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

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Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies. / Quell, Jan D.; Römisch-Margl, Werner; Colombo, Marco; Krumsiek, Jan; Evans, Anne M.; Mohney, Robert; Salomaa, Veikko; De Faire, Ulf; Groop, Leif C.; Agakov, Felix; Looker, Helen C.; McKeigue, Paul M.; Colhoun, Helen M.; Kastenmüller, Gabi.

In: Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, Vol. 1071, 2017, p. 58-67.

Research output: Contribution to journalArticle

Harvard

Quell, JD, Römisch-Margl, W, Colombo, M, Krumsiek, J, Evans, AM, Mohney, R, Salomaa, V, De Faire, U, Groop, LC, Agakov, F, Looker, HC, McKeigue, PM, Colhoun, HM & Kastenmüller, G 2017, 'Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies', Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, vol. 1071, pp. 58-67. https://doi.org/10.1016/j.jchromb.2017.04.002

APA

Quell, J. D., Römisch-Margl, W., Colombo, M., Krumsiek, J., Evans, A. M., Mohney, R., Salomaa, V., De Faire, U., Groop, L. C., Agakov, F., Looker, H. C., McKeigue, P. M., Colhoun, H. M., & Kastenmüller, G. (2017). Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1071, 58-67. https://doi.org/10.1016/j.jchromb.2017.04.002

CBE

Quell JD, Römisch-Margl W, Colombo M, Krumsiek J, Evans AM, Mohney R, Salomaa V, De Faire U, Groop LC, Agakov F, Looker HC, McKeigue PM, Colhoun HM, Kastenmüller G. 2017. Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences. 1071:58-67. https://doi.org/10.1016/j.jchromb.2017.04.002

MLA

Vancouver

Author

Quell, Jan D. ; Römisch-Margl, Werner ; Colombo, Marco ; Krumsiek, Jan ; Evans, Anne M. ; Mohney, Robert ; Salomaa, Veikko ; De Faire, Ulf ; Groop, Leif C. ; Agakov, Felix ; Looker, Helen C. ; McKeigue, Paul M. ; Colhoun, Helen M. ; Kastenmüller, Gabi. / Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies. In: Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences. 2017 ; Vol. 1071. pp. 58-67.

RIS

TY - JOUR

T1 - Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies

AU - Quell, Jan D.

AU - Römisch-Margl, Werner

AU - Colombo, Marco

AU - Krumsiek, Jan

AU - Evans, Anne M.

AU - Mohney, Robert

AU - Salomaa, Veikko

AU - De Faire, Ulf

AU - Groop, Leif C.

AU - Agakov, Felix

AU - Looker, Helen C.

AU - McKeigue, Paul M.

AU - Colhoun, Helen M.

AU - Kastenmüller, Gabi

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Biochemical pathway prediction

KW - Metabolic network reconstruction

KW - Metabolite identification

KW - Non-targeted metabolomics

KW - Reaction prediction

UR - http://www.scopus.com/inward/record.url?scp=85018979296&partnerID=8YFLogxK

U2 - 10.1016/j.jchromb.2017.04.002

DO - 10.1016/j.jchromb.2017.04.002

M3 - Article

C2 - 28479069

AN - SCOPUS:85018979296

VL - 1071

SP - 58

EP - 67

JO - Journal of Chromatography. B

JF - Journal of Chromatography. B

SN - 1873-376X

ER -