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NPJ Syst Biol Appl. 2017 Sep 21;3:28. doi: 10.1038/s41540-017-0029-9. eCollection 2017.

Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
2
Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.
3
DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany.
4
Department of Restorative Dentistry, Periodontology, Endodontology, Preventive and Pediatric Dentistry, Unit of Periodontology, University Medicine Greifswald, Greifswald, Germany.
5
Institute of Bioinformatics and Systems Biology, Helmholtz-Zentrum München, Neuherberg, Germany.
6
Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Doha, Qatar.
7
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
8
Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands.
9
German Center for Diabetes Research (DZD), Neuherberg, Germany.

Abstract

The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.

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