Skip to main content

In Vivo Predictive Toxicogenomics

  • Protocol
Essential Concepts in Toxicogenomics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 460))

Summary

Reference databases consisting of large sample numbers and high-dimensional microarray data are now available for the investigation of adverse events in animal model systems such as the rat. This large volume of data, accompanied by appropriate study designs, compound and dose selection procedure, and minimization of technical and biological confounding effects, can yield successful predictive models for a variety of hypotheses. The process of training, validating, and implementing predictive models is cyclical and complex. This chapter highlights individual decisions that need to be made before, during, and after a model or set of models has been trained, with an emphasis on proper statistical methods and suitable interpretation of the results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hamadeh, H.K., Bushel, P.R., Jayadev, S., Martin, K., DiSorbo, O., Sieber, S., et al. (2002) Gene expression analysis reveals chemical-specific profiles. Toxicol. Sci. 67, 219–231.

    Article  CAS  PubMed  Google Scholar 

  2. Waring, J.F., Jolly, R.A., Ciurlionis, R., Lum, P.Y., Praestgaard, J.T., Morfitt, D.C., et al. (2001) Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol. Appl. Pharmacol.175, 28–42.

    Article  CAS  PubMed  Google Scholar 

  3. Huang, Q., Jin, X., Gaillard, E.T., Knight, B.L., Pack, F.D., Stoltz, J.H., et al. (2004) Gene expression profiling reveals multiple toxicity endpoints induced by hepatotoxicants. Mutat. Res. 549, 147–167.

    CAS  PubMed  Google Scholar 

  4. Kramer, J.A., Curtiss, S.W., Kolaja, K.L., Alden, C.L., Blomme, E.A., Curtiss, W.C., et al. (2004) Acute molecular markers of rodent hepatic carcinogenesis identified by transcription profiling. Chem. Res. Toxicol. 17,463–470.

    Article  CAS  PubMed  Google Scholar 

  5. Bushel, P.R., Hamadeh, H.K., Bennett, L., Green, J., Ableson, A., Misener, S., et al. (2002) Computational selection of distinct class- and subclass-specific gene expression signatures. J. Biomed. Inform. 35, 160–170.

    Article  PubMed  Google Scholar 

  6. Roberts, R.A., Michel, C., Coyle, B., Freathy, C., Cain, K., and Boitier, E. (2004) Regulation of apoptosis by peroxisome proliferators. Toxicol. Lett. 149,37–41.

    Article  CAS  PubMed  Google Scholar 

  7. Ramos, K.S., Chacon, E., and Acosta, D., Jr. (1996) Toxic responses of the heart and vascular systems. In: Casarett and Doull’s Toxicology: The Basic Science of Poisons (Klaassen, C.D., ed.), McGraw-Hill, New York, pp. 487–527.

    Google Scholar 

  8. Steiner, G., Suter, L., Boess, F., Gasser, R., de Vera, M.C., Albertini, S., and Ruepp, S. (2004) Discriminating different classes of toxicants by transcript profiling. Environ. Health Perspect. 112, 1236–1248.

    Article  CAS  PubMed  Google Scholar 

  9. Porter, M.W., Castle, A.L., Orr, M.S., and Mendrick, D.L. (2003) Predictive toxicogenomics. In: An Introduction to Toxicogenomics (Burczynski, M.E., ed.), CRC Press, Boca Raton, pp. 225–259.

    Google Scholar 

  10. Hamadeh, H.K., Bushel, P.R., Jayadev, S., DiSorbo, O., Bennett, L., Li, L., et al. (2002) Prediction of compound signature using high density gene expression profiling. Toxicol. Sci. 67, 232–240.

    Article  CAS  PubMed  Google Scholar 

  11. Pahl, A., and Brune, K. (2002) Stabilization of gene expression profiles in blood after phlebotomy. Clin. Chem. 48, 2251–2253.

    CAS  PubMed  Google Scholar 

  12. Ellinger-Ziegelbauer, H., Stuart, B., Wahle, B., Bomann, W., and Ahr, H.J. (2004) Characteristic expression profiles induced by genotoxic carcinogens in rat liver. Toxicol. Sci. 77, 19–34.

    Article  CAS  PubMed  Google Scholar 

  13. Ellinger-Ziegelbauer, H., Stuart, B., Wahle, B., Bomann, W., and Ahr, H.J. (2005) Comparison of the expression profiles induced by genotoxic and nongenotoxic carcinogens in rat liver. Mutat. Res.575, 61–84.

    CAS  PubMed  Google Scholar 

  14. Kier, L.D., Neft, R., Tang, L., Suizu, R., Cook, T., Onsurez, K., et al. (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, 101–113.

    CAS  PubMed  Google Scholar 

  15. Natsoulis, G., El Ghaoui, L., Lanckriet, G.R., Tolley, A.M., Leroy, F., Dunlea, S., et al. (2005) Classification of a large microarray data set: algorithm comparison and analysis of drug signatures. Genome Res. 15, 724–736.

    Article  CAS  PubMed  Google Scholar 

  16. Carter, S.L., Eklund, A.C., Mecham, B.H., Kohane, I.S., and Szallasi, Z. (2005) Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC Bioinformatics 6, 107.

    Article  PubMed  Google Scholar 

  17. Mecham, B.H., Klus, G.T., Strovel, J., Augustus, M., Byrne, D., Bozso, P., et al. (2004) Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Res.32, e74.

    Article  PubMed  Google Scholar 

  18. Pennie, W., Pettit, S.D., and Lord, P.G. (2004) Toxicogenomics in risk assessment: an overview of an HESI collaborative research program. Environ. Health Perspect. 112, 417–419.

    Article  PubMed  Google Scholar 

  19. Mattingly, C.J., Colby, G.T., Forrest, J.N., and Boyer, J.L. (2003) The Comparative Toxicogenomics Database (CTD). Environ. Health Perspect. 111,793–795.

    Article  CAS  PubMed  Google Scholar 

  20. Tong, W., Cao, X., Harris, S., Sun, H., Fang, H., Fuscoe, J., et al. (2003) ArrayTrack—supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research. Environ. Health Perspect. 111, 1819–1826.

    Article  CAS  PubMed  Google Scholar 

  21. Waters, M., Boorman, G., Bushel, P., Cunningham, M., Irwin, R., Merrick, A., et al. (2003) Systems toxicology and the Chemical Effects in Biological Systems (CEBS) knowledge base. EHP Toxicogenomics 111, 15–28.

    CAS  PubMed  Google Scholar 

  22. Edgar, R., Domrachev, M., and Lash, A.E. (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210.

    Article  CAS  PubMed  Google Scholar 

  23. Tsai, C.A., Lee, T.C., Ho, I.C., Yang, U.C., Chen, C.H., and Chen, J.J. (2005) Multi-class clustering and prediction in the analysis of microarray data. Math. Biosci. 193, 79–100.

    Article  CAS  PubMed  Google Scholar 

  24. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537.

    Article  CAS  PubMed  Google Scholar 

  25. Nguyen, D.V., and Rocke, D.M. (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18,39–50.

    Article  CAS  PubMed  Google Scholar 

  26. Li, T., Zhang, C., and Ogihara, M. (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20, 2429–2437.

    Article  CAS  PubMed  Google Scholar 

  27. Nadon, R., and Shoemaker, J. (2002) Statistical issues with microarrays: processing and analysis. Trends Genet. 18, 265–271.

    Article  CAS  PubMed  Google Scholar 

  28. Zien, A., Aigner, T., Zimmer, R., and Lengauer, T. (2001) Centralization: a new method for the normalization of gene expression data. Bioinformatics 17 (Suppl 1), S323–331.

    PubMed  Google Scholar 

  29. Choe, S.E., Boutros, M., Michelson, A.M., Church, G.M., and Halfon, M.S. (2005) Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biol. 6, R16.

    Article  PubMed  Google Scholar 

  30. Irizarry, R.A., Bolstad, B.M., Collin, F., Cope, L.M., Hobbs, B., and Speed, T.P. (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15.

    Article  PubMed  Google Scholar 

  31. Kreil, D.P., and Russell, R.R. (2005) There is no silver bullet—a guide to low-level data transforms and normalisation methods for microarray data. Brief Bioinform. 6, 86–97.

    Article  CAS  PubMed  Google Scholar 

  32. Desai, V.G., Moland, C.L., Branham, W.S., Delongchamp, R.R., Fang, H., Duffy, P.H., et al. (2004) Changes in expression level of genes as a function of time of day in the liver of rats. Mutat. Res. 549, 115–129.

    CAS  PubMed  Google Scholar 

  33. Kita, Y., Shiozawa, M., Jin, W., Majewski, R.R., Besharse, J.C., Greene, A.S., and Jacob, H.J. (2002) Implications of circadian gene expression in kidney, liver and the effects of fasting on pharmacogenomic studies. Pharmacogenetics 12, 55–65.

    Article  CAS  PubMed  Google Scholar 

  34. Boorman, G.A., Blackshear, P.E., Parker, J.S., Lobenhofer, E.K., Malarkey, D.E., Vallant, M.K., et al. (2005) Hepatic gene expression changes throughout the day in the Fischer rat: implications for toxicogenomic experiments. Toxicol. Sci.86, 185–193.

    Article  CAS  PubMed  Google Scholar 

  35. Gerwien, R., and Hyde, C. (2003) Reducing the risk of drug discovery: the application of predictive modeling to preclinical development. Preclinica 1, 247–252.

    Google Scholar 

  36. Inza, I., Larranaga, P., Blanco, R., and Cerrolaza, A.J. (2004) Filter versus wrapper gene selection approaches in DNA microarray domains. Artif. Intell. Med. 31, 91–103.

    Article  PubMed  Google Scholar 

  37. Furlanello, C., Serafini, M., Merler, S., and Jurman, G. (2003) Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinformatics 4, 54.

    Article  PubMed  Google Scholar 

  38. Ambroise, C., and McLachlan, G.J. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. U. S. A. 99, 6562–6566.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Porter, M.W. (2008). In Vivo Predictive Toxicogenomics. In: Mendrick, D.L., Mattes, W.B. (eds) Essential Concepts in Toxicogenomics. Methods in Molecular Biology™, vol 460. Humana Press. https://doi.org/10.1007/978-1-60327-048-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-60327-048-9_6

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-638-2

  • Online ISBN: 978-1-60327-048-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics