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- Title
Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.
- Authors
Zhou, Zhiwei; Luo, Mingdu; Zhang, Haosong; Yin, Yandong; Cai, Yuping; Zhu, Zheng-Jiang
- 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. Unknown metabolite annotation is a grand challenge in untargeted metabolomics. Here, the authors develop knowledge-guided multi-layer networking (KGMN) to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics.
- Subjects
BIOLOGICAL systems; ANNOTATIONS; CHEMICAL synthesis; MASS spectrometry; METABOLOMICS
- Publication
Nature Communications, 2022, Vol 13, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-022-34537-6