When evaluating compound similarity, addressing multiple sources of information to reach conclusions about common pharmaceutical and/or toxicological mechanisms of action is a crucial strategy. In ...this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-based protein target prediction analysis, and a cross-study/cross-platform meta-analysis of in vitro and in vivo human and rat transcriptomics data derived from public resources (i.e., the diXa data warehouse). Hierarchical clustering of the outcome scores of the separate analyses did not result in a satisfactory grouping of compounds considering their known toxic mechanism as described in literature. However, a combined analysis of multiple data types may hypothetically compensate for missing or unreliable information in any of the single data types. We therefore performed an integrated clustering analysis of all three data sets using the R-based tool iClusterPlus. This indeed improved the grouping results. The compound clusters that were formed by means of iClusterPlus represent groups that show similar gene expression while simultaneously integrating a similarity in structure and protein targets, which corresponds much better with the known mechanism of action of these toxicants. Using an integrative systems biology approach may thus overcome the limitations of the separate analyses when grouping liver toxicants sharing a similar mechanism of toxicity.
N-nitroso compounds (NOC) are genotoxic, carcinogenic to animals, and may play a role in human cancer development. Because the gastro-intestinal tract is an important route of exposure through ...endogenous nitrosation, we hypothesize that NOC exposure targets genetic processes relevant in colon carcinogenesis. To investigate these genomic responses, we analyzed the transcriptomic effects of genotoxic concentrations of two nitrosamides, N-methyl-N′-nitro-N-nitrosoguanidine (MNNG, 1μM) and N-methyl-N-nitrosurea (MNU, 1mM), and four nitrosamines, N-nitrosodiethylamine (NDEA, 50mM), N-nitrosodimethylamine (NDMA, 100mM), N-nitrosopiperidine (NPIP, 40mM), and N-nitrosopyrrolidine (NPYR, 100mM), in the human colon carcinoma cell line Caco-2. Gene Ontology gene group, consensus motif gene group and biological pathway analysis revealed that nitrosamides had little effect on gene expression after 24 h of exposure, whereas nitrosamines had a strong impact on the transcriptomic profile. Analyses showed modifications of cell cycle regulation and apoptosis pathways for nitrosamines which was supported by flow cytometric analysis. We found additional modifications in gene groups and pathways of oxidative stress and inflammation, which suggest an increase in oxidative stress and proinflammatory immune response upon nitrosamine exposure, although less distinct for NDMA. Furthermore, NDEA, NPIP, and NPYR most strongly affected several developmental motif gene groups and pathways, which may influence developmental processes. Many of these pathways and gene groups are implicated in the carcinogenic process and their modulation by nitrosamine exposure may therefore influence the development of colon cancer. In summary, our study has identified pathway modifications in human colon cells which may be associated with cancer risk of nitrosamine exposure in the human colon.
The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in ...vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC). MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.
Chemical carcinogenesis can be induced by genotoxic (GTX) or non-genotoxic (NGTX) carcinogens. GTX carcinogens have a well-described mode of action. However, the complex mechanisms by which NGTX ...carcinogens act are less clear and may result in conflicting results between species e.g. Wy-14,643 (Wy). We hypothesise that common microRNA response pathways exist for each class of carcinogenic agents. Therefore, this study compares and integrates mRNA and microRNA expression profiles following short term acute exposure (24 and 48h) to three GTX aflatoxin B1 (AFB1), benzoapyrene (BaP) and cisplatin (CisPl) or three NGTX (2,3,7,8-tetrachloordibenzodioxine (TCDD), cyclosporine A (CsA) and Wy) carcinogens in primary mouse hepatocytes. Discriminative gene sets, microRNAs (not for 24h) and processes were identified following 24 and 48h of exposure. From the three discriminative microRNAs found following 48h of exposure, mmu-miR-503-5p revealed to have an interaction with mRNA target gene cyclin D2 (Ccnd2 - 12444) which was involved in the discriminative process of p53 signalling and metabolism. Following exposure to NGTX carcinogens Mmu-miR-503-5p may have an oncogenic function by stimulating Ccnd2 possibly leading to a tumourigenic cell cycle progression. By contrast, after GTX carcinogen exposure it may have a tumour-suppressive function (repressing Ccnd2) leading to cell cycle arrest and to increased DNA repair activities. In addition, compound-specific microRNA-mRNA interactions mmu-miR-301b-3p-Papss2 (for AFB1), as well as mmu-miR-29b-3p-Col4a2 and mmu-miR-24-3p-Flna (for BaP) were found to contribute to a better understanding of microRNAs in cell cycle arrest and the impairment of the DNA damage repair, an important hallmark of GTX-induced carcinogenesis. Overall, our results indicate that microRNAs represent yet another relevant intracellular regulatory level in chemical carcinogenesis.
Considering genetic variability in population studies focusing on the health risk assessment of exposure to environmental carcinogens may provide improved insights in individual environmental cancer ...risks. Therefore, the current study aims to determine the impact of genetic polymorphisms on the relationship between exposure and gene expression, by identifying exposure-dependently coregulated genes and genetic pathways. Statistical analysis based on mixed models, was performed to relate gene expression data from 134 subjects to exposure measurements of multiple carcinogens, 28 polymorphisms, age, sex and biomarkers of cancer risk. We evaluated the combined exposure to cadmium, lead, polychlorinated biphenyls, p,p'-dichlorodiphenyldichloroethylene, hexachlorobenzene and 1-OH-pyrene, and the outcome was biologically interpreted by using ConsensusPathDB, thereby focusing on carcinogenesis-related pathways. We found generic and carcinogenesis-related pathways deregulated in both sexes, but males showed a stronger transcriptome response than females. We highlighted NOTCH1, CBR1, ITGB3, ITGA4, ADI1, HES1, NCOA2 and SMARCA2 in view of their direct link with cancer development. Two of these, NOTCH1 and ITGB3, are also known to respond to PCBs and cadmium chloride exposure in rodents and to lead in humans. Subjects carrying a high number of risk alleles appear more responsive to combined carcinogen exposure with respect to the induced expression of some of these cancer-related genes, which may be indicative of increased cancer risk as a consequence of environmental factors.
Comparing time courses of gene expression with time courses of phenotypic data may provide new insights in cellular mechanisms. In this study, we compared the performance of five pattern recognition ...methods with respect to their ability to relate genes and phenotypic data: one classical method (k-means) and four methods especially developed for time series Short Time-series Expression Miner (STEM), Linear Mixed Model mixtures, Dynamic Time Warping for -Omics and linear modeling with R/Bioconductor limma package. The methods were evaluated using data available from toxicological studies that had the aim to relate gene expression with phenotypic endpoints (i.e. to develop biomarkers for adverse outcomes). Additionally, technical aspects (influence of noise, number of time points and number of replicates) were evaluated on simulated data.
None of the methods outperforms the others in terms of biology. Linear modeling with limma is mostly influenced by noise. STEM is mostly influenced by the number of biological replicates in the dataset, whereas k-means and linear modeling with limma are mostly influenced by the number of time points. In most cases, the results of the methods complement each other. We therefore provide recommendations to integrate the five methods.
The Matlab code for the simulations performed in this research is available in the Supplementary Data (Word file). The microarray data analysed in this paper are available at ArrayExpress (E-TOXM-22 and E-TOXM-23) and Gene Expression Omnibus (GSE39291). The phenotypic data are available in the Supplementary Data (Excel file). Links to the pattern recognition tools compared in this paper are provided in the main text.
d.hendrickx@maastrichtuniversity.nl
Supplementary data are available at Bioinformatics online.
The γH2AX assay has recently been suggested as a new in vitro assay for detecting genotoxic (GTX) properties of chemicals. This assay is based on the phosphorylation of H2AX histone in response to ...DNA damage i.e. induction of double-strand breaks (DSBs). Quantification of γH2AX foci using flow cytometry can rapidly detect DNA damage induced by chemicals that cause DNA DSBs. Up to now, only few compounds have been tested with this assay. The main goal of this study was to compare the performance of this automated γH2AX assay with that of standard in vitro genotoxicity assays in predicting in vivo genotoxicity. HepG2 cells were exposed to 64 selected compounds with known GTX properties and subsequently analysed for induction of γH2AX foci. The results of this assay were compared with public data from standard in vitro genotoxicity tests. Accuracy, sensitivity and specificity in predicting in vivo genotoxicity, using the γH2AX assay alone or in combinations with conventional assays, were calculated. Both the γH2AX assay and the bacterial mutagenicity test (Ames) were highly specific for in vivo GTX, whereas chromosomal aberration/micronucleus test (CA/MN) resulted in highest sensitivity. The currently widely used in vitro genotoxicity test battery-Ames test, mouse lymphoma assay (MLA) and CA/MN test-resulted in low accuracy (55-65%) to predict in vivo genotoxicity. Interestingly, the inclusion of γH2AX assay in the standard battery, instead of MLA assay, resulted in higher accuracy (62-70%) compared with other combinations. Advantage of the γH2AX assay in HepG2 cells is its high sensitivity to detect DNA-reactive GTX compounds, although the reduced sensitivity for compounds that require metabolic activation needs to be improved. In conclusion, the automated γH2AX assay can be a useful, fast and cost-effective human cell-based tool for early screening of compounds for in vivo genotoxicity.
Blueberries contain relatively large amounts of different phytochemicals, which are suggested to have chemopreventive properties, but little information is available on the underlying molecular modes ...of action. This study investigates whole genome gene expression changes in lymphocytes of 143 humans after a 4-week blueberry-apple juice dietary intervention. Differentially expressed genes and genes correlating with the extent of antioxidant protection were identified in four subgroups. The magnitude of the preventive effect after the intervention differed between these four subgroups. Furthermore, subjects in two groups carried genetic polymorphisms that were previously found to influence the chemopreventive response. Pathway analysis of the identified genes showed strong but complex gene expression changes in pathways signaling for apoptosis, immune response, cell adhesion, and lipid metabolism. These pathways indicate increased apoptosis, upgraded growth control, induced immunity, reduced platelet aggregation and activation, blood glucose homeostasis, and regulation of fatty acid metabolism. Based on these observations, we hypothesize that combining transcriptomic data with phenotypic markers of oxidative stress may provide insight into the relevant cellular processes and genetic pathways, which contribute to the antioxidant response of complex mixtures of phytochemicals, such as found in blueberry-apple juice.
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