PT - JOURNAL ARTICLE AU - Zhiwei Zhou AU - Mingdu Luo AU - Haosong Zhang AU - Yandong Yin AU - Yuping Cai AU - Zheng-Jiang Zhu TI - Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic network AID - 10.1101/2022.06.02.494523 DP - 2022 Jan 01 TA - bioRxiv PG - 2022.06.02.494523 4099 - http://biorxiv.org/content/early/2022/06/03/2022.06.02.494523.short 4100 - http://biorxiv.org/content/early/2022/06/03/2022.06.02.494523.full AB - 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 grand challenge in untargeted metabolomics. Here, we developed an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrated 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 applied 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 were validated with in-silico MS/MS tools. Finally, we successfully validated 5 unknown metabolites through the repository-mining and the syntheses of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites towards deciphering dark matters in untargeted metabolomics.Competing Interest StatementThe authors have declared no competing interest.