ABSTRACT
To integrate the information from genome to phenome, metabolomic approaches may have an intermediary bridge-building role (Fiehn et al.
1Soybean Physiology Research Team, National Institute of Crop Science, Tsukuba, Japan. 2Rhizosphere Environment Research Team, National Agricultural Research Center for Hokkaido Region, Sapporo, Japan. 3Department of Life Sciences, The University of West Indies, Jamaica. 4Graduate School of Biosphere Science, Hiroshima University, Higashi Hiroshima, Japan. 5Graduate School of Agriculture, Hokkaido University, Sapporo, Japan. 6Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo, Japan. *Corresponding author: shinano@affrc.go.jp
2001). The metabolomics approach offers not only comprehensive analysis of a large number of metabolites, but also targets several compounds simultaneously. Metabolome has been developed to investigate comprehensive analysis of metabolites, and the terms are designated mainly by how they focus on the targeting (Nielsen and Oliver 2005). Metabolomics study has progressed especially using gas chromatography mass spectrometry (GC-MS) because of enormous efforts in developing methodological standards and updating a large number of metabolites in plants (e.g., Fiehn et al. 2000; Roessner et al. 2000). Other methodological applications have also been introduced, including fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), liquid chromatography mass spectrometry (LC-MS), capillary electrophoresis mass spectrometry (CE-MS), and nuclear magnetic resonance (NMR). There are several leading references on metabolome analysis itself such as reviewed by Villas-Bôas et al. (2007). Recent progress in plant metabolomics is mainly focusing on specific model plants such as Arabidopsis thaliana. This is not only because of the accessibility to its genomics data, but also large efforts have been made to develop tools to integrate transcriptomics and metabolomics data such as KaPPA view (http:/kpv.kazusa.or.jp/kappa-view/) and Batch Learning (BL)-SOM (Hirai et al. 2004, 2005). However, the advantage of using metabolomics is the possibility to demonstrate the metabolic flow or its physiological traits in non-model plants such as tea (Pongsuwan et al. 2008) and spinach (Okazaki et al. 2008). In this chapter, our recent progress in soybean metabolomics is introduced. Importantly, soybean belongs to Fabaceae, and contains a large number of secondary metabolites such as flavonoids, isoflavonoids, etc. Though metabolomics concentrates on comprehensive analysis of overall metabolites in an organism, one major problem is that > 1,000,000 compounds are considered to exist in the plant kingdom (Wink 1988; Weckwerth 2003; Oksman-Caldentey and Inzé 2004), and even in one plant species 5,000-25,000 different compounds may exist (Trethewey 2004), and their quantitative variation is large (Windsor et al. 2005). The number seems to increase with improved analytical methods (Last et al. 2007), and still there are no reliable procedures to cover all plant compounds.
