MetaTag: A Biochemical Reaction-Guided Design Strategy for New Metabolite Discovery
Cong Li, Qianrui Xu, Liying Zhang, Zhaoyi Liu, Jing Shi, Zixian Jia, Yinghao Cao, Haoran Chen, Quan Liu, Congyu Ke, Zhixing Chen, Lifeng Yang, Sichang Wang, Lin WangAbstract
Most small-molecule features detected in untargeted metabolomics remain unannotated, representing the dark matter of the metabolome. Here, we introduce MetaTag, a biochemical-reaction-guided framework that generates high-probability novel metabolites by combining abundant endogenous metabolites with common enzymatic transformations derived from KEGG pathways. In this proof-of-concept implementation, we focused on N-acylation and N-alkylation reactions. We synthesized and characterized a physical library of 1068 compounds, creating a public MS/MS database (https://mtdb-website.vercel.app). Screening against 27 rat tissues matched 180 previously unreported metabolites with 6 at level 1 (confirmed by RT, formula, MS/MS) and 174 at level 2 (confirmed by RT, formula). These metabolites exhibited tissue-specific distributions and participated in the interorgan exchange during fasting. Functional interrogation of one such metabolite, N-propionyltaurine, demonstrated its role as a redox-sensitive regulator in brown adipocytes. This biochemistry-guided strategy provides a scalable platform for the discovery of previously uncharacterized bioactive metabolites.