IPA Analysis of Metabolomics Data Including Cross-Platform Integration with Transcriptomics Data from a Diabetic Mouse Model

OVERVIEW Type 2 (or non insulin dependent) diabetes is one of the most common diseases in the western world, with over 150 million afflicted worldwide. Currently, none of the available animal models used to aid research into diagnostics and new anti-diabetic drugs reflect all disease aspects in man. More detailed model characterization compared to man is therefore essential to help make better decisions on animal testing. Non-targeted metabolomics technologies have the potential for providing novel biomarkers of disease and drug efficacy and are increasingly being incorporated into biomarker exploration studies. Contextualization of metabolomics results is enhanced by integration with study data from other platforms such as transcriptomics, thus linking known metabolites and genes to relevant pathways and phenotypes, i.e. mapping genotype to phenotype. In this white paper we have described NMR-based metabolomic and transcriptomic results from the db/db diabetic mouse, one of the most extensively studied animal models of severe type 2 diabetes. To assist with cross platform integration we have used IPA-Metabolomics™ analysis. IPA-Metabolomics is powered by the Ingenuity Knowledge Base, a repository of molecular interactions, regulatory events, signaling and metabolic pathways, and gene to phenotype associations. The structures of 82 metabolites were identified that discriminated between the urines of diabetic db/db and control db/+ mice. Of these, 70 mapped to biological functions or pathways in IPA. Expected diabetes-related changes in lipid, gluconeogenesis, mitochondrial dysfunction and oxidative stress, and protein and amino acid metabolism were characterized by the metabolomic markers. Metabolites were also linked to changes in leptin, branched chain amino acid degradation, and vitamin metabolism. Follow-up analytical and biological validation studies to verify both the identification and biological reproducibility of the results are essential to confirm initial findings from non-targeted metabolomics studies. In this work, the integration of metabolomics and transcriptomics data via IPA-Metabolomics has facilitated the contextualization of probable markers of processes in the db/db mice related to clinical manifestations of diabetes. This has provided useful input into protocol development for biomarker validation studies, highlighting metabolites that may reflect processes of interest, and focusing in on possible biomarkers.