An interaction map of circulating metabolites, immune gene networks, and their genetic regulation
AuthorNath, AP; Ritchie, SC; Byars, SG; Fearnley, LG; Havulinna, AS; Joensuu, A; Kangas, AJ; Soininen, P; Wennerstrom, A; Milani, L; ...
Source TitleGenome Biology
University of Melbourne Author/sByars, Sean; Inouye, Michael; Abraham, Gad; Ritchie, Scott; Fearnley, Liam; Nath, Artika
Melbourne School of Population and Global Health
Medical Biology (W.E.H.I.)
Document TypeJournal Article
CitationsNath, A. P., Ritchie, S. C., Byars, S. G., Fearnley, L. G., Havulinna, A. S., Joensuu, A., Kangas, A. J., Soininen, P., Wennerstrom, A., Milani, L., Metspalu, A., Mannisto, S., Wurtz, P., Kettunen, J., Raitoharju, E., Kahonen, M., Juonala, M., Palotie, A., Ala-Korpela, M. ,... Inouye, M. (2017). An interaction map of circulating metabolites, immune gene networks, and their genetic regulation. GENOME BIOLOGY, 18 (1), https://doi.org/10.1186/s13059-017-1279-y.
Access StatusOpen Access
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.
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