Toxicogenomics analysis

BoverhofDRet al (2006) Comparative toxicogenomic analysis of the hepatotoxic effects of TCDD in Sprague Dawley rats and C57BL/6 mice. Toxicol Sci 94 398-116. doi kfll 00 (pii) 10.1093/toxsci/kfl 100... 

N Jai A, Boverhof DR, Dere E, et al. Comparative temporal toxicogenomic analysis of TCDD- and TCDF-mediated hepatic effects in immature female C57BL/6 mice. Toxicol Sci. 2008 103(2) 285-297. 

Peterson RL, Casciotti L, Block L, Goad ME, Tong Z, Meehan JT, Jordan RA, Vinlove MP, Markiewicz VR, Weed CA, Dorner AJ. Mechanistic toxicogenomic analysis of WAY-144122 administration in Sprague-Dawley rats. Toxicol Appl Pharmacol 2004 196(l) 80-94. 

Gong P et al., Toxicogenomic analysis provides new insights into molecular mechanisms of the sublethal toxicity of 2,4,6-trinitrotoluene in Eisenia fetida, Environ, Sci. Technol., 41, 8195, 2007. 

Routine toxicogenomic analysis for compounds prior to innovation... 

Burchiel, S.W. et al., Analysis of genetic and epigenetic mechanisms of toxicity potential roles of toxicogenomics and proteomics in toxicology, Toxicol. Sci., 59, 193, 2001. 

Cross comparisons across toxicogenomic datasets provides new statistical questions and subsequent challenges in data analysis. Meta-analyses may integrate datasets from multiple experimental studies consisting of different models (species, source), platforms (array type), statistical techniques (normalization) and design. The first challenge that needs to be addressed is how to properly make comparisons across datasets. To normalize datasets, better results may be achieved when data is first normalized internally and then externally (88). Secondly, equivalent and current annotation is needed to identify common genes across platforms, models, etc. 

Knudsen S (2004). Guide to Analysis of DNA Microarray Data. 2nd ed. Wiley-Liss, Hoboken, N.J. Pennie W, Pettit SD, Lord PG. Toxicogenomics in risk assessment an overview of an HESl collaborative research program. Environ Health Perspect 2004 112 417 19. 

Metabonomics has received considerable attention in the toxicological community, but based on published literature, metabonomic applications do not appear to be as widely used as transcriptomics (toxicogenomics). However, this is not a reflection on the utility of the approach, but more likely a consequence of the need to have a complex infrastructure, particularly with respect to data analysis and interpretation in order to carry out the technology. 

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