Abstract
Introduction
Sulfur-containing metabolites (S-metabolites) in organisms including plants have unique benefits to humans. So far, few analytical methods have explored such metabolites.
Objectives
We aimed to develop an automatic chemically assigning platform by metabolomics and chemoinformatics with 34S labeling to identify the molecular formula of S-metabolites.
Methods
Direct infusion analysis using Fourier transform ion cyclotron resonance-mass spectrometry provided ultra-high-resolution data including clearly separated isotopic ions—15N, 34S, 18O, and 13C2—in the flower, silique, leaf, stem, and root of non-labeled and 34S-labeled Arabidopsis thaliana. Chemoinformatic analysis assigned several elemental compositions of S-metabolites to the acquired S-containing monoisotopic ions using mass accuracy and peak resolution in the non-labeled metabolome data. Possible elemental compositions were characterized on the basis of diagnostic scores of the exact mass and isotopic ion pattern, and a database search. By comparing elemental compositions assigned to the 34S-labeled data with those assigned to the non-labeled data, the elemental composition of S-metabolites were determined. The determined elemental compositions were surveyed using the in-house database, which stores molecular formulae downloaded from metabolome databases.
Results
We identified 35 molecular formulae for known S-metabolites and characterized 72 for unknown. Chemoinformatics required around 1.5 min to analyze a pair of the non-labeled and 34S-labeled data of the organ.
Conclusion
In this study, we developed an automation platform for automatically identifying the presence of S-metabolites. We identified the molecular formula of known S-metabolites, which are accessible in free databases, together with that of unknown. This analytical method did not focus on identifying the structure of S-metabolites, but on the automatic identification of their molecular formula.
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References
Afendi, F. M., et al. (2012). KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research. Plant and Cell Physiology, 53, e1.
Bottcher, C., et al. (2008). Metabolome analysis of Biosynthetic mutants reveals a diversity of metabolic changes and allows identification of a large number of new compounds in arabidopsis. Plant Physiology, 147, 2107–2120.
Brown, P. D., Tokuhisa, J. G., Reichelt, M., & Gershenzon, J. (2003). Variation of glucosinolate accumulation among different organs and developmental stages of Arabidopsis thaliana. Phytochemistry, 62, 471–481.
Frolkis, A., et al. (2010). SMPDB: the small molecule pathway database. Nucleic Acids Research, 38, D480–D487.
Giavalisco, P., et al. (2011). Elemental formula annotation of polar and lipophilic metabolites using C-13, N-15 and S-34 isotope labelling, in combination with high- resolution mass spectrometry. Plant J, 68, 364–376.
Glaser, K., Kanawati, B., Kubo, T., Schmitt-Kopplin, P., & Grill, E. (2014). Exploring the Arabidopsis sulfur metabolome. Plant J, 77, 31–45.
Guo, A. C., et al. (2013). ECMDB: the E. coli metabolome database. Nucleic Acids Research, 41, D625–D630.
Han, J., et al. (2008). Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics, 4, 128–140.
Hastings, J., et al. (2013). The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Research, 41, D456–D463.
Jewison, T., et al. (2012). YMDB: the yeast metabolome database. Nucleic Acids Research, 40, D815–D820.
Kind, T., & Fiehn, O. (2007). Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics, 8, 1.
Lim, E., et al. (2010). T3DB: a comprehensively annotated database of common toxins and their targets. Nucleic Acids Research, 38, D781–D786.
Miura, D., Tsuji, Y., Takahashi, K., Wariishi, H., & Saito, K. (2010). A strategy for the determination of the elemental composition by fourier transform ion cyclotron resonance mass spectrometry based on isotopic peak ratios. Analytical Chemistry, 82, 5887–5891.
Nakabayashi, R., & Saito, K. (2013). Metabolomics for unknown plant metabolites. Analytical and Bioanalytical Chemistry, 405, 5005–5011.
Nakabayashi, R., & Saito, K. (2015). Integrated metabolomics for abiotic stress responses in plants. Current Opinion in Plant Biology, 24, 10–16.
Nakabayashi, R., & Saito, K. (2016). Ultrahigh resolution metabolomics for S-containing metabolites. Current Opinion in Biotechnology, 43, 8–16.
Nakabayashi, R., Yang, Z., Nishizawa, T., Mori, T., & Saito, K. (2015). Top-down targeted metabolomics reveals a sulfur-containing metabolite with inhibitory activity against angiotensin-converting enzyme in Asparagus officinalis. Journal of Natural Products, 78, 1179–1183.
Nakabayashi, R., et al. (2013). Combination of liquid chromatography-fourier transform ion cyclotron resonance-mass spectrometry with 13C-labeling for chemical assignment of sulfur-containing metabolites in onion bulbs. Analytical Chemistry, 85, 1310–1315.
Nakabayashi, R., et al. (2016). Chemical assignment of structural isomers of sulfur-containing metabolites in garlic by liquid chromatography-fourier transform ion cyclotron resonance-mass spectrometry. Journal of Nutrition, 146, 397S–402S.
Newman, D. J., & Cragg, G. M. (2012). Natural products as sources of new drugs over the 30 years from 1981 to 2010. Journal of Natural Products, 75, 311–335.
Tsugawa, H., et al. (2015). MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods, 12, 523–526.
Tsugawa, H., et al. (2016). Hydrogen rearrangement rules: computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Analytical Chemistry, 88, 7946–7958.
Wishart, D. S., et al. (2006). DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34, D668–D672.
Wishart, D. S., et al. (2007). HMDB: the human metabolome database. Nucleic Acids Research, 35, D521–D526.
Acknowledgments
We would like to thank Yutaka Yamada and Tetsuya Sakurai (RIKEN CSRS) for managing the metabolome data. This work was partially supported by Strategic International Collaborative Research Program (SICORP), JST, and Japan Advanced Plant Science Network. H.T. was supported by a grant-in-aid for scientific research (C) 15K01812.
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Ryo Nakabayashi and Hiroshi Tsugawa have equally contributed to this work.
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Nakabayashi, R., Tsugawa, H., Mori, T. et al. Automation of chemical assignment for identifying molecular formula of S-containing metabolites by combining metabolomics and chemoinformatics with 34S labeling. Metabolomics 12, 168 (2016). https://doi.org/10.1007/s11306-016-1115-5
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DOI: https://doi.org/10.1007/s11306-016-1115-5