@article{AgaBarfknechtHallahanGottmannetal.2020,
  author    = {Aga-Barfknecht, Heja and Hallahan, Nicole and Gottmann, Pascal and J{\"a}hnert, Markus and Osburg, Sophie and Schulze, Gunnar and Kamitz, Anne and Arends, Danny and Brockmann, Gudrun and Schallschmidt, Tanja and Lebek, Sandra and Chadt, Alexandra and Al-Hasani, Hadi and Joost, Hans-Georg and Sch{\"u}rmann, Annette and Vogel, Heike},
  title     = {Identification of novel potential type 2 diabetes genes mediating beta-cell loss and hyperglycemia using positional cloning},
  series = {Frontiers in genetics},
  volume    = {11},
  journal   = {Frontiers in genetics},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-8021},
  doi       = {10.3389/fgene.2020.567191},
  pages     = {11},
  year      = {2020},
  abstract  = {Type 2 diabetes (T2D) is a complex metabolic disease regulated by an interaction of genetic predisposition and environmental factors. To understand the genetic contribution in the development of diabetes, mice varying in their disease susceptibility were crossed with the obese and diabetes-prone New Zealand obese (NZO) mouse. Subsequent whole-genome sequence scans revealed one major quantitative trait loci (QTL),Nidd/DBAon chromosome 4, linked to elevated blood glucose and reduced plasma insulin and low levels of pancreatic insulin. Phenotypical characterization of congenic mice carrying 13.6 Mbp of the critical fragment of DBA mice displayed severe hyperglycemia and impaired glucose clearance at week 10, decreased glucose response in week 13, and loss of beta-cells and pancreatic insulin in week 16. To identify the responsible gene variant(s), further congenic mice were generated and phenotyped, which resulted in a fragment of 3.3 Mbp that was sufficient to induce hyperglycemia. By combining transcriptome analysis and haplotype mapping, the number of putative responsible variant(s) was narrowed from initial 284 to 18 genes, including gene models and non-coding RNAs. Consideration of haplotype blocks reduced the number of candidate genes to four (Kti12,Osbpl9,Ttc39a, andCalr4) as potential T2D candidates as they display a differential expression in pancreatic islets and/or sequence variation. In conclusion, the integration of comparative analysis of multiple inbred populations such as haplotype mapping, transcriptomics, and sequence data substantially improved the mapping resolution of the diabetes QTLNidd/DBA. Future studies are necessary to understand the exact role of the different candidates in beta-cell function and their contribution in maintaining glycemic control.},
  language  = {en}
}
@article{DelperoArendsSprechertetal.2022,
  author    = {Delpero, Manuel and Arends, Danny and Sprechert, Maximilian and Krause, Florian and Kluth, Oliver and Sch{\"u}rmann, Annette and Brockmann, Gudrun A. and Hesse, Deike},
  title     = {Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse},
  series = {International journal of obesity / North American Association for the Study of Obesity},
  volume    = {46},
  journal   = {International journal of obesity / North American Association for the Study of Obesity},
  number    = {2},
  publisher = {Nature Publ. Group},
  address   = {Avenel, NJ},
  issn      = {0307-0565},
  doi       = {10.1038/s41366-021-00991-3},
  pages     = {307 -- 315},
  year      = {2022},
  abstract  = {Background The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with impaired glucose metabolism using the obese lines BFMI861-S1 and BFMI861-S2, which are genetically closely related, but differ in several traits. BFMI861-S1 is insulin resistant and stores ectopic fat in the liver, whereas BFMI861-S2 is insulin sensitive. Methods In generation 10, 397 males of an advanced intercross line (AIL) BFMI861-S1 x BFMI861-S2 were challenged with a high-fat, high-carbohydrate diet and phenotyped over 25 weeks. QTL-analysis was performed after selective genotyping of 200 mice using the GigaMUGA Genotyping Array. Additional 197 males were genotyped for 7 top SNPs in QTL regions. For the prioritization of positional candidate genes whole genome sequencing and gene expression data of the parental lines were used. Results Overlapping QTL for gonadal adipose tissue weight and blood glucose concentration were detected on chromosome (Chr) 3 (95.8-100.1 Mb), and for gonadal adipose tissue weight, liver weight, and blood glucose concentration on Chr 17 (9.5-26.1 Mb). Causal modeling suggested for Chr 3-QTL direct effects on adipose tissue weight, but indirect effects on blood glucose concentration. Direct effects on adipose tissue weight, liver weight, and blood glucose concentration were suggested for Chr 17-QTL. Prioritized positional candidate genes for the identified QTL were Notch2 and Fmo5 (Chr 3) and Plg and Acat2 (Chr 17). Two additional QTL were detected for gonadal adipose tissue weight on Chr 15 (67.9-74.6 Mb) and for body weight on Chr 16 (3.9-21.4 Mb). Conclusions QTL mapping together with a detailed prioritization approach allowed us to identify candidate genes associated with traits of the metabolic syndrome. In addition, we provided evidence for direct and indirect genetic effects on blood glucose concentration in the insulin-resistant mouse line BFMI861-S1.},
  language  = {en}
}