Review and meta-analysis of gene expression biomarkers predictive of chemical-induced genotoxicity in vivo

There is growing recognition across broad sectors of the toxicology community that gene expression biomarkers have the potential to identify genotoxic and nongenotoxic carcinogens through a weight-of-evidence approach, providing opportunities to reduce reliance on the 2-year bioassay to identify carcinogens. In August 2022, a workshop within the International Workshops on Genotoxicity Testing (IWGT) was held to critically review current methods to identify genotoxicants using various 'omics profiling methods. Here, we describe the findings of a workshop subgroup focused on the state of the science regarding the use of biomarkers to identify chemicals that act as genotoxicants in vivo. A total of 1341 papers were screened to identify those that were most relevant. While six published biomarkers with characterized accuracy were initially examined, four of the six were not considered further, because they had not been tested for classification accuracy using additional sets of chemicals or other transcript profiling platforms. Two independently derived biomarkers used in conjunction with standard computational techniques can identify genotoxic chemicals in vivo (rat liver or both rat and mouse liver) on different gene expression profiling platforms. The biomarkers have predictive accuracies of ≥92%. These biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies using short-term rodent exposures to identify genotoxic and nongenotoxic chemicals that cause cancer.

Four functional genotoxic marker genes (Bax, Btg2, Ccng1, and Cdkn1a) discriminate genotoxic hepatocarcinogens from non-genotoxic hepatocarcinogens and non-genotoxic non-hepatocarcinogens in rat public toxicogenomics data, Open TG-GATEs

BACKGROUND

Previously, Japanese Environmental Mutagen and Genome Society/Mammalian Mutagenicity Study Group/Toxicogenomics Study Group (JEMS/MMS toxicogenomic study group) proposed 12 genotoxic marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) to discriminate genotoxic hepatocarcinogens (GTHCs) from non-genotoxic hepatocarcinogens (NGTHCs) and non-genotoxic non-hepatocarcinogens (NGTNHCs) in mouse and rat liver using qPCR and RNA-Seq and confirmed in public rat toxicogenomics data, Open TG-GATEs, by principal component analysis (PCA). On the other hand, the U.S. Environmental Protection Agency (US EPA) suggested seven genotoxic marker genes (Bax, Btg2, Ccng1, Cgrrf1, Cdkn1a, Mgmt, and Tmem47) with Open TG-GATEs data. Four genes (Bax, Btg2, Ccng1, and Cdkn1a) were common in these two studies. In the present study, we examined the performance of these four genes in Open TG-GATEs data using PCA.

RESULTS

The study's findings are of paramount significance, as these four genes proved to be highly effective in distinguishing five typical GTHCs (2-acetylaminofluorene, aflatoxin B1, 2-nitrofluorene, N-nitrosodiethylamine and N-nitrosomorpholine) from seven typical NGTHCs (clofibrate, ethanol, fenofibrate, gemfibrozil, hexachlorobenzene, phenobarbital, and WY-14643) and 11 NGTNHCs (allyl alcohol, aspirin, caffeine, chlorpheniramine, chlorpropamide, dexamethasone, diazepam, indomethacin, phenylbutazone, theophylline, and tolbutamide) by PCA at 24 h after a single administration with 100% accuracy. These four genes also effectively distinguished two typical GTHCs (2-acetylaminofluorene and N-nitrosodiethylamine) from seven NGTHCs and ten NGTNHCs by PCA on 29 days after 28 days-repeated administrations, with a similar or even better performance compared to the previous 12 genes. Furthermore, the study's analysis revealed that the three intermediate GTHC/NGTHCs (methapyrilene, monocrotaline, and thioacetamide, which were negative in the Salmonella test but positive in the in vivo rat liver test) were located in the intermediate region between typical GTHCs and typical NGTHCs by PCA.

CONCLUSIONS

The present results unequivocally demonstrate the availability of four genotoxic marker genes ((Bax, Btg2, Ccng1, and Cdkn1a) and PCA in discriminating GTHCs from NGTHCs and NGTNHCs in Open TG-GATEs. These findings strongly support our recommendation that future rat liver in vivo toxicogenomics tests prioritize these four genotoxic marker genes, as they have proven to be highly effective in discriminating between different types of hepatocarcinogens.

Short-term in vivo testing to discriminate genotoxic carcinogens from non-genotoxic carcinogens and non-carcinogens using next-generation RNA sequencing, DNA microarray, and qPCR

Next-generation RNA sequencing (RNA-Seq) has identified more differentially expressed protein-coding genes (DEGs) and provided a wider quantitative range of expression level changes than conventional DNA microarrays. JEMS·MMS·Toxicogenomics group studied DEGs with targeted RNA-Seq on freshly frozen rat liver tissues and on formalin-fixed paraffin-embedded (FFPE) rat liver tissues after 28 days of treatment with chemicals and quantitative real-time PCR (qPCR) on rat and mouse liver tissues after 4 to 48 h treatment with chemicals and analyzed by principal component analysis (PCA) as statics. Analysis of rat public DNA microarray data (Open TG-GATEs) was also performed. In total, 35 chemicals were analyzed [15 genotoxic hepatocarcinogens (GTHCs), 9 non-genotoxic hepatocarcinogens (NGTHCs), and 11 non-genotoxic non-hepatocarcinogens (NGTNHCs)]. As a result, 12 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) were proposed to discriminate GTHCs from NGTHCs and NGTNHCs. U.S. Environmental Protection Agency studied DEGs induced by 4 known GTHCs in rat liver using DNA microarray and proposed 7 biomarker genes, Bax, Bcmp1, Btg2, Ccng1, Cdkn1a, Cgr19, and Mgmt for GTHCs. Studies involving the use of whole-transcriptome RNA-Seq upon exposure to chemical carcinogens in vivo have also been performed in rodent liver, kidney, lung, colon, and other organs, although discrimination of GTHCs from NGTHCs was not examined. Candidate genes published using RNA-Seq, qPCR, and DNA microarray will be useful for the future development of short-term in vivo studies of environmental carcinogens using RNA-Seq.

Quinoline is more genotoxic than 4-methylquinoline in hiHeps cells and rodent liver

Environmental pollutants, such as quinoline (QN) and 4-methylquinoline (4-MeQ), may be genotoxic and carcinogenic. Earlier studies, including in vitro genotoxicity tests, indicated that 4-MeQ is more mutagenic than QN. However, we hypothesized that the methyl group of 4-MeQ favors detoxication over bioactivation, and this factor may be overlooked in in vitro tests that do not incorporate supplementation with cofactors for enzymes that catalyze conjugation reactions. We used human induced hepatocyte cells (hiHeps), which express such enzymes, and compared the genotoxicity of 4-MeQ and QN. We also carried out an in vivo micronucleus (MN) test in rat liver, since 4-MeQ is not genotoxic in rodent bone marrow. In the Ames test and the Tk gene mutation assay, with rat S9 activation, 4-MeQ was more mutagenic than QN. However, QN induced significantly higher MN frequencies in hiHeps and rat liver than did 4-MeQ. Furthermore, QN upregulated genotoxicity marker genes much more than did 4-MeQ. We also investigated the roles of two important detoxication enzymes, UDP-glucuronosyltransferases (UGTs) and cytosolic sulfotransferases (SULTs). When hiHeps were preincubated with hesperetin (UGT inhibitor) and 2,6-dichloro-4-nitrophenol (SULT inhibitor), MN frequencies were elevated approximately 1.5-fold for 4-MeQ, whereas no significant effects were seen for QN. This study shows that QN is more genotoxic than 4-MeQ, when the roles of SULTs and UGTs in detoxication are considered and our results may improve understanding the structure-activity relationships of quinoline derivatives.

Machine learning guided prediction of liquid chromatography-mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products

The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to cause cancer, low-level quantitation is required to protect patients. A quick and easy method of determining the liquid chromatography-mass spectrometry (LC/MS) conditions for high-sensitivity analysis of GTIs may prospectively accelerate pharmaceutical development. In this study, a quantitative structure-property relationship (QSPR) model was developed for predicting the ionization efficiency of compounds using liquid-chromatography-mass spectrometry (LC/MS) parameters and molecular descriptors. Before implementing the QSPR prediction model, linear regression analysis was performed to model the relationship between the ionization efficiency and the LC/MS parameters for each compound. Comparison of the predicted peak areas with the experimentally observed peak areas showed good agreement based on the coefficient of determination (R² > 0.96). The machine learning-based QSPR approach begins with computation of the molecular descriptors expressing the physicochemical properties of a compound, followed by a genetic algorithm-based feature selection. Linear and nonlinear regression were performed, and support vector machine (SVM) was selected as the best machine learning algorithm for the prediction. The SVM algorithm was developed and optimized using 1031 experimental data points for nine compounds, including well-known GTIs. Validation of the model by comparison of the predicted and observed relative ionization efficiencies (RIE) showed a high coefficient of determination (R² = 0.96) and low root mean squared error value (RMSE = 0.118). Finally, this established prediction model was applied to hydrophilic interaction liquid chromatography coupled with MS for a new compound in new mobile phase compositions and new MS parameter settings. The RMSE of the predicted versus observed RIE was 0.203. This prediction accuracy was sufficient to determine the starting point of the LC/MS method development. The methodology demonstrated in this study can be used to determine the LC/MS conditions for high sensitivity analysis of GTIs.

Using FFPE RNA-Seq with 12 marker genes to evaluate genotoxic and non-genotoxic rat hepatocarcinogens

Introduction

Various challenges have been overcome with regard to applying 'omics technologies for chemical risk assessments. Previously we published results detailing targeted mRNA sequencing (RNA-Seq) on a next generation sequencer using intact RNA derived from freshly frozen rat liver tissues. We successfully discriminated genotoxic hepatocarcinogens (GTHCs) from non-genotoxic hepatocarcinogens (NGTHCs) using 11 selected marker genes. Based on this, we next attempted to use formalin-fixed paraffin-embedded (FFPE) pathology specimens for RNA-Seq analyses.

Findings

In this study we performed FFPE RNA-Seq to compare a typical GTHC, 2-acetylaminofluorene (AAF) to genotoxicity equivocal p-cresidine (CRE). CRE is used as a synthetic chemical intermediate, and this compound is classified as an IARC 2B carcinogen and is mutagenic in S. typhimurium, which is non-genotoxic to rat livers as assessed by single strand DNA damage analysis. RNA-Seq was used to examine liver FFPE samples obtained from groups of five 10-week-old male F344 rats that were fed with chemicals (AAF: 0.025% and CRE: 1% in food) for 4 weeks or from controls that were fed a basal diet. We extracted RNAs from FFPE samples and RNA-Seq was performed on a MiniSeq (Illumina) using the TruSeq custom RNA panel. AAF induced remarkable differences in the expression of eight genes (Aen, Bax, Btg2, Ccng1, Gdf15, Mbd1, Phlda3 and Tubb4b) from that in the control group, while CRE only induced expression changes in Gdf15, as shown using Tukey's test. Gene expression profiles for nine genes (Aen, Bax, Btg2, Ccng1, Cdkn1a, Gdf15, Mbd1, Phlda3, and Plk2) differed.between samples treated with AAF and CRE. Finally, principal component analysis (PCA) of 12 genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) using our previous Open TG-GATE data plus FFPE-AAF and FFPE-CRE successfully differentiated FFPE-AAF, as GTHC, from FFPE-CRE, as NGHTC.

Conclusion

Our results suggest that FFPE RNA-Seq and PCA are useful for evaluating typical rat GTHCs and NGTHCs.

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post-modified non-negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

Evaluation of 12 mouse marker genes in rat toxicogenomics public data, Open TG-GATEs: Discrimination of genotoxic from non-genotoxic hepatocarcinogens

Previously, we proposed 12 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2 and Tubb4b) to discriminate mouse genotoxic hepatocarcinogens (GTHC) from non-genotoxic hepatocarcinogens (NGTHC). This was determined by qPCR and principal component analysis (PCA), as the aim of an in vivo short-term screening for genotoxic hepatocarcinogens. For this paper, we conducted an application study of the 12 mouse marker genes to rat data, Open TG-GATEs (public data). We analyzed five typical rat GTHC (2-acetamodofluorene, aflatoxin B1, 2-nitrofluorene, N-nitrosodiethylamine and N-nitrosomorpholine), and not only seven typical rat NGTHC (clofibrate, ethanol, fenofibrate, gemfibrozil, hexachlorobenzene, phenobarbital and WY-14643) but also 11 non-genotoxic non-hepatocarcinogens (NGTNHC; allyl alcohol, aspirin, caffeine, chlorpheniramine, chlorpropamide, dexamethasone, diazepam, indomethacin, phenylbutazone, theophylline and tolbutamide) from Open TG-GATEs. The analysis was performed at 3, 6, 9 and 24 h after a single administration and 4, 8, 15 and 29 days after repeated administrations. We transferred Open TG-GATEs DNA microarray data into log₂ data using the "R Project for Statistical Computing". GTHC-specific dose-dependent gene expression changes were observed and significance assessed with the Williams test. Similar significant changes were observed during 3-24 h and 4-29 days, assessed with Welch's t-test, except not for NGTHC or NGTNHC. Significant differential changes in gene expression were observed between GTHC and NGTHC in 11 genes (except not Tubb4b) and between GTHC and NGTNHC in all 12 genes at 24 h and 10 genes (except Ccnf and Mbd1) at 29 days, per Tukey's test. PCA successfully discriminated GTHC from NGTHC and NGTNHC at 24 h and 29 days. The results demonstrate that 12 previously proposed mouse marker genes are useful for discriminating rat GTHC from NGTHC and NGTNHC from Open TG-GATEs.

Using RNA-Seq with 11 marker genes to evaluate 1,4-dioxane compared with typical genotoxic and non-genotoxic rat hepatocarcinogens

It has long been unclear whether 1,4-dioxane (DO) is a genotoxic hepatocarcinogen (GTHC). Therefore, the present study aimed to evaluate rat GTHCs and non-genotoxic hepatocarcinogens (NGTHCs) via selected gene expression patterns in the liver, as determined by next generation sequencing-targeted mRNA sequencing (RNA-Seq) and principal component analysis (PCA). Previously, we selected 11 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) to discriminate GTHCs and NGTHCs. In the present study, we quantified changes in the expression of these genes following DO treatment, and compared them with treatment with two typical rat GTHCs, N-nitrosodiethylamine (DEN) and 3,3'-dimethylbenzidine·2HCl (DMB), and a typical rat NGTHC, di(2-ethylhexyl)phthalate (DEHP). RNA-Seq was conducted on liver samples from groups of five male, 10-week-old F344 rats after 4 weeks' feeding of chemicals in the water or the food. Rats in the control group were given water and a basal diet. Significant changes in gene expression in experimental groups compared with the control group were observed in eight genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Phlda3 and Plk2), as shown by Tukey's test. Gene expression profiles of the 11 genes under DO treatment differed significantly from those with DEN and DMB, as well as DEHP. Gene expression profiles with DO treatment differed partially from those with typical GTHCs for five genes (Bax, Btg2, Cdkn1a, Lrp1 and Plk2) and were substantially different from treatment with a typical NGTHC (DEHP) for nine genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Mbd1, Phlda3 and Tubb4b) as determined by Tukey's test. Finally, PCA successfully differentiated GTHCs from DEHP and DO with the 11 genes. The present results suggest that RNA-Seq and PCA are useful to evaluate rat typical GTHCs and typical NGTHCs. DO was suggested to result in a different intermediate gene expression profile from typical GTHCs and NGTHC.

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

Toxicogenomics (TGx) is an important tool to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) using toxicogenomic datasets from the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. With this method, we investiagted three important questions that were not addressed in our previous study: (1) is a 1-day in vivo short-term assay able to replace the 28-day standard and expensive toxicological assay? (2) are some biological processes more conservative across different preclinical testing systems than others? and (3) do these preclinical testing systems have the similar resolution in differentiating drugs by their therapeutic uses? For question 1, a high similarity was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term and more expensive in vivo model systems. There was a moderate similarity between rat primary hepatocytes and in vivo repeat-dose studies (PRank score = 0.71) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies. To address question 2, we limited the analysis to gene sets relevant to specific toxicogenomic pathways and we found that pathways such as lipid metabolism were consistently over-represented in all three assay systems. For question 3, all three preclinical assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utility in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utility and application of TGx assays.

Collaborative studies in toxicogenomics in rodent liver in JEMS·MMS; a useful application of principal component analysis on toxicogenomics

Toxicogenomics is a rapidly developing discipline focused on the elucidation of the molecular and cellular effects of chemicals on biological systems. As a collaborative study group of Toxicogenomics/JEMS·MMS, we conducted studies on hepatocarcinogens in rodent liver in which 100 candidate marker genes were selected to discriminate genotoxic hepatocarcinogens from non-genotoxic hepatocarcinogens. Differential gene expression induced by 13 chemicals were examined using DNA microarray and quantitative real-time PCR (qPCR), including eight genotoxic hepatocarcinogens [o-aminoazotoluene, chrysene, dibenzo[a,l]pyrene, diethylnitrosamine (DEN), 7,12-dimethylbenz[a]anthracene, dimethylnitrosamine, dipropylnitrosamine and ethylnitrosourea (ENU)], four non-genotoxic hepatocarcinogens [carbon tetrachloride, di(2-ethylhexyl)phthalate (DEHP), phenobarbital and trichloroethylene] and a non-genotoxic non-hepatocarcinogen [ethanol]. Using qPCR, 30 key genes were extracted from mouse livers at 4 h and 28 days following dose-dependent gene expression alteration induced by DEN and ENU: the most significant changes in gene expression were observed at 4 h. Next, we selected key point times at 4 and 48 h from changes in time-dependent gene expression during the acute phase following administration of chrysene by qPCR. We successfully showed discrimination of eight genotoxic hepatocarcinogens [2-acetylaminofluorene, 2,4-diaminotoluene, diisopropanolnitrosamine, 4-dimethylaminoazobenzene, 4-(methylnitsosamino)-1-(3-pyridyl)-1-butanone, N-nitrosomorpholine, quinoline and urethane] from four non-genotoxic hepatocarcinogens [1,4-dichlorobenzene, dichlorodiphenyltrichloroethane, DEHP and furan] using qPCR and principal component analysis. Additionally, we successfully identified two rat genotoxic hepatocarcinogens [DEN and 2,6-dinitrotoluene] from a nongenotoxic-hepatocarcinogen [DEHP] and a non-genotoxic non-hepatocarcinogen [phenacetin] at 4 and 48 h. The subsequent gene pathway analysis by Ingenuity Pathway Analysis extracted the DNA damage response, resulting from the signal transduction of a p53-class mediator leading to the induction of apoptosis. The present review of these studies suggests that application of principal component analysis on the gene expression profile in rodent liver during the acute phase is useful to predict genotoxic hepatocarcinogens in comparison to non-genotoxic hepatocarcinogens and/or non-carcinogenic hepatotoxins.

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.

Dose-dependent alterations in gene expression in mouse liver induced by diethylnitrosamine and ethylnitrosourea and determined by quantitative real-time PCR

We examined the dose-dependency of gene expression changes for 51 genes in mouse liver treated with two N-nitroso genotoxic hepatocarcinogens, diethylnitrosamine (DEN) and ethylnitrosourea (ENU) by quantitative real-time PCR (qPCR). DEN (3, 9, 27 and 80mg/kg bw) or ENU (6, 17, 50 and 150mg/kg bw) was injected intraperitoneally into groups of five male 9-week-old B6C3F(1) mice and the livers were dissected after 4h and 28 days. Total RNA from pooled livers was reverse-transcribed to cDNA and the amount of each gene was quantified by qPCR. Results were analyzed by hierarchical and k-means clustering and ingenuity pathway analysis (IPA). The most characteristic result was a similar dose-dependency of gene expression changes with DEN and ENU. Twenty-one genes exhibited a distinct dose-dependent increase in expression at 4h for both carcinogens [Bax, Btg2, Ccng1, Cdkn1a, Cyp4a10, Cyp21a1, Fos, Gadd45b, Gdf15, Hmox1, Hspb1, Isg20l1, Jun, Mbd1, Mdm2, Myc, Net1, Plk2, Ppp1r3c, Rcan1 and Tubb2c], although the increase in gene expression due to ENU was generally weaker than that due to DEN. Only Gdf15 showed a dose-dependent increase in expression at 28 days for both carcinogens. The differences between DEN and ENU were in the expression of additional genes (7 for DEN and 8 for ENU). IPA extracted five gene networks: Network-1 included genes related to cancer and cell cycle arrest and associated with Bax, Btg2, Ccng1, Cdkn1a, Gadd45b, Gdf15, Hspb1, Mdm2 and Plk2 and Network-2 was related to DNA replication, recombination, repair and cell death and associated with Cyp21a1, Gdf15, Ppp1r3c, Rcan1 and Tubb2c. The present results show a distinct dose-dependency of gene expression changes induced by DEN and ENU. These changes were associated with cancer, cell cycle arrest, DNA replication, recombination, repair and cell death and were seen not only at 4h but also, for some, at 28 days after administration.

Detection of micronucleated cells and gene expression changes in glandular stomach of mice treated with stomach-targeted carcinogens

To assess the genotoxicity of chemicals on the stomach, we developed in vivo assays that can detect micronucleus induction and gene expression changes in epithelial cells of the glandular stomach in mice. Male BALB/c mice were orally given a single dose (100 mg/kg) of N-nitroso-N-methylurea (MNU) or N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) as stomach-targeted carcinogens. The glandular stomach was excised at 4h, 3 and 4 days after administration, and a single cell suspension of epithelial cells was prepared from the everted glandular stomach by EDTA treatment. For determination of micronucleus induction, gastric epithelial cells on days 3 and 4 after administration were fixed with 10% neutral-buffered formalin, stained with a combination of AO-DAPI, and analyzed under fluorescence microscopy. We also examined the induction of micronuclei in peripheral blood of these mice on days 2 and 3 after administration. Moreover, total RNA was extracted from gastric epithelial cells at 4h after administration, and p21 and plk2 expression was analyzed using a quantitative RT-PCR technique. 1) A significant increase of micronucleated cells was observed in the glandular stomach in mice treated with N-nitroso-N-methylurea (MNU) or N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) compared to mice treated with vehicle. 2) In peripheral blood, induction of micronuclei was observed in mice treated with MNU but not with MNNG. 3) p21 and plk2, which related to cell cycle arrest, were up-regulated in the glandular stomach in mice treated with MNU or MNNG compared to mice treated with vehicle. The present study showed that these assays using glandular stomach may help to evaluate the genotoxicity of chemicals after oral administration.