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.
References
[1]
Kaplowitz, N. Idiosyncratic drug hepatotoxicity. Nat. Rev. Drug Discov?2005, 4, 489–499, doi:10.1038/nrd1750. 15931258
[2]
Marchetti, S; Mazzanti, R; Beijnen, JH; Schellens, JH. Concise review: Clinical relevance of drug drug and herb drug interactions mediated by the ABC transporter ABCB1 (MDR1, P-glycoprotein). Oncologist?2007, 12, 927–941, doi:10.1634/theoncologist.12-8-927. 17766652
[3]
Haddad, A; Davis, M; Lagman, R. The pharmacological importance of cytochrome CYP3A4 in the palliation of symptoms: Review and recommendations for avoiding adverse drug interactions. Support. Care Cancer?2007, 15, 251–257, doi:10.1007/s00520-006-0127-5. 17139496
[4]
Kodama, S; Negishi, M. Phenobarbital confers its diverse effects by activating the orphan nuclear receptor car. Drug Metab. Rev?2006, 38, 75–87, doi:10.1080/03602530600569851. 16684649
[5]
Kitano, H. Towards a theory of biological robustness. Mol. Syst. Biol?2007, 3, 137. 17882156
[6]
Boverhof, DR; Zacharewski, TR. Toxicogenomics in risk assessment: applications and needs. Toxicol. Sci?2006, 89, 352–360, doi:10.1093/toxsci/kfj018. 16221963
[7]
Waring, JF; Jolly, RA; Ciurlionis, R; Lum, PY; Praestgaard, JT; Morfitt, DC; Buratto, B; Roberts, C; Schadt, E; Ulrich, RG. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol. Appl. Pharmacol?2001, 175, 28–42, doi:10.1006/taap.2001.9243. 11509024
[8]
Waring, JF; Ciurlionis, R; Jolly, RA; Heindel, M; Ulrich, RG. Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol. Lett?2001, 120, 359–368, doi:10.1016/S0378-4274(01)00267-3. 11323195
[9]
Ashburner, M; Ball, CA; Blake, JA; Botstein, D; Butler, H; Cherry, JM; Davis, AP; Dolinski, K; Dwight, SS; Eppig, JT; Harris, MA; Hill, DP; Issel-Tarver, L; Kasarskis, A; Lewis, S; Matese, JC; Richardson, JE; Ringwald, M; Rubin, GM; Sherlock, G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet?2000, 25, 25–29, doi:10.1038/75556. 10802651
[10]
Goto, S; Bono, H; Ogata, H; Fujibuchi, W; Nishioka, T; Sato, K; Kanehisa, M. Organizing and computing metabolic pathway data in terms of binary relations. Pac. Symp. Biocomput?1997, 175–186.
[11]
Dahlquist, KD; Salomonis, N; Vranizan, K; Lawlor, SC; Conklin, BR. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet?2002, 31, 19–20, doi:10.1038/ng0502-19. 11984561
[12]
Kiyosawa, N; Ando, Y; Manabe, S; Yamoto, T. Toxicogenomic biomarkers for liver toxicity. J. Toxicol. Pathol?2009, 22, 35–52, doi:10.1293/tox.22.35. 22271975
[13]
Kier, LD; Neft, R; Tang, L; Suizu, R; Cook, T; Onsurez, K; Tiegler, K; Sakai, Y; Ortiz, M; Nolan, T; Sankar, U; Li, AP. 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. Mutat. Res?2004, 549, 101–113, doi:10.1016/j.mrfmmm.2003.11.015. 15120965
[14]
Ellinger-Ziegelbauer, H; Gmuender, H; Bandenburg, A; Ahr, HJ. Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies. Mutat. Res?2008, 637, 23–39, doi:10.1016/j.mrfmmm.2007.06.010. 17689568
[15]
Uehara, T; Hirode, M; Ono, A; Kiyosawa, N; Omura, K; Shimizu, T; Mizukawa, Y; Miyagishima, T; Nagao, T; Urushidani, T. A toxicogenomics approach for early assessment of potential non-genotoxic hepatocarcinogenicity of chemicals in rats. Toxicology?2008, 250, 15–26, doi:10.1016/j.tox.2008.05.013. 18619722
[16]
Sawada, H; Takami, K; Asahi, SA. Toxicogenomic approach to drug-induced phospholipidosis: Analysis of its induction mechanism and establishment of a novel in vitro screening system. Toxicol. Sci?2005, 83, 282–292. 15342952
[17]
Hirode, M; Ono, A; Miyagishima, T; Nagao, T; Ohno, Y; Urushidani, T. Gene expression profiling in rat liver treated with compounds inducing phospholipidosis. Toxicol. Appl. Pharmacol?2008, 229, 290–299, doi:10.1016/j.taap.2008.01.036. 18355885
[18]
Kiyosawa, N; Ito, K; Sakuma, K; Niino, N; Kanbori, M; Yamoto, T; Manabe, S; Matsunuma, N. Evaluation of glutathione deficiency in rat livers by microarray analysis. Biochem. Pharmacol?2004, 68, 1465–1475, doi:10.1016/j.bcp.2004.05.053. 15345336
[19]
Kiyosawa, N; Uehara, T; Gao, W; Omura, K; Hirode, M; Shimizu, T; Mizukawa, Y; Ono, A; Miyagishima, T; Nagao, T; Urushidani, T. Identification of glutathione depletion-responsive genes using phorone-treated rat liver. J. Toxicol. Sci?2007, 32, 469–486, doi:10.2131/jts.32.469. 18198479
[20]
Edgar, R; Domrachev, M; Lash, AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic. Acids Res?2002, 30, 207–210, doi:10.1093/nar/30.1.207. 11752295
[21]
Brazma, A; Parkinson, H; Sarkans, U; Shojatalab, M; Vilo, J; Abeygunawardena, N; Holloway, E; Kapushesky, M; Kemmeren, P; Lara, GG; Oezcimen, A; Rocca-Serra, P; Sansone, SA. Array Express—A public repository for microarray gene expression data at the EBI. Nucleic Acids Res?2003, 31, 68–71, doi:10.1093/nar/gkg091. 12519949
[22]
Waters, M; Stasiewicz, S; Merrick, BA; Tomer, K; Bushel, P; Paules, R; Stegman, N; Nehls, G; Yost, KJ; Johnson, CH; Gustafson, SF; Xirasagar, S; Xiao, N; Huang, CC; Boyer, P; Chan, DD; Pan, Q; Gong, H; Taylor, J; Choi, D; Rashid, A; Ahmed, A; Howle, R; Selkirk, J; Tennant, R; Fostel, J. CEBS—Chemical Effects in Biological Systems: A public data repository integrating study design and toxicity data with microarray and proteomics data. Nucleic Acids Res?2008, 36, D892–D900. 17962311
[23]
Mattingly, CJ; Rosenstein, MC; Davis, AP; Colby, GT; Forrest, JN, Jr; Boyer, JL. The comparative toxicogenomics database: A cross-species resource for building chemical-gene interaction networks. Toxicol. Sci?2006, 92, 587–595, doi:10.1093/toxsci/kfl008. 16675512
[24]
Hayes, KR; Vollrath, AL; Zastrow, GM; McMillan, BJ; Craven, M; Jovanovich, S; Rank, DR; Penn, S; Walisser, JA; Reddy, JK; Thomas, RS; Bradfield, CA. EDGE: A centralized resource for the comparison, analysis, and distribution of toxicogenomic information. Mol. Pharmacol?2005, 67, 1360–1368, doi:10.1124/mol.104.009175. 15662043
[25]
Gehlenborg, N; O’Donoghue, SI; Baliga, NS; Goesmann, A; Hibbs, MA; Kitano, H; Kohlbacher, O; Neuweger, H; Schneider, R; Tenenbaum, D; Gavin, AC. Visualization of omics data for systems biology. Nat. Methods?2010, 7, S56–S68, doi:10.1038/nmeth.1436. 20195258
[26]
Gentleman, RC; Carey, VJ; Bates, DM; Bolstad, B; Dettling, M; Dudoit, S; Ellis, B; Gautier, L; Ge, Y; Gentry, J; Hornik, K; Hothorn, T; Huber, W; Iacus, S; Irizarry, R; Leisch, F; Li, C; Maechler, M; Rossini, AJ; Sawitzki, G; Smith, C; Smyth, G; Tierney, L; Yang, JY; Zhang, J. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol?2004, 5, R80, doi:10.1186/gb-2004-5-10-r80. 15461798
[27]
R: A Language and Environment for Statistical Computing; R Development Core Team: Vienna, Australia, 2008.
[28]
Shannon, P; Markiel, A; Ozier, O; Baliga, NS; Wang, JT; Ramage, D; Amin, N; Schwikowski, B; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res?2003, 13, 2498–2504, doi:10.1101/gr.1239303. 14597658
[29]
Urushidani, T. Prediction of hepatotoxicity based on the toxicogenomics database. In Hepatotoxicity from Genomics to in Vitro and in Vivo Models; Sahu, S, Ed.; John Wiley & Sons: Hoboken, NJ, USA, 2008; pp. 507–529.
[30]
Kiyosawa, N; Shiwaku, K; Hirode, M; Omura, K; Uehara, T; Shimizu, T; Mizukawa, Y; Miyagishima, T; Ono, A; Nagao, T; Urushidani, T. 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. J. Toxicol. Sci?2006, 31, 433–448, doi:10.2131/jts.31.433. 17202759
[31]
Kiyosawa, N; Ando, Y; Watanabe, K; Niino, N; Manabe, S; Yamoto, T. Scoring multiple toxicological endpoints using a toxicogenomic database. Toxicol. Lett?2009, 188, 91–97, doi:10.1016/j.toxlet.2009.03.011. 19446240
[32]
Mah, N; Thelin, A; Lu, T; Nikolaus, S; Kuhbacher, T; Gurbuz, Y; Eickhoff, H; Kloppel, G; Lehrach, H; Mellgard, B; Costello, CM; Schreiber, S. A comparison of oligonucleotide and cDNA-based microarray systems. Physiol. Genomics?2004, 16, 361–370, doi:10.1152/physiolgenomics.00080.2003. 14645736
[33]
Severgnini, M; Bicciato, S; Mangano, E; Scarlatti, F; Mezzelani, A; Mattioli, M; Ghidoni, R; Peano, C; Bonnal, R; Viti, F; Milanesi, L; De Bellis, G; Battaglia, C. Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment. Anal. Biochem?2006, 353, 43–56, doi:10.1016/j.ab.2006.03.023. 16624241
[34]
Waring, JF; Ulrich, RG; Flint, N; Morfitt, D; Kalkuhl, A; Staedtler, F; Lawton, M; Beekman, JM; Suter, L. Interlaboratory evaluation of rat hepatic gene expression changes induced by methapyrilene. Environ. Health Perspect?2004, 112, 439–448, doi:10.1289/ehp.6643. 15033593
[35]
Chu, TM; Deng, S; Wolfinger, R; Paules, RS; Hamadeh, HK. Cross-site comparison of gene expression data reveals high similarity. Environ. Health Perspect?2004, 112, 449–455, doi:10.1289/ehp.6787. 15033594
[36]
Fielden, MR; Nie, A; McMillian, M; Elangbam, CS; Trela, BA; Yang, Y; Dunn, RT, II; Dragan, Y; Fransson-Stehen, R; Bogdanffy, M; Adams, SP; Foster, WR; Chen, SJ; Rossi, P; Kasper, P; Jacobson-Kram, D; Tatsuoka, KS; Wier, PJ; Gollub, J; Halbert, DN; Roter, A; Young, JK; Sina, JF; Marlowe, J; Martus, HJ; Aubrecht, J; Olaharski, AJ; Roome, N; Nioi, P; Pardo, I; Snyder, R; Perry, R; Lord, P; Mattes, W; Car, BD. Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. Toxicol. Sci?2008, 103, 28–34. 18281259
Baker, VA; Harries, HM; Waring, JF; Duggan, CM; Ni, HA; Jolly, RA; Yoon, LW; de Souza, AT; Schmid, JE; Brown, RH; Ulrich, RG; Rockett, JC. Clofibrate-induced gene expression changes in rat liver: A cross-laboratory analysis using membrane cDNA arrays. Environ. Health Perspect?2004, 112, 428–438, doi:10.1289/ehp.6677. 15033592
[39]
Ulrich, RG; Rockett, JC; Gibson, GG; Pettit, SD. Overview of an interlaboratory collaboration on evaluating the effects of model hepatotoxicants on hepatic gene expression. Environ. Health Perspect?2004, 112, 423–427, doi:10.1289/ehp.6675. 15033591
[40]
Manoli, T; Gretz, N; Grone, HJ; Kenzelmann, M; Eils, R; Brors, B. Group testing for pathway analysis improves comparability of different microarray datasets. Bioinformatics?2006, 22, 2500–2506, doi:10.1093/bioinformatics/btl424. 16895928
[41]
Ma’ayan, A. Insights into the organization of biochemical regulatory networks using graph theory analyses. J. Biol. Chem?2009, 284, 5451–5455. 18940806
[42]
Kiyosawa, N; Manabe, S; Sanbuissho, A; Yamoto, T. Gene set-level network analysis using a toxicogenomics database. Genomics?2010, 96, 39–49, doi:10.1016/j.ygeno.2010.03.014. 20363313
[43]
Crzegorczyk, M; Husmeier, D; Werhli, AV. Reverse engineering gene regulatory networks with various machine learning methods. In Analysis of Microarray Data—A Network Approach; Emmert-Streib, F, Dehmer, M, Eds.; Wiley-VCH: Hoboken, NJ, USA, 2008.
[44]
Lau, SS; Monks, TJ. The contribution of bromobenzene to our current understanding of chemically-induced toxicities. Life Sci?1988, 42, 1259–1269, doi:10.1016/0024-3205(88)90219-6. 3280935