Refine
Has Fulltext
- no (2)
Document Type
- Article (2)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
- QTL (1)
- apoptosis (1)
- beta-cell (1)
- diabetes (1)
- proliferation (1)
Institute
Current attempts to prevent and manage type 2 diabetes have been moderately effective, and a better understanding of the molecular roots of this complex disease is important to develop more successful and precise treatment options.
Recently, we initiated the collective diabetes cross, where four mouse inbred strains differing in their diabetes susceptibility were crossed with the obese and diabetes-prone NZO strain and identified the quantitative trait loci (QTL) Nidd13/NZO, a genomic region on chromosome 13 that correlates with hyperglycemia in NZO allele carriers compared to B6 controls.
Subsequent analysis of the critical region, harboring 644 genes, included expression studies in pancreatic islets of congenic Nidd13/NZO mice, integration of single-cell data from parental NZO and B6 islets as well as haplotype analysis.
Finally, of the five genes (Acot12, S100z, Ankrd55, Rnf180, and Iqgap2) within the polymorphic haplotype block that are differently expressed in islets of B6 compared to NZO mice, we identified the calcium-binding protein S100z gene to affect islet cell proliferation as well as apoptosis when overexpressed in MINE cells. In summary, we define S100z as the most striking gene to be causal for the diabetes QTL Nidd13/NZO by affecting beta-cell proliferation and apoptosis. Thus, S100z is an entirely novel diabetes gene regulating islet cell function.
To explore the genetic determinants of obesity and Type 2 diabetes (T2D), the German Center for Diabetes Research (DZD) conducted crossbreedings of the obese and diabetes-prone New Zealand Obese mouse strain with four different lean strains (B6, DBA, C3H, 129P2) that vary in their susceptibility to develop T2D. Genome-wide linkage analyses localized more than 290 quantitative trait loci (QTL) for obesity, 190 QTL for diabetes-related traits and 100 QTL for plasma metabolites in the out-cross populations. A computational framework was developed that allowed to refine critical regions and to nominate a small number of candidate genes by integrating reciprocal haplotype mapping and transcriptome data. The efficiency of the complex procedure was demonstrated for one obesity QTL. The genomic interval of 35 Mb with 502 annotated candidate genes was narrowed down to six candidates. Accordingly, congenic mice retained the obesity phenotype owing to an interval that contains three of the six candidate genes. Among these the phospholipase PLA2G4A exhibited an elevated expression in adipose tissue of obese human subjects and is therefore a critical regulator of the obesity locus. Together, our broad and complex approach demonstrates that combined- and comparative-cross analysis exhibits improved mapping resolution and represents a valid tool for the identification of disease genes.