Format

Send to

Choose Destination
PLoS Genet. 2015 Jun 18;11(6):e1005274. doi: 10.1371/journal.pgen.1005274. eCollection 2015 Jun.

The Human Blood Metabolome-Transcriptome Interface.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
2
Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany; Institute of Human Genetics, Technische Universität München, Neuherberg, Germany.
3
Institute of Experimental Genetics, Genome Analysis Center Helmholtz Zentrum München, Neuherberg, Germany; Faculty of Experimental Genetics, Technische Universität München, Freising-Weihenstephan, Germany; German Center for Cardiovascular Disease Research (DZHK e.V.), partner-site Munich, Munich, Germany.
4
Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.
5
Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research (DZD e.V.), partner-site Düsseldorf, Düsseldorf, Germany.
6
German Center for Cardiovascular Disease Research (DZHK e.V.), partner-site Munich, Munich, Germany; Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Cardiovascular Disease Research (DZHK e.V.), partner-site Munich, Munich, Germany.
7
Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
8
Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research (DZD e.V.), partner-site Düsseldorf, Düsseldorf, Germany; Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
9
Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany; Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany.
10
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany; Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha, Qatar.
11
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.
12
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; Department of Mathematics, Technische Universität München, Garching, Germany.

Abstract

Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the 'human blood metabolome-transcriptome interface' (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease.

PMID:
26086077
PMCID:
PMC4473262
DOI:
10.1371/journal.pgen.1005274
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center