Chemicalome and metabolome matching approach to elucidating biological metabolic networks of complex mixtures

  • Gong P
  • Cui N
  • Wu L
 et al. 
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Abstract

Global metabolite identification of complex compound mixtures in biological systems is a very challenging task. Herein, we developed and validated a chemicalome to metabolome matching approach by taking herbal medicine as an example to delineate the metabolic networks of complex systems. This approach consists of five steps of data processing including raw data output, endogenous background subtraction, parent compound and metabolite differentiation, chemicalome to metabolome correlation, and the final validation via manual fragment comparison. Chemicalome to metabolome correlation, the core step of this approach, was performed based on matching the accurate mass differences of pseudomolecular ions between them with the accurate mass changes of known metabolic pathways and validating the matches by validation ions. A step-forward approach that confers a gradual identification of metabolites generated from different steps (1-4) and types (degradation, phase I/II, or mixed) of metabolic reactions was further proposed for chemicalome to metabolome matching. This approach was validated to be very useful and powerful for the metabolite identification of a single compound, a homologous compound mixture, and a complex herbal system. Using this approach, all metabolites (162) detected from urine samples of rats treated with Mai-Luo-Ning injection could be linked to their respective parent compounds, and 143 of them were supported by the final validation via manual fragment analysis. In most cases, more than 80% of the automatic matching results could be supported by the manual fragment validations. A complex metabolic network showing all the possible links between precursors and metabolites was successfully constructed. This study provides a generally applicable approach to global metabolite identification of complex compound mixtures in complex matrixes.

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Authors

  • Ping Gong

  • Nan Cui

  • Liang Wu

  • Yan Liang

  • Kun Hao

  • Xiangyang Xu

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