S2: Garuda Platform as a tool for Open Toxicology
Progress of Percellome Toxicogenomics Project, and the use of Garuda Platform as a tool for Open Toxicology
National Institute of Health Sciences, Japan
Section Chief, Division of Cellular & Molecular Toxicology
1 Division of Cellular and Molecular Toxicology, Biological Safety Research Center, National Institute of Health Sciences, Tokyo, Japan
2 Japan Bioassay Research Center, Japan Organization of Occupational Health and Safety, Kanagawa, Japan
Funding Source: The Percellome Projects are supported by the Health and Labour Sciences Research Grant, MHLW, Japan
Percellome project was initiated in order to reinforce and eventually replace the “safety factor (uncertainty factor)” widely used for the extrapolation of experimental animal data to humans in Toxicology. Since the Toxicology must be prepared for any unexpected responses, the approach must be comprehensive. Thus, our Percellome project attempts to comprehensively identify the transcriptomic networks induced by xenobiotics. For this attempt, we adopted phenotype-independent approach, simply because not all the changes in mRNA expression can be anchored to the symptoms or overt phenotypes. Consequently, there emerged a need to pile up a large amount of transcriptomic data for the identification of networks. This can be compared to the situation when the electron microscope was invented; consensus on its never-seen-before images was gained by compilation of the images to organize textbooks and atlases. For this need, a normalization method designated as “Percellome” was developed (BMC Genomics 7:64, 2006) for microarrays and quantitative PCR to generate absolute copy numbers of each mRNA per one cell (in average). Absolutized expression data are visualized as 45,000 layers of 3-D graphs (time x dose x copy number per cell) corresponding to the probe sets of the Affymetrix MOE430 2.0 GeneChip. Up to now, the time- and dose-dependent alterations of gene expression induced in mouse liver (4 time points x 4 dose levels, triplicate, 48 per chemical) are studied on 130 chemicals. Now, data from repeated dosage, various organs, inhalation, and fetus are added. Here, we report two case studies on estragole, a flavor and pentachlorophenol (PCP), a pesticide, discovering that estragole and PCP affect unreported transcriptomic networks of the PPAR-alpha and the interferon signaling networks, respectively with systems biology analyses including the introduction of the Garuda Project, especially on the upstream/promoter analysis.