Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics

Xiaotao Shen, Ruohong Wang, Xin Xiong, Yandong Yin, Yuping Cai, Zaijun Ma, Nan Liu and Zheng-Jiang Zhu ()
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Xiaotao Shen: Chinese Academy of Sciences
Ruohong Wang: Chinese Academy of Sciences
Xin Xiong: Chinese Academy of Sciences
Yandong Yin: Chinese Academy of Sciences
Yuping Cai: Chinese Academy of Sciences
Zaijun Ma: Chinese Academy of Sciences
Nan Liu: Chinese Academy of Sciences
Zheng-Jiang Zhu: Chinese Academy of Sciences

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: Abstract Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.

Date: 2019
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DOI: 10.1038/s41467-019-09550-x

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