Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Zhiwei Zhou, Mingdu Luo, Haosong Zhang, Yandong Yin, Yuping Cai and Zheng-Jiang Zhu ()
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Zhiwei Zhou: Chinese Academy of Sciences
Mingdu Luo: Chinese Academy of Sciences
Haosong Zhang: Chinese Academy of Sciences
Yandong Yin: Chinese Academy of Sciences
Yuping Cai: Chinese Academy of Sciences
Zheng-Jiang Zhu: Chinese Academy of Sciences

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100–300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

Date: 2022
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DOI: 10.1038/s41467-022-34537-6

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