Practical Application of Toxicogenomics for Profiling Toxicant-Induced Biological Perturbations

A systems-level understanding of molecular perturbations is crucial for evaluating chemical-induced toxicity risks appropriately, and for this purpose comprehensive gene expression analysis or toxicogenomics investigation is highly advantageous. The recent accumulation of toxicity-associated gene sets (toxicogenomic biomarkers), enrichment in public or commercial large-scale microarray database and availability of open-source software resources facilitate our utilization of the toxicogenomic data. However, toxicologists, who are usually not experts in computational sciences, tend to be overwhelmed by the gigantic amount of data. In this paper we present practical applications of toxicogenomics by utilizing biomarker gene sets and a simple scoring method by which overall gene set-level expression changes can be evaluated efficiently. Results from the gene set-level analysis are not only an easy interpretation of toxicological significance compared with individual gene-level profiling, but also are thought to be suitable for cross-platform or cross-institutional toxicogenomics data analysis. Enrichment in toxicogenomics databases, refinements of biomarker gene sets and scoring algorithms and the development of user-friendly integrative software will lead to better evaluation of toxicant-elicited biological perturbations.

[1]  Adriano V. Werhli,et al.  Reverse Engineering Gene Regulatory Networks with Various Machine Learning Methods , 2008 .

[2]  Avi Ma’ayan.  Insights into the Organization of Biochemical Regulatory Networks Using Graph Theory Analyses* , 2009, Journal of Biological Chemistry.

[3]  Mark Craven,et al.  EDGE: A Centralized Resource for the Comparison, Analysis, and Distribution of Toxicogenomic Information , 2005, Molecular Pharmacology.

[4]  N. Kiyosawa,et al.  Evaluation of glutathione deficiency in rat livers by microarray analysis. , 2004, Biochemical pharmacology.

[5]  M Kanehisa,et al.  Organizing and computing metabolic pathway data in terms of binary relations. , 1997, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[6]  Steven C. Lawlor,et al.  GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways , 2002, Nature Genetics.

[7]  M. Negishi,et al.  Phenobarbital Confers its Diverse Effects by Activating the Orphan Nuclear Receptor Car , 2006, Drug metabolism reviews.

[8]  Mellar P. Davis,et al.  The pharmacological importance of cytochrome CYP3A4 in the palliation of symptoms: review and recommendations for avoiding adverse drug interactions , 2007, Supportive Care in Cancer.

[9]  N. Kiyosawa,et al.  Scoring multiple toxicological endpoints using a toxicogenomic database. , 2009, Toxicology letters.

[10]  Ivan Rusyn,et al.  Multicenter study of acetaminophen hepatotoxicity reveals the importance of biological endpoints in genomic analyses. , 2007, Toxicological sciences : an official journal of the Society of Toxicology.

[11]  N. Kiyosawa,et al.  A toxicogenomics approach for early assessment of potential non-genotoxic hepatocarcinogenicity of chemicals in rats. , 2008, Toxicology.

[12]  Roland Eils,et al.  Group testing for pathway analysis improves comparability of different microarray datasets , 2006, Bioinform..

[13]  J. Waring,et al.  Interlaboratory evaluation of rat hepatic gene expression changes induced by methapyrilene. , 2004, Environmental health perspectives.

[14]  Matthew A. Hibbs,et al.  Visualization of omics data for systems biology , 2010, Nature Methods.

[15]  N. Kiyosawa,et al.  Gene set-level network analysis using a toxicogenomics database. , 2010, Genomics.

[16]  Neil Kaplowitz,et al.  Idiosyncratic drug hepatotoxicity , 2005, Nature Reviews Drug Discovery.

[17]  Atsushi Ono,et al.  Identification of glutathione depletion-responsive genes using phorone-treated rat liver. , 2007, The Journal of toxicological sciences.

[18]  Atsushi Ono,et al.  Gene expression profiling in rat liver treated with compounds inducing phospholipidosis. , 2008, Toxicology and applied pharmacology.

[19]  Darrell R Boverhof,et al.  Toxicogenomics in risk assessment: applications and needs. , 2006, Toxicological sciences : an official journal of the Society of Toxicology.

[20]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[21]  H. Kitano.  Towards a theory of biological robustness , 2007, Molecular systems biology.

[22]  Kenji Takami,et al.  A toxicogenomic approach to drug-induced phospholipidosis: analysis of its induction mechanism and establishment of a novel in vitro screening system. , 2004, Toxicological sciences : an official journal of the Society of Toxicology.

[23]  Shibing Deng,et al.  Cross-site comparison of gene expression data reveals high similarity. , 2004, Environmental health perspectives.

[24]  R G Ulrich,et al.  Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. , 2001, Toxicology and applied pharmacology.

[25]  R G Ulrich,et al.  Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. , 2001, Toxicology letters.

[26]  J. Waring,et al.  Clofibrate-induced gene expression changes in rat liver: a cross-laboratory analysis using membrane cDNA arrays. , 2004, Environmental health perspectives.

[27]  N. Kiyosawa,et al.  Utilization of a one-dimensional score for surveying chemical-induced changes in expression levels of multiple biomarker gene sets using a large-scale toxicogenomics database. , 2006, The Journal of toxicological sciences.

[28]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[29]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[30]  N. Kiyosawa,et al.  Toxicogenomic Biomarkers for Liver Toxicity , 2009, Journal of toxicologic pathology.

[31]  T. Urushidani.  Prediction of Hepatotoxicity Based on the Toxicogenomics Database , 2008 .

[32]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[33]  Michael C. Rosenstein,et al.  The comparative toxicogenomics database: a cross-species resource for building chemical-gene interaction networks. , 2006, Toxicological sciences : an official journal of the Society of Toxicology.

[34]  Hans Lehrach,et al.  A comparison of oligonucleotide and cDNA-based microarray systems. , 2004, Physiological genomics.

[35]  J. Schellens,et al.  Concise review: Clinical relevance of drug drug and herb drug interactions mediated by the ABC transporter ABCB1 (MDR1, P-glycoprotein). , 2007, The oncologist.

[36]  R. Ulrich,et al.  Overview of an interlaboratory collaboration on evaluating the effects of model hepatotoxicants on hepatic gene expression. , 2004, Environmental health perspectives.

[37]  Yumiko Sakai,et al.  Applications of microarrays with toxicologically relevant genes (tox genes) for the evaluation of chemical toxicants in Sprague Dawley rats in vivo and human hepatocytes in vitro. , 2004, Mutation research.

[38]  T. Monks,et al.  The contribution of bromobenzene to our current understanding of chemically-induced toxicities. , 1988, Life sciences.

[39]  M. Fielden,et al.  Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. , 2008, Toxicological sciences : an official journal of the Society of Toxicology.

[40]  Pierre R. Bushel,et al.  CEBS—Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data , 2007, Nucleic Acids Res..

[41]  M. Severgnini,et al.  Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment. , 2006, Analytical Biochemistry.

[42]  Sergio Contrino,et al.  ArrayExpress—a public repository for microarray gene expression data at the EBI , 2004, Nucleic Acids Res..

[43]  H. Ellinger-Ziegelbauer,et al.  Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies. , 2008, Mutation research.