Discrimination of carcinogens by hepatic transcript profiling in rats following 28-day administration

A Feedback Circuitry between Polycomb Signaling and Fructose-1, 6-Bisphosphatase Enables Hepatic and Renal Tumorigenesis

Suppression of gluconeogenesis elevates glycolysis and is commonly observed in tumors derived from gluconeogenic tissues including liver and kidney, yet the definitive regulatory mechanism remains elusive. Here, we screened an array of transcription regulators and identified the enhancer of zeste homolog 2 (EZH2) as a key factor that inhibits gluconeogenesis in cancer cells. Specifically, EZH2 repressed the expression of a rate-limiting gluconeogenic enzyme fructose-1, 6-bisphosphatase 1 (FBP1) and promoted tumor growth primarily through FBP1 suppression. Furthermore, EZH2 was upregulated by genotoxins that commonly induce hepatic and renal tumorigenesis. Genotoxin treatments augmented EZH2 acetylation, leading to reduced association between EZH2 and its E3 ubiquitin ligase SMURF2. Consequently, EZH2 became less ubiquitinated and more stabilized, promoting FBP1 attenuation and tumor formation. Intriguingly, FBP1 physically interacted with EZH2, competed for EZH2 binding, and dissembled the polycomb complex. Therefore, FBP1 suppresses polycomb-initiated transcriptional responses and constitutes a double-negative feedback loop indispensable for EZH2-promoted tumorigenesis. Finally, EZH2 and FBP1 levels were inversely correlated in tumor tissues and accurately predicted patient survival. This work reveals an unexpected cross-talk between epigenetic and metabolic events, and identifies a new feedback circuitry that highlights EZH2 inhibitors as liver and kidney cancer therapeutics. SIGNIFICANCE: A novel feedback loop involving EZH2 and suppression of the gluconeogenesis enzyme FBP1 promotes hepatocellular cancer growth.See related commentary by Leithner, p. 657.

A decision tree-based integrated testing strategy for tailor-made carcinogenicity evaluation of test substances using genotoxicity test results and chemical spaces

Genotoxicity evaluation has been widely used to estimate the carcinogenicity of test substances during safety evaluation. However, the latest strategies using genotoxicity tests give more weight to sensitivity; therefore, their accuracy has been very low. For precise carcinogenicity evaluation, we attempted to establish an integrated testing strategy for the tailor-made carcinogenicity evaluation of test materials, considering the relationships among genotoxicity test results (Ames, in vitro mammalian genotoxicity and in vivo micronucleus), carcinogenicity test results and chemical properties (molecular weight, logKow and 179 organic functional groups). By analyzing the toxicological information and chemical properties of 230 chemicals, including 184 carcinogens in the Carcinogenicity Genotoxicity eXperience database, a decision tree for carcinogenicity evaluation was optimised statistically. A decision forest model was generated using a machine-learning method-random forest-which comprises thousands of decision trees. As a result, balanced accuracies in cross-validation of the optimised decision tree and decision forest model, considering chemical space (71.5% and 75.5%, respectively), were higher than balanced accuracy of an example regulatory decision tree (54.1%). Moreover, the statistical optimisation of tree-based models revealed significant organic functional groups that would cause false prediction in standard genotoxicity tests and non-genotoxic carcinogenicity (e.g., organic amide and thioamide, saturated heterocyclic fragment and aryl halide). In vitro genotoxicity tests were the most important parameters in all models, even when in silico parameters were integrated. Although external validation is required, the findings of the integrated testing strategies established herein will contribute to precise carcinogenicity evaluation and to determine new mechanistic hypotheses of carcinogenicity.

Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies With Different Degrees of Completeness and Scientific Confidence

Adverse outcome pathways (AOPs) offer a pathway-based toxicological framework to support hazard assessment and regulatory decision-making. However, little has been discussed about the scientific confidence needed, or how complete a pathway should be, before use in a specific regulatory application. Here we review four case studies to explore the degree of scientific confidence and extent of completeness (in terms of causal events) that is required for an AOP to be useful for a specific purpose in a regulatory application: (i) Membrane disruption (Narcosis) leading to respiratory failure (low confidence), (ii) Hepatocellular proliferation leading to cancer (partial pathway, moderate confidence), (iii) Covalent binding to proteins leading to skin sensitization (high confidence), and (iv) Aromatase inhibition leading to reproductive dysfunction in fish (high confidence). Partially complete AOPs with unknown molecular initiating events, such as 'Hepatocellular proliferation leading to cancer', were found to be valuable. We demonstrate that scientific confidence in these pathways can be increased though the use of unconventional information (eg, computational identification of potential initiators). AOPs at all levels of confidence can contribute to specific uses. A significant statistical or quantitative relationship between events and/or the adverse outcome relationships is a common characteristic of AOPs, both incomplete and complete, that have specific regulatory uses. For AOPs to be useful in a regulatory context they must be at least as useful as the tools that regulators currently possess, or the techniques currently employed by regulators.

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances.

Discovery of characteristic molecular signatures for the simultaneous prediction and detection of environmental pollutants

Gene expression data may be very promising for the classification of toxicant types, but the development and application of transcriptomic-based gene classifiers for environmental toxicological applications are lacking compared to the biomedical sciences. Also, simultaneous classification across a set of toxicant types has not been investigated extensively. In the present study, we determined the transcriptomic response to three types of ubiquitous toxicants exposure in two types of human cell lines (HepG2 and HL-60), which are useful in vitro human model for evaluation of toxic substances that may affect human hepatotoxicity (e.g., polycyclic aromatic hydrocarbon [PAH] and persistent organic pollutant [POP]) and human leukemic myelopoietic proliferation (e.g., volatile organic compound [VOC]). The findings demonstrate characteristic molecular signatures that facilitated discrimination and prediction of the toxicant type. To evaluate changes in gene expression levels after exposure to environmental toxicants, we utilized 18 chemical substances; nine PAH toxicants, six VOC toxicants, and three POP toxicants. Unsupervised gene expression analysis resulted in a characteristic molecular signature for each toxicant group, and combination analysis of two separate multi-classifications indicated 265 genes as surrogate markers for predicting each group of toxicants with 100 % accuracy. Our results suggest that these expression signatures can be used as predictable and discernible surrogate markers for detection and prediction of environmental toxicant exposure. Furthermore, this approach could easily be extended to screening for other types of environmental toxicants.

Distinguishing between genotoxic and non-genotoxic hepatocarcinogens by gene expression profiling and bioinformatic pathway analysis

A rapid and sensitive method to determine the characteristics of carcinogens is needed. In this study, we used a microarray-based genomics approach, with a short-term in vivo model, in combination with insights from statistical and mechanistic analyses to determine the characteristics of carcinogens. Carcinogens were evaluated based on the different mechanisms involved in the responses to genotoxic carcinogens and non-genotoxic carcinogens. Gene profiling was performed at two time points after treatment with six training and four test carcinogens. We mapped the DEG (differentially expressed gene)-related pathways to analyze cellular processes, and we discovered significant mechanisms that involve critical cellular components. Classification results were further supported by Comet and Micronucleus assays. Mechanistic studies based on gene expression profiling enhanced our understanding of the characteristics of different carcinogens. Moreover, the efficiency of this study was demonstrated by the short-term nature of the animal experiments that were conducted.

Comparison of genotoxicant-modified transcriptomic responses in conventional and epigenetically stabilized primary rat hepatocytes with in vivo rat liver data

The concept of mechanistic toxicogenomics implies that compound-induced changes in gene expression profiles provide valuable information about their mode of action. A growing number of research groups have presented evidence that whole-genome gene expression profiling techniques might be used as tools for in vivo and in vitro generation of gene signatures and elucidation of molecular mechanisms after exposure to toxic compounds. An important issue to be investigated is the in vivo relevance of in vitro-obtained data. In the current study, we compare the gene expression profiles generated in vitro, after exposing conventional and epigenetically stabilized primary rat hepatocytes to well-known genotoxic hepatocarcinogens (aflatoxin B1, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone and 2-nitrofluorene) with those derived in vivo after oral exposure of rats to these compounds. Similar statistical tools were applied on both sets of data. The major molecular pathways affected in the in vivo setting were DNA damage, detoxification and cell survival response, as previously described. In the conventional hepatocyte cultures, two of the three genotoxicants showed quite similar responses as in vivo with respect to these pathways. The third compound (2-nitrofluorene) revealed in vitro response which was not observed in vivo. In the epigenetically stabilized hepatocytes, in contrast to what was expected, the responses were less relevant for the in vivo situation. This study highlights the importance of in vitro/in vivo comparison of data that are generated using in vitro models and shows that conventional primary rat hepatocyte cultures represent an appropriate in vitro model to retrieve mechanistic information on the exposure to genotoxicants.

Discrimination of genotoxic and non-genotoxic hepatocarcinogens by statistical analysis based on gene expression profiling in the mouse liver as determined by quantitative real-time PCR

The general aim of the present study is to discriminate between mouse genotoxic and non-genotoxic hepatocarcinogens via selected gene expression patterns in the liver as analyzed by quantitative real-time PCR (qPCR) and statistical analysis. qPCR was conducted on liver samples from groups of 5 male, 9-week-old B6C3F(1) mice, at 4 and 48h following a single intraperitoneal administration of chemicals. We quantified 35 genes selected from our previous DNA microarray studies using 12 different chemicals: 8 genotoxic hepatocarcinogens (2-acetylaminofluorene, 2,4-diaminotoluene, diisopropanolnitrosamine, 4-dimethylaminoazobenzene, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, N-nitrosomorpholine, quinoline and urethane) and 4 non-genotoxic hepatocarcinogens (1,4-dichlorobenzene, dichlorodiphenyltrichloroethane, di(2-ethylhexyl)phthalate and furan). A considerable number of genes exhibited significant changes in their gene expression ratios (experimental group/control group) analyzed statistically by the Dunnett's test and Welch's t-test. Finally, we distinguished between the genotoxic and non-genotoxic hepatocarcinogens by statistical analysis using principal component analysis (PCA) of the gene expression profiles for 7 genes (Btg2, Ccnf, Ccng1, Lpr1, Mbd1, Phlda3 and Tubb2c) at 4h and for 12 genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2 and Tubb2c) at 48h. Seven major biological processes were extracted from the gene ontology analysis: apoptosis, the cell cycle, cell proliferation, DNA damage, DNA repair, oncogenes and tumor suppression. The major, biologically relevant gene pathway suggested was the DNA damage response pathway, resulting from signal transduction by a p53-class mediator leading to the induction of apoptosis. Eight genes (Aen, Bax, Btg2, Ccng1, Cdkn1a, Gdf15, Phlda3 and Plk2) that are directly associated with Trp53 contributed to the PCA. The current findings demonstrate a successful discrimination between genotoxic and non-genotoxic hepatocarcinogens, using qPCR and PCA, on 12 genes associated with a Trp53-mediated signaling pathway for DNA damage response at 4 and 48 h after a single administration of chemicals.

New Short Term Prediction Method for Chemical Carcinogenicity by Hepatic Transcript Profiling following 28-Day Toxicity Tests in Rats

We have previously shown the hepatic gene expression profiles of carcinogens in 28-day toxicity tests were clustered into three major groups (Group-1 to 3). Here, we developed a new prediction method for Group-1 carcinogens which consist mainly of genotoxic rat hepatocarcinogens. The prediction formula was generated by a support vector machine using 5 selected genes as the predictive genes and predictive score was introduced to judge carcinogenicity. It correctly predicted the carcinogenicity of all 17 Group-1 chemicals and 22 of 24 non-carcinogens regardless of genotoxicity. In the dose-response study, the prediction score was altered from negative to positive as the dose increased, indicating that the characteristic gene expression profile emerged over a range of carcinogen-specific doses. We conclude that the prediction formula can quantitatively predict the carcinogenicity of Group-1 carcinogens. The same method may be applied to other groups of carcinogens to build a total system for prediction of carcinogenicity.