Metabolite discovery through global annotation of untargeted metabolomics data

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...

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Vydáno v:	Nature methods Ročník 18; číslo 11; s. 1377 - 1385
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Nature Publishing Group 01.11.2021
Témata:	Algorithms Animals Annotations Chromatography, Liquid - methods Data Curation - methods Data Curation - standards Fragmentation Global optimization Isotopes Liquid chromatography Liver - metabolism Mass spectrometry Mass spectroscopy Metabolites Metabolome Metabolomics Metabolomics - methods Metabolomics - standards Mice Network management systems Optimization Saccharomyces cerevisiae - metabolism Scientific imaging Spectroscopy Tandem Mass Spectrometry - methods Taurine Thiamine Yeast
ISSN:	1548-7091, 1548-7105, 1548-7105
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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. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
AbstractList	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. 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.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. 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.The NetID algorithm annotates untargeted LC-MS metabolomics data by combining known biochemical and metabolomic principles with a global network optimization strategy.
Author	Zeng, Xianfeng Xing, Xi Chen, Ziyang Muscarella, Antonio D Lu, Wenyun Teng, Xin Singh, Mona Chen, Li Cowan, Alexis Kennedy, Brandon J Wang, Lin Shen, Yihui Campagna, Shawn R McReynolds, Melanie R Lato, Ashley M Rabinowitz, Joshua D
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Snippet	Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain... Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain...
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SubjectTerms	Algorithms Animals Annotations Chromatography, Liquid - methods Data Curation - methods Data Curation - standards Fragmentation Global optimization Isotopes Liquid chromatography Liver - metabolism Mass spectrometry Mass spectroscopy Metabolites Metabolome Metabolomics Metabolomics - methods Metabolomics - standards Mice Network management systems Optimization Saccharomyces cerevisiae - metabolism Scientific imaging Spectroscopy Tandem Mass Spectrometry - methods Taurine Thiamine Yeast
Title	Metabolite discovery through global annotation of untargeted metabolomics data
URI	https://www.ncbi.nlm.nih.gov/pubmed/34711973 https://www.proquest.com/docview/2592764703 https://www.proquest.com/docview/2590128090
Volume	18
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