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ItemAn interaction map of circulating metabolites, immune gene networks, and their genetic regulationNath, AP ; Ritchie, SC ; Byars, SG ; Fearnley, LG ; Havulinna, AS ; Joensuu, A ; Kangas, AJ ; 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 ; Ripatti, S ; Lehtimaki, T ; Abraham, G ; Raitakari, O ; Salomaa, V ; Perola, M ; Inouye, M (BMC, 2017-08-01)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.
ItemElevated serum alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortalityRitchie, SC ; Kettunen, J ; Brozynska, M ; Nath, AP ; Havulinna, AS ; Maennist, S ; Perola, M ; Salomaa, V ; Ala-Korpela, M ; Abraham, G ; Wuertz, P ; Inouye, M ; Feng, Y-M (PUBLIC LIBRARY SCIENCE, 2019-10-23)BACKGROUND: GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown. METHODS: We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT. RESULTS: Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10-10), influenza and pneumonia (HR = 1.37, P = 6×10-10), and liver diseases (HR = 1.81, P = 1×10-6). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways. CONCLUSIONS: This study clarifies the molecular underpinnings of the GlycA biomarker's associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.