Metabolomics

, 12:168

Automation of chemical assignment for identifying molecular formula of S-containing metabolites by combining metabolomics and chemoinformatics with 34S labeling

  • Ryo Nakabayashi
  • Hiroshi Tsugawa
  • Tetsuya Mori
  • Kazuki Saito
Original Article

DOI: 10.1007/s11306-016-1115-5

Cite this article as:
Nakabayashi, R., Tsugawa, H., Mori, T. et al. Metabolomics (2016) 12: 168. doi:10.1007/s11306-016-1115-5

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.

Keywords

Metabolomics FTICR-MS S-containing metabolite Chemical assignment Molecular formula 

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ryo Nakabayashi
    • 1
  • Hiroshi Tsugawa
    • 1
  • Tetsuya Mori
    • 1
  • Kazuki Saito
    • 1
    • 2
  1. 1.RIKEN Center for Sustainable Resource ScienceYokohamaJapan
  2. 2.Graduate School of Pharmaceutical SciencesChiba UniversityChibaJapan

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