Advertisement

Public Consortium Efforts in Toxicogenomics

  • William B. Mattes
Part of the Methods in Molecular Biology™ book series (MIMB, volume 460)

Summary

Public consortia provide a forum for addressing questions requiring more resources than one organization alone could bring to bear and engaging many sectors of the scientific community. They are particular well suited for tackling some of the questions encountered in the field of toxicogenomics, where the number of studies and microarray analyses would be prohibitively expensive for a single organization to carry out. Five consortia that stand out in the field of toxicogenomics are the Institutional Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Committee on the Application of Genomics to Mechanism Based Risk Assessment, the Toxicogenomics Research Consortium, the MicroArray Quality Control (MAQC) Consortium, the InnoMed PredTox effort, and the Predictive Safety Testing Consortium. Collectively, these consortia efforts have addressed issues such as reproducibility of microarray results, standard practice for assays and analysis, relevance of microarray results to conventional end points, and robustness of statistical models on diverse data sets. Their results demonstrate the impact that the pooling of resources, experience, expertise, and insight found in consortia can have.

Key Words

consortia public-private partnerships standards toxicogenomics 

References

  1. 1.
    Schena, M., Shalon, D., Davis, R. W., and Brown, P. O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470.CrossRefPubMedGoogle Scholar
  2. 2.
    DeRisi, J. L., Iyer, V. R., and Brown, P. O. (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686.CrossRefPubMedGoogle Scholar
  3. 3.
    Anderson, N. L., Taylor, J., Hofmann, J. P., Esquer-Blasco, R., Swift, S., and Anderson, N. G. (1996) Simultaneous measurement of hundreds of liver proteins: application in assessment of liver function. Toxicol. Pathol. 24, 72–76.CrossRefPubMedGoogle Scholar
  4. 4.
    Kegelmeyer, A. E., Sprankle, C. S., Horesovsky, G. J., and Butterworth, B. E. (1997) Differential display identified changes in mRNA levels in regenerating livers from chloroform-treated mice. Mol. Carcinog. 20, 288–297.CrossRefPubMedGoogle Scholar
  5. 5.
    Rodi, C. P., Bunch, R. T., Curtiss, S. W., Kier, L. D., Cabonce, M. A., Davila, J. C., et al. (1999) Revolution through genomics in investigative and discovery toxicology. Toxicol. Pathol. 27, 107–110.CrossRefPubMedGoogle Scholar
  6. 6.
    Amundson, S. A., Bittner, M., Chen, Y., Trent, J., Meltzer, P., and Fornace, A. J. (1999) Fluorescent cDNA microarray hybridization reveals complexity and heterogeneity of cellular genotoxic stress responses. Oncogene 18, 3666–3672.CrossRefPubMedGoogle Scholar
  7. 7.
    Farr, S., and Dunn, R. T., 2nd. (1999) Concise review: gene expression applied to toxicology. Toxicol. Sci. 50, 1–9.CrossRefPubMedGoogle Scholar
  8. 8.
    Fornace, A. J., Amundson, S. A., Bittner, M., Myers, T. G., Meltzer, P., Weinstein, J. N., and Trent, J. (1999) The complexity of radiation stress responses: analysis by informatics and functional genomics approaches. Gene. Expr. 7, 387–400.PubMedGoogle Scholar
  9. 9.
    Jelinsky, S. A., and Samson, L. D. (1999) Global response of Saccharomyces cerevisiae to an alkylating agent. Proc. Natl. Acad. Sci. U. S. A. 96, 1486–1491.CrossRefPubMedGoogle Scholar
  10. 10.
    Nuwaysir, E. F., Bittner, M., Trent, J., Barrett, J. C., and Afshari, C. A. (1999) Microarrays and toxicology: the advent of toxicogenomics. Mol. Carcinog. 24, 153–159.CrossRefPubMedGoogle Scholar
  11. 11.
    Lockhart, D. J., Dong, H., Byrne, M. C., Follettie, M. T., Gallo, M. V., Chee, M. S., et al. (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 14, 1675–1680.CrossRefPubMedGoogle Scholar
  12. 12.
    Shalon, D., Smith, S. J., and Brown, P. O. (1996) A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 6, 639–645.CrossRefPubMedGoogle Scholar
  13. 13.
    Stimpson, D. I., Cooley, P. W., Knepper, S. M., and Wallace, D. B. (1998) Parallel production of oligonucleotide arrays using membranes and reagent jet printing. BioTechniques 25, 886–890.PubMedGoogle Scholar
  14. 14.
    Ermolaeva, O., Rastogi, M., Pruitt, K. D., Schuler, G. D., Bittner, M. L., Chen, Y., et al. (1998) Data management and analysis for gene expression arrays. Nat. Genet. 20, 19–23.CrossRefPubMedGoogle Scholar
  15. 15.
    Bassett, D. E., Eisen, M. B., and Boguski, M. S. (1999) Gene expression informatics—it’s all in your mine. Nat. Genet. 21, 51–55.CrossRefPubMedGoogle Scholar
  16. 16.
    Zhang, M. Q. (1999) Large-scale gene expression data analysis: a new challenge to computational biologists. Genome Res. 9, 681–688.PubMedGoogle Scholar
  17. 17.
    Brazma, A., and Vilo, J. (2000) Gene expression data analysis. FEBS Lett. 480, 17–24.CrossRefPubMedGoogle Scholar
  18. 18.
    ILSI. About ILSI. Available at www.ilsi.org/AboutILSI/.
  19. 19.
    ILSI. Application of Genomics to Mechanism-based Risk Assessment. Available at www.hesiglobal.org/Committees/TechnicalCommittees/Genomics/.
  20. 20.
    Parkinson, H., Kapushesky, M., Shojatalab, M., Abeygunawardena, N., Coulson, R., Farne, A., et al. (2007) ArrayExpress–a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 35, D747–750.CrossRefPubMedGoogle Scholar
  21. 21.
  22. 22.
    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.CrossRefPubMedGoogle Scholar
  23. 23.
    Baker, V. A., Harries, H. M., Waring, J. F., Duggan, C. M., Ni, H. A., Jolly, R. A., et al. (2004) Clofibrate-induced gene expression changes in rat liver: a cross-laboratory analysis using membrane cDNA arrays. Environ. Health Perspect. 112, 428–438.CrossRefPubMedGoogle Scholar
  24. 24.
    Ulrich, R. G., Rockett, J. C., Gibson, G. G., and Pettit, S. D. (2004) Overview of an interlaboratory collaboration on evaluating the effects of model hepatotoxicants on hepatic gene expression. Environ. Health Perspect. 112, 423–427.CrossRefPubMedGoogle Scholar
  25. 25.
    Thompson, K. L., Afshari, C. A., Amin, R. P., Bertram, T. A., Car, B., Cunningham, M., et al. (2004) Identification of platform-independent gene expression markers of cisplatin nephrotoxicity. Environ. Health Perspect. 112, 488–494.CrossRefPubMedGoogle Scholar
  26. 26.
    Kramer, J. A., Pettit, S. D., Amin, R. P., Bertram, T. A., Car, B., Cunningham, M., et al. (2004) Overview on the application of transcription profiling using selected nephrotoxicants for toxicology assessment. Environ. Health Perspect. 112, 460–464.CrossRefPubMedGoogle Scholar
  27. 27.
    Amin, R. P., Vickers, A. E., Sistare, F., Thompson, K. L., Roman, R. J., Lawton, M., et al. (2004) Identification of putative gene based markers of renal toxicity. Environ. Health Perspect. 112, 465–479.CrossRefPubMedGoogle Scholar
  28. 28.
    Jolly, R. A., Goldstein, K. M., Wei, T., Gao, H., Chen, P., Huang, S., et al. (2005) Pooling samples within microarray studies: a comparative analysis of rat liver transcription response to prototypical toxicants. Physiol. Genomics 22, 346–355.CrossRefPubMedGoogle Scholar
  29. 29.
    Waring, J. F., Ulrich, R. G., Flint, N., Morfitt, D., Kalkuhl, A., Staedtler, F., et al. (2004) Interlaboratory evaluation of rat hepatic gene expression changes induced by methapyrilene. Environ. Health Perspect. 112, 439–448.CrossRefPubMedGoogle Scholar
  30. 30.
    Chu, T. M., Deng, S., Wolfinger, R., Paules, R. S., and Hamadeh, H. K. (2004) Cross-site comparison of gene expression data reveals high similarity. Environ. Health Perspect. 112, 449–455.CrossRefPubMedGoogle Scholar
  31. 31.
    Hamadeh, H. K., Knight, B. L., Haugen, A. C., Sieber, S., Amin, R. P., Bushel, P. R., et al. (2002) Methapyrilene toxicity: anchorage of pathologic observations to gene expression alterations. Toxicol. Pathol. 30, 470–482.PubMedGoogle Scholar
  32. 32.
    Mattes, W. B. (2004) Annotation and cross-indexing of array elements on multiple platforms. Environ. Health Perspect. 112, 506–510.CrossRefPubMedGoogle Scholar
  33. 33.
    Goodsaid, F. M., Smith, R. J., and Rosenblum, I. Y. (2004) Quantitative PCR deconstruction of discrepancies between results reported by different hybridization platforms. Environ. Health Perspect. 112, 456–460.CrossRefPubMedGoogle Scholar
  34. 34.
    NIEHS. Introduction to the Toxicogenomics Research Consortium (TRC). Available at www.niehs.nih.gov/dert/trc/intro.htm.
  35. 35.
    Medlin, J. (2002) Toxicogenomics research consortium sails into uncharted waters. Environ. Health Perspect. 110, A744–746.CrossRefPubMedGoogle Scholar
  36. 36.
    Hosack, D. A., Dennis, G., Jr., Sherman, B. T., Lane, H. C., Lempicki, R. A., Yang, J., and Gao, W. (2003) Identifying biological themes within lists of genes with EASE. Genome Biol. 4, R70.CrossRefPubMedGoogle Scholar
  37. 37.
    Camon, E., Magrane, M., Barrell, D., Binns, D., Fleischmann, W., Kersey, P., et al. (2003) The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res. 13, 662–672.CrossRefPubMedGoogle Scholar
  38. 38.
    Tong, W., Lucas, A. B., Shippy, R., Fan, X., Fang, H., Hong, H., et al. (2006) Evaluation of external RNA controls for the assessment of microarray performance. Nat. Biotechnol. 24, 1132–1139.CrossRefPubMedGoogle Scholar
  39. 39.
    Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., et al. (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161.CrossRefPubMedGoogle Scholar
  40. 40.
    Canales, R. D., Luo, Y., Willey, J. C., Austermiller, B., Barbacioru, C. C., Boysen, C., et al. (2006) Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotechnol. 24, 1115–1122.CrossRefPubMedGoogle Scholar
  41. 41.
    Patterson, T. A., Lobenhofer, E. K., Fulmer-Smentek, S. B., Collins, P. J., Chu, T. M., Bao, W., Fet al. (2006) Performance comparison of one-color and two-color platforms within the Microarray Quality Control (MAQC) project. Nat. Biotechnol. 24, 1140–1150.CrossRefPubMedGoogle Scholar
  42. 42.
    Guo, L., Lobenhofer, E. K., Wang, C., Shippy, R., Harris, S. C., Zhang, L., et al. (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat. Biotechnol. 24, 1162–1169.CrossRefPubMedGoogle Scholar
  43. 43.
    InnoMed PredTox. Available at www.innomed-predtox.com/.
  44. 44.
    FDA. (2006) Critical Path Opportunities Report and List (HHS, ed.). FDA, Bethesda, MD.Google Scholar
  45. 45.
    FDA. FDA and the Critical Path Institute Announce Predictive Safety Testing Consortium. Available at www.fda.gov/bbs/topics/news/2006/NEW01337.html.
  46. 46.
    Fielden, M. R., Brennan, R., and Gollub, J. (2007) A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals. Toxicol. Sci. 99, 90–100.CrossRefPubMedGoogle Scholar
  47. 47.
    Nie, A. Y., McMillian, M., Parker, J. B., Leone, A., Bryant, S., Yieh, L., et al. (2006) Predictive toxicogenomics approaches reveal underlying molecular mechanisms of nongenotoxic carcinogenicity. Mol. Carcinog. 45, 914–933.CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • William B. Mattes
    • 1
  1. 1.Department of ToxicologyThe Critical Path InstituteRockville

Personalised recommendations