Literature DB >> 17327692

Optimization of an animal test protocol for toxicogenomics studies (ii); a cross-laboratory gene expression analysis.

Kayo Sumida1, Koichi Saito, Kenji Oeda, Masanori Otsuka, Kazunari Tsujimura, Hideki Miyaura, Masaru Sekijima, Koji Nakayama, Yukiko Kawano, Yuki Kawakami, Makoto Asamoto, Tomoyuki Shirai.   

Abstract

Toxicogenomics is a promising new tool for prediction of chemical toxicities including carcinogenicity in a relatively short period. However, it is important to develop a reliable animal test protocol for toxicogenomics studies. The preparation of RNA and tissues is also crucial, since it greatly influences outcomes of gene expression analysis. We proposed an animal test protocol for toxicogenomics studies. In the present study, we examined an animal test protocol by comparing biological and gene expression data from different laboratories running identical in vivo studies on the same microarray platform. The results gave good correspondence in all three laboratories at the level of biological responses and gene expression, especially for genes whose expression changes were quite large. As the fold change or the signal values become smaller, however, discrepancies occur in gene expression data. For example, one laboratory shows an opposite directional change to the other two or no change. The results of hierarchical clustering and principal component analysis (PCA) demonstrated all samples from the three laboratories being clearly divided between control and treatment. Examination of the reproducibility of gene expression data across laboratories using the proposed animal test protocol thus confirmed only minor differences, which was expected to present no problems for gene expression analysis.

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Year:  2007        PMID: 17327692     DOI: 10.2131/jts.32.33

Source DB:  PubMed          Journal:  J Toxicol Sci        ISSN: 0388-1350            Impact factor:   2.196


  2 in total

1.  Discrimination of carcinogens by hepatic transcript profiling in rats following 28-day administration.

Authors:  Hiroshi Matsumoto; Yoshikuni Yakabe; Koichi Saito; Kayo Sumida; Masaru Sekijima; Koji Nakayama; Hideki Miyaura; Fumiyo Saito; Masanori Otsuka; Tomoyuki Shirai
Journal:  Cancer Inform       Date:  2009-11-13

2.  Coincidence between transcriptome analyses on different microarray platforms using a parametric framework.

Authors:  Tomokazu Konishi; Fumikazu Konishi; Shigeru Takasaki; Kohei Inoue; Koji Nakayama; Akihiko Konagaya
Journal:  PLoS One       Date:  2008-10-29       Impact factor: 3.240

  2 in total

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