Toxicogenomic biomarkers for liver toxicity

Abstract

Toxicogenomics (TGx) is a widely used technique in the preclinical stage of drug development to investigate the molecular mechanisms of toxicity. A number of candidate TGx biomarkers have now been identified and are utilized for both assessing and predicting toxicities. Further accumulation of novel TGx biomarkers will lead to more efficient, appropriate and cost effective drug risk assessment, reinforcing the paradigm of the conventional toxicology system with a more profound understanding of the molecular mechanisms of drug-induced toxicity. In this paper, we overview some practical strategies as well as obstacles for identifying and utilizing TGx biomarkers based on microarray analysis. Since clinical hepatotoxicity is one of the major causes of drug development attrition, the liver has been the best documented target organ for TGx studies to date, and we therefore focused on information from liver TGx studies. In this review, we summarize the current resources in the literature in regard to TGx studies of the liver, from which toxicologists could extract potential TGx biomarker gene sets for better hepatotoxicity risk assessment.

Keywords: biomarker; liver; microarray; toxicogenomics.

Fig. 1.

General flow of a TGx study. The general flow of a TGx study is presented. Conventional toxicologic parameters, such as body / organ weights, histopathological findings, blood chemistry and toxico / pharmacokinetics, and functional genomics information, such as microarray data, are collected. The genomics data sets are huge and need to be organized into a well-designed database. Interpretation of the genomics data depends on the quality of the database, and analytical tools and an experienced researchers’ interdisciplinary knowledge and skills in biology, toxicology, statistics and computational sciences. A number of issues are yet to be determined to establish a standard operating procedure (SOP) for the public, including the content / format of the final report, recording items, statistical analysis to be performed for genomics data, etc. All the information should be appropriately recorded so that the obtained TGx data can be exchangeable across laboratories.

Fig. 2.

Characterization of hepatic toxicity profile. An example of characterizing the hepatic toxicity profile is presented. In this figure, six TGx biomarker gene sets associated with a) phase I drug metabolizing enzyme (DME), b) PPARα-regulated genes, c) cell proliferation, d) glutathione depletion, e) inflammation and f) oxidative stress are used to assess toxicity profiles based on the microarray data for rat livers treated with one of 90 chemicals. The microarray data was retrieved from TG-GATEs, a TGx database developed by the Toxicogenomics Project in Japan (TGP), after obtaining permission. The expression changes for each biomarker set were summarized and estimated using the TGP1 score, and the TGP1 score was subjected to hierarchical clustering. The red and blue colors indicate that the genes included in the TGx biomarker were generally up- or down-regulated, respectively, and the black color indicates that the expression level of the TGx biomarker gene sets did not show characteristic changes as a whole. Ideally, chemicals that do not affect the expression levels of genes included in the TGx biomarker would be desirable drug candidates. This strategy is applied to rank the chemicals based on the toxicity profiling.

Fig. 3.

Identification and application of TGx biomarkers for assessing glutathione depletion. A model case for identifying the candidate TGx biomarkers associated with glutathione depletion-type (acetaminophen-type) liver injury is presented. Rats were treated with a glutathione depletor L-buthionine (S, R)-sulfoximine (BSO), and GeneChip analysis was conducted on the liver. (A) A total of 69 probe sets were identified whose signal values were inversely correlated with the hepatic glutathione content. (B) The validity of the 69 probe sets as candidate TGx biomarkers for evaluation of glutathione depletion was evaluated by PCA using time-course microarray data for rat livers treated with acetaminophen. The 69 probe sets clearly classified the animal groups following acetaminophen treatment, and the acetaminophen group was clustered for 24 h together with the BSO-treated rats, suggesting that glutathione homeostasis was highly affected at this time point. Reprinted from Reference, with permission from Elsevier.

Fig. 4.

Toxicity prediction by Support Vector Machine algorithm. Support Vector Machine is a popular discriminant analysis algorithm. The first step in this algorithm is to prepare a training data set, such as microarray data for a “carcinogenic compound (positive)” and “non-carcinogenic compound (negative)”. Next, a classifier is developed with the training data using the machine learning algorithm. By using the developed classifier, one can predict a positive / negative outcome (carcinogenic / non-carcinogenic outcome in the figure) for a test compound with an unknown toxicological profile. The accuracy of the prediction by the classifier can be estimated by cross-validation using the training data set. Gray and green indicate ‘Positive’ and ‘Negative’ classification areas, respectively. Red spots indicate the support vectors used for the classification of the test data set.

Fig. 5.

Overcoming the discrepancy between old and new GeneChip data. Even within the same GeneChip platform, the inconsistency in microarray data is evident among the different generations of rat GeneChips, namely RG U34A and RAE 230A arrays, and this hinders utilization of ‘legacy TGx knowledge’ obtained from older microarrays. (A) The median signal values of the vehicle-treated rats were adjusted between the RG U34A and RAE 230A GeneChip data. The results for 4 representative genes are presented. (B) Principal component analysis using baseline-corrected RG U34A and RAE 230A GeneChip data was performed using the glutathione depletion-associated genes presented in Fig. 3. Adjustment of the baseline signal levels considerably improved the data compatibility between the RG U34A and RAE 230A GeneChip data; the spots for each treated chemical moved closer together (cf. inside area of the dashed circles). Reprinted from Reference, with permission from Elsevier.

Fig. 6.

Species-specific regulation of the hepatic Cyp17a1 gene elicited by o,p’-DDT. Correlation analysis between mice and rats was performed using differentially expressed orthologous genes in the liver elicited by o,p’-DDT. The temporal profiles of the o,p’-DDT-treated mouse liver198 and those of the o,p’-DDT-treated rat liver were compared by determining the Pearson’s correlation of the temporal gene expression (fold change) and significance (p1[t] value by empirical Bayesian analysis) between orthologs, and the results of this comparison are presented as a scatter plot. Correlations of gene expression and significance approaching 1.0 indicate that the behaviors of the orthologous genes are similar and would fall within the upper right quadrant. (A) Orthologs tended to localize in the upper- or lower-right quadrants, indicating that the temporal gene expression changes for o,p’-DDT-treated mouse and rat liver are comparable. However, poor correlations between the temporal p1(t) values and gene expression fold changes would fall within the lower left quadrant. Cyp17a1, one of the poor-correlation genes, fell into this quadrant, suggesting that significant differences exist between the rat and mouse othologue expression profiles. (B) The hepatic Cyp17a1 gene expression levels following o,p’-DDT treatment were compared between rats and mice by QRT-PCR. Significant species-specific regulation of hepatic CYP17a1 gene was observed. * P < 0.05 by a two-way ANOVA followed by pairwise comparisons using Tukey’s test.