Diabetologie und Stoffwechsel 2014; 9 - P111
DOI: 10.1055/s-0034-1374968

Novel diabetes QTL on chromosomes 1, 9, 11 and 13 identified in an NZOxC57BL/6J backcross population

A Kamitz 1, N Hallahan 1, R Burkhardt 2, G Schulze 1, M Jähnert 1, R Kluge 1, W Jonas 1, HG Joost 1, A Schürmann 1
  • 1German Institute of Human Nutrition (DIfE) Potsdam-Rehbruecke, Departments of Experimental Diabetology and Pharmacology, Potsdam, Germany
  • 2Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig, Germany

Background: The New Zealand Obese (NZO) mouse is a model for human polygenic obesity and type 2 diabetes. Outcross experiments with lean strains led to the discovery of various quantitative trait loci (QTL) and the subsequent identification of diabetes and obesity genes by positional cloning. The aim was to identify novel diabetes genes and suppressors in a backcross population of the NZO with the diabetes resistant C57/BL6J (B6) mouse.

Methods: The phenotype of 600 mice of the backcross population on high-fat diet (45% fat) was monitored (body weight, blood glucose, insulin sensitivity, body composition). Furthermore, metabolomics profiles (28 amino acids, 35 acylcarnitines) were assessed.

Results: Linkage analyses confirmed significant QTL on chr. 1 (Nob3) and 4 (Nidd1) for body weight and blood glucose, respectively, a QTL for cholesterol on chr. 5, as well as Nidd3 on chr. 11 for plasma insulin. Additionally, novel QTL for total pancreatic insulin on chr 1, 9, distal 11 and 13 were identified harboring 37, 25, 22 and 12 differentially expressed genes in islets, respectively between parental strains (eg: Ifi202b, Dusp3). The novel metabolomic analysis resulted in the identification of 8 QTL for amino acids and 5 QTL for acylcarnitines that partly overlap with obesity and diabetes traits.

Conclusion: In the NZO-B6 backcross 4 new QTL affecting β-cell function have been identified harboring differentially expressed islet genes which will be further studied. The analysis of metabolomic profiles might allow the identification of predictive markers for the disease.