MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry (EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MS(n) data of 2337 authentic compounds of metabolites, 11 545 EI-MS and 834 other-MS data of 10,286 volatile natural and synthetic compounds, and 3045 ESI-MS(2) data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI-MS(2) data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS(2) data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI-MS(2) data on an identical compound under different collision-induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21-23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data.

Understanding plant metabolism as an integrated system is essential for metabolic engineering aimed at the effective production of compounds useful to human life and the global environment. The ''omics'' approach integrates transcriptome and metabolome data into a single data set and can lead to the identification of unknown genes and their regulatory networks involved in metabolic pathways of interest. One of the intriguing, although poorly described metabolic pathways in plants is the biosynthesis of glucosinolates (GSLs), a group of bioactive secondary products derived from amino acids that are found in the family Brassicaceae. Here we report the discovery of two R2R3-Myb transcription factors that positively control the biosynthesis of GSLs in Arabidopsis thaliana by an integrated omics approach. Combined transcriptome coexpression analysis of publicly available, condition-independent data and the condition-specific (i.e., sulfur-deficiency) data identified Myb28 and Myb29 as candidate transcription factor genes specifically involved in the regulation of aliphatic GSL production. Analysis of a knockout mutant and ectopic expression of the gene demonstrated that Myb28 is a positive regulator for basal-level production of aliphatic GSLs. Myb29 presumably plays an accessory function for methyl jasmonate-mediated induction of a set of aliphatic GSL biosynthetic genes. Overexpression of Myb28 in Arabidopsis-cultured suspension cells, which do not normally synthesize GSLs, resulted in the production of large amounts of GSLs, suggesting the possibility of efficient industrial production of GSLs by manipulation of these transcription factors. A working model for regulation of GSL production involving these genes, renamed Production of Methionine-Derived Glucosinolate (PMG) 1 and 2, are postulated.coexpression ͉ functional genomics ͉ transcriptomics

Jasmonic acid (JA) and methyl jasmonate (MeJA), collectively known as JAs, regulate diverse physiological processes in plants, including the response to wounding. Recent reports suggest that a cyclopentenone precursor of JA, 12-oxo-phytodienoic acid (OPDA), can also induce gene expression. However, little is known about the physiological significance of OPDA-dependent gene expression. We used microarray analysis of approximately 21,500 Arabidopsis (Arabidopsis thaliana) genes to compare responses to JA, MeJA, and OPDA treatment. Although many genes responded identically to both OPDA and JAs, we identified a set of genes (OPDA-specific response genes [ORGs]) that specifically responded to OPDA but not to JAs. ORGs primarily encoded signaling components, transcription factors, and stress response-related genes. One-half of the ORGs were induced by wounding. Analysis using mutants deficient in the biosynthesis of JAs revealed that OPDA functions as a signaling molecule in the wounding response. Unlike signaling via JAs, OPDA signaling was CORONATINE INSENSITIVE 1 independent. These results indicate that an OPDA signaling pathway functions independently of JA/MeJA signaling and is required for the wounding response in Arabidopsis.

Drought is the major environmental threat to agricultural production and distribution worldwide. Adaptation by plants to dehydration stress is a complex biological process that involves global changes in gene expression and metabolite composition. Here, using one type of functional genomics analysis, metabolomics, we characterized the metabolic phenotypes of Arabidopsis wild-type and a knockout mutant of the NCED3 gene (nc3-2) under dehydration stress. NCED3 plays a role in the dehydration-inducible biosynthesis of abscisic acid (ABA), a phytohormone that is important in the dehydration-stress response in higher plants. Metabolite profiling performed using two types of mass spectrometry (MS) systems, gas chromatography/time-of-flight MS (GC/TOF-MS) and capillary electrophoresis MS (CE-MS), revealed that accumulation of amino acids depended on ABA production, but the level of the oligosaccharide raffinose was regulated by ABA independently under dehydration stress. Metabolic network analysis showed that global metabolite-metabolite correlations occurred in dehydration-increased amino acids in wild-type, and strong correlations with raffinose were reconstructed in nc3-2. An integrated metabolome and transcriptome analysis revealed ABA-dependent transcriptional regulation of the biosynthesis of the branched-chain amino acids, saccharopine, proline and polyamine. This metabolomics analysis revealed new molecular mechanisms of dynamic metabolic networks in response to dehydration stress.

Since the completion of genome sequences of model organisms, functional identification of unknown genes has become a principal challenge in biology. Postgenomics sciences such as transcriptomics, proteomics, and metabolomics are expected to discover gene functions. This report outlines the elucidation of gene-togene and metabolite-to-gene networks via integration of metabolomics with transcriptomics and presents a strategy for the identification of novel gene functions. Metabolomics and transcriptomics data of Arabidopsis grown under sulfur deficiency were combined and analyzed by batch-learning self-organizing mapping. A group of metabolites/genes regulated by the same mechanism clustered together. The metabolism of glucosinolates was shown to be coordinately regulated. Three uncharacterized putative sulfotransferase genes clustering together with known glucosinolate biosynthesis genes were candidates for involvement in biosynthesis. In vitro enzymatic assays of the recombinant gene products confirmed their functions as desulfoglucosinolate sulfotransferases. Several genes involved in sulfur assimilation clustered with O-acetylserine, which is considered a positive regulator of these genes. The genes involved in anthocyanin biosynthesis clustered with the gene encoding a transcriptional factor that up-regulates specifically anthocyanin biosynthesis genes. These results suggested that regulatory metabolites and transcriptional factor genes can be identified by this approach, based on the assumption that they cluster with the downstream genes they regulate. This strategy is applicable not only to plant but also to other organisms for functional elucidation of unknown genes.In the era of post-genomics, a systematic and comprehensive understanding of the complex events of life is a great concern in biology. The first step in this process is to identify all gene functions and gene-to-gene networks as the components of the system, the whole events of life. The metabolome is the final product of a series of gene actions. Hence, metabolomics has a potential to elucidate gene functions and networks, especially when integrated with transcriptomics. A promising approach is pair-wise transcript-metabolite correlation analysis, which can reveal unexpected correlations and bring to light candidate genes for modifying the metabolite content (1). Gene functions involved in the specific pathway of interest have been identified by the integration of transcript and targeted metabolic profiling in experimental systems where the pathway was activated (2-6). However, up to now, no gene function has been identified by non-targeted analyses of the transcriptome and metabolome. In this report, we analyzed the non-targeted metabolome and transcriptome of a model plant Arabidopsis under sulfur (S) 1 deficiency whose genome sequencing has been completed. Our strategy for integrated analyses using batch-learning-selforganizing mapping (BL-SOM) (7-9) enabled the identification of gene-to-gene and metabolite-to-gene networks and new gene fun...