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Metabolite discovery through global annotation of untargeted metabolomics data

Nature Methods volume 18, pages 1377–1385 (2021)Cite this article

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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.

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Fig. 1: A global network optimization approach for untargeted metabolomics data annotation (NetID).
Fig. 2: Utility of global network optimization.
Fig. 3: Identification of thiamine-derived metabolites in yeast using NetID.
Fig. 4: Discovery of mammalian taurine derivatives using NetID.
Fig. 5: Global optimization of metabolomics data annotation and metabolite discovery with NetID.

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Data availability

All LC-MS data, including the yeast and mouse metabolomics datasets, the 13C labeling datasets, and more than 2,000 targeted MS2 files collected from the liver data in mzXML format were deposited in MassIVE (ID no. MSV000087434). R code for generating NetID statistics and for performing false discovery rate analysis in Fig. 2 and Extended Data Fig. 1 is provided in GitHub (https://github.com/LiChenPU/NetID/releases/tag/v1.0) and Zenodo (https://zenodo.org/record/5508337). The atom difference rule table is provided in Supplementary Data 1, the peak table for the yeast negative-mode data, as well as the NetID annotation results, putative metabolite list, and manual curation results are provided in Supplementary Data 2, an in-house retention time list for known metabolites is provided in Supplementary Data 3, the HMDB, YMDB, PubChemLite and PubChemLite_bio reference compound databases (customized to contain relevant information) are provided in Supplementary Data 4–7, and MS2 spectra of newly discovered metabolites are provided in Supplementary Data 8.

Code availability

NetID was developed mainly in R, and used a mixture of IBM ILOG CPLEX Optimization Studio, Matlab and Python. NetID code and example files are available for non-commercial use in GitHub at https://github.com/LiChenPU/NetID/releases/tag/v1.0 and Zenodo at https://zenodo.org/record/5508337, under the GNU General Public License v3.0. User guide and pseudocode are provided in Supplementary Notes 3 and 4.

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Acknowledgements

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.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China

    Li Chen & Ziyang Chen

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Li Chen, Wenyun Lu, Lin Wang, Xi Xing, Xin Teng, Xianfeng Zeng, Antonio D. Muscarella, Yihui Shen, Alexis Cowan, Melanie R. McReynolds, Mona Singh & Joshua D. Rabinowitz

  3. Department of Chemistry, Princeton University, Princeton, NJ, USA

    Wenyun Lu, Lin Wang, Xi Xing, Xianfeng Zeng, Melanie R. McReynolds & Joshua D. Rabinowitz

  4. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Ziyang Chen, Alexis Cowan & Joshua D. Rabinowitz

  5. Lotus Separations, LLC, Department of Chemistry, Princeton University, Princeton, NJ, USA

    Brandon J. Kennedy

  6. Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA

    Ashley M. Lato & Shawn R. Campagna

  7. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Mona Singh

  8. Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA

    Joshua D. Rabinowitz

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Contributions

L.C., M.S. and J.D.R. conceived the project. L.C., X.X. and Z.C. wrote the NetID algorithm code. W.L., L.W., X.Z., A.C. and M.R.M. performed the mice experiments. L.W., W.L. and L.C. performed the experiments on yeast. L.C., W.L., L.W. and X.X. analyzed the LC-MS and LC-MS/MS data. X.T., A.D.M. and Y.S. contributed to coding development. B.J.K., A.M.L. and S.R.C. synthesized taurine-related compounds. L.C. and J.D.R. wrote the paper. All of the authors discussed the results and commented on the paper.

Corresponding author

Correspondence to Joshua D. Rabinowitz.

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The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Pieter Dorrestein and Justin van der Hooft for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Characterization of NetID network.

(a) Summary table of the candidate annotation step in NetID workflow. (b) Visualization of the optimal network obtained from negative mode LC-MS analysis of baker’s yeast, containing 4851 nodes and 9699 connections. Metabolite and putative metabolite peaks are in green and artifact peaks in purple. (c) Connectivity of NetID network from the yeast negative-mode dataset.

Extended Data Fig. 2 Examples of putative metabolites in yeast negative-mode dataset.

(a-c) Subnetwork surrounding glutathione (a), glycerophosphocholine (b), and xanthurenic acid (c). (d) Peak properties and annotations for putative metabolites (yellow nodes) in subnetworks (a)-(c).

Extended Data Fig. 3 Evaluation of annotation false discovery rate (FDR) and fraction gold-standard peaks annotated correctly using different reference databases.

The four tested reference compound databases are HMDB (human metabolomics database), PBCM (short for PubChemLite.0.2.0, zenodo.org/record/3611238), PBCM_BIO (short for PubChemLite_BioPathway, a subset of biopathway related entries in PubChemLite.0.2.0) and YMDB (yeast metabolomics database). (a) False discovery rate estimated using target-decoy strategy. (b) Fraction of 314 manually curated ‘ground truth’ annotations made correctly. For A and B, each individual data point (circle) is from a different randomized decoy library. N = 10 randomized libraries were tested for each reference compound database. Boxes show median and IQR and whiskers extend to largest and smallest value no further than ±1.5 × IQR from hinge.

Extended Data Fig. 4 Subnetwork surrounding thiamine with additional known structures.

Nodes, connections, and formulae are direct output of NetID. Boxes with structures were manually added.

Extended Data Fig. 5 Evidence for the additional thiamine-derived metabolites.

Similar to Fig. 3, adding unlabeled thiamine to [U-13C]glucose culture media, yeast uptake the unlabeled thiamine, resulting in unlabeled thiamine, M + 4 labeled thiamine + [C4H6O3] and thiamine + [C4H8O] species (n = 5). The proposed formulae are also supported by m/z measured by high-resolution mass-spectrometry. Bar represents mean values and error bar indicates s.d.

Extended Data Fig. 6 Subnetwork surrounding taurine with additional known structures.

Nodes, connections, and formulae are direct output of NetID. Boxes with structures were manually added.

Extended Data Fig. 7 SelTOCSY NMR confirmation of the structure of the chemically synthesized N-glucosyl-taurine.

The final crude material is a mixture of glucose, taurine, and N-glucosyl-taurine at 5.2% (pink line). Comparing N-glucosyl-taurine (yellow) to alpha- (blue) and beta-glucose (green) NMR experiments indicate that C1 of the glucosyl group connects the amine group of taurine in α-position.

Extended Data Fig. 8 Glucosyl-taurine is a liver metabolite, not ex vivo reaction product.

To test for ex vivo production of glucosyl-taurine, liver extract (with or without spiked 55 μM [U-13C]glucose) or extraction buffer (40:40:20 ACN:MeOH:H2O + NH4HCO3 or 50:50 MeOH:H2O) containing pure glucose and taurine were incubated at 5 °C for the indicated duration. Metabolites formed by ex vivo reactions typically accumulate upon sample incubation, while glucosyl-taurine does not. Moreover, there is minimal assimilation of [U-13C]glucose into glucosyl-taurine to make M + 6 glucosyl-taurine in liver extract, and, while trace glucosyl-taurine can be formed abiotically in acetonitrile:methanol:water at pH = 7, the observed biological quantity is 100-fold greater.

Supplementary information

Supplementary Information

Supplementary Tables 1–5, Supplementary Notes 1–4.

Reporting Summary

Supplementary Data 1

Atom difference rule table

Supplementary Data 2

NetID annotation for the yeast negative-mode dataset

Supplementary Data 3

In-house retention time list

Supplementary Data 4

HMDB reference compound database

Supplementary Data 5

YMDB reference compound database

Supplementary Data 6

PubChemLite reference compound database

Supplementary Data 7

PubChemLite_bio reference compound database

Supplementary Data 8

MS2 spectra of newly discovered metabolites

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Chen, L., Lu, W., Wang, L. et al. Metabolite discovery through global annotation of untargeted metabolomics data. Nat Methods 18, 1377–1385 (2021). https://doi.org/10.1038/s41592-021-01303-3

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