Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics

• Published in: Nature communications Vol. 10; no. 1; p. 1516
• Main Authors: Shen, Xiaotao, Wang, Ruohong, Xiong, Xin, Yin, Yandong, Cai, Yuping, Ma, Zaijun, Liu, Nan, Zhu, Zheng-Jiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.04.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 140/58; 631/114/2390; 631/1647/296; 631/1647/48; 631/45/320; Algorithms; Animals; Annotations; Biological properties; Biological samples; Chromatography, Liquid - methods; Data acquisition; Databases, Factual; Drosophila - metabolism; Gene Expression Regulation; Gluconeogenesis; Humanities and Social Sciences; Mass spectrometry; Mass spectroscopy; Metabolic Networks and Pathways; Metabolic pathways; Metabolism; Metabolites; Metabolome; Metabolomics; Metabolomics - instrumentation; Metabolomics - methods; Models, Biological; multidisciplinary; Recursive methods; Sampling methods; Science; Science (multidisciplinary); Spectra; Tandem Mass Spectrometry - methods; Transcriptome
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-09550-x

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis. Untargeted metabolomics detects large numbers of metabolites but their annotation remains challenging. Here, the authors develop a metabolic reaction network-based recursive algorithm that expands metabolite annotation by taking advantage of the mass spectral similarity of reaction-paired neighbor metabolites.