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Quell, J. ; Römisch-Margl, W. ; Colombo, M.* ; Krumsiek, J. ; Evans, A.M.* ; Mohney, R.P.* ; Salomaa, V.* ; de Faire, U.* ; Groop, L.C.* ; Agakov, F.V.* ; Looker, H.C.* ; McKeigue, P.M.* ; Colhoun, H.M.* ; Kastenmüller, G.

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies.

J. Chromatogr. B 1071, 58-67 (2017)
Postprint DOI PMC
Open Access Green
Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Biochemical Pathway Prediction ; Metabolic Network Reconstruction ; Metabolite Identification ; Non-targeted Metabolomics ; Reaction Prediction; Mass-spectrometry; Cardiovascular Risk; Metabolomics; Networks; Population; Identify; Trends
ISSN (print) / ISBN 1570-0232
e-ISSN 1873-376X
Journal Journal of Chromatography
Quellenangaben Volume: 1071, Issue: , Pages: 58-67 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Bioinformatics and Systems Biology (IBIS)