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Metabolomic analysis of insulin resistance across different mouse strains and diets.
Stöckli, Jacqueline; Fisher-Wellman, Kelsey H; Chaudhuri, Rima; Zeng, Xiao-Yi; Fazakerley, Daniel J; Meoli, Christopher C; Thomas, Kristen C; Hoffman, Nolan J; Mangiafico, Salvatore P; Xirouchaki, Chrysovalantou E; Yang, Chieh-Hsin; Ilkayeva, Olga; Wong, Kari; Cooney, Gregory J; Andrikopoulos, Sofianos; Muoio, Deborah M; James, David E.
Afiliación
  • Stöckli J; From the Charles Perkins Centre, School of Life and Environmental Sciences, the University of Sydney, Sydney NSW 2006, Australia.
  • Fisher-Wellman KH; the Garvan Institute of Medical Research, Sydney NSW 2010, Australia.
  • Chaudhuri R; the Duke Molecular Physiology Institute, Duke University, Durham, North Carolina 27708.
  • Zeng XY; From the Charles Perkins Centre, School of Life and Environmental Sciences, the University of Sydney, Sydney NSW 2006, Australia.
  • Fazakerley DJ; From the Charles Perkins Centre, School of Life and Environmental Sciences, the University of Sydney, Sydney NSW 2006, Australia.
  • Meoli CC; From the Charles Perkins Centre, School of Life and Environmental Sciences, the University of Sydney, Sydney NSW 2006, Australia.
  • Thomas KC; the Garvan Institute of Medical Research, Sydney NSW 2010, Australia.
  • Hoffman NJ; From the Charles Perkins Centre, School of Life and Environmental Sciences, the University of Sydney, Sydney NSW 2006, Australia.
  • Mangiafico SP; From the Charles Perkins Centre, School of Life and Environmental Sciences, the University of Sydney, Sydney NSW 2006, Australia.
  • Xirouchaki CE; the Department of Medicine, University of Melbourne, Melbourne VIC 3010, Australia, and.
  • Yang CH; the Department of Medicine, University of Melbourne, Melbourne VIC 3010, Australia, and.
  • Ilkayeva O; the Department of Medicine, University of Melbourne, Melbourne VIC 3010, Australia, and.
  • Wong K; the Duke Molecular Physiology Institute, Duke University, Durham, North Carolina 27708.
  • Cooney GJ; the Duke Molecular Physiology Institute, Duke University, Durham, North Carolina 27708.
  • Andrikopoulos S; the Sydney Medical School, the University of Sydney, Sydney NSW 2006, Australia.
  • Muoio DM; the Department of Medicine, University of Melbourne, Melbourne VIC 3010, Australia, and.
  • James DE; the Duke Molecular Physiology Institute, Duke University, Durham, North Carolina 27708.
J Biol Chem ; 292(47): 19135-19145, 2017 11 24.
Article en En | MEDLINE | ID: mdl-28982973
Insulin resistance is a major risk factor for many diseases. However, its underlying mechanism remains unclear in part because it is triggered by a complex relationship between multiple factors, including genes and the environment. Here, we used metabolomics combined with computational methods to identify factors that classified insulin resistance across individual mice derived from three different mouse strains fed two different diets. Three inbred ILSXISS strains were fed high-fat or chow diets and subjected to metabolic phenotyping and metabolomics analysis of skeletal muscle. There was significant metabolic heterogeneity between strains, diets, and individual animals. Distinct metabolites were changed with insulin resistance, diet, and between strains. Computational analysis revealed 113 metabolites that were correlated with metabolic phenotypes. Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sensitivity, we identified C22:1-CoA, C2-carnitine, and C16-ceramide as the best classifiers. Strikingly, when these three metabolites were combined into one signature, they classified mice based on insulin sensitivity more accurately than each metabolite on its own or other published metabolic signatures. Furthermore, C22:1-CoA was 2.3-fold higher in insulin-resistant mice and correlated significantly with insulin resistance. We have identified a metabolomic signature composed of three functionally unrelated metabolites that accurately predicts whole-body insulin sensitivity across three mouse strains. These data indicate the power of simultaneous analysis of individual, genetic, and environmental variance in mice for identifying novel factors that accurately predict metabolic phenotypes like whole-body insulin sensitivity.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Resistencia a la Insulina / Biología Computacional / Dieta / Metaboloma / Metabolómica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: J Biol Chem Año: 2017 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Resistencia a la Insulina / Biología Computacional / Dieta / Metaboloma / Metabolómica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: J Biol Chem Año: 2017 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Estados Unidos