• Title/Summary/Keyword: High density-functional cDNA microarray

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Toxicogenomic analysis of Effects of Bisphenol A on Japanese Medaka fish using high density-functional cDNA microarray

  • Jiho Min;Park, Kyeong-Seo;Hong, Han-Na;Gu, Man-Bock
    • Proceedings of the Korea Society of Environmental Toocicology Conference
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    • 2003.10a
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    • pp.173-173
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    • 2003
  • With the introduction of DNA microarrays, a high throughput analysis of gene expression is now possible as a replacement to the traditional time-consuming Southern-blot analysis. This cDNA microarray should be ahighly favored technology in the area of molecular toxicology or analysis of environmental stresses.In this study, therefore, we developed a novel cDNA microarray for analyzing stress-specific responses in japanese Medaka fish. In the design and fabrication of this stress specific functional cDNA microarray, 123 different genes in Medaka fish were selected from eighteen different stress responsive groups and spotted on a 25${\times}$75 mm glass surface. After exposure of the fish to bisphenol A which is the one of the well-known endocrine disrupting chemicals (EDCs), over 1 or 10 days, the responses of the DNA chip were found to show distinct expression patterns according to the mode of toxic actions from environmental toxicants. As a results, they showed specific gene expression pattern to bisphenol A, additionally, the chemical spesific biomarkers could be suggested based on the chip analysis data. Therefore, this chip can be used to monitor stress responses of unknown and/or known toxic chemicals using Medaka fish and may be used for the further development of biomarkers by utilizing the gene expression patterns for known contaminants.

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Application of Bioinformatics for the Functional Genomics Analysis of Prostate Cancer Therapy

  • Mousses, Spyro
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2000.11a
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    • pp.74-82
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    • 2000
  • Prostate cancer initially responds and regresses in response to androgen depletion therapy, but most human prostate cancers will eventually recur, and re-grow as an androgen independent tumor. Once these tumors become hormone refractory, they usually are incurable leading to death for the patient. Little is known about the molecular details of how prostate cancer cells regress following androgen ablation and which genes are involved in the androgen independent growth following the development of resistance to therapy. Such knowledge would reveal putative drug targets useful in the rational therapeutic design to prevent therapy resistance and control androgen independent growth. The application of genome scale technologies have permitted new insights into the molecular mechanisms associated with these processes. Specifically, we have applied functional genomics using high density cDNA microarray analysis for parallel gene expression analysis of prostate cancer in an experimental xenograft system during androgen withdrawal therapy, and following therapy resistance, The large amount of expression data generated posed a formidable bioinformatics challenge. A novel template based gene clustering algorithm was developed and applied to the data to discover the genes that respond to androgen ablation. The data show restoration of expression of androgen dependent genes in the recurrent tumors and other signaling genes. Together, the discovered genes appear to be involved in prostate cancer cell growth and therapy resistance in this system. We have also developed and applied tissue microarray (TMA) technology for high throughput molecular analysis of hundreds to thousands of clinical specimens simultaneously. TMA analysis was used for rapid clinical translation of candidate genes discovered by cDNA microarray analysis to determine their clinical utility as diagnostic, prognostic, and therapeutic targets. Finally, we have developed a bioinformatic approach to combine pharmacogenomic data on the efficacy and specificity of various drugs to target the discovered prostate cancer growth associated candidate genes in an attempt to improve current therapeutics.

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