Metabolite discovery through global annotation of untargeted metabolomics data

Chen, Li; Lu, Wenyun; Wang, Lin; Xing, Xi; Chen, Ziyang; Teng, Xin; Zeng, Xianfeng; Muscarella, Antonio D.; Shen, Yihui; Cowan, Alexis; McReynolds, Melanie R.; Kennedy, Brandon J.; Lato, Ashley M.; Campagna, Shawn R.; Singh, Mona; Rabinowitz, Joshua D.

doi:10.1038/s41592-021-01303-3

Metabolite discovery through global annotation of untargeted metabolomics data

Journal Article · Thu Oct 28 00:00:00 EDT 2021 · Nature Methods

DOI:https://doi.org/10.1038/s41592-021-01303-3· OSTI ID:1855984

Chen, Li ^[1]; ^[2]; ^[2]; Xing, Xi ^[2]; Chen, Ziyang ^[3]; Teng, Xin ^[2]; Zeng, Xianfeng ^[2]; ^[2]; Shen, Yihui ^[2]; Cowan, Alexis ^[2]; ^[2]; ^[2]; ^[4]; Campagna, Shawn R. ^[4]; Singh, Mona ^[2]; ^[5]

Fudan University, Shanghai (China); Princeton University, NJ (United States); University of Illinois
Princeton University, NJ (United States)
Fudan University, Shanghai (China); Princeton University, NJ (United States)
University of Tennessee, Knoxville, TN (United States)
Princeton University, NJ (United States); Ludwig Institute for Cancer Research, Princeton, NJ (United States)

Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak–peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Furthermore, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.

View Accepted Manuscript (DOE)

Research Organization:: CABBI, Urbana, IL (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0018420

OSTI ID:: 1855984

Journal Information:: Nature Methods, Journal Name: Nature Methods Journal Issue: 11 Vol. 18; ISSN 1548-7091

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Additional file 3 of Metabolomic investigation of urinary extracellular vesicles for early detection and screening of lung cancer Yang, Qinsi; Luo, Jiaxin; Xu, Hao figshare https://doi.org/10.6084/m9.figshare.22826709.v1	dataset	January 2023
Additional file 4 of Metabolomic investigation of urinary extracellular vesicles for early detection and screening of lung cancer Yang, Qinsi; Luo, Jiaxin; Xu, Hao figshare https://doi.org/10.6084/m9.figshare.22826712	dataset	January 2023
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