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Metabolites. 2018 Sep 15;8(3). pii: E51. doi: 10.3390/metabo8030051.

Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas.

Author information

1
Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31555 Toulouse, France. Clement.Frainay@inra.fr.
2
Eawag: Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland. emma.schymanski@uni.lu.
3
Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg. emma.schymanski@uni.lu.
4
Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany. Sneumann@ipb-halle.de.
5
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher Platz 5e, 04103 Leipzig, Germany. Sneumann@ipb-halle.de.
6
Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31555 Toulouse, France. Benjamin.Merlet@inra.fr.
7
The International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon CEDEX 08, France. SalekR@iarc.fr.
8
Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31555 Toulouse, France. Fabien.Jourdan@inra.fr.
9
Metabolomics Platform, IISPV, Department of Electronic Engineering, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, 43007 Tarragona, Spain. oscar.yanes@urv.cat.
10
Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Monforte de Lemos 3-5, 28029 Madrid, Spain. oscar.yanes@urv.cat.

Abstract

The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.

KEYWORDS:

mass spectral libraries; metabolic networks; metabolite annotation; metabolomics data mapping

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