A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease

James A. Timmons, Philip J. Atherton, Ola Larsson, Sanjana Sood, Ilya O. Blokhin, Robert J. Brogan, Claude Henry Volmar, Andrea R. Josse, Cris Slentz, Claes Wahlestedt, Stuart M. Phillips, Bethan E. Phillips, Iain J. Gallagher, William E. Kraus

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)7772-7792
Number of pages21
JournalNucleic acids research
Issue number15
StatePublished - Sep 6 2018
Externally publishedYes

ASJC Scopus subject areas

  • Genetics


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