A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease. Issue 15 (9th July 2018)

• Record Type: Journal Article
• Authors: Timmons, James A; Atherton, Philip J; Larsson, Ola; Sood, Sanjana; Blokhin, Ilya O; Brogan, Robert J; Volmar, Claude-Henry; Josse, Andrea R; Slentz, Cris; Wahlestedt, Claes; Phillips, Stuart M; Phillips, Bethan E; Gallagher, Iain J; Kraus, William E
• Is Part Of: Nucleic acids research. Volume 46:Issue 15(2018)
• Page Start: 7772
• Page End: 7792
• Publication Date: 2018-07-09
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky570
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Genome-wide association studies (GWAS), relying on hundreds of thousands of individuals, have revealed >200 genomic loci linked to metabolic disease (MD). Loss of insulin sensitivity (IS) is a key component of MD and we hypothesized that discovery of a robust IS transcriptome would help reveal the underlying genomic structure of MD. Using 1, 012 human skeletal muscle samples, detailed physiology and a tissue-optimized approach for the quantification of coding (>18, 000) and non-coding (>15, 000) RNA (ncRNA), we identified 332 fasting IS-related genes (CORE-IS). Over 200 had a proven role in the biochemistry of insulin and/or metabolism or were located at GWAS MD loci. Over 50% of the CORE-IS genes responded to clinical treatment; 16 quantitatively tracking changes in IS across four independent studies ( P = 0.0000053: negatively: AGL, G0S2, KPNA2, PGM2, RND3 and TSPAN9 and positively: ALDH6A1, DHTKD1, ECHDC3, MCCC1, OARD1, PCYT2, PRRX1, SGCG, SLC43A1 and SMIM8 ). A network of ncRNA positively related to IS and interacted with RNA coding for viral response proteins ( P < 1 × 10 −48 ), while reduced amino acid catabolic gene expression occurred without a change in expression of oxidative-phosphorylation genes. We illustrate that combining in-depth physiological phenotyping with robust RNA profiling methods, identifies molecular networks which are highly consistent with the genetics and biochemistry of human metabolic disease.