TY - JOUR
T1 - Metabolite discovery through global annotation of untargeted metabolomics data
AU - Chen, Li
AU - Lu, Wenyun
AU - Wang, Lin
AU - Xing, Xi
AU - Chen, Ziyang
AU - Teng, Xin
AU - Zeng, Xianfeng
AU - Muscarella, Antonio D.
AU - Shen, Yihui
AU - Cowan, Alexis
AU - McReynolds, Melanie R.
AU - Kennedy, Brandon J.
AU - Lato, Ashley M.
AU - Campagna, Shawn R.
AU - Singh, Mona
AU - Rabinowitz, Joshua D.
N1 - Funding Information:
This work was supported by a Department of Energy (DOE) grant (no. DE-SC0012461 to J.D.R.), the Center for Advanced Bioenergy and Bioproducts Innovation (grant no. DE-SC0018420, subcontract to J.D.R.), NIH grant R50CA211437 to W.L. and the Howard Hughes Medical Institute and Burroughs Wellcome Fund via the PDEP and Hanna H. Gray Fellows Programs to M.R.M. The authors thank I. Pelczer at the NMR facility of the Department of Chemistry at Princeton University for the NMR analysis, the Metabolomics and Lipidomics Mass Spectrometry Core Facility of IMIB at Fudan University for additional mass spectrometry support, and X. Su and Y. An for scientific discussion and help. The Center for Advanced Bioenergy and Bioproducts Innovation and the Center for Bioenergy Innovation are both US Department of Energy Bioenergy Research Centers supported by the Office of Biological and Environmental Research in the DOE Office of Science. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the US Department of Energy.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/11
Y1 - 2021/11
N2 - 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. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
AB - 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. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
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U2 - 10.1038/s41592-021-01303-3
DO - 10.1038/s41592-021-01303-3
M3 - Article
C2 - 34711973
AN - SCOPUS:85117905982
SN - 1548-7091
VL - 18
SP - 1377
EP - 1385
JO - Nature Methods
JF - Nature Methods
IS - 11
ER -