期刊
METABOLOMICS
卷 11, 期 6, 页码 1815-1833出版社
SPRINGER
DOI: 10.1007/s11306-015-0829-0
关键词
Epidemiology; Metabolic networks; Metabolomics; Gender differences; Systems biology
资金
- Helmholtz Postdoctoral Programme, Initiative and Networking Fund
- Studienstiftung des Deutschen Volkes
- Standortagentur Tirol
- Dutch Science Organization (ZonMW-VENI) [916.14.023]
- Biomedical Research Program funds at Weill Cornell Medical College in Qatar - Qatar Foundation
- RFBR (Russian Foundation for Basic Research)-Helmholtz Joint Research Group
- European Research Council (starting grant LatentCauses)
- German Federal Ministry of Education and Research (BMBF)
- Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German National Genome Research Network (NGFN)
- Munich Center of Health Sciences (MC Health) as part of LMUinnovativ
- European Union's Seventh Framework Programme [FP7-Health-F5-2012, 305280]
The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.
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