Genome biologyGenomicsPeer-reviewed journal article
An interaction map of circulating metabolites, immune gene networks and their genetic regulation
Artika P. NathiD, Scott C. RitchieiD, Sean ByarsiD, Liam G. FearnleyiD, Aki Samuli HavulinnaiD, Anni JoensuuiD, Antti J. KangasiD, Pasi Soininen, Annika Wennerström, Lili A. MilaniiD, Andres MetspaluiD, Satu MännistöiD, Peter WürtziD, Johannes KettuneniD, Emma RaitoharjuiD, Mika A. P. KähöneniD, Markus JuonalaiD, Aarno V. PalotieiD, Mika Ala‐KorpelaiD, Samuli RipattiiD, Terho J. LehtimäkiiD, Gad AbrahamiD, Olli Tuomas RaitakariiD, Veikko V SalomaaiD, Markus PerolaiD, Michael InouyeiD
Abstract
The interaction between metabolism and the immune system plays a central role in many cardiometabolic diseases. We integrated blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts, including a subset with 7-year follow-up sampling. We identified topologically robust gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules showed complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) revealed five modules with mQTLs of both cis and trans effects. The strongest mQTL was in ARHGEF3 (rs1354034) and affected a module enriched for platelet function. Mast cell/basophil and neutrophil function modules maintained their metabolite associations during 7-year follow-up, while our strongest mQTL in ARHGEF3 also displayed clear temporal stability. This study provides a detailed map of natural variation at the blood immuno-metabolic interface and its genetic basis, and facilitates subsequent studies to explain inter-individual variation in cardiometabolic disease.
- organism: human
- assay: bulk RNA-seq
- assay: metabolomics
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 53
- FWCI
- 4.71
- Field percentile
- top 5%
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