Metabolite discovery through global annotation of untargeted metabolomics data
Abstract
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.
- Authors:
-
[2];
[2];
[2];
[2];
[2];
[3];
[4]
- Fudan University, Shanghai (China); Princeton University, NJ (United States)
- 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)
- Publication Date:
- Research Org.:
- CABBI, Urbana, IL (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1855984
- Grant/Contract Number:
- SC0018420
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Methods
- Additional Journal Information:
- Journal Volume: 18; Journal Issue: 11; Journal ID: ISSN 1548-7091
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Computational models; Metabolomics
Citation Formats
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, and Rabinowitz, Joshua D. Metabolite discovery through global annotation of untargeted metabolomics data. United States: N. p., 2021.
Web. doi:10.1038/s41592-021-01303-3.
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. Metabolite discovery through global annotation of untargeted metabolomics data. United States. https://doi.org/10.1038/s41592-021-01303-3
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, and Rabinowitz, Joshua D. Thu .
"Metabolite discovery through global annotation of untargeted metabolomics data". United States. https://doi.org/10.1038/s41592-021-01303-3. https://www.osti.gov/servlets/purl/1855984.
@article{osti_1855984,
title = {Metabolite discovery through global annotation of untargeted metabolomics data},
author = {Chen, Li and Lu, Wenyun and Wang, Lin and Xing, Xi and Chen, Ziyang and Teng, Xin and Zeng, Xianfeng and Muscarella, Antonio D. and Shen, Yihui and Cowan, Alexis and McReynolds, Melanie R. and Kennedy, Brandon J. and Lato, Ashley M. and Campagna, Shawn R. and Singh, Mona and Rabinowitz, Joshua D.},
abstractNote = {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.},
doi = {10.1038/s41592-021-01303-3},
journal = {Nature Methods},
number = 11,
volume = 18,
place = {United States},
year = {Thu Oct 28 00:00:00 EDT 2021},
month = {Thu Oct 28 00:00:00 EDT 2021}
}
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