Christina Neuschl

Mapping of quantitative trait loci controlling lifespan in the short‐lived fish Nothobranchius furzeri– a new vertebrate model for age research

The African annual fish Nothobranchius furzeri emerged as a new model for age research over recent years. Nothobranchius furzeri show an exceptionally short lifespan, age‐dependent cognitive/behavioral decline, expression of age‐related biomarkers, and susceptibility to lifespan manipulation. In addition, laboratory strains differ largely in lifespan. Here, we set out to study the genetics of lifespan determination. We crossed a short‐ to a long‐lived strain, recorded lifespan, and established polymorphic markers. On the basis of genotypes of 411 marker loci in 404 F2 progeny, we built a genetic map comprising 355 markers at an average spacing of 5.5 cM, 22 linkage groups (LGs) and 1965 cM. By combining marker data with lifespan values, we identified one genome‐wide highly significant quantitative trait locus (QTL) on LG 9 (P < 0.01), which explained 11.3% of the F2 lifespan variance, and three suggestive QTLs on LG 11, 14, and 17. We characterized the highly significant QTL by synteny analysis, because a genome sequence of N. furzeri was not available. We located the syntenic region on medaka chromosome 5, identified candidate genes, and performed fine mapping, resulting in a c. 40% reduction of the initial 95% confidence interval. We show both that lifespan determination in N. furzeri is polygenic, and that candidate gene detection is easily feasible by cross‐species analysis. Our work provides first results on the way to identify loci controlling lifespan in N. furzeri and illustrates the potential of this vertebrate species as a genetic model for age research.

ATR-FTIR spectroscopy reveals genomic loci regulating the tissue response in high fat diet fed BXD recombinant inbred mouse strains

BackgroundObesity-associated organ-specific pathological states can be ensued from the dysregulation of the functions of the adipose tissues, liver and muscle. However, the influence of genetic differences underlying gross-compositional differences in these tissues is largely unknown. In the present study, the analytical method of ATR-FTIR spectroscopy has been combined with a genetic approach to identify genetic differences responsible for phenotypic alterations in adipose, liver and muscle tissues.ResultsMice from 29 BXD recombinant inbred mouse strains were put on high fat diet and gross-compositional changes in adipose, liver and muscle tissues were measured by ATR-FTIR spectroscopy. The analysis of genotype-phenotype correlations revealed significant quantitative trait loci (QTL) on chromosome 12 for the content of fat and collagen, collagen integrity, and the lipid to protein ratio in adipose tissue and on chromosome 17 for lipid to protein ratio in liver. Using gene expression and sequence information, we suggest Rsad2 (viperin) and Colec11 (collectin-11) on chromosome 12 as potential quantitative trait candidate genes. Rsad2 may act as a modulator of lipid droplet contents and lipid biosynthesis; Colec11 might play a role in apoptopic cell clearance and maintenance of adipose tissue. An increased level of Rsad2 transcripts in adipose tissue of DBA/2J compared to C57BL/6J mice suggests a cis-acting genetic variant leading to differential gene activation.ConclusionThe results demonstrate that the analytical method of ATR-FTIR spectroscopy effectively contributed to decompose the macromolecular composition of tissues that accumulate fat and to link this information with genetic determinants. The candidate genes in the QTL regions may contribute to obesity-related diseases in humans, in particular if the results can be verified in a bigger BXD cohort.

Genetic factors contributing to obesity and body weight can act through mechanisms affecting muscle weight, fat weight, or both

Genetic loci for body weight and subphenotypes such as fat weight have been mapped repeatedly. However, the distinct effects of different loci and physiological interactions among different traits are often not accounted for in mapping studies. Here we used the method of structural equation modeling to identify the specific relationships between genetic loci and different phenotypes influencing body weight. Using this technique, we were able to distinguish genetic loci that affect adiposity from those that affect muscle growth. We examined the high body weight-selected mouse lines NMRI8 and DU6i and the intercross populations NMRI8 x DBA/2 and DU6i x DBA/2. Structural models help us understand whether genetic factors affect lean mass and fat mass pleiotropically or nonpleiotropically. Sex has direct effects on both fat and muscle weight but also influences fat weight indirectly via muscle weight. Three genetic loci identified in these two crosses showed exclusive effects on fat deposition, and five loci contributed exclusively to muscle weight. Two additional loci showed pleiotropic effects on fat and muscle weight, with one locus acting in both crosses. Fat weight and muscle weight were influenced by epistatic effects. We provide evidence that significant fat loci in strains selected for body weight contribute to fat weight both directly and indirectly via the influence on lean weight. These results shed new light on the action of genes in quantitative trait locus regions potentially influencing muscle and fat mass and thus controlling body weight as a composite trait.

Multiple-trait QTL mapping for body and organ weights in a cross between NMRI8 and DBA/2 mice

Multiple-trait analyses have been shown to improve the detection of quantitative trait loci (QTLs) with multiple effects. Here we applied a multiple-trait approach on obesity- and growth-related traits that were surveyed in 275 F2 mice generated from an intercross between the high body weight selected line NMRI8 and DBA/2 as lean control. The parental lines differed 2.5-fold in body weight at the age of 6 weeks. Within the F2 population, the correlations between body weight and weights of abdominal fat weight, muscle, liver and kidney at the age of 6 weeks were about 0.8. A least squares multiple-trait QTL analysis was performed on these data to understand more precisely the cause of the genetic correlation between body weight, body composition traits and weights of inner organs. Regions on Chr 1, 2, 7 and 14 for body weights at different early ages and regions on Chr 1, 2, 4, 7, 14, 17 and 19 for organ weights at 6 weeks were found to have significant multiple effects at the genome-wide level.

Features of the metabolic syndrome in the Berlin Fat Mouse as a model for human obesity.

Background: The Berlin Fat Mouse BFMI860 is a polygenic obesity mouse model which harbors a natural major gene defect resulting in early onset of obesity. To elucidate adult bodily responses in BFMI860 mice that develop juvenile obesity, we studied features of the metabolic syndrome at 20 weeks. Methods: We examined fat deposition patterns, adipokines, lipid profiles in serum, glucose homeostasis, and insulin sensitivity in mice that were fed either a standard maintenance (SMD) or a high-fat diet (HFD). Results: Like many obese humans, BFMI860 mice showed hyperleptinemia accompanied by hypoadiponectinemia already at SMD that was further unbalanced as a result of HFD. Furthermore, BFMI860 mice had high triglyceride concentrations. However, triglyceride clearance after an oral oil gavage was impaired on SMD but improved on HFD. The oral and intraperitoneal glucose as well as the insulin tolerance tests provided evidence for reduced insulin sensitivity under SMD and insulin resistance on HFD. BFMI860 mice can maintain normal glucose clearance over a wide range of feeding conditions according to an adaptation via increasing the insulin concentrations. Conclusions: BFMI860 mice show obesity, dyslipidemia, and insulin resistance as three major components of the metabolic syndrome. As these mice develop the described phenotype as a result of a major gene defect, they are a unique model for the investigation of genetic and pathophysiological mechanisms underlying the observed features of the metabolic syndrome and to search for potential strategies to revert the adverse effects under controlled conditions.

RandoMate: a program for the generation of random mating schemes for small laboratory animals.

Advanced intercross lines (AIL) have proven to be a powerful tool in genetic research to map complex genetic traits. The advantage of AIL is the high enrichment of visible recombination events to fine map the position of the target gene. Therefore, AIL are generated under the avoidance of inbreeding. We developed an online software tool, RandoMate, that generates random mating schemes such that only animals from different families are paired. When animals have to be selected randomly for mating, RandoMate optimizes the mating scheme such that all families contribute equally to the next generation. RandoMate uses a divide-and-conquer algorithm to define a mating scheme without brother-sister matings for all animals of a generation. If not all animals can be considered for the next generation, the mating scheme maximizes the randomness of the occurrences of animals from their families to make the family contributions as equal as possible. RandoMate is freely available at http://www2.hu-berlin.de/RandoMate.

Genetic control of lipids in the mouse cross DU6i x DBA/2.

An F2 pedigree based on the mouse lines DU6i and DBA/2 with extremely different growth and obesity characteristics was generated to search for QTLs affecting serum concentrations of triglycerides (TG), total cholesterol (CHOL), HDL cholesterol (HDL-C), and LDL cholesterol (LDL-C). Compared with many other studies, we searched for spontaneous genetic variants contributing to high lipid levels under a standard breeding diet. Significant QTLs for CHOL were identified on chromosomes 4 and 6, and a female-specific locus on chromosome 3. QTLs for HDL-C were detected on chromosome 11 for both sexes, and on chromosome 1 for females. These QTLs are located in syntenic human regions that have QTLs that have not been previously confirmed in animal studies. LDL-C QTLs have been mapped for both sexes to chromosome 8 and in males on chromosome 13. Epistatic interactions that significantly accounted for the phenotypic variance of HDL-C, CHOL, and LDL-C serum concentrations were also detected with one interaction between chromosomes 8 and 15, accounting for 22% of the observed variance in LDL-C levels. The identified loci coincide in part with regions controlling growth and obesity. Thus, multiple genes or pleiotropic effects may be assumed. The identified QTLs for cholesterol and its transport proteins as subcomponents of risk for coronary heart disease will further improve our understanding of the genetic net controlling plasma lipid concentrations.

Relationship between obesity phenotypes and genetic determinants in a mouse model for juvenile obesity

Obesity, a state of imbalance between lean mass and fat mass, is important for the etiology of diseases affected by the interplay of multiple genetic and environmental factors. Although genome-wide association studies have repeatedly associated genes with obesity and body weight, the mechanisms underlying the interaction between the muscle and adipose tissues remain unknown. Using 351 mice (at 10 wk of age) of an intercross population between Berlin Fat Mouse Inbred (BFMI) and C57BL/6NCrl (B6N) mice, we examined the causal relationships between genetic variations and multiple traits: body lean mass and fat mass, adipokines, and bone mineral density. Furthermore, evidence from structural equation modeling suggests causality among these traits. In the BFMI model, juvenile obesity affects lean mass and impairs bone mineral density via adipokines secreted from the white adipose tissues. While previous studies have indicated that lean mass has a causative effect on adiposity, in the Berlin Fat Mouse model that has been selected for juvenile obesity (at 9 wk of age) for >90 generations, however, the causality is switched from fat mass to lean mass. In addition, linkage studies and statistical modeling have indicated that quantitative trait loci on chromosomes 5 and 6 affect both lean mass and fat mass. These lines of evidence indicate that the muscle and adipose tissues interact with one another and the interaction is modulated by genetic variations that are shaped by selections. Experimental examinations are necessary to verify the biological role of the inferred causalities.

Positional cloning of diabetes genes.

Several mouse strains are diabetic already at the juvenile age or develop diabetes mellitus during their life. Before these strains become diabetic, they often show several or all features of the metabolic syndrome, which is very similar to the etiology of diabetes in humans. Under the assumption that natural mutations are responsible for the development of diabetes in those mouse strains, they are valuable resources for the identification of diabetes genes and modifiers. Usually, several steps are necessary to detect the causative genes in the genome. These include the initial identification of the genomic regions contributing to the disease which is typically done by linkage mapping in an F(2) intercross or backcross population, fine mapping of the identified chromosomal interval to narrow down the target region carrying the causative genetic variation and subsequent functional and genetic characterization of the target gene or a small subset of genes. Here, we give a general overview on genetic models and the strategy for identifying diabetes genes and provide a specific protocol for the mapping and fine mapping of chromosomal regions carrying diabetes genes.