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

Author

Listed:
  • Zhiwei Zhou

    (Chinese Academy of Sciences)

  • Mingdu Luo

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Haosong Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yandong Yin

    (Chinese Academy of Sciences)

  • Yuping Cai

    (Chinese Academy of Sciences)

  • Zheng-Jiang Zhu

    (Chinese Academy of Sciences
    Shanghai Key Laboratory of Aging Studies)

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.

Suggested Citation

  • Zhiwei Zhou & Mingdu Luo & Haosong Zhang & Yandong Yin & Yuping Cai & Zheng-Jiang Zhu, 2022. "Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34537-6
    DOI: 10.1038/s41467-022-34537-6
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    References listed on IDEAS

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    1. Robin Schmid & Daniel Petras & Louis-Félix Nothias & Mingxun Wang & Allegra T. Aron & Annika Jagels & Hiroshi Tsugawa & Johannes Rainer & Mar Garcia-Aloy & Kai Dührkop & Ansgar Korf & Tomáš Pluskal & , 2021. "Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Ken H. Liu & Choon M. Lee & Grant Singer & Preeti Bais & Francisco Castellanos & Michael H. Woodworth & Thomas R. Ziegler & Colleen S. Kraft & Gary W. Miller & Shuzhao Li & Young-Mi Go & Edward T. Mor, 2021. "Large scale enzyme based xenobiotic identification for exposomics," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Zhiwei Zhou & Mingdu Luo & Xi Chen & Yandong Yin & Xin Xiong & Ruohong Wang & Zheng-Jiang Zhu, 2020. "Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    4. Florian Huber & Lars Ridder & Stefan Verhoeven & Jurriaan H Spaaks & Faruk Diblen & Simon Rogers & Justin J J van der Hooft, 2021. "Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-18, February.
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    Cited by:

    1. Jiali Lv & Chang Pan & Yuping Cai & Xinyue Han & Cheng Wang & Jingjing Ma & Jiaojiao Pang & Feng Xu & Shuo Wu & Tianzhang Kou & Fandong Ren & Zheng-Jiang Zhu & Tao Zhang & Jiali Wang & Yuguo Chen, 2024. "Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Mingdu Luo & Yandong Yin & Zhiwei Zhou & Haosong Zhang & Xi Chen & Hongmiao Wang & Zheng-Jiang Zhu, 2023. "A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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