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Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics

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Book cover Computational Methods and Data Analysis for Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2104))

Abstract

High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.

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Acknowledgments

T.N. acknowledges support by the IMPRS-PMPG program and A.R.F. the support of Max Planck Society. E. G. acknowledges the support by the Deutsche Forschungsgemeinschaft Excellence Initiative to the University of Heidelberg and by the Centre National de la Recherche Scientifique.

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Authors and Affiliations

  1. Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany

    Thomas Naake & Alisdair R. Fernie

  2. Institute of Plant Molecular Biology, University of Strasbourg, Strasbourg, France

    Emmanuel Gaquerel

  3. Centre for Organismal Studies, University of Heidelberg, Heidelberg, Germany

    Emmanuel Gaquerel

Authors
  1. Thomas Naake
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  2. Emmanuel Gaquerel
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  3. Alisdair R. Fernie
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Corresponding author

Correspondence to Alisdair R. Fernie .

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Editors and Affiliations

  1. Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Shuzhao Li

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Naake, T., Gaquerel, E., Fernie, A.R. (2020). Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_12

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  • DOI: https://doi.org/10.1007/978-1-0716-0239-3_12

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0238-6

  • Online ISBN: 978-1-0716-0239-3

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