include reactive oxidative metabolites and glutathione depletion in a PBPK model for β-chloroprene

Introduction: ß-chloroprene (2-chloro-1,3-butadiene; CP) causes lung tumors after inhalation exposures in rats and mice. Mice develop these tumors at lower exposures than rats. In rats CP exposures cause depletion of lung glutathione (GSH). Methods: PBPK models developed to relate the appearance of mouse lung tumors with rates of CP metabolism to reactive metabolites or total amounts metabolized during exposures have been expanded to include production of reactive metabolites from CP. The extended PBPK model describes both the unstable oxirane metabolite, 2-CEO, and metabolism of the more stable oxirane, 1-CEO, to reactive metabolites via microsomal oxidation to a diepoxide, and linked production of these metabolites to a PK model predicting GSH depletion with increasing CP exposure. Key information required to develop the model were available from literature studies identifying: 1) microsomal metabolites of CP, and 2) in vitro rates of clearance of CP and 1-CEO from active microsomal preparations from mice, rats, hamsters and humans. Results: Model simulation of concentration dependence of disproportionate increases in reactive metabolite concentrations as exposures increases and decreases in tissue GSH are consistent with the dose-dependence of tumor formation. At the middle bioassay concentrations with a lung tumor incidence, the predicted tissue GSH is less than 50% background. These simulations of reduction in GSH are also consistent with the gene expression results showing the most sensitive pathways are Nrf2-regulation of oxidative stress and GSH metabolism. Discussion: The PBPK model is used to correlate predicted tissue exposure to reactive metabolites with toxicity and carcinogenicity of CP.

GAPDH: A common housekeeping gene with an oncogenic role in pan-cancer

Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is one of the most prominent housekeeping proteins and is widely used as an internal control in some semi-quantitative assays. In addition to glycolysis, GAPDH is involved in several cancer-related biological processes and has been reported to be commonly dysregulated in multiple cancer types. Therefore, its role in the physiological process of cancer needs to be urgently elucidated. Pan-cancer analysis indicated that GAPDH is ubiquitously highly expressed in most cancer types, and that patients with a high GAPDH expression of in tumor tissues have a poor prognosis. The concordance of GAPDH expression in tumors with the infiltration of immune cells and immune checkpoints implies a certain association between GAPDH and the tumor microenvironment as well as tumor development. Gene Set Enrichment Analysis revealed that GAPDH may contribute to multiple important cancer-related pathways and biological processes. Multi-omics analysis and in vitro cell experiments revealed that GAPDH overexpression is regulated by DNA copy number amplification and promoter methylation modification. Importantly, a transcription factor, forkhead box M1 (FOXM1), which is capable of regulating GAPDH expression, was also identified and was confirmed to be an oncogene and ubiquitously highly expressed in multiple cancer types. Semi-quantitative chromatin immunoprecipitation, quantitative PCR, and dual-luciferase assays showed that FOXM1 mainly binds to the promoter region of GAPDH in two cancer cell lines. The present findings revealed the implication of GAPDH in tumor development, thus bringing attention to this important molecule and casting doubts on its role as an internal reference gene in cancer studies.

A computational system for Bayesian benchmark dose estimation of genomic data in BBMD

BACKGROUND

Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the "best" model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision.

OBJECTIVE

By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose-response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress.

METHODS

The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties.

RESULTS

This system was tested and validated versus 24 previously published in-vivo microarray dose-response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78-0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established.

DISCUSSION

Overall, this study demonstrates that dose-response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose-response data analysis with outstanding functionalities to quantify uncertainty from various sources.

Toxicogenomics provides insights to toxicity pathways of neonicotinoids to aquatic insect, Chironomus dilutus

Neonicotinoid insecticides have posed a great threat to non-target organisms, yet the mechanisms underlying their toxicity are not well characterized. Major modes of action (MoAs) of imidacloprid were analyzed in an aquatic insect Chironomus dilutus. Lethal and sublethal outcomes were assessed in the midges after 96-h exposure to imidacloprid. Global transcriptomic profiles were determined using de novo RNA-sequencing to more holistically identify toxicity pathways. Transcriptional 10% biological potency values derived from ranked KEGG pathways and GO terms were 0.02 (0.01-0.08) (mean (95% confidence interval) and 0.05 (0.04-0.06) μg L^-1, respectively, which were more sensitive than those from phenotypic traits (10% lethal concentration: 0.44 (0.23-0.79) μg L^-1; 10% burrowing behavior concentration: 0.30 (0.22-0.43) μg L^-1). Major MoAs of imidacloprid in aquatic species were identified as follows: the activation of nicotinic acetylcholine receptors (nAChRs) induced by imidacloprid impaired organisms' nerve system through calcium ion homeostasis imbalance and mitochondrial dysfunction, which posed oxidative stress and DNA damage and eventually caused death of organisms. The current investigation highlighted that imidacloprid affected C. dilutus at environmentally relevant concentrations, and elucidated toxicity pathways derived from gene alteration to individual outcomes, calling for more attention to toxicity of neonicotinoids to aquatic organisms.

Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach

The ever-increasing number of chemicals and complex mixtures demands a high-throughput and cost-effective approach for chemical safety assessment. High-throughput transcriptomics (HTT) is promising in investigating genome-scale perturbation of chemical exposure in concentration-dependent manner. However, the application of HTT has been limited due to lack of methodology for single chemicals and mixture assessment. This study aimed to evaluate the ability of a newly-developed human reduced transcriptomics (RHT) approach to assess pathway-based profiles of single chemicals, and to develop a biological pathway-based approach for benchmarking mixture potency using single chemical-based prediction model. First, concentration-dependent RHT were used to qualitatively and quantitatively differentiate pathway-based patterns of different chemicals, using three model toxicants, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), triclosan (TCS) and 5-Chloro-6-hydroxy-2,2',4,4'-tetrabromodiphenyl ether (5-Cl-6-OH-BDE-47). AHR-regulated genes and pathways were most sensitively induced by TCDD, while TCS and 5-Cl-6-OH-BDE-47 were much less potent in AHR-associated activation, which was concordant with known MoA of each single chemical. Second, two artificial mixtures and their components of twelve individual chemicals were performed with concentration-dependent RHT. Concentration addition (CA) and independent action (IA) models were used to predict transcriptional potency of mixtures from transcriptomics of individual chemicals. For overall bioactivity, CA and IA models can both predict potency of observed responses within 95% confidence interval. For specific biological processes, multiple biological processes such as hormone signaling and DNA damage can be predicted using CA models for mixtures. The concentration-dependent RHT can provide a powerful approach for qualitative and quantitative assessment of biological pathway perturbated by environment chemical and mixtures.

A toxicogenomic approach for the risk assessment of the food contaminant acetamide

Acetamide (CAS 60-35-5) is detected in common foods. Chronic rodent bioassays led to its classification as a group 2B possible human carcinogen due to the induction of liver tumors in rats. We used a toxicogenomics approach in Wistar rats gavaged daily for 7 or 28 days at doses of 300 to 1500 mg/kg/day (mkd) to determine a point of departure (POD) and investigate its mode of action (MoA). Ki67 labeling was increased at doses ≥750 mkd up to 3.3-fold representing the most sensitive apical endpoint. Differential gene expression analysis by RNA-Seq identified 1110 and 1814 differentially expressed genes in male and female rats, respectively, following 28 days of treatment. Down-regulated genes were associated with lipid metabolism while up-regulated genes included cell signaling, immune response, and cell cycle functions. Benchmark dose (BMD) modeling of the Ki67 labeling index determined the BMD10 lower confidence limit (BMDL10) as 190 mkd. Transcriptional BMD modeling revealed excellent concordance between transcriptional POD and apical endpoints. Collectively, these results indicate that acetamide is most likely acting through a mitogenic MoA, though specific key initiating molecular events could not be elucidated. A POD value of 190 mkd determined for cell proliferation is suggested for risk assessment purposes.

Extended Analysis and Evidence Integration of Chloroprene as a Human Carcinogen

β-Chloroprene is used in the production of polychloroprene, a synthetic rubber. In 2010, Environmental Protection Agency (EPA) published the Integrated Risk Information System "Toxicological Review of Chloroprene," concluding that chloroprene was "likely to be carcinogenic to humans." This was based on findings from a 1998 National Toxicology Program (NTP) study showing multiple tumors within and across animal species; results from occupational epidemiological studies; a proposed mutagenic mode of action; and structural similarities with 1,3-butadiene and vinyl chloride. Using mouse data from the NTP study and assuming a mutagenic mode of action, EPA calculated an inhalation unit risk (IUR) for chloroprene of 5 × 10^-4 per µg/m³ . This is among the highest IURs for chemicals classified by IARC or EPA as known or probable human carcinogens and orders of magnitude higher than the IURs for carcinogens such as vinyl chloride, benzene, and 1,3-butadiene. Due to differences in pharmacokinetics, mice appear to be uniquely responsive to chloroprene exposure compared to other animals, including humans, which is consistent with the lack of evidence of carcinogenicity in robust occupational epidemiological studies. We evaluated and integrated all lines of evidence for chloroprene carcinogenicity to assess whether the 2010 EPA IUR could be scientifically substantiated. Due to clear interspecies differences in carcinogenic response to chloroprene, we applied a physiologically based pharmacokinetic model for chloroprene to calculate a species-specific internal dose (amount metabolized/gram of lung tissue) and derived an IUR that is over 100-fold lower than the 2010 EPA IUR. Therefore, we recommend that EPA's IUR be updated.

Incorporation of in vitro metabolism data and physiologically based pharmacokinetic modeling in a risk assessment for chloroprene

Objective: To develop a physiologically based pharmacokinetic (PBPK) model for chloroprene in the mouse, rat and human, relying only on in vitro data to estimate tissue metabolism rates and partitioning, and to apply the model to calculate an inhalation unit risk (IUR) for chloroprene.Materials and methods: Female B6C3F1 mice were the most sensitive species/gender for lung tumors in the 2-year bioassay conducted with chloroprene. The PBPK model included tissue metabolism rate constants for chloroprene estimated from results of in vitro gas uptake studies using liver and lung microsomes. To assess the validity of the PBPK model, a 6-hr, nose-only chloroprene inhalation study was conducted with female B6C3F1 mice in which both chloroprene blood concentrations and ventilation rates were measured. The PBPK model was then used to predict dose measures - amounts of chloroprene metabolized in lungs per unit time - in mice and humans.Results: The mouse PBPK model accurately predicted in vivo pharmacokinetic data from the 6-hr, nose-only chloroprene inhalation study. The PBPK model was used to conduct a cancer risk assessment based on metabolism of chloroprene to reactive epoxides in the lung, the target tissue in mice. The IUR was over100-fold lower than the IUR from the EPA Integrated Risk Information System (IRIS), which was based on inhaled chloroprene concentration. The different result from the PBPK model risk assessment arises from use of the more relevant tissue dose metric, amount metabolized, rather than inhaled concentrationDiscussion and conclusions: The revised chloroprene PBPK model is based on the best available science, including new test animal in vivo validation, updated literature review and a Markov-Chain Monte Carlo analysis to assess parameter uncertainty. Relying on both mouse and human metabolism data also provides an important advancement in the use of quantitative in vitro to in vivo extrapolation (QIVIVE). Inclusion of the best available science is especially important when deriving a toxicity value based on species extrapolation for the potential carcinogenicity of a reactive metabolite.

Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions

Recently, numerous organizations, including governmental regulatory agencies in the U.S. and abroad, have proposed using data from New Approach Methodologies (NAMs) for augmenting and increasing the pace of chemical assessments. NAMs are broadly defined as any technology, methodology, approach or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. High-throughput transcriptomics (HTTr) is a type of NAM that uses gene expression profiling as an endpoint for rapidly evaluating the effects of large numbers of chemicals on in vitro cell culture systems. As compared to targeted high-throughput screening (HTS) approaches that measure the effect of chemical X on target Y, HTTr is a non-targeted approach that allows researchers to more broadly characterize the integrated response of an intact biological system to chemicals that may affect a specific biological target or many biological targets under a defined set of treatment conditions (time, concentration, etc.). HTTr screening performed in concentration-response mode can provide potency estimates for the concentrations of chemicals that produce perturbations in cellular response pathways. Here, we discuss study design considerations for HTTr concentration-response screening and present a framework for the use of HTTr-based biological pathway-altering concentrations (BPACs) in a screening-level, risk-based chemical prioritization approach. The framework involves concentration-response modeling of HTTr data, mapping gene level responses to biological pathways, determination of BPACs, in vitro-to-in vivo extrapolation (IVIVE) and comparison to human exposure predictions.

Tissue- and strain-specific effects of a genotoxic carcinogen 1,3-butadiene on chromatin and transcription

Epigenetic effects of environmental chemicals are under intense investigation to fill existing knowledge gaps between environmental/occupational exposures and adverse health outcomes. Chromatin accessibility is one prominent mechanism of epigenetic control of transcription, and understanding of the chemical effects on both could inform the causal role of epigenetic alterations in disease mechanisms. In this study, we hypothesized that baseline variability in chromatin organization and transcription profiles among various tissues and mouse strains influence the outcome of exposure to the DNA damaging chemical 1,3-butadiene. To test this hypothesis, we evaluated DNA damage along with comprehensive quantification of RNA transcripts (RNA-seq), identification of accessible chromatin (ATAC-seq), and characterization of regions with histone modifications associated with active transcription (ChIP-seq for acetylation at histone 3 lysine 27, H3K27ac). We collected these data in the lung, liver, and kidney of mice from two genetically divergent strains, C57BL/6J and CAST/EiJ, that were exposed to clean air or to 1,3-butadiene (~600 ppm) for 2 weeks. We found that tissue effects dominate differences in both gene expression and chromatin states, followed by strain effects. At baseline, xenobiotic metabolism was consistently more active in CAST/EiJ, while immune system pathways were more active in C57BL/6J across tissues. Surprisingly, even though all three tissues in both strains harbored butadiene-induced DNA damage, little transcriptional effect of butadiene was observed in liver and kidney. Toxicologically relevant effects of butadiene in the lung were on the pathways of xenobiotic metabolism and inflammation. We also found that variability in chromatin accessibility across individuals (i.e., strains) only partially explains the variability in transcription. This study showed that variation in the basal states of epigenome and transcriptome may be useful indicators for individuals or tissues susceptible to genotoxic environmental chemicals.

Integration of the TGx-28.65 genomic biomarker with the flow cytometry micronucleus test to assess the genotoxicity of disperse orange and 1,2,4-benzenetriol in human TK6 cells

In vitro gene expression signatures to predict toxicological responses can provide mechanistic context for regulatory testing. We previously developed the TGx-28.65 genomic biomarker from a database of gene expression profiles derived from human TK6 cells exposed to 28 well-known compounds. The biomarker comprises 65 genes that can classify chemicals as DNA damaging or non-DNA damaging. In this study, we applied the TGx-28.65 genomic biomarker in parallel with the in vitro micronucleus (MN) assay to determine if two chemicals of regulatory interest at Health Canada, disperse orange (DO: the orange azo dye 3-[[4-[(4-Nitrophenyl)azo]phenyl] benzylamino]propanenitrile) and 1,2,4-benzenetriol (BT: a metabolite of benzene) are genotoxic or non-genotoxic. Both chemicals caused dose-dependent declines in relative survival and increases in apoptosis. A strong significant increase in MN induction was observed for all concentrations of BT; the top two concentrations of DO also caused a statistically significant increase in MN, but these increases were <2-fold above controls. TGx-28.65 analysis classified BT as genotoxic at all three concentrations and DO as genotoxic at the mid and high concentrations. Thus, although DO only caused a small increase in MN, this response was sufficient to induce a cellular DNA damage response. Benchmark dose modeling confirmed that BT is much more potent than DO. The results strongly suggest that follow-up work is required to assess whether DO and BT are also genotoxic in vivo. This is particularly important for DO, which may require metabolic activation by bacterial gut flora to fully induce its genotoxic potential. Our previously published data and this proof of concept study suggest that the TGx-28.65 genomic biomarker has the potential to add significant value to existing approaches used to assess genotoxicity.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.

Combining transcriptomics and PBPK modeling indicates a primary role of hypoxia and altered circadian signaling in dichloromethane carcinogenicity in mouse lung and liver

Dichloromethane (DCM) is a lung and liver carcinogen in mice at inhalation exposures≥2000ppm. The modes of action (MOA) of these responses have been attributed to formation of genotoxic, reactive metabolite(s). Here, we examined gene expression in lung and liver from female B6C3F1 mice exposed to 0, 100, 500, 2000, 3000 and 4000ppm DCM for 90days. We also simulated dose measures - rates of DCM oxidation to carbon monoxide (CO) in lung and liver and expected blood carboxyhemoglobin (HbCO) time courses with a PBPK model inclusive of both conjugation and oxidation pathways. Expression of large numbers of genes was altered at 100ppm with maximal changes in the numbers occurring by 500 or 2000ppm. Most changes in genes common to the two tissues were related to cellular metabolism and circadian clock. At the lower concentrations, the changes in metabolism-related genes were discordant - up in liver and down in lung. These processes included organelle biogenesis, TCA cycle, and respiratory electron transport. Changes in circadian cycle genes - primarily transcription factors - showed strong concentration-related response at higher concentrations (Arntl, Npas2, and Clock were down-regulated; Cry2, Wee1, Bhlhe40, Per3, Nr1d1, Nr1d2 and Dbp) were up-regulated with similar directionality in both tissues. Overall, persistently elevated HbCO from DCM oxidation appears to cause extended periods of hypoxia, leading to altered circadian coupling to cellular metabolism. The dose response for altered circadian processes correlates with the cancer outcome. We found no evidence of changes in genes indicative of responses to cytotoxic, DNA-reactive metabolites.

The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions

BACKGROUND

The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions.

OBJECTIVE

Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures.

METHODS

New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling.

DISCUSSION

NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams.

CONCLUSIONS

While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.

Weighted gene co-expression network analysis of pneumocytes under exposure to a carcinogenic dose of chloroprene

AIMS

Occupational exposure to chloroprene via inhalation may lead to acute toxicity and chronic pulmonary diseases, including lung cancer. Currently, most research is focused on epidemiological studies of chloroprene production workers. The specific molecular mechanism of carcinogenesis by chloroprene in lung tissues still remains obscure, and specific candidate therapeutic targets for lung cancer are lacking. The present study identifies specific gene modules and valuable hubs associated with lung cancer.

MAIN METHODS

We downloaded the dataset GSE40795 from the Gene Expression Omnibus (GEO) and divided the dataset into the non-carcinogenic dose chloroprene exposed mice group and the carcinogenic dose chloroprene exposed mice group. With a systemic biological view, we discovered significantly altered gene modules between the two groups and identified hub genes in the carcinogenic dose exposed group using weighted co-expression network analysis (WGCNA).

KEY FINDINGS

A total of 2434 differentially expressed genes were identified. Twelve gene modules with multiple biological activities were related to the carcinogenesis of chloroprene in lung tissue. Seven hub genes that were critical for the carcinogenesis of chloroprene in lung tissue were ultimately identified, including Cftr, Hip1, Tbl1x, Ephx1, Cbr3, Antxr2 and Ccnd2. They were implicated in inflammatory response, cell transformation, gene transcription regulation, phase II detoxification, angiogenesis, cell adhesion, motility and the cell cycle.

SIGNIFICANCE

The seven hub genes may become valuable candidates for risk assessment biomarkers and therapeutic targets in lung cancer.

Using gene expression profiling to evaluate cellular responses in mouse lungs exposed to V2O5 and a group of other mouse lung tumorigens and non-tumorigens

Many compounds test positive for lung tumors in two-year NTP carcinogenicity bioassays in B6C3F1 mice. V2O5 was identified as a lung carcinogen in this assay, leading to its IARC (International Agency for Research on Cancer) classification as group 2b or a "possible" human carcinogen. To assess potential tumorigenic mode of action of V2O5, we compared gene expression and gene ontology enrichment in lung tissue of female B6C3F1 mice exposed for 13 weeks to a V2O5 particulate aerosol at a tumorigenic level (2.0 mg/m(3)). Relative to 12 other compounds also tested for carcinogenicity in 2-year bioassays in mice, there were 1026 differentially expressed genes with V2O5, of which 483 were unique to V2O5. Ontology analysis of the 1026 V2O5 differentially expressed genes showed enrichment for hyaluronan and sphingolipid metabolism, adenylate cyclase functions, c-AMP signaling and PKA activation/signaling. Enrichment of lipids/lipoprotein metabolism and inflammatory pathways were consistent with previously reported clinical findings. Enrichment of c-AMP and PKA signaling pathways may arise due to inhibition of phosphatases, a known biological action of vanadate. We saw no enrichment for DNA-damage, oxidative stress, cell cycle, or apoptosis pathway signaling in mouse lungs exposed to V2O5 which is in contrast with past studies evaluating in vivo gene expression in target tissues of other carcinogens (arsenic, formaldehyde, naphthalene and chloroprene).

Time- and concentration-dependent genomic responses of the rat airway to inhaled nickel subsulfide

OBJECTIVE

To provide insights into the mode of action for Ni3S2 lung carcinogenicity by examining gene expression changes in target cells after inhalation exposure.

METHODS

Gene expression changes were determined in micro-dissected lung broncho-alveolar cells from Fischer 344 rats following inhalation of Ni3S2 at 0.0, 0.04, 0.08, 0.15, and 0.60 mg/m(3) (0.03, 0.06, 0.11, and 0.44 mgNi/m(3)) for one and four weeks (6h/day, 5 days/week).

RESULTS

Broncho-alveolar lavage fluid evaluation and lung histopathology provided evidence of inflammation only at the two highest concentrations, which were similar to those tested in the 2-year bioassay. The number of statistically significant up- and down-regulated genes decreased markedly from one to four weeks of exposure, suggesting adaptation. Cell signal pathway enrichment at both time-points primarily reflected responses to toxicity, including inflammatory and proliferative signaling. While proliferative signaling was up-regulated at both time points, some inflammatory signaling reversed from down-regulation at 1 week to up-regulation at 4 weeks.

CONCLUSIONS

These results support a mode of action for Ni3S2 carcinogenicity driven by chronic toxicity, inflammation and proliferation, leading to mis-replication, rather than by direct genotoxicity. Benchmark dose (BMD) analysis identified the lowest pathway transcriptional BMD exposure concentration as 0.026 mgNi/m(3), for apoptosis/survival signaling. When conducted on the basis of lung Ni concentration the lowest pathway BMD was 0.64 μgNi/g lung, for immune/inflammatory signaling.

IMPLICATIONS

These highly conservative BMDs could be used to derive a point of departure in a nonlinear risk assessment for Ni3S2 toxicity and carcinogenicity.

Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments

Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcript abundance, a greater dynamic range, and detection of novel transcripts. However, previous comparisons of the 2 technologies have not covered dose-response experiments that are relevant to toxicology. Male F344 rats were exposed for 13 weeks to 5 doses of bromobenzene, and liver gene expression was measured using both microarrays and RNA-seq. Multiple normalization methods were evaluated for each technology, and gene expression changes were statistically analyzed using both analysis of variance and benchmark dose (BMD). Fold-change values were highly correlated between the 2 technologies, whereas the -log p values showed lower correlation. RNA-seq detected fewer statistically significant genes at lower doses, but more significant genes based on fold change except when a negative binomial transformation was applied. Overlap in genes significant by both p value and fold change was approximately 30%-40%. Random sampling of the RNA-seq data showed an equivalent number of differentially expressed genes compared with microarrays at ~5 million reads. Quantitative RT-PCR of differentially expressed genes uniquely identified by each technology showed a high degree of confirmation when both fold change and p value were considered. The mean dose-response expression of each gene was highly correlated between technologies, whereas estimates of sample variability and gene-based BMD values showed lower correlation. Differences in BMD estimates and statistical significance may be due, in part, to differences in the dynamic range of each technology and the degree to which normalization corrects genes at either end of the scale.