RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database.

The fragment pattern analysis of tandem mass spectrometry (MS/MS) has long been used for the structural characterization of metabolites. The construction of a plant-specific MS/MS data resource and database will enable complex phytochemical structures to be narrowed down to candidate structures. Therefore, a web-based database of MS/MS data pertaining to phytochemicals was developed and named ReSpect (RIKEN tandem mass spectral database). Of the 3595 metabolites in ReSpect, 76% were derived from 163 literature reports, whereas the rest was obtained from authentic standards. As a main web application of ReSpect, a fragment search was established based on only the m/z values of query data and records. The confidence levels of the annotations were managed using the MS/MS fragmentation association rule, which is an algorithm for discovering common fragmentations in MS/MS data. Using this data resource and database, a case study was conducted for the annotation of untargeted MS/MS data that were selected after quantitative trait locus analysis of the accessions (Gifu and Miyakojima) of a model legume Lotus japonicus. In the case study, unknown metabolites were successfully narrowed down to putative structures in the website.

Arabidopsis bile acid:sodium symporter family protein 5 is involved in methionine-derived glucosinolate biosynthesis.

Glucosinolates (GSLs) are a group of plant secondary metabolites that have repellent activity against herbivore insects and pathogens, and anti-carcinogenic activity in humans. They are produced in plants of the Brassicaceae and other related families. Biosynthesis of GSLs from precursor amino acids takes place in two subcellular compartments; amino acid biosynthesis and side chain elongation occur mainly in the chloroplast, whereas the following core structure synthesis takes place in the cytosol. Although the genes encoding biosynthetic enzymes of GSLs are well known in Arabidopsis thaliana, the transporter genes responsible for translocation of biosynthetic intermediates between the chloroplast and cytosol are as yet unidentified. In this study, we identified the bile acid:sodium symporter family protein 5 (BASS5) gene in Arabidopsis as a candidate transporter gene involved in methionine-derived GSL (Met-GSL) biosynthesis by means of transcriptome co-expression analysis. Knocking out BASS5 resulted in a decrease of Met-GSLs and concomitant increase of methionine. A transient assay using fluorescence fusion proteins indicated a chloroplastic localization of BASS5. These results supported the idea that BASS5 plays a role in translocation across the chloroplast membranes of the biosynthetic intermediates of Met-GSLs.

Omics-based approaches to methionine side chain elongation in Arabidopsis: characterization of the genes encoding methylthioalkylmalate isomerase and methylthioalkylmalate dehydrogenase.

Glucosinolates (GSLs) are secondary metabolites in Brassicaceae plants synthesized from amino acids. Methionine-derived GSLs (Met-GSLs) with diverse side chains of various lengths are the major GSLs in Arabidopsis. Methionine chain elongation enzymes are responsible for variations in chain length in Met-GSL biosynthesis. The genes encoding methionine chain elongation enzymes are considered to have been recruited from the leucine biosynthetic pathway in the course of evolution. Among them, the genes encoding methylthioalkylmalate synthases and aminotransferases have been identified; however, the remaining genes that encode methylthioalkylmalate isomerase (MAM-I) and methylthioalkylmalate dehydro-genase (MAM-D) remain to be identified. In a previous study based on transcriptome co-expression analysis, we identified candidate genes for the large subunit of MAM-I and MAM-D. In this study, we confirmed their predicted functions by targeted GSL analysis of the knockout mutants, and named the respective genes MAM-IL1/AtleuC1 and MAM-D1/AtIMD1. Metabolic profiling of the knockout mutants of methionine chain elongation enzymes, conducted by means of widely targeted metabolomics, implied that these enzymes have roles in controlling metabolism from methionine to primary and methionine-related secondary metabolites. As shown here, an omics-based approach is an efficient strategy for the functional elucidation of genes involved in metabolism.

Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants.

Metabolomics is an 'omics' approach that aims to analyze all metabolites in a biological sample comprehensively. The detailed metabolite profiling of thousands of plant samples has great potential for directly elucidating plant metabolic processes. However, both a comprehensive analysis and a high throughput are difficult to achieve at the same time due to the wide diversity of metabolites in plants. Here, we have established a novel and practical metabolomics methodology for quantifying hundreds of targeted metabolites in a high-throughput manner. Multiple reaction monitoring (MRM) using tandem quadrupole mass spectrometry (TQMS), which monitors both the specific precursor ions and product ions of each metabolite, is a standard technique in targeted metabolomics, as it enables high sensitivity, reproducibility and a broad dynamic range. In this study, we optimized the MRM conditions for specific compounds by performing automated flow injection analyses with TQMS. Based on a total of 61,920 spectra for 860 authentic compounds, the MRM conditions of 497 compounds were successfully optimized. These were applied to high-throughput automated analysis of biological samples using TQMS coupled with ultra performance liquid chromatography (UPLC). By this analysis, approximately 100 metabolites were quantified in each of 14 plant accessions from Brassicaceae, Gramineae and Fabaceae. A hierarchical cluster analysis based on the metabolite accumulation patterns clearly showed differences among the plant families, and family-specific metabolites could be predicted using a batch-learning self-organizing map analysis. Thus, the automated widely targeted metabolomics approach established here should pave the way for large-scale metabolite profiling and comparative metabolomics.