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Anu Loukola,
Monica Chadha, Sharron G Penn,
David Rank,
David V Conti,
Deborah Thompson,
Mine Cicek,
Brad Love,
Vesna Bivolarevic,
Qiner Yang,
Yalin Jiang,
David K Hanzel,
Katherine Dains,
Pamela L Paris,
Graham Casey,
John S Witte
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ABSTRACT: Genes involved in the testosterone biosynthetic pathway - such as CYP17A1, CYP3A4, and SRD5A2 - represent strong candidates for affecting prostate cancer. Previous work has detected associations between individual variants in these three genes and prostate cancer risk and aggressiveness. To more comprehensively evaluate CYP17A1, CYP3A4, and SRD5A2, we undertook a two-phase study of the relationship between their genotypes/haplotypes and prostate cancer. Phase I of the study first searched for single-nucleotide polymorphisms (SNPs) in these genes by resequencing 24 individuals from the Coriell Polymorphism Discovery Resource, 92-110 men from prostate cancer case-control sibships, and by leveraging public databases. In all, 87 SNPs were discovered and genotyped in 276 men from case-control sibships. Those SNPs exhibiting preliminary case-control allele frequency differences, or distinguishing (ie, 'tagging') common haplotypes across the genes, were identified for further study (24 SNPs in total). In Phase II of the study, the 24 SNPs were genotyped in an additional 841 men from case-control sibships. Finally, associations between genotypes/haplotypes in CYP17A1, CYP3A4, and SRD5A2 and prostate cancer were evaluated in the total case-control sample of 1117 brothers from 506 sibships. Family-based analyses detected associations between prostate cancer risk or aggressiveness and a number of CYP3A4 SNPs (P-values between 0.006 and 0.05), a CYP3A4 haplotype (P-values 0.05 and 0.009 in nonstratified and stratified analysis, respectively), and two SRD5A2 SNPs in strong linkage disequilibrium (P=0.02). Undertaking a two-phase study comprising SNP discovery, haplotype tagging, and association analyses allowed us to more fully decipher the relation between CYP17A1, CYP3A4, and SRD5A2 and prostate cancer.
European Journal of HumanGenetics 05/2004; 12(4):321-32. · 4.40 Impact Factor
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Michael A Shultz,
Lu Zhang,
Yi-Zhong Gu,
Gregory L Baker,
Michelle V Fannuchi,
Allan M Padua,
William A Gurske,
Dexter Morin, Sharron G Penn,
Stevan B Jovanovich,
Charles G Plopper,
Alan R Buckpitt
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ABSTRACT: A key challenge in measuring gene expression changes in the lung in response to site-selective toxicants is differentiating between target and nontarget areas. The toxicity for the cytotoxicant 1-nitronaphthalene is highly localized in the airway epithelium. Target cells comprise but a fraction of the total lung cell mass; measurements from whole lung homogenates are not likely to reflect what occurs at the target site. Additionally, the use of generic microarrays to measure expression in airway epithelium may not provide a good representation of transcripts present at the site of toxic action. cDNA libraries from airway and alveolar subcompartments of rat lung were sequenced for the development of a custom microarray representative of these lung regions. We identified 7,460 nonredundant rat lung sequences. Nearly 30% of the sequences on this array are not present on the Affymetrix Rat GeneChip 230. A 20,000-element microarray was developed that delineates differences in gene expression between subcompartments. This is the first in a series of articles employing this microarray for detecting gene expression changes during acute injury produced by 1-nitronaphthalene and subsequent repair.
American Journal of Respiratory Cell and Molecular Biology 04/2004; 30(3):296-310. · 5.13 Impact Factor
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ABSTRACT: Traditional models of toxicity have relied on dissecting chemical action into pharmacokinetic and pharmacodynamic processes. However, the integration of genomic information with toxicology will enhance our basic understanding of these processes and significantly change the way we apply toxicological information to risk assessment and regulatory problems. In this article, we summarize the application of gene expression information and polymorphism discovery to four areas in toxicology: toxicity testing, cross-species extrapolation, understanding mechanism of action, and susceptibility.
Environmental Health Perspectives 01/2003; 110 Suppl 6:919-23. · 7.04 Impact Factor
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Russell S Thomas,
David R Rank, Sharron G Penn,
Gina M Zastrow,
Kevin R Hayes,
Kalyan Pande,
Edward Glover,
Tomi Silander,
Mark W. Craven,
Janardan K Reddy,
Stevan B Jovanovich,
Christopher A Bradfield
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ABSTRACT: We have developed an approach to classify toxicants based upon their influence on profiles of mRNA transcripts. Changes in liver gene expression were examined after exposure of mice to 24 model treatments that fall into five well-studied toxicological categories: peroxisome proliferators, aryl hydrocarbon receptor agonists, noncoplanar polychlorinated biphenyls, inflammatory agents, and hypoxia-inducing agents. Analysis of 1200 transcripts using both a correlation-based approach and a probabilistic approach resulted in a classification accuracy of between 50 and 70%. However, with the use of a forward parameter selection scheme, a diagnostic set of 12 transcripts was identified that provided an estimated 100% predictive accuracy based on leave-one-out cross-validation. Expansion of this approach to additional chemicals of regulatory concern could serve as an important screening step in a new era of toxicological testing.
11/2002;
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ABSTRACT: In many microarray experiments, relatively few intra- and inter-array replicate measurements are made due to significant cost limitations and sample availability. Compounding this problem is a lack of robust statistical methods for analyzing gene expression data with limited experimental replicates. As a result, the interpretation of the results of these experiments are difficult with little understanding of the probability of type I and type II errors.
The variability in a series of replicate microarray measurements was modelled using a combination of parametric and non-parametric methods. A 3-dimensional surface was created for the conditional distribution of the variability given the mean signal intensity in both the Cy3 and Cy5 channels. The results were used as the basis for developing statistical methods for analyzing gene expression data with limited experimental replicates.
The statistical analysis scripts are available upon request.
Bioinformatics 09/2002; 18(8):1064-72. · 5.47 Impact Factor
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ABSTRACT: The Ahr locus encodes for the aryl hydrocarbon receptor (AHR), which plays an important toxicological and developmental role. Sequence variation in this gene was studied in 13 different mouse lines that included eight laboratory strains, two Mus musculus subspecies and three additional Mus species. The data presented represent the largest study of sequence variation across multiple mouse lines in a single gene (approximately equal to 15.9 kb/mouse line). Among all mice, the average frequency of all polymorphisms in the intronic regions was 20.3 variants/kb and the average exonic frequency was 14.1 variants/kb. For substitutions alone, the average frequencies in the intronic and exonic regions for all mice were 13.3 and 8.9 substitutions/kb, respectively. Between laboratory strains, the average intronic and exonic frequencies for all polymorphisms dropped to 5.4 and 2.9 variants/kb, respectively. There were 111 non-synonymous polymorphisms that resulted in 42 different amino acid changes, of which only 10 amino acid changes had been previously identified. Based on the nucleotide sequence, the phylogenetic history of the gene showed mice from the Ahr(b2) and Ahr(d) alleles in separate branches while mice from the Ahr(b1) and Ahr(b3) alleles exhibited a more complex history. Evolutionarily, the AHR protein as a whole appears to be under purifying selective pressure (K(a) : K(s) ratio = 0.237). Despite significant functional constraint in the basic helix-loop-helix and PAS domains, ligand binding is not constrained to the high-affinity allele, which supports further the role of the AHR in development and its importance beyond the adaptive response to environmental toxicants.
Pharmacogenetics 04/2002; 12(2):151-63.
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Methods in Enzymology 02/2002; 357:198-205. · 2.04 Impact Factor
