Centers for Nutrient-Gene Interactions
Department of Medicine
University of Alabama, Birmingham
Stephen Barnes, PhD
"Center for Nutrition-Gene Interaction in Cancer Prevention"
This integrated effort will provide data on the roles of polyphenols and soy isoflavones in breast and prostate cancer, and of dietary soy on the first onset of menstruation. The focus is to identify genetic pathways that distinguish cancer cell development from normal cells, and how dietary polyphenols with documented chemopreventive activity impact these pathways. A combination of genomics, DNA microarray, and proteomic approaches will be used in cell culture and animal chemoprevention studies. These will be augmented by an epidemiological cohort study of primarily Asian girls who are soy and non-soy consumers to determine if this dietary component changes the timing of menarche, a risk factor for breast cancer. To accommodate the molecular approaches, part of the Center will focus on developing and evaluating new statistical procedures for analyzing the types of multidimensional data produced by current experimental approaches.
The identification of pathways that may distinguish mammary cancer development, and the extent to which dietary polyphenols with documented chemopreventive activity modulate these pathways.
Dietary Polyphenols and Chemoprevention in Rodent Models of Breast and Prostate Cancer (Leader, Dr. Coral Lamartiniere, Department of Pharmacology & Toxicology, UAB).
- To investigate the potential of the polyphenols, genistein, EGCG and resveratrol, alone and in combination, to protect against mammary cancer in the dimethylbenz(a)anthracene (DMBA)–induced rat model.
- To investigate the potential of these polyphenols to regulate mammary gland maturation and cell proliferation.
- To perform gene expression analysis in mammary glands of rats exposed to polyphenols.
- To identify proteins that are differentially expressed in mammary glands of rats treated with polyphenols.
- To investigate the potential of the polyphenols, genistein and resveratrol, alone and in combination, to protect against prostate cancer in transgenic rats that spontaneously develop prostate cancer.
- To investigate the potential of resveratrol to enhance prostate development, to enhance cell differentiation, and to regulate cell proliferation.
- To use oligonucleotide-based microarray analysis for comparing gene expression profiles of prostates of transgenic rats exposed to polyphenols.
- To identify proteins that are differentially expressed in prostates of transgenic rats treated with polyphenols.
The Effect of a Soy Diet on the Timing of Menarche (Leader, Dr. Pamela Horn-Ross, Northern California Cancer Center).
- To determine the relationship between isoflavone consumption and the onset of menarche.
- To explore how polymorphic variation in genes in the steroid hormone pathway affect the onset of menarche.
- To the extent possible, to examine the joint effects of isoflavones and genetic variation on menarche.
- To establish a resource containing plasma, serum, urine, DNA, and epidemiologic data that can be used to address a multitude of issues in the future, including the relationship between dietary polyphenols, genotype, protein expression, and pubertal maturation and hormone levels.
Statistical Analysis of High Dimensional Data (Leader, Dr. David Allison, Department of Biostatistics, UAB).
- To develop and evaluate new statistical procedures for analysis of the types of data produced by the high dimensional nature of projects 1 and 2.
Research is supported by four cores.
Administration Core that reports on center activities and organizes regular interaction between all the investigators and project leaders (Director, Dr. Stephen Barnes, Department of Pharmacology & Toxicology, UAB; Associate Director, Dr. Clinton Grubbs, Department of Surgery, UAB)
Genomics Core responsible for gene sequencing and SNP analysis of specific variants and microarray analysis (Director, Dr. Lisa Guay-Woodford, Department of Medicine, UAB)
Proteomics Core responsible for protein analysis (Director, Dr. Helen Kim, Department of Pharmacology & Toxicology, UAB)
Biostatistics/Bioinformatics Core that provides support to all projects and cores (Director, Dr. Grier Page, Department of Biostatistics)
Barnes S, Prasain JK, Wang C-C, et al. Applications of LC-MS in the study of the uptake, distribution, metabolism and excretion of bioactive polyphenols from dietary supplements. Life Sciences 2006;78: 2054-2059.
Kim H, Deshane J, Barnes S, Meleth S. Proteomics analysis of the actions of grape seed extract in rat brain: technological and biological implications for the study of the actions of psychoactive compounds. Life Sciences 2006;78: 2060-2065.
Liu C, Yu S, Zinn K, et al. Murine mammary carcinoma exosomes promote tumor growth by suppression of NK cell function. J Immunol 2006;176, 1375-1385.
Kim K, Page GP, Beasley T M, et al. A proposed metric for assessing the measurement quality of individual microarrays. BMC Bioinformatics 2006; 7:35.
Horn-Ross PL, Barnes S, Lee VS, et al. Reliability and validity of an assessment of usual phytoestrogen consumption (United States) Cancer Causes Control 2006;17:85-93.
Barnes S, et al. (2005) Genistein and polyphenols in the study of cancer prevention: chemistry, biology, statistics, and experimental design. In Kaput J, Rodrigues R (eds) Discovering the Path to Personalized Nutrition, 1st ed.
Barnes S, Prasain JK. Current Progress in the use of Traditional Medicines and Nutraceuticals. Curr Opin Plant Biol 2005;8: 324-328.
Beasley TM, Holt JK, Allison DB. Comparison of linear weighting schemes for perfect match and mismatch gene expression levels from microarray data. Am J Pharmacogenomics 2005;5(3):197-205.
Beasley T, Brand J, Long J (2005) The use of nonparametric procedues in the statistical analysis of microarray data. In Allison D et al (eds) DNA microarrays and related Genomic Techniques, 1 ed. Boca Raton: CRC Press.
Edwards J, Ghosh P (2005) Bayesian Analysis of Microarray data. In Allison D, et al. (eds) DNA microarrays and related Genomics Techniques, 1 ed. Boca Raton: CRC Press.
Gadbury GL, et al (2005) The role of sample size on measures of uncertainty and power. In Allison DB et al. (eds.) In DNA Microarrays and Related Genomics Techniques, 1 ed. Boca Raton: CRC.
Garge N, Page GP, Sprague AP. Reproducible Clusters from Microarray Research: Whither? BMC Bioinformatics 2005;6:Suppl 2:S10.
Gorman V, Zhang K (2005) Cluster stability. In Allison D et al (eds) DNA microarrays and Related Genomics Techniques, 1 ed. Boca Raton: CRC.
Hedlund TE, Maroni PD, Ferucci PG, et al. Long-term dietary habits influence soy isoflavone metabolism in caucasian men: selective accumulation of isoflavonoids within prostatic fluid. J Nutr 2005;135:1400-1406.
Kaput J, Ordovas JM, Ferguson L, et al. Director: Barnes, Stephen U54 CA100949-03 Zucker, J-D. The Case for Strategic International Alliances to Harness Nutritional Genomics for Public and Personal Health. Br J Nutr 2005;94:623-632.
Meleth S, Deshane J, Satyadas A, Kim H. Analysis of 2D Gel Proteomic Data – A Comparison of Three Different Protocols. Presented at Fifth International Conference on Hybrid Intelligent Systems, Rio de Janeiro, Brazil, November 6-9, 2005.
Page G, Ruden D (2005) Combining high dimensional biological data to study complex diseases and quantitative traits. In Allison D, et al. (eds) DNA microarrays and related genomic techniques, 1st ed. Boca-Raton: CRR Press.
ST-onge M, et al. (2005) Design & Analysis of Microarray Studies for Obesity Research. In Moussa N (ed) Genomics of obesity, 1 ed. Boca Raton: CRC.
Trivedi P, Edwards JW, Wang J, et al. HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data. BMC.Bioinformatics 2005;6(1):86.
Yin W, Gadbury G, Samaranayake V. Power and Type I Error in a Global Test of Differential Genetic Expression. Proceedings of the American Statistical Association, Biometrics Section 2005:441- 446.
Zakharkin SO, Kim K, Mehta T, et al. Sources of variation in Affymetrix microarray experiments. BMC Bioinformatics 2005;6:214.
Barnes S. Soy isoflavones--phytoestrogens and what else? J Nutr 2004;134(5):1225S-8S.
Chen DT, Chen JJ, Soong SJ. Probe rank approaches for gene selection in oligonucleotide arrays with a small number of replicates. Bioinformatics 2005;21(12):2861-6.
Kim H, Page GP, Barnes S. Proteomics and mass spectrometry in nutrition research. Nutrition 2004;20(1):155-65.
Lee SA, Wen W, Xiang YB, et al. Assessment of dietary isoflavone intake among middle-aged Chinese men. J Nutr 2007;137(4):1011-6.
Malik M, Bakshi CS, McCabe K, et al. Matrix metalloproteinase 9 activity enhances host susceptibility to pulmonary infection with type A and B strains of Francisella tularensis. J Immunol 2007;178(2):1013-20.
Mehta TS, Zakharkin SO, Gadbury GL, Allison DB. Epistemological issues in omics and high-dimensional biology: give the people what they want. Physiol Genomics 2006;28(1):24-32.
Prasain JK, Wang CC, Barnes S. Mass spectrometric methods for the determination of flavonoids in biological samples. Free Radic Biol Med 2004;37(9):1324-50.
Prasain JK, Xu J, Kirk M, et al. Differential biliary excretion of genistein metabolites following intraduodenal and intravenous infusion of genistin in female rats. J Nutr 2006;136(12):2975-9.
Redden DT, Allison DB. The effect of assortative mating upon genetic association studies: spurious associations and population substructure in the absence of admixture. Behav Genet 2006;36(5):678-86.
Redden DT, Divers J, Vaughan LK, et al. Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model. PLoS Genet 2006;2(8):e137.
Sarkar P, Sarkar S, Ramesh V, et al. Proteomic analysis of mice hippocampus in simulated microgravity environment. J Proteome Res 2006;5(3):548-53.
Zakharkin SO, Kim K, Bartolucci AA, et al. Optimal allocation of replicates for measurement evaluation studies. Genomics Proteomics Bioinformatics 2006;4(3):196-202.
Zhang K, Qin Z, Chen T, et al. HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms. Bioinformatics 2005;21(1):131-4.
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