Predictive toxicogenomics

An Introduction to Toxicogenomics

Volumn , Issue , 2003, Pages 225-259

  Porter, Mark W ^a   Castle, Arthur L ^a   Orr, Michael S ^a,b   Mendrick, Donna L ^a  

^a Gene Logic Laboratories Inc   (United States)

^b PFIZER INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84888660214     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (92)

1. Farr, S. and Dunn, R.T., II, Concise review: gene expression applied to toxicology, Toxicol. Sci., 50, 1, 1999.
2. Waring, J.F. and Ulrich, R.G., The impact of genomics-based technologies on drug safety evaluations, Annu. Rev. Pharmacol. Toxicol., 40, 335, 2000.
3. Rockett, J.C. and Dix, D.J., Application of DNA arrays to toxicology, Environ. Health Perspect., 107, 681, 1999.
4. Afshari, C.A. et al., Application of complementary DNA microarray technology to carcinogen identification, toxicology, and drug safety evaluation, Cancer Res., 59, 4759, 1999.
5. Lovett, R.A., Toxicologists brace for genomics revolution, Science, 289, 536, 2000.
6. Pennie, W.D. et al., The principles and practice of toxicogenomics: applications and opportunities, Toxicol. Sci., 54, 277, 2000.
7. Kerr, M.K. and Churchill, G.A., Statistical design and the analysis of gene expression microarray data, Genet. Res., 77, 123, 2001.
8. Golub, T.R. et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, 531, 1999.
9. Raychaudhuri, S. et al., Pattern recognition of genomic features with microarrays: site typing of Mycobacterium tuberculosis strains, Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 286, 2000.
10. Park, PJ. et al., A nonparametric scoring algorithm for identifying informative genes from microarray data, Pac. Symp. Biocomput., 52, 2001.
11. Kerr, M.K. and Churchill, G.A., Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments, Proc. Natl. Acad. Sci. USA, 98, 8961, 2001.
12. Callow, M.J. et al., Microarray expression profiling identifies genes with altered expression in HDL-deficient mice, Genome Res., 10, 2022, 2000.
13. Hamadeh, H.K. et al., Prediction of compound signature using high density gene expression profiling, Toxicol. Sci., 67, 232, 2002.
14. Butte, A.J. et al., Determining significant fold differences in gene expression analysis, Pac. Symp. Biocomput., 6, 2001.
15. Tsien, C.L. et al., On reporting fold differences, Pac. Symp. Biocomput., 496, 2001.
16. Manduchi, E. et al., Generation of patterns from gene expression data by assigning confidence to differentially expressed genes, Bioinformatics, 16, 685, 2000.
17. Claverie, J.M., Computational methods for the identification of differential and coordinated gene expression, Hum. Mol. Genet., 8, 1821, 1999.
18. Alon, U. et al., Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Natl. Acad. Sci. USA, 96, 6745, 1999.
19. Perou, C.M. et al., Distinctive gene expression patterns in human mammary epithelial cells and breast cancers, Proc. Natl. Acad. Sci. USA, 96, 9212, 1999.
20. Furey, T.S. et al., Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16, 906, 2000.
21. Alizadeh, A.A. et al., Towards a novel classification of human malignancies based on gene expression patterns, J. Pathol., 195, 41, 2001.
22. Bittner, M. et al., Molecular classification of cutaneous malignant melanoma by gene expression profiling, Nature, 406, 536, 2000.
23. Califano, A. et al., Analysis of gene expression microarrays for phenotype classification, Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 75, 2000.
24. Raychaudhuri, S. et al., Principal components analysis to summarize microarray experiments: application to sporulation time series, Pac. Symp. Biocomput., 455, 2000.
25. Brown, M.P. et al., Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. USA, 97, 262, 2000.
26. DeRisi, J.L. et al., Exploring the metabolic and genetic control of gene expression on a genomic scale, Science, 278, 680, 1997.
27. Cho, R.J. et al., A genome-wide transcriptional analysis of the mitotic cell cycle, Mol. Cell, 2, 65, 1998.
28. Spellman, P.T. et al., Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Mol. Biol. Cell, 9, 3273, 1998.
29. Alizadeh, A.A. et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature, 403, 503, 2000.
30. Raychaudhuri, S. et al., Basic microarray analysis: grouping and feature reduction, Trends Biotechnol., 19, 189, 2001.
31. Khan, J. et al., Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nat. Med., 7, 673, 2001.
32. Cheng, Y. and Church, G.M., Biclustering of expression data, Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 93, 2000.
33. Sorlie, T. et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications, Proc. Natl. Acad. Sci. USA, 98, 10869, 2001.
34. Waring, J.F. et al., Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles, Toxicol. Appl. Pharmacol., 175, 28, 2001.
35. Planet, PJ. et al., Systematic analysis of DNA microarray data: ordering and interpreting patterns of gene expression, Genome Res., 11, 1149, 2001.
36. Eisen, M.B. et al., Cluster analysis and display of genome-wide expression patterns, Proc. Natl. Acad. Sci. USA, 95, 14863, 1998.
37. Michaels, G.S. et al., Cluster analysis and data visualization of large-scale gene expression data, Pac. Symp. Biocomput., 42, 1998.
38. Eisen, M.B. and Brown, P.O., DNA arrays for analysis of gene expression, Methods Enzymol., 303, 179, 1999.
39. Sherlock, G., Analysis of large-scale gene expression data, Curr. Opin. Immunol., 12, 201, 2000.
40. Heyer, L.J. et al., Exploring expression data: identification and analysis of coexpressed genes, Genome Res., 9, 1106, 1999.
41. Aronow, B.J. et al., Microarray analysis of trophoblast differentiation: gene expression reprogramming in key gene function categories, Physiol. Genomics, 6, 105, 2001.
42. Huang, J. et al., Effects of ischemia on gene expression, J. Surg. Res., 99, 222, 2001.
43. Dysvik, B. and Jonassen, I., J-Express: exploring gene expression data using JAVA, Bioinformatics, 17, 369, 2001.
44. Tamayo, P. et al., Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation, Proc. Natl. Acad. Sci. USA, 96, 2907, 1999.
45. Toronen, P. et al., Analysis of gene expression data using self-organizing maps, FEBS Lett., 451, 142, 1999.
46. Altman, R.B. and Raychaudhuri, S., Whole-genome expression analysis: challenges beyond clustering, Curr. Opin. Struct. Biol., 11, 340, 2001.
47. Yeung, K.Y. et al., Validating clustering for gene expression data, Bioinformatics, 17, 309, 2001.
48. McAdams, H.H. and Shapiro, L., Circuit simulation of genetic networks, Science, 269, 650, 1995.
49. Maki, Y. et al., Development of a system for the inference of large scale genetic networks, Pac. Symp. Biocomput., 446, 2001.
50. van Someren, E.P. et al., Linear modeling of genetic networks from experimental data, Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 355, 2000.
51. Hartemink, A.J. et al., Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks, Pac. Symp. Biocomput., 422, 2001.
52. D’Haeseleer, P. et al., Linear modeling of mRNA expression levels during CNS development and injury, Pac. Symp. Biocomput., 41, 1999.
53. Wessels, L.F. et al., A comparison of genetic network models, Pac. Symp. Biocomput., 508, 2001.
54. Akutsu, T. et al., Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function, J. Comput. Biol., 7, 331, 2000.
55. Akutsu, T. et al., Inferring qualitative relations in genetic networks and metabolic pathways, Bioinformatics, 16, 727, 2000.
56. Liang, S. et al., Reveal, a general reverse engineering algorithm for inference of genetic network architectures, Pac. Symp. Biocomput., 18, 1998.
57. Koza, J.R. et al., Reverse engineering of metabolic pathways from observed data using genetic programming, Pac. Symp. Biocomput., 434, 2001.
58. Ambroise, C. and McLachlan, G.J., Selection bias in gene extraction on the basis of microarray geneexpression data, Proc. Natl. Acad. Sci. USA, 99, 6562, 2002.
59. Ronchetti, R. et al., Robust linear model selection by cross-validation, JASA, 92, 1017, 1997.
60. Shao, J., Linear model selection by cross-validation, JASA, 88, 486, 1993.
61. Efron, B. and Tibshirani, R., An Introduction to the Bootstrap, Chapman & Hall, London, 1993.
62. Tibshirani, R., A comparison of some error estimates for neural network models, Neural Comput., 8, 152, 1996.
63. Plutowski, M.E., Survey: Cross-Validation in Theory and in Practice, www.emotivate.com/CvSur-vey.doc, 1996.
64. Castle, A.L. et al., Predictive modeling of hepatotoxicants using microarrays and a linear discriminant modeling approach, Toxicol. Sci., 66(1-S), 274, 2002.
65. Johnson, D.E. and Wolfgang, G.H., Assessing the potential toxicity of new pharmaceuticals, Curr. Top. Med. Chem., 1, 233, 2001.
66. Soffers, A.E. et al., Computer-modeling-based QSARs for analyzing experimental data on biotransformation and toxicity, Toxicol. In Vitro, 15, 539, 2001.
67. Rosenkranz, H.S. et al., Development, characterization and application of predictive-toxicology models, SAR QSAR Environ. Res., 10, 277, 1999.
68. Smoyer, W.E. and Mundel, P., Regulation of podocyte structure during the development of nephrotic syndrome, J. Mol. Med., 76, 172, 1998.
69. Diamond, J.R. and Karnovsky, M.J., Focal and segmental glomerulosclerosis following a simgle intravenous dose of puromycin aminonucleoside, Am. J. Pathol., 122, 481, 1986.
70. Smoyer, W.E. et al., Podocyte alpha-actinin induction precedes foot process effacement in experimental nephrotic syndrome, Am. J. Physiol., 273, F150-F157, 1997.
71. Shirato, I. et al., Cytoskeletal changes in podocytes associated with foot process effacement in Masugi nephritis, Am. J. Pathol., 148, 1283, 1996.
72. Kaplan, J.M. et al., Mutations in ACTN4, encoding alpha-actinin-4, cause familial focal segmental glomerulosclerosis, Nat. Genet., 24, 251, 2000.
73. Woolf, T.F. et al., Bioactivation and irreversible binding of the cognition activator tacrine using human and rat liver microsomal preparations: species difference, Drug Metab. Dispos., 21, 874, 1993.
74. Fitten, L.J. et al., Long-term oral administration of memory-enhancing doses of tacrine in mice: a study of potential toxicity and side effects, J. Gerontol., 42, 681, 1987.
75. Mayeux, R. and Sano, M., Treatment of Alzheimer’s disease, N. Engl. J. Med., 341, 1670, 1999.
76. Nordberg, A. and Svensson, A.L., Cholinesterase inhibitors in the treatment of Alzheimer’s disease. A comparison of tolerability and pharmacology, Drug Saf., 19, 465, 1998.
77. Taylor, P., Anticholinesterase agents, in Goodman and Gilman’s Pharmacologic Basis of Therapeutics, Hardman, J.G., Ed., McGraw-Hill, New York, 1996, chap. 8.
78. Porter, M.W. et al., Identification through gene expression of a toxic response in the rat for a compound that exhibits overt toxicity in humans, but not in rats, Toxicol. Sci., 66 (1-S), 295, 2002.
79. Hessel, G. et al., Correlation between the severity of acute hepatic necrosis induced by acetaminophen and serum aminotransferase levels in fasted and sucrose-fed rats, Braz. J. Med. Biol. Res., 29, 793, 1996.
80. Buttar, H.S. et al., Serum enzyme activities and hepatic triglyceride levels in acute and subacute acetaminophen-treated rats, Toxicology, 6, 9, 1976.
81. Tarloff, J.B. et al., Sex- and age-dependent acetaminophen hepato- and nephrotoxicity in Spra-gue-Dawley rats: role of tissue accumulation, nonprotein sulfhydryl depletion, and covalent binding, Fund. Appl. Toxicol., 30, 13, 1996.
82. Klaasen, C.D., Casarett and Doull’s Toxicology: The Basic Science of Poisons, Fifth ed., McGraw-Hill, New York, 1996, p. 1.
83. Vermeulen, N.P. et al., Molecular aspects of paracetamol-induced hepatotoxicity and its mechanism-based prevention, Drug Metab. Rev., 24, 367, 1992.
84. Zaher, H. et al., Protection against acetaminophen toxicity in CYP1A2 and CYP2E1 double-null mice, Toxicol. Appl. Pharmacol., 152, 193, 1998.
85. Mitchell, J.R. et al., Acetaminophen-induced hepatic necrosis. I. Role of drug metabolism, J. Pharmacol. Exp. Ther., 187, 185, 1973.
86. Dahlin, D.C. et al., N-acetyl-p-benzoquinone imine: a cytochrome P-450-mediated oxidation product of acetaminophen, Proc. Natl. Acad. Sci. USA, 81, 1327, 1984.
87. Davidson, D.G. and Eastham, W.N., Acute liver necrosis following overdose of paracetamol, Br. Med. J., 5512, 497, 1966.
88. Coles, B. et al., The spontaneous and enzymatic reaction of N-acetyl-p-benzoquinonimine with glutathione: a stopped-flow kinetic study, Arch. Biochem. Biophys., 264, 253, 1988.
89. Rollins, D.E. and Buckpitt, A.R., Liver cytosol catalyzed conjugation of reduced glutathione with a reactive metabolite of acetaminophen, Toxicol. Appl. Pharmacol., 47, 331, 1979.
90. Hinson, J.A. et al., 3-(glutathion-S-yl)acetaminophen: a biliary metabolite of acetaminophen, Drug Metab. Dispos., 10, 47, 1982.
91. Deneke, S.M. and Fanburg, B.L., Regulation of cellular glutathione, Am. J. Physiol., 257, L163-L173, 1989.
92. Chan, K. et al., An important function of Nrf2 in combating oxidative stress: detoxification of acetaminophen, Proc. Natl. Acad. Sci. USA, 98, 4611, 2001.