디지털콘텐츠학회 논문지 (Journal of Digital Contents Society)

  • 제11권1호
  • /
  • Pages.99-104
  • /
  • 2010
  • /
  • 1598-2009(pISSN)
  • /
  • 2287-738X(eISSN)
한국디지털콘텐츠학회 (Digital Contents Society)
권호 표지

바이오 디지털 콘텐츠를 이용한 독성의 분석

Analysis of toxicity using bio-digital contents

  • 강진석 (남서울대학교 임상병리학과)
  • 투고 : 2009.11.23
  • 심사 : 2010.03.31
  • 발행 : 2010.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

화학물질은 생체에 들어오면 여러 가지 독성반응을 나타내는데, 독성반응에 따른 유전자 발현을 분석하기 위해 바이오 칩 등을 이용한 신기술이 확산되면서 바이오 디지털 콘텐츠가 다량으로 생성되고 있다. 이 콘텐츠는 그 자체로는 의미가 적고 컴퓨터를 이용한 분석과 보정과정을 거쳐 생물학적으로 의미 있는 값들을 선별하여야 한다. 이런 콘텐츠에는 유전자들의 발현 양상 측정을 목적으로 하는 유전체학(genomics), 유전자의 발현 양상을 측정하는 전사체학(transcriptomics), 단백질의 발현을 측정하는 단백체학(proteomics), 대사체의 발현을 측정하는 대사체학(metabolomics) 등이 있으며, 이를 통칭하여 오믹스(omics)라고 부른다. 오믹스 기술을 독성을 연구하는 분야에 접목한 것이 독성유전체학(toxicogenomics)이며, 이에 대한 콘텐츠를 분석함으로써 독성을 예측하고 독성기전을 규명할 수 있다. 독성분석에 있어서 초기 단계의 분석은 향후 만성독성의 예측에 있어서 중요한 부분을 차지하고 있다. 바이오 디지털 콘텐츠를 이용하여 독성을 예측함에 있어 기존의 방법보다 더 빠르고 정확하게 예측하기 위해서는 많은 정보에 대한 분석기술의 진보가 필요하다. 또, 바이오 디지털 콘텐츠를 이용한 독성예측에 있어서 전체세포보다는 생물학적 현상을 일으키는 특이세포에서 이런 정보를 얻는 것이 중요하다고 생각된다. 또, 향후 바이오 디지털 콘텐츠 분석은 전략적 실험설계에 의한 데이터가 분석되고 축적되어야 하고, 분석알고리즘을 통한 네트워크 분석이 이루어져야 하며, 통합적 데이터 구축을 통해 이루어져야 할 것으로 생각된다.

Numerous bio-digital contents have been produced by new technology using biochip and others for analyzing early chemical-induced genes. These contents have little meaning by themselves, and so they should be modified and extracted after consideration of biological meaning. These include genomics, transcriptomics, protenomics, metabolomics, which combined into omics. Omics tools could be applied into toxicology, forming a new field of toxicogenomics. It is possible that approach of toxicogenomics can estimate toxicity more quickly and accurately by analyzing gene/protein/metabolite profiles. These approaches should help not only to discover highly sensitive and predictive biomarkers but also to understand molecular mechanism(s) of toxicity, based on the development of analysing technology. Furthermore, it is important that bio-digital contents should be obtained from specific cells having biological events more than from whole cells. Taken together, many bio-digital contents should be analyzed by careful calculating algorism under well-designed experimental protocols, network analysis using computational algorism and related profound databases.

키워드

참고문헌

  1. Pennie W, Pettit SD, Lord PG. Toxicogenomics in risk assessment: an overview of an HESI collaborative research program. Environ Health Perspect 2004;112:417-9. https://doi.org/10.1289/ehp.6674
  2. Clarke PA, te Poele R, Workman P. Gene expression microarray technologies in the development of new therapeutic agents. Eur J Cancer 2004;40:2560-91. https://doi.org/10.1016/j.ejca.2004.07.024
  3. Canales RD, Luo Y, Willey JC, et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol 2006;24:1115-22. https://doi.org/10.1038/nbt1236
  4. Shippy R, Fulmer-Smentek S, Jensen RV, et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat Biotechnol 2006;24:1123-31. https://doi.org/10.1038/nbt1241
  5. Guo L, Lobenhofer EK, Wang C, et al. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol 2006;24:1162-9. https://doi.org/10.1038/nbt1238
  6. Corvi R, Ahr HJ, Albertini S, et al. Meeting report: Validation of toxicogenomics-based test systems: ECVAM-ICCVAM/NICEATM considerations for regulatory use. Environ Health Perspect 2006;114:420-9. https://doi.org/10.1289/ehp.114-a420
  7. Bulera SJ, Eddy SM, Ferguson E, et al. RNA expression in the early characterization of hepatotoxicants in Wistar rats by high-density DNA microarrays. Hepatology 2001;33:1239-58. https://doi.org/10.1053/jhep.2001.23560
  8. Bulera SJ, Eddy SM, Ferguson E, et al. RNA expression in the early characterization of hepatotoxicants in Wistar rats by high-density DNA microarrays. mer Electronics, Vol.48, No.2, pp. 366-375, 2002. https://doi.org/10.1109/TCE.2002.1010143
  9. Minami K, Saito T, Narahara M, et al. Relationship between hepatic gene expression profiles and hepatotoxicity in five typical hepatotoxicant-administered rats. Toxicol Sci 2005;87:296-305. https://doi.org/10.1093/toxsci/kfi235
  10. Huang Q, Jin X, Gaillard ET, et al. Gene expression profiling reveals multiple toxicity endpoints induced by hepatotoxicants. Mutat Res 2004;549:147-68. https://doi.org/10.1016/j.mrfmmm.2003.12.020
  11. Waring JF, Jolly RA, Ciurlionis R, et al. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol Appl Pharmacol 2001;175:28-42. https://doi.org/10.1006/taap.2001.9243
  12. Ellinger-Ziegelbauer H, Stuart B, Wahle B, et al. Characteristic expression profiles induced by genotoxic carcinogens in rat liver. Toxicol Sci 2004;77:19-34. https://doi.org/10.1093/toxsci/kfh016
  13. de Longueville F, Atienzar FA, Marcq L, et al. Use of a low-density microarray for studying gene expression patterns induced by hepatotoxicants on primary cultures of rat hepatocytes. Toxicol Sci 2003;75:378-92. https://doi.org/10.1093/toxsci/kfg196
  14. Kang JS, Jeong YK, Suh SK, et al. Assessment of feasibility for developing toxicogenomics biomarkers by comparing in vitro and in vivo genomic profiles specific to liver toxicity induced by acetaminophen. Mol & Cellular Toxicol 2007;3:177-84.
  15. Kang JS, Jeong YK, Shin JH, et al. Comparing in vitro and in vivo genomic profiles specific to liver toxicity induced by thioacetamide. The J of Applied Pharm 2007;15:252-60. https://doi.org/10.4062/biomolther.2007.15.4.252
  16. Kang JS, Yum Y, N., Han E, et al. Evaluation of potential biomarkers for thioacetamide-induced hepatotoxicity using siRNA. The Journal of Applied Pharmacology 2008;16:197-202.
  17. Kang JS, Wanibuchi H, Murai T, et al. Analysis of gene expression in different stages of MeIQx-induced rat hepatocarcinogenesis. Oncol Rep 2007;17:747-52.
  18. Chuma M, Sakamoto M, Yamazaki K, et al. Expression profiling in multistage hepatocarcinogenesis: identification of HSP70 as a molecular marker of early hepatocellular carcinoma. Hepatology 2003;37:198-207. https://doi.org/10.1053/jhep.2003.50022
  19. Yamashita S, Nomoto T, Abe M, et al. Persistence of gene expression changes in stomach mucosae induced by short-term N-methyl-N'-nitro-N-nitroso guanidine treatment and their presence in stomach cancers. Mutat Res 2004;549:185-93. https://doi.org/10.1016/j.mrfmmm.2003.10.009
  20. Iida M, Anna CH, Hartis J, et al. Changes in global gene and protein expression during early mouse liver carcinogenesis induced by non-genotoxic model carcinogens oxazepam and Wyeth-14,643. Carcinogenesis 2003;24:757-70. https://doi.org/10.1093/carcin/bgg011
  21. Nishida K, Mine S, Utsunomiya T, et al. Globalanal ysis of altered gene expressions during the process of esophageal squamous cell carcinogenesis in the rat: a study combined with a laser microdissection and a cDNA microarray. Cancer Res 2005;65:401-9.
  22. Shan L, Yu M, Schut HA, et al. Susceptibility of rats to mammary gland carcinogenesis by the food-derived carcinogen 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) varies with age and is associated with the induction of differential gene ex pression. Am J Pathol 2004;165:191-202. https://doi.org/10.1016/S0002-9440(10)63288-9
  23. Kuramoto T, Morimura K, Yamashita S, et al. Etiology-specific gene expression profiles in rat mammary carcinomas. Cancer Res 2002;62:3592-7.
  24. Shan L, He M, Yu M, et al. cDNA microarray profiling of rat mammary gland carcinomas induced by 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine and 7,12-dimethylbenz[a]anthracene. Carcinogenesis 2002;23:1561-8. https://doi.org/10.1093/carcin/23.10.1561
  25. Shan L, Yu M, Snyderwine EG. Gene expression profiling of chemically induced rat mammary gland cancer. Carcinogenesis 2005;26:503-9. https://doi.org/10.1093/carcin/bgh330
  26. Sukata T, Uwagawa S, Ozaki K, et al. alpha(2)-Macroglobulin: a novel cytochemical marker characterizing preneoplastic and neoplastic rat liver lesions negative for hitherto established cytochemical markers. Am J Pathol 2004;165:1479-88. https://doi.org/10.1016/S0002-9440(10)63406-2
  27. Michel C, Desdouets C, Sacre-Salem B, et al. Liver gene expression profiles of rats treated with clofibric acid: comparison of whole liver and laser capture microdissected liver. Am J Pathol 2003;163:2191-9. https://doi.org/10.1016/S0002-9440(10)63577-8
  28. Suzuki S, Asamoto M, Tsujimura K, et al. Specific differences in gene expression profile revealed by cDNA microarray analysis of glutathione S-transferase placental form (GST-P) immunohistochemically positive rat liver foci and surrounding tissue. Carcinogenesis 2004;25:439-43.