Implications of diet and exercise with interaction of allelic variations in the Berlin Fat Mouse line


Genetic variations as well as environmental impacts like diet and exercise affect the fat deposition of an organism. This project addresses both aspects in two different approaches: Within the first approach, methods for the detailed in silico characterization of mutations were developed while the second approach investigated the influence of exercise and diet on fat deposition in a genetically predisposed mouse model. To characterize genetic variations in silico, the software-tools CandiSNPer and NovelSNPer have been developed. CandiSNPer charakterizes SNPs (single nucleotide polymorphism) that were found statistically significantly associated with a trait of interest in genome-wide association studies. In a first step, this tool finds SNPs which are in linkage disequilibirum with the given SNP (start-SNP). In a second step, all reference SNPs are listed with the annotation such as chromosomal position, type of SNP (e.g. startgain, startloss SNPs), nearest gene, conservation score. Thus, the potential effect of the SNP on the phenotype can be assessed. The software-tool NovelSNPer characterizes SNPs as well as insertions and deletions from Next-Generation-Sequencing datasets. Due to the automatic monitoring of databases, NovelSNPer decides in a very short time period if found variants are already represented in standard databases or if they are novel. To gain further insight into the relationship between significant and causal SNPs, we had a closer look onto all SNPs that were identified as associated with a trait in 600 studies as of June 2010. We found that non-synonymous SNPs were four times as often associated with a trait as expected according to their abundance on standard genotyping platforms. Putatively causal SNPs were, furthermore, often found in membrane associated genes and in signal transduction genes. In the second part of the project, the influences of the onset of continuous exercise (from the childhood or early adulthood) on body composition and metabolic parameters were investigated. The investigations were performed with the Berlin Fat Mouse Inbred (BFMI) line. BFMI mice show a higher fat deposition in adipose tissues as well as in organs due to its genetic predisposition. Besides the high fatness, this mouse line exhibits features of the metabolic syndrome such as high insulin and triglyceride concentrations and a lowered glucose tolerance. Although the motivation for BFMI mice to exercise is relatively low, exercise from childhood on reduced genetically determined fat deposition on both, a high-fat and a standard diet, and led to diminished insulin, triglyceride and cholesterol concentrations in adulthood. The energy intake was not elevated due to exercise during childhood. Onset of exercise from early adulthood on increased daily energy intake. Hence, within this group body weight and fat content was stabilized but not reduced. Nevertheless, the animals had an improved glucose tolerance in comparison to the mice with exercise from childhood or control mice without exercise. This led to the assumption that the impact of exercise on glucose homeostasis decreases as soon as the animal is adapted to physical activity. Despite the different effect of the onset of exercise, the average daily running activity was the same in all animals. In cooperation with the Proteomcenter of the Ruhr-Universität Bochum, the effect of the exercise on standard diet was examined in muscle and adipose tissue in a proteome analysis. In the muscular tissue, only few differences could be detected in the proteome between the exercise groups (about 1% differentially expressed proteins). The differences were mainly detected in proteins belonging to the metabolism. In the adipose tissue, clear differences were found between the onset of the exercise at childhood compared to onset from adulthood and the control group. More than 300 differentially expressed protein spots could be detected (about 10% of the protein spots), which was in part due to the elevated number of antigen-presenting cells (e.g. macrophages).


Principal investigators
Brockmann, Gudrun A. Prof. Dr. habil. rer. nat. (Details) (Breeding Biology and Molecular Animal Breeding)

Financer
BMBF

Duration of project
Start date: 06/2008
End date: 12/2011

Research Areas
Fettansatz, genetische Varianten, Prävention, Bioinformatik

Publications
Aßmus J, Schmitt AO, Bortfeldt RH, Brockmann GA (2011). NovelSNPer: A fast tool for the identification and characterization of novel SNPs and InDels. Adv Bioinformatics 2011:657341. PMID: 22110502.

Brockmann GA, Tsaih SW, Neuschl C, Churchill GA, Li R. Genetic factors contributing to obesity and body weight can act through mechanisms affecting muscle weight, fat weight, or both. Physiol Genomics. 2009 Jan 8;36(2):114-126. PMID: 18984673.

Cox A, Ackert-Bicknell CL, Dumont BL, Ding Y, Bell JT, Brockmann GA, Wergedal JE, Bult C, Paigen B, Flint J, Tsaih SW, Churchill GA, Broman KW (2009). A new standard genetic map for the laboratory mouse. Genetics 182(4):1335-1344. PMID: 19535546.

Günther T, Schmitt AO, Bortfeldt RH, Hinney A, Hebebrand J, Brockmann GA (2011).Where in the genome are significant single nucleotide polymorphisms from genome-wide association studies located? OMICS 15(7-8):507-512. PMID: 21699402.

Hageman RS, Wagener A, Hantschel C, Svenson KL, Churchill GA, Brockmann GA (2010). High-fat diet leads to tissue-specific changes reflecting risk factors for diseases in DBA/2J mice. Physiol Genomics 42(1):55-66. PMID: 20215417.

Hantschel C, Wagener A, Neuschl C, Teupser D, Brockmann GA (2011). Features of the metabolic syndrome in the Berlin Fat Mouse as a model for human obesity. Obes Facts 4(4):270-277. PMID: 21921649.

Meyer CW, Wagener A, Rink N, Hantschel C, Heldmaier G, Klingenspor M, Brockmann GA (2009). High energy digestion efficiency and altered lipid metabolism contribute to obesity in BFMI mice. Obesity (Silver Spring) 17(11):1988-1993. PMID: 19390516.

Neuschl C, Hantschel C, Wagener A, Schmitt AO, Illig T, Brockmann GA (2010). A unique genetic defect on chromosome 3 is responsible for juvenile obesity in the Berlin Fat Mouse. Int J Obes (Lond) 34(12):1706-1714. PMID: 20498659.

Schäfer N, Wagener A, Hantschel C, Mauel S, Gruber AD, Brockmann GA (2011). IGF-I contributes to glucose homeostasis in the Berlin Fat Mouse Inbred line. Growth Factors 29(6):298-309. PMID: 22023218.

Schmitt AO, Al-Hasani H, Cheverud JM, Pomp D, Bünger L, Brockmann GA (2007). Finemapping of mouse QTLs for fatness using SNP data. OMICS. 11(4):341-350. PMID: 18092907.

Schmitt AO, Assmus J, Bortfeldt RH, Brockmann GA (2010). CandiSNPer: a web tool for the identification of candidate SNPs for causal variants. Bioinformatics 26(7):969-970. PMID: 20172942.

Schmitt AO, Dempfle A, Brockmann GA (2007). Deletions in the genomes of fifteen inbred mouse lines and their possible implications for fat accumulation. JZhejiang Univ Sci B 8(11):777-781. PMID: 17973337.

Wagener A, Goessling HF, Schmitt AO, Mauel S, Gruber AD, Reinhardt R, Brockmann GA (2010). Genetic and diet effects on Ppar-α and Ppar-γ signaling pathways in the Berlin Fat Mouse Inbred line with genetic predisposition for obesity. Lipids Health Dis 10;9:99. PMID: 20831792.

Wagener A, Schmitt AO, Aksu S, Schlote W, Neuschl C, Brockmann GA (2006). Genetic,sex, and diet effects on body weight and obesity in the Berlin Fat Mouse Inbred lines. Physiol Genomics 27(3):264-270. PMID:16912068.

Widiker S, Karst S, Wagener A, Brockmann GA (2010). High-fat diet leads to a decreased methylation of the Mc4r gene in the obese BFMI and the lean B6 mouselines. J Appl Genet 51(2):193-197. PMID: 20453306.

Last updated on 2022-09-09 at 01:07