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Krumsiek, J. ; Suhre, K. ; Evans, A.M.* ; Mitchell, M.W.* ; Mohney, R.P.* ; Milburn, M.V.* ; Wägele, B. ; Römisch-Margl, W. ; Illig, T. ; Adamski, J. ; Gieger, C. ; Theis, F.J. ; Kastenmüller, G.

Mining the unknown: A systems approach to metabolite identification combining genetic and metabolic information.

PLoS Genet. 8:e1003005 (2012)
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Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter GENOME-WIDE ASSOCIATION; CHRONIC KIDNEY-DISEASE; BILIRUBIN LEVELS; RECONSTRUCTION; VARIANTS; BIOLOGY; COMMON; LOCUS; ACID; TOOL
Sprache englisch
Veröffentlichungsjahr 2012
HGF-Berichtsjahr 2012
ISSN (print) / ISBN 1553-7390
e-ISSN 1553-7404
Zeitschrift PLoS Genetics
Quellenangaben Band: 8, Heft: 10, Seiten: , Artikelnummer: e1003005 Supplement: ,
Verlag Public Library of Science (PLoS)
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)
Molekulare Endokrinologie und Metabolismus (MEM)
Research Unit Molecular Epidemiology (AME)
Institute of Genetic Epidemiology (IGE)
Institute of Experimental Genetics (IEG)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
30201 - Metabolic Health
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
90000 - German Center for Diabetes Research
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
PSP-Element(e) G-503700-001
G-505600-001
G-504200-003
G-503700-004
G-504100-001
G-501900-061
PubMed ID 23093944
DOI 10.1371/journal.pgen.1003005
WOS ID WOS:000310528400029
Scopus ID 84868112190
Erfassungsdatum 2012-10-30
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