Analysis of cell death inducing compounds
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Biomarkers for early detection of toxicity hold the promise of improving the failure rates in drug development. In the present study, gene expression levels were measured using full-genome RAE230 version 2 Affymetrix GeneChips on rat liver tissue 48 h after administration of six different compounds, three toxins (ANIT, DMN and NMF) and three non-toxins (Caeruelein, Dinitrophenol and Rosiglitazone). We identified three gene transcripts with exceptional predictive performance towards liver toxicity and/or changes in histopathology. The three genes were: glucokinase regulatory protein (GCKR), ornithine aminotransferase (OAT) and Cytochrome P450, subfamily IIC (mephenytoin 4-hydroxylase) (Cyp2C29). RT-PCR for these three genes was performed and four additional compounds were included for validation. The quantitative RT-PCR analysis confirmed the findings based on the microarray data and using the three genes a classification rate of 55 of 57 samples was achieved for the classification of not toxic versus toxic. The single most promising biomarker (OAT) alone resulted in a surprisingly 100% correctly classified samples. OAT has not previously been linked to toxicity and cell death in the literature and the novel finding represents a putative hepatotoxicity biomarker.
KeywordsToxicogenomics Cell death Ornithine aminotransferase (OAT) Glucokinase regulatory protein (GCKR) Cytochrome P450—subfamily IIC (mephenytoin 4-hydroxylase) (Cyp2C29)
Control (untreated) animal
Cytochrome P450, family 2, subfamily C
Flavin containing monooxygenase 1
Glucokinase regulatory protein
I would like to thank the COMET I consortium for giving me access to the tissues analyzed in this study. Homepage of the COMET consortium: http://www.bc-comet.sk.med.ic.ac.uk/
I would also like to thank Anne-Marie Mølck from Novo Nordisk with help on toxicity, Nina Hagen for inputs on P450, Lene Normann Nielsen on help with the QPCR and Kristine Dahlin from DTU with assistance in the laboratory.
- Gunther EC, Gerwien RW, Bento P, Heyes MP, Stone DJ (2003) Prediction of clinical drug efficacy by classification of drug—induced genomic expression profiles in vitro. Society for Neuroscience Abstract Viewer and Itinerary Planner 2003, Abstract-2003Google Scholar
- Heinloth AN, Irwin RD, Boorman GA, Nettesheim P, Fannin RD, Sieber SO, Snell ML, Tucker CJ, Li L, Travlos GS, Vansant G, Blackshear PE, Tennant RW, Cunningham ML, Paules RS (2004) Gene expression profiling of rat livers reveals indicators of potential adverse effects. Toxicol Sci 80(1):193–202PubMedCrossRefGoogle Scholar
- Johnson PH, Walker RP, Jones SW, Stephens K, Meurer J, Zajchowski DA, Luke MM, Eeckman F, Tan Y, Wong L, Parry G, Morgan TK, McCarrick MA, Monforte J (2002) Multiplex gene expression analysis for high-throughput drug discovery: screening and analysis of compounds affecting genes overexpressed in cancer cells. Mol Cancer Ther 1(14):1293–1304PubMedGoogle Scholar
- Kier LD, Neft R, Tang L, Suizu R, Cook T, Onsurez K, Tiegler K, Sakai Y, Ortiz M, Nolan T, Sankar U, Li AP (2004) 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 549(1–2):101–113PubMedGoogle Scholar
- Morgan ET (2001) Regulation of cytochrome P450 by inflammatory mediators: why and how? Drug Metabo Dispos 29(3):207–212Google Scholar