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Genome Biol. 2017 Aug 1;18(1):146. doi: 10.1186/s13059-017-1279-y.

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation.

Author information

1
Department of Microbiology and Immunology, The University of Melbourne, Parkville, 3010, Victoria, Australia.
2
Systems Genomics Lab, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
3
Department of Pathology, The University of Melbourne, Parkville, 3010, Victoria, Australia.
4
School of BioSciences, The University of Melbourne, Parkville, 3010, Victoria, Australia.
5
National Institute for Health and Welfare, Helsinki, 00271, Finland.
6
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, 00014, Finland.
7
Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, 90014, Finland.
8
NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, 70211, Finland.
9
University of Tartu, Estonian Genome Center, Tartu, 51010, Estonia.
10
Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland.
11
Biocenter Oulu, University of Oulu, Oulu, 90014, Finland.
12
Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, 33014, Tampere, Finland.
13
Department of Clinical Physiology, University of Tampere and Tampere University Hospital, FI-33521, Tampere, Finland.
14
Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, FI-20520, Turku, Finland.
15
Murdoch Childrens Research Institute, Parkville, 3052, Victoria, Australia.
16
Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
17
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
18
Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA.
19
Computational Medicine, School of Social and Community Medicine, University of Bristol, Bristol, BS8 1TH, UK.
20
Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
21
Department of Public Health, University of Helsinki, Helsinki, 00014, Finland.
22
Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20520, Finland.
23
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520, Finland.
24
Department of Microbiology and Immunology, The University of Melbourne, Parkville, 3010, Victoria, Australia. minouye@baker.edu.au.
25
Systems Genomics Lab, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. minouye@baker.edu.au.
26
Department of Pathology, The University of Melbourne, Parkville, 3010, Victoria, Australia. minouye@baker.edu.au.
27
School of BioSciences, The University of Melbourne, Parkville, 3010, Victoria, Australia. minouye@baker.edu.au.

Abstract

BACKGROUND:

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up.

RESULTS:

We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus.

CONCLUSIONS:

This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.

PMID:
28764798
PMCID:
PMC5540552
DOI:
10.1186/s13059-017-1279-y
[Indexed for MEDLINE]
Free PMC Article

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