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There is a need of reliable, rapid, and cost-effective analysis technique to evaluate food and crop compositions, which are important to improve their qualities and quantities. Prior to fast GC-FID development, metabolic fingerprints, and predictive models obtained from a conventional GC-FID were evaluated by comparison to those derived from GC-TOF-MS. A similar chromatographic pattern with higher sensitivity of polyphenol compounds including epicatechin gallate (ECg) and epigallocatechin gallate (EGCg) had been achieved by using conventional GC-FID. Fast gas chromatograph coupled with flame ionization detector (GC-FID) has been carried out with 10 m x 0.18 mm id x 0.20 microm df capillary column. The analysis time per sample was reduced to less than 14 min compared to those of a conventional GC-FID (38 min) and GC-TOF-MS (28 min). The fast GC-FID also offered reliable retention time reproducibility without significant loss of peak resolution. Projections to latent structures by means of partial least squares (PLS) with orthogonal signal correction filtering (OSC) was applied to the fast GC-FID data. The predictive model showed good model fit and predictability with RMSEP of 3.464, suggesting that fast GC-FID based metabolic fingerprinting could be an alternative method for the prediction of Japanese green tea quality.

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In this study, metabolite profiling was demonstrated as a useful tool to plot a specific metabolic pathway, which is regulated by phytochrome A (phyA). Etiolated Arabidopsis wild-type (WT) and phyA mutant seedlings were irradiated with either far-red light (FR) or white light (W). Primary metabolites of the irradiated seedlings were profiled by gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) to obtain new insights on phyA-regulated metabolic pathways. Comparison of metabolite profiles in phyA and WT seedlings grown under FR revealed a number of metabolites that contribute to the differences between phyA and the WT. Several metabolites, including some amino acids, organic acids, and major sugars, as well as putrescine, were found in smaller amounts in WT compared with the content in phyA seedlings grown under FR. There were also significant differences between metabolite profiles of WT and phyA seedlings during de-etiolation under W. The polyamine biosynthetic pathway was investigated further, because putrescine, one of the polyamines existing in a wide variety of living organisms, was found to be present in lower amounts in WT than in phyA under both light conditions. The expression levels of polyamine biosynthesis-related genes were investigated by quantitative real-time RT-PCR. The gene expression profiles revealed that the arginine decarboxylase 2 (ADC2) gene was transcribed less in the WT than in phyA seedlings under both light conditions. This finding suggests that ADC2 is negatively regulated by phyA during photomorphogenesis. In addition, S-adenosylmethionine decarboxylase 2 and 4 (SAMDC2 and SAMDC4) were found to be regulated by phyA but in a different manner from the regulation of ADC2.

Cell suspension cultures are now recognized as important model materials for plant bioscience and biotechnology. Very few studies of metabolic comparisons between cell cultures and original plants have been reported, even though the biological identity of cultured cells with the normally grown plant is of great importance. In this study, a comparison of the metabolome for primary metabolites extracted from the leaves of Arabidopsis thaliana and cultured cells from an Arabidopsis suspension culture (cell line T87) was performed. The results suggest that although cell suspension cultures and Arabidopsis leaves showed similarities in the common primary metabolite profile, nonetheless, moderate differences in quantitative profile were revealed.

Metabolomics is distinct from conventional metabolism studies in that it addresses whole cellular activities rather than just focusing on enzymes, reactions, or metabolites. Metabolomics research currently confronts a problem associated with high-throughput data acquisition technologies including mass spectrometry, which have facilitated simultaneous detection and quantification of large variety of metabolite-derivative peaks without appropriate assignment of metabolites (Hall 2006). To assign the metabolites to peaks of spectra, we need to survey natural products reported in the literatures, which is a very daunting data collection process. So to feasibly incorporate peak information to metabolite, we have developed a metabolite database concerning speciesmetabolite relations called KNApSAcK (Sinbo et al. 2004), which currently contains 49,165 speciesmetabolite relations involving 24,847 metabolites. There are at least three publicly available databases concerning natural products, PubChem (Wheeler et al. 2006), KEGG (Kanehisa et al. 2008), and KNApSAcK (Shinbo et al. 2006). The PubChem database is comprised of records for over 19.6 million compounds with over 11 million unique structures including small molecules, particularly diagnostic and therapeutic agents, but is inconvenient for the purpose of assigning metabolites to spectral peaks, because there is no information on the origin of compounds such as they are synthetic or natural compounds. In KEGG, the metabolic pathways are constructed by interspecies gene relations such as orthologs and paralogs, so metabolite-species relations can be obtained via information of enzymes. However, the KEGG database mainly focuses on metabolites related to known metabolic pathways and includes around 13,000 metabolites. On the other hand, the relationships between metabolites and their biological origins have been addressed systematically in the KNApSAcK database. So KNApSAcK database makes it possible to assign metabolites to spectral peaks tractably.In the present study, we review the current status of KNApSAcK database and it's application to Abstract Since 2004, we have been developing a metabolite database concerning species-metabolite relations called KNApSAcK, which currently contains 49,165 species-metabolite relations incorporating 24,847 metabolites. In the present study, we report current status of KNApSAcK database and it's application to metabolomics fields and propose a new algorithm for detecting fragmentation patterns in a complicated mixture such as a plant tissue and a new scheme for analyzing spectral information leading to peak annotation of GC-MS spectra. When considering samples corresponding to a variety of species in addition to model species, KNApSAcK DB has strong potential for contribution to metabolomics research by way of applying it not only to simple metabolite search but also to further metabolomics analysis. An approach to peak detection in GC-MS chromatograms and application of KNApSAcK database in prediction of candidate metabolites