Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.
Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.
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DOI：
10.1038/s41467-022-34537-6
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发表时间：
2022-11-04
影响因子：
16.6
通讯作者：
Zhu, Zheng-Jiang
中科院分区：
文献类型：
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作者：
Zhou, Zhiwei;Luo, Mingdu;Zhang, Haosong;Yin, Yandong;Cai, Yuping;Zhu, Zheng-Jiang
基于液相色谱-质谱（LC-MS）的非靶向代谢组学允许测量代谢组中已知和未知的代谢物。然而，未知的代谢物注释是非靶向代谢组学的主要挑战。在这里，我们开发了一种方法，即知识引导的多层网络（KGMN），以实现非靶向代谢组学中从已知到未知的全局代谢物注释。KGMN方法集成了三层网络，包括基于知识的代谢反应网络、知识引导的质谱/质谱相似网络和全局峰相关网络。为了证明这一原理，我们将KGMN应用于体外酶反应系统和不同的生物样品中，在每个数据集中注释了约100-300个假定的未知数。其中，bbb80 %的未知代谢物被硅质谱/质谱工具证实。最后，我们通过库挖掘和化学标准物合成验证了常见MS/MS文库中缺失的5种代谢物。总之，KGMN方法实现了有效的未知注释，并大大推进了对模式生物常见生物样本中反复出现的未知代谢物的发现，从而破译了非靶向代谢组学中的暗物质。未知代谢物注释是非靶向代谢组学的一个巨大挑战。在这里，作者开发了知识引导的多层网络（KGMN），以便在非靶向代谢组学中实现从已知到未知的全局代谢物注释。
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.
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影响因子：
8.6
作者：
Djoumbou-Feunang, Yannick;Fiamoncini, Jarlei;Wishart, David S.
通讯作者：
Wishart, David S.
影响因子：
4.3
作者：
da Silva RR;Wang M;Nothias LF;van der Hooft JJJ;Caraballo-Rodríguez AM;Fox E;Balunas MJ;Klassen JL;Lopes NP;Dorrestein PC
通讯作者：
Dorrestein PC
影响因子：
14.9
作者：
Allen F;Pon A;Wilson M;Greiner R;Wishart D
通讯作者：
Wishart D
影响因子：
7.4
作者：
Domingo-Almenara X;Montenegro-Burke JR;Benton HP;Siuzdak G
通讯作者：
Siuzdak G
DOI：
10.1038/s41579-021-00621-9
发表时间：
2022-03
期刊：
Nature reviews. Microbiology
影响因子：
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作者：
通讯作者：
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