Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics

Haosong Zhang, Xinhao Zeng, Yandong Yin and Zheng-Jiang Zhu ()
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Haosong Zhang: Chinese Academy of Sciences
Xinhao Zeng: Chinese Academy of Sciences
Yandong Yin: Chinese Academy of Sciences
Zheng-Jiang Zhu: Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Metabolite annotation in untargeted metabolomics remains challenging due to the vast structural diversity of metabolites. Network-based approaches have emerged as powerful strategies, particularly for annotating metabolites lacking chemical standards. Here, we develop a two-layer interactive networking topology that integrates data-driven and knowledge-driven networks to enhance metabolite annotation. A comprehensive metabolic reaction network is curated using graph neural network-based prediction of reaction relationships, enhancing both coverage and network connectivity. Experimental data are pre-mapped onto this network via sequential MS1 matching, reaction relationship mapping, and MS2 similarity constraints. The generated networking topology enables interactive annotation propagation with over 10-fold improved computational efficiency. In common biological samples, it annotates over 1600 seed metabolites with chemical standards and >12,000 putatively annotated metabolites through network-based propagation. Notably, two previously uncharacterized endogenous metabolites absent from human metabolome databases have been discovered. Overall, this strategy significantly improves the coverage, accuracy, and efficiency of metabolite annotation and is freely available as MetDNA3.

Date: 2025
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DOI: 10.1038/s41467-025-63536-6

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