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Comparative transcriptomic analyses to scrutinize the assumption that genotoxic PAHs exert effects via a common mode of action.

ABSTRACT: In this study, the accuracy of the assumption that genotoxic, carcinogenic polycyclic aromatic hydrocarbons (PAHs) act via similar mechanisms of action as benzo(a)pyrene (BaP), the reference PAH used in the human health risk assessment of PAH-containing complex mixtures, was investigated. Adult male Muta™Mouse were gavaged for 28 days with seven individual, genotoxic PAHs. Global gene expression profiles in forestomach, liver, and lung (target tissues of exposure) were determined at 3 days post-exposure. The results are compared with our previously published results from mice exposed to BaP via the same exposure regimen. Although all PAHs showed enhanced ethoxyresorufin-O-deethylase activity, DNA adduct formation, and lacZ mutant frequency in the lungs, the unsupervised cluster analysis of differentially expressed genes revealed that the transcriptional changes are both PAH- and tissue-specific, with lung showing the most response. Further bioinformatics-/pathway-based analysis revealed that all PAHs induce expression of genes associated with carcinogenic processes, including DNA damage response, immune/inflammatory response, or cell signaling processes; however, the type of pathways and the magnitude of change varied for each PAH and were not the same as those observed for BaP. Benchmark dose modeling showed transcriptomic data closely reflected the known tumor incidence for the individual PAHs in each tissue. Collectively, the results suggest that the underlying mechanisms of PAH-induced toxicity leading to tumorigenesis are tissue-specific and not the same for all PAHs; based on the tissue type considered, use of BaP as a reference chemical may overestimate or underestimate the carcinogenic potential of PAHs.

SUBMITTER: Labib S

PROVIDER: S-EPMC5043007 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Comparative transcriptomic analyses to scrutinize the assumption that genotoxic PAHs exert effects via a common mode of action.

Labib S S Williams A A Guo C H CH Leingartner K K Arlt V M VM Schmeiser H H HH Yauk C L CL White P A PA Halappanavar S S

Archives of toxicology 20150916 10

In this study, the accuracy of the assumption that genotoxic, carcinogenic polycyclic aromatic hydrocarbons (PAHs) act via similar mechanisms of action as benzo(a)pyrene (BaP), the reference PAH used in the human health risk assessment of PAH-containing complex mixtures, was investigated. Adult male Muta™Mouse were gavaged for 28 days with seven individual, genotoxic PAHs. Global gene expression profiles in forestomach, liver, and lung (target tissues of exposure) were determined at 3 days post- ...[more]

PMID: 26377693

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Project description:Gene expression analysis has been established as a tool for the characterization of genotoxic mechanisms of chemical mutagens. This approach has been shown to differentiate between DNA reactive genotoxins and non-DNA reactive or indirectly-acting genotoxins. In this context, it has been suggested that expression analysis is capable of distinguishing compounds that cause DNA damage from those that interfere with mitotic spindle function. Formaldehyde (FA) is known to be a DNA-reactive substance which mainly induces chromosomal damage in cultured mammalian cells. However, there has been concern that FA might also act as an aneugen (i.e., induce aneuploidy) but recent cytogenetic studies did not support this assumption. To further characterize FA's genotoxic mode of action, we now used gene expression profiling as a molecular tool to differentiate between clastogenic and aneugenic activity. TK6 cells were exposed to FA for 4 and 24 h and changes in gene expression were analyzed using a whole-genome human microarray. Results were compared to the expression profiles of two DNA-damaging clastogens (methyl methanesulfonate [MMS] and ethyl methanesulfonate [EMS]) and two aneugens (colcemid [COL] and vincristine [VCR]). The gene expression profiles indicated that clastogens and aneugens induce discriminable gene expression patterns. The expression profile of FA showed more similarities to clastogens than to aneugens. Hierarchical clustering analysis as well as several class prediction algorithms revealed a much closer relationship of FA with clastogens than with aneugens. A pathway analysis of differentially regulated genes also demonstrated an overall better agreement of FA with clastogens than with aneugens. Altogether, the results of this study revealed great similarities in gene expression in response to FA and clastogens but did not support an aneugenic activity of FA. Gene expression analysis of untreated TK6 cells or TK6 cells treated with formaldehyde, the DNA-damaging clastogens methyl methanesulfonate or ethyl methanesulfonate, or the aneugens colcemid or vincristine.

Project description:Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium iodide to serve as a pan-DNA dye, fluorescent antibodies against γH2AX, phospho-histone H3, and p53, and fluorescent microspheres for absolute nuclei counts. The assay was applied to TK6 cells and 67 diverse reference chemicals that served as a training set. Exposure was for 24 hrs in 96-well plates, and unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. At 4- and 24-hrs aliquots were removed and added to microtiter plates containing the reagent mix. Following a brief incubation period robotic sampling facilitated walk-away data acquisition. Univariate analyses identified biomarkers and time points that were valuable for classifying agents into one of three groups: clastogenic, aneugenic, or non-genotoxic. These mode of action predictions were optimized with a forward-stepping process that considered Wald test p-values, receiver operator characteristic curves, and pseudo R(2) values, among others. A particularly high performing multinomial logistic regression model was comprised of four factors: 4 hr γH2AX and phospho-histone H3 values, and 24 hr p53 and polyploidy values. For the training set chemicals, the four-factor model resulted in 94% concordance with our a priori classifications. Cross validation occurred via a leave-one-out approach, and in this case 91% concordance was observed. A test set of 17 chemicals that were not used to construct the model were evaluated, some of which utilized a short-term treatment in the presence of a metabolic activation system, and in 16 cases mode of action was correctly predicted. These initial results are encouraging as they suggest a machine learning strategy can be used to rapidly and reliably predict new chemicals' genotoxic mode of action based on data from an efficient and highly scalable multiplexed assay.

Project description:BackgroundComparative transcriptomic analysis is a powerful approach for investigating the molecular mechanisms underlying various physiological and pathological processes, including liver disease. The liver is a vital organ with diverse functions, including metabolism and detoxification. In vitro models of liver cells, such as HepG2, Huh7, and Hep3B, have been widely used to study liver biology and pathology. However, there is limited information on the heterogeneity of these cell lines at the transcriptomic level.ObjectiveThis study aimed to conduct a comparative transcriptomic analysis of three common liver cell lines (HepG2, Huh7, and Hep3B) using publicly available RNA-sequencing data. In addition, we compared these cell lines to primary hepatocytes, cells isolated directly from liver tissue and considered the gold standard for studying liver function and disease.MethodsOur study included sequencing data with the following criteria: total number of reads over 20,000,000, average read length of over 60 base pairs, Illumina sequencing, and non-treated cells. The data for the three cell lines were compiled: HepG2 (97 samples), Huh7 (39 samples), and Hep3B (16 samples). We performed differential gene expression analysis using the DESeq2 package, principal component analysis, hierarchical clustering on principal components, and correlation analysis to explore the heterogeneity within each cell line.ResultsWe identified numerous genes and pathways differentially expressed between HepG2, Huh7, and Hep3B, such as oxidative phosphorylation, cholesterol metabolism, and DNA damage. We report that the expression levels of important genes differ significantly between primary hepatocytes and liver cell lines.ConclusionOur study provides new insights into the transcriptional heterogeneity of commonly used liver cell lines and highlights the importance of considering specific cell line. Consequently, transferring results without considering the heterogeneity of cell lines is impractical and may lead to inaccurate or distorted conclusions.

Project description:The in vitro MultiFlow® DNA Damage Assay multiplexes γH2AX, p53, phospho-histone H3, and polyploidization biomarkers into a single flow cytometric analysis. The current report describes a tiered sequential data analysis strategy based on data generated from exposure of human TK6 cells to a previously described 85 chemical training set and a new pharmaceutical-centric test set (n = 40). In each case, exposure was continuous over a range of closely spaced concentrations, and cell aliquots were removed for analysis following 4 and 24 hr of treatment. The first data analysis step focused on chemicals' genotoxic potential, and for this purpose, we evaluated the performance of a machine learning (ML) ensemble, a rubric that considered fold increases in biomarkers against global evaluation factors (GEFs), and a hybrid strategy that considered ML and GEFs. This first tier further used ML output and/or GEFs to classify genotoxic activity as clastogenic and/or aneugenic. Test set results demonstrated the generalizability of the first tier, with particularly good performance from the ML ensemble: 35/40 (88%) concordance with a priori genotoxicity expectations and 21/24 (88%) agreement with expected mode of action (MoA). A second tier applied unsupervised hierarchical clustering to the biomarker response data, and these analyses were found to group certain chemicals, especially aneugens, according to their molecular targets. Finally, a third tier utilized benchmark dose analyses and MultiFlow biomarker responses to rank genotoxic potency. The relevance of these rankings is supported by the strong agreement found between benchmark dose values derived from MultiFlow biomarkers compared to those generated from parallel in vitro micronucleus analyses. Collectively, the results suggest that a tiered MultiFlow data analysis pipeline is capable of rapidly and effectively identifying genotoxic hazards while providing additional information that is useful for modern risk assessments-MoA, molecular targets, and potency. Environ. Mol. Mutagen. 60:513-533, 2019. © 2019 Wiley Periodicals, Inc.

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Comparative transcriptomic analyses to scrutinize the assumption that genotoxic PAHs exert effects via a common mode of action.

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Comparative transcriptomic analyses to scrutinize the assumption that genotoxic PAHs exert effects via a common mode of action.

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