Identification of potential biomarkers of genotoxicity and carcinogenicity in L5178Y mouse lymphoma cells by cDNA microarray analysis

Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures

One of the most difficult challenges for risk assessment is evaluation of chemicals that predominately co-occur in mixtures like polycyclic aromatic hydrocarbons (PAHs). We previously developed a classification model in which systems biology data collected from mice short-term after chemical exposure accurately predict tumor outcome. The present study demonstrates translation of this approach into a human in vitro model in which chemical-specific bioactivity profiles from 3D human bronchial epithelial cells (HBEC) classify PAHs by carcinogenic potency. Gene expression profiles were analyzed from HBEC exposed to carcinogenic and non-carcinogenic PAHs and classification accuracies were identified for individual pathway-based gene sets. Posterior probabilities of best performing gene sets were combined via Bayesian integration resulting in a classifier with four gene sets, including aryl hydrocarbon receptor signaling, regulation of epithelial mesenchymal transition, regulation of angiogenesis, and cell cycle G2-M. In addition, transcriptional benchmark dose modeling of benzo[a]pyrene (BAP) showed that the most sensitive gene sets to BAP regulation were largely dissimilar from those that best classified PAH carcinogenicity challenging current assumptions that BAP carcinogenicity (and subsequent mode of action) is reflective of overall PAH carcinogenicity. These results illustrate utility of using systems toxicology approaches to analyze global gene expression towards carcinogenic hazard assessment.

Comparative transcriptome analysis between the short-term stress and long-term adaptation of the Ruditapes philippinarum in response to benzo[a]pyrene

In order to monitor the pollution of polycyclic aromatic hydrocarbons (PAHs) in the seawater environment, screening biomarkers capable of monitoring PAHs is the focus of many studies. The transcriptomic profiles of the digestive gland tissue from the R. philippinarum groups after the exposure to BaP (4 μg/L) at four time points (0, 0.5, 6 and 15 days) were investigated to globally screen the key genes and pathways involved in the responses to short-term stress and long-term adaptation of BaP resistance. By comparative transcriptome analysis, 233, 282 and 58 differentially expressed genes (DEGs) were identified at 0.5 day, 6 day and 15 day (vs 0 day). The differential expression genes were related to stress response, detoxification metabolic process and innate immunity. DEGs of each group at different stages were clustered in six profiles based on gene expression pattern. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis were used on all genes to determine the biological functions and processes. We selected Multidrug resistance protein 3 (MRP3), transcriptional regulator ATRX-like isoform X2 (ATRX) as biomarker indicator genes for short-term pollution monitoring and NADH dehydrogenase [ubiquinone] 1 (NQO1), Complement C1q-like protein 4 (C1q), Glutathione-S-transferase theta (GST), E3 ubiquitin-protein ligase (E3) for long-term pollution monitoring based on the different expression patterns and the function in detoxification and antioxidant defense system. Besides, the expression of seven genes was measured through Quantitative real-time PCR (qPCR) according to their gene expression patterns which was confirmed by the DGE analysis. Taken together, adoption of transcriptomic analysis to explore the bivalves' mRNA abundance changes and detoxification metabolic mechanism under the BaP stress at different time points can aid the development of sensitive and informed molecular endpoints for application towards ecotoxicogenomic monitoring of bivalves.

A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70-90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens' adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention.

RNA-Seq provides new insights in the transcriptome responses induced by the carcinogen benzo[a]pyrene

Whole-genome transcriptome measurements are pivotal for characterizing molecular mechanisms of chemicals and predicting toxic classes, such as genotoxicity and carcinogenicity, from in vitro and in vivo assays. In recent years, deep sequencing technologies have been developed that hold the promise of measuring the transcriptome in a more complete and unbiased manner than DNA microarrays. Here, we applied this RNA-seq technology for the characterization of the transcriptomic responses in HepG2 cells upon exposure to benzo[a]pyrene (BaP), a well-known DNA damaging human carcinogen. Based on EnsEMBL genes, we demonstrate that RNA-seq detects ca 20% more genes than microarray-based technology but almost threefold more significantly differentially expressed genes. Functional enrichment analyses show that RNA-seq yields more insight into the biology and mechanisms related to the toxic effects caused by BaP, i.e., two- to fivefold more affected pathways and biological processes. Additionally, we demonstrate that RNA-seq allows detecting alternative isoform expression in many genes, including regulators of cell death and DNA repair such as TP53, BCL2 and XPA, which are relevant for genotoxic responses. Moreover, potentially novel isoforms were found, such as fragments of known transcripts, transcripts with additional exons, intron retention or exon-skipping events. The biological function(s) of these isoforms remain for the time being unknown. Finally, we demonstrate that RNA-seq enables the investigation of allele-specific gene expression, although no changes could be observed. Our results provide evidence that RNA-seq is a powerful tool for toxicology, which, compared with microarrays, is capable of generating novel and valuable information at the transcriptome level for characterizing deleterious effects caused by chemicals.

Microarray data analysis and mining tools

Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously. Arrays have been applied to studies in gene expression, genome mapping, SNP discrimination, transcription factor activity, toxicity, pathogen identification and many other applications. In this paper we concentrate on discussing various bioinformatics tools used for microarray data mining tasks with its underlying algorithms, web resources and relevant reference. We emphasize this paper mainly for digital biologists to get an aware about the plethora of tools and programs available for microarray data analysis. First, we report the common data mining applications such as selecting differentially expressed genes, clustering, and classification. Next, we focused on gene expression based knowledge discovery studies such as transcription factor binding site analysis, pathway analysis, protein- protein interaction network analysis and gene enrichment analysis.

Toxicogenomics: transcription profiling for toxicology assessment

Toxicogenomics, the application of transcription profiling to toxicology, has been widely used for elucidating the molecular and cellular actions of chemicals and other environmental stressors on biological systems, predicting toxicity before any functional damages, and classification of known or new toxicants based on signatures of gene expression. The success of a toxicogenomics study depends upon close collaboration among experts in different fields, including a toxicologist or biologist, a bioinformatician, statistician, physician and, sometimes, mathematician. This review is focused on toxicogenomics studies, including transcription profiling technology, experimental design, significant gene extraction, toxicological results interpretation, potential pathway identification, database input and the applications of toxicogenomics in various fields of toxicological study.

Application of toxicogenomics to genetic toxicology risk assessment

Based on the assumption that compounds having similar toxic modes of action induce specific gene expression changes, the toxicity of unknown compounds can be predicted after comparison of their molecular fingerprints with those obtained with compounds of known toxicity. These predictive models will therefore rely on the characterization of marker genes. Toxicogenomics (TGX) also provides mechanistic insight into the mode of toxicity, and can therefore be used as an adjunct to the standard battery of genotoxicity tests. Promising results, highlighting the ability of TGX to differentiate genotoxic from non-genotoxic carcinogens, as well as DNA-reactive from non-DNA reactive genotoxins, have been reported. Additional data suggested the possibility of ranking genotoxins according to the nature of their interactions with DNA. This new approach could contribute to the improvement of risk assessment. TGX could be applied as a follow-up testing strategy in case of positive in vitro genotoxicity findings, and could contribute to improve our ability to identify the molecular mechanism of action and to possibly better assess dose-response curves. TGX has been found to be less sensitive than the standard genotoxicity end-points, probably because it measures the whole cell population response, when compared with standard tests designed to detect rare events in a small number of cells. Further validation will be needed (1) to better link the profiles obtained with TGX to the established genotoxicity end-points, (2) to improve the gene annotation tools, and (3) to standardise study design and data analysis and to better evaluate the impact of variability between platforms and bioinformatics approaches.