Type 2 diabetes mouse model TallyHo carries an obesity gene on chromosome 6 that exaggerates dietary obesity

Jung Han Kim1, Taryn P. Stewart1, Weidong Zhang2, Hyoung Yon Kim1, Patsy M. Nishina2 and Jürgen K. Naggert2

1 Department of Nutrition, The University of Tennessee, Knoxville, Tennessee
2 The Jackson Laboratory, Bar Harbor, Maine


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
The TallyHo (TH) mouse strain is a polygenic model for Type 2 diabetes with obesity. Genetic analysis in backcross progeny from a cross between F1 [C57BL/6J (B6) x TH] and TH mice mapped a quantitative trait locus (QTL) named TH-associated body weight 2 (tabw2) to chromosome 6. The TH-derived allele is associated with increased body weight. As a first step to identify the molecular basis of this obesity QTL, we constructed a congenic line of mice on the B6 genetic background that carries a genomic region from TH mice containing tabw2. Congenic mice homozygous for tabw2 (B6.TH-tabw2/tabw2) fed a chow diet exhibited slightly, but significantly, higher body weight and body fat and plasma leptin levels compared with controls (B6.TH-+/+). This difference was exacerbated when the animals were maintained on a high-fat and high-sucrose (HFS) diet. The diet-induced obesity in tabw2 congenic mice is accompanied by hyperleptinemia, mild hyperinsulinemia, impaired glucose tolerance, and reduced glucose uptake in adipose tissue in response to insulin administration. Using F2 progeny fed a HFS diet from an intercross of B6.TH-tabw2/+ mice, we were able to refine the map position of the tabw2 obesity susceptibility locus to a 15-cM region (95% confidence interval) extending distally from the marker D6Mit102. In summary, tabw2 congenic mice are a new animal model for diet-induced obesity that will be valuable for the study of gene-diet interactions.

diabesity; quantitative trait loci; gene-diet interactions; congenic mice


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
CURRENTLY, the prevalence of overweight and obesity, defined as a body mass index [BMI; body weight (in kg)/height (in m2)] > 25, is about 64% of adults in the United States (1999–2000 National Health and Nutrition Examination Survey, www.cdc.gov/nchs/products/pubs/pubd/hestats/obese/obse99.htm). The genetic contribution to human obesity has been appreciated through twin, adoption, and family studies, demonstrating that an individual with obese relatives has a higher risk for being obese than individuals that do not have a family history of obesity (29, 50, 51). Estimates from twin studies suggest that 50–70% of the total variance in BMI can be accounted for by genetic factors (1). The incidence of obesity has increased worldwide in parallel with industrialization, which has been accompanied by improved availability of food, especially foods high in fat and sucrose content (37). The obesity epidemic, therefore, may reflect the interactions between existing genetic susceptibility factors in predisposed individuals and an obesity-promoting environment (3, 6), such as high-fat and high-sucrose (HFS) diets. The participation of gene-diet interactions in the regulation of body weight is suggested by the wider distribution of BMI in energy-enriched environments compared with energy-restricted environments (38).

In light of the challenges that genetic studies of obesity pose in human populations, animal models have been used that allow for control of the environment and the genetic makeup of the study population. Over 100 loci related to obesity have been genetically mapped in mice using quantitative trait loci (QTLs) mapping approaches (48). These QTLs are thought to more closely mimic the presumed polygenic inheritance of obesity in humans. However, the molecular identification of these loci has not been straightforward, leaving the causative genes unidentified.

Gene-diet interactions in the development of obesity are also observed in animal models. For instance, certain inbred strains of mice become markedly obese when fed high-fat diets, whereas others do not gain weight (13, 35, 53, 54, 66). Several loci linked to high-fat diet-induced obesity have been localized through QTL mapping using inbred mouse strains. Again, the specific genes underlying these QTLs remain unknown. Although polygenic mouse models of obesity may closely mimic the genetics of human obesity, they still pose many of the same problems for identification of the underlying genes. However, in the case of animal models, polygenic traits can be reduced to simpler monogenic traits through the development of congenic strains (43), and the effects of each gene can then be studied in isolation. In this simpler system, conventional genetic mapping and positional cloning methods can be applied for gene identification.

The TallyHo (TH) mouse strain is a newly established polygenic model for Type 2 diabetes with obesity (20). TH mice are characterized by insulin resistance, hyperinsulinemia, hyperglycemia (in males), obesity, and dyslipidemia associated with increased triglyceride, free fatty acid, and cholesterol levels. Although they are obese, TH female mice are normally not hyperglycemic. It has been demonstrated, however, that the TH Y chromosome (Chr) does not carry an allele necessary for the development of hyperglycemia in TH male mice (20). Gender dimorphism for diabetes is commonly observed in mice (19, 27, 28), and protection from developing hyperglycemia in female mice has been attributed to low hepatic estrogen sulfotransferase activity (26). Conversely, elevated levels of estrogen sulfotransferase activity in females of some mouse strains are associated with an androgenized state of liver metabolism and the development of hyperglycemia (17).

In the present study, we mapped a QTL linked to body weight and generated congenic mice carrying the obesity QTL derived from the TH strain on a C57BL/6J (B6) genetic background. The congenic mice develop obesity, which is exacerbated by HFS diet feeding. A genetic analysis using the congenic mice has been carried out and used to refine the obesity QTL map location on Chr 6.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Animals
All mice were allowed free access to food and water in a temperature- and humidity-controlled room with a 12:12-h light-dark cycle. Mice were weaned onto standard rodent chow [4% fat, Harlan Teklad Rodent Diet (W) 8604, Harlan Teklad; Madison, WI]. At 4 wk of age, experimental mice were either fed a HFS diet (32% kcal from fat and 25% kcal from sucrose, no. 12266B, Research Diets; New Brunswick, NJ) or chow for 10 wk. All animal studies were carried out with the approval of The University of Tennessee Animal Care and Use Committee. Mice were euthanized by CO2 asphyxiation.

QTL Mapping for Obesity in TH Mice
Previously, we conducted a genome-wide genetic study for hyperglycemia and obesity in TH mice using backcross (BC) male mice obtained from a cross between F1 female (B6 female x TH male) and TH male mice (20). The first cohort of 69 BC male mice from the cross was genotyped with 64 simple sequence length polymorphism (SSLP) markers spanning the genome and phenotyped for plasma glucose levels and body weights. Details in genetic crosses and phenotyping have been described by Kim et al. (20). The computer program MapManager QT (31) was used for QTL mapping analysis using the 69 BC male mice.

Construction of Congenic Mice
Mice congenic for the obesity QTL [TH-associated body weight 2 (tabw2)] genomic region were generated by marker-assisted backcrossing (42). First, B6 female and TH male mice were crossed to yield F1 (or N1) progeny, which were then backcrossed to B6 mice. The resulting N2 progeny were genotyped with flanking SSLP markers to select heterozygotes for the QTL interval, which were then again backcrossed to B6 mice. This procedure was repeated for 10 cycles of backcrossing at which point 2 heterozygotes were intercrossed to yield offspring that were either homozygous for TH alleles [B6.TH-tabw2/tabw2 (tabw2)] or homozygous for B6 alleles [wild-type (WT) B6.TH-+/+]. Homozygous mice were then interbred to maintain the congenic lines. The congenic segment was ~46.4 cM [Mouse Genome Database (MGD)] in length, extending from the marker locus D6Mit273 to D6Mit339 (Fig. 1).



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Fig. 1. Interval mapping for body weights (20 wk) in 69 male progeny from a F1 [C57BL/6J (B6) x TallyHo (TH)] x TH backcross using the MapManager QT program. The numbers on the top of the vertical dotted lines indicate the F-ratio statistics for the 90th and 95th percentile derived from 1,000 random permutations of the experimental data on the genotypes at 1-cM spacing. The congenic region is indicated on the left. The genetic positions (in cM) of marker loci were based on the Mouse Genome Database (MGD), and the physical distances (in Mb) between markers were based on Ensemble. Chr, chromosome.

 
Genotyping
Genomic DNA was isolated from tail tips of mice using proteinase K and two series of salt precipitation steps (7, 49). SSLP markers (MapPairs, Mouse Markers, Invitrogen; Carlsbad, CA) were assayed after PCR amplification of genomic DNA by 3% metaphor (no. 50184, FMC; Rockland, ME) and 1% agarose (no. 0710-500G, Amresco; Solon, OH) gel electrophoresis and visualized with ethidium bromide (no. E-1510, Sigma; St. Louis, MO) staining (43).

Body Weight Gain and Fat Pad Weight in tabw2 Congenic Mice
Mice were weaned at 4 wk of age and fed the HFS diet or chow for 10 wk. Body weights were measured daily. At the end of the study, mice were euthanized, five white fat pads [inguinal, epididymal, mesenteric, retroperitoneal (including perirenal), and subscapular] were dissected and weighed, and carcass weights (body weight without the five fat pads) were measured.

Plasma Glucose, Insulin, and Leptin Levels
Blood was drawn between 7:30 and 10:30 AM from the retroorbital plexus using heparinized microcapillary tubes (no. 22-362-566, Fisher Scientific; Pittsburgh, PA), and plasma was obtained by centrifugation (1,200 g) at 4°C. Plasma glucose levels were determined using a commercial colorimetric kit (no. 635-100, Sigma). Plasma insulin and leptin levels were determined using RIA kits (nos. RI-13K and RL-83K, respectively, Linco Research; St. Charles, MO).

Intraperitoneal Glucose Tolerance Test
Mice were fasted overnight and injected intraperitoneally with glucose (1 mg/g body wt) in saline. Blood was collected via the retroorbital plexus using a heparinized microcapillary tube at 0, 30, 60, and 120 min after injection. Plasma glucose levels were then determined using a commercial colorimetric kit as described above.

Tissue Glucose Uptake
Mice fasted overnight were injected intravenously through the tail vein with a bolus of 2-deoxy-D-glucose-1, 2-[3H](N) [2-DG; 10 µCi/mouse (69), no. D-4539, Sigma] in saline either with or without insulin (0.1 units, no. I-5500, Sigma) and euthanized 30 min after injection. Epididymal fat pads were then collected, washed, blot dried, weighed, and dissolved in 1 M NaOH at 60°C. Incorporated radioactivity was counted in a scintillation counter (LS3801, Beckman; Fullerton, CA). Uptake of 2-DG was expressed as counts per minute divided by protein content.

Tissue Protein Analysis
Tissue was homogenized in Krebs-Ringer bicarbonate buffer (no. K4002, Sigma). Protein content of homogenates was determined by the modified micro-Lowry method using a commercial kit with bovine serum albumin as a standard (no. 690-A, Sigma).

Genetic Fine Mapping of the tabw2 Locus
To map the tabw2 locus more precisely, we mated WT mice with tabw2 mice, and the resultant F1 progeny were interbred to generate an F2 population. The F2 mice were then fed the HFS diet for 10 wk beginning at 4 wk of age. Mice were weighed weekly and euthanized after the 10-wk diet treatment. The five white fat pads described above and the brown fat pad were dissected and weighed, and carcass weights were measured as described above. Genomic DNA samples were prepared from all F2 mice as described above and genotyped with SSLP markers including D6Mit273, -93, -29, -102, -108, and -339. DNAs from recombinant animals were further genotyped with 16 additional SSLP markers located within the congenic region. These included D6Mit188, -209, -320, -246, -5, -210, -129, -162, -321, -228, -248, -40, -263, -362, -100, and -284. A QTL mapping analysis was then carried out in this F2 population by the method developed by Sen and Churchill (45) to fine map the tabw2 locus (see below).

QTL Mapping Using F2 Progeny From an Intercross of F1 (B6.TH-+/+ x B6.TH-tabw2/tabw2) Mice
The QTL analysis using F2 mice from an intercross of F1 (B6.TH-+/+ x B6.TH-tabw2/tabw2) mice was carried out to refine the tabw2 map position using a statistical package, Pseudomarker, written in MATLAB (Mathworks; Natick, MA) (45). The software can be downloaded from http://www.jax.org/sta/churchill/labsite/index.html. The pseudomarker approach uses multiple imputations of the genotypes on a regular grid of genome-wide locations conditional on the observed marker data. The logarithm of odds (LOD) score for each marker location was calculated by averaging the LOD scores over imputations. Pairwise Pearson correlations were calculated for all phenotypes.

Main scans.
The main scan assumes a one-QTL model and scans through each pseudomarker location to detect whether there is a QTL present. To refine the tabw2 map position, initial QTL analysis was performed scanning at 5-cM spacing for each phenotype with litter size as an additive covariate. One thousand permutations of the phenotype values were used to determine 63%, 10%, 5%, and 1% threshold values (10, 24).

Pairwise scans.
The pairwise scan assumes a two-QTL model and scans through pairs of pseudomarkers. Analysis using pairwise scans at 5-cM spacing was also performed for each phenotype. All possible pairs of QTL locations on each location were tested for association with the phenotype. The likelihood from the full model (pseudomarker pair and the interaction between them) and the null model (no genetic effect) were compared, and LOD scores were calculated. In addition, LOD scores from comparing the likelihood from the full model and the additive model (with only the main effects of pseudomarkers but no interaction) were also calculated.

Stepwise regression models.
One can identify QTLs and possible covariates that explain the variations of the phenotype under study by the main scans and pairwise scans. The multiple regression model takes into account QTLs, covariates, QTL-QTL interactions, and QTL-covariate interactions. Type III sum of squares and P values were calculated for all terms in the regression model. Terms were dropped sequentially until all of the terms in the model were significant at the 5% level. The final model was used to determine whether any QTL, covariate, or QTL-QTL interaction made any contribution to cause the variations for the phenotype under study.

Confidence interval of QTL.
The posterior probability density (PPD) for QTL locations was estimated. Confidence intervals were constructed based on the density distribution of each QTL (45).

Principal component analysis.
Principal component analysis is a method for dimensionality reduction in data sets containing multiple correlated variables (62). Principal component analysis decomposes the correlated phenotypes into independent components, with the first principal component capturing the largest proportion of the variance underlying the correlated phenotypes. The first principal component was used as a new phenotype and the main scans, pairwise scans and stepwise regressions were performed on this new phenotype.

RT-PCR and Sequencing of the Histamine Receptor H1 (Hrh1) Gene
Total RNA was extracted from the whole brain using the RNeasy kit (no. 75842, Qiagen; Valencia, CA). The RNA (10 µg) was reverse transcribed with Superscript RT (no. 11904-018, Invitrogen; Carlsbad, CA) using oligo d(T)12–18 as a primer according to the manufacturer's instructions. Single-strand cDNA was diluted with water [1:5 (vol/vol)], and 2 µl of the diluate were used to amplify histamine receptor H1 (Hrh1) gene cDNA by the Expand Long Template PCR System (no. 1681842, Roche; Indianapolis, IN) using primers F1 (5'-ACTGGAGGCTGCCCTTGTG-3') and R1 (5'-CCCTCTTGGACATCAGACGTT-3') derived from the Hrh1 coding region sequences (Genbank NM_008285). The PCR products were purified (no. K3051-2, Clontech; Palo Alto, CA) after agarose gel electrophoresis and directly sequenced (Prism, Applied Biosystems; Foster City, CA) with the primers originally used in the PCR.

Statistical Analysis for Phenotypic Characterization
Data analysis was conducted by ANOVA with StatView 5.0 (Abacus Concepts; Berkeley, CA). All data are presented as means ± SE.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Mapping of Tabw2 in BC Progeny From F1 (B6 x TH) x TH Mice
During the initial phases of mapping TH diabesity (obesity and diabetes) susceptibility loci in 69 male BC progeny from an F1 (B6 x TH) x TH backcross (20) using the MapManager QT program, we noted a QTL on Chr 6 (at marker D6Mit230) with a modest effect on plasma glucose levels, accounting for 11% of the phenotypic variance (Table 1). Mice homozygous for the TH allele exhibited higher plasma glucose levels than mice heterozygous for B6 and TH alleles. This locus, however, showed a more significant effect on body weight in the backcross, accounting for 16% of the phenotypic variance, and again the TH allele was associated with higher body weights (Table 1). Interval mapping and permutation tests using the MapManager QT program placed the peak F score for the body weight QTL between markers D6Mit102 and D6Mit327 (Fig. 1). This body weight QTL was named tabw2 after the originally reported body weight QTL, tabw (20). Interestingly, tabw2 appears to interact with the major diabetes QTL TH-associated non-insulin-dependent diabetes 1 (tanidd1) on Chr 19 reported in Kim et al. (20) to increase plasma glucose levels; mice that are homozygous for TH alleles at both tabw2 and tanidd1 loci have significantly higher plasma glucose levels than animals that are heterozygous at both loci or animals that are heterozygous at the tabw2 locus but homozygous for the TH allele at the tanidd1 locus (Table 2).


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Table 1. Plasma glucose levels (20–26 wk) and body weights (20 wk) at marker D6Mit230 in BC male progeny from a cross between F1 (B6 x TH) and TH mice

 

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Table 2. Plasma glucose levels for the given genotypes at tabw2 (at marker D6Mit230) and tanidd1 (at marker D19Mit103) obtained from BC male progeny from a cross between F1 (B6 x TH) and TH mice

 
Mice Congenic for the Tabw2 Region Became Obese
A congenic line on the B6 genetic background was established by introgressing, for 10 generations, a TH-derived genomic fragment defined by the flanking markers D6Mit273 and D6Mit339 (Fig. 1). At the 10th backcross generation, mice were interbred to obtain experimental animals, tabw2 and WT mice, for characterization.

Four-week-old male tabw2 and WT mice were fed chow or the HFS diet for 10 wk. At 4 wk of age, tabw2 mice were slightly, but significantly, heavier than WT mice [15.0 ± 0.35 g (n = 10) vs. 13.4 ± 0.44 g (n = 9), means ± SE, P = 0.004], and the higher body weight in tabw2 mice was still maintained after 14 wk on a chow diet (Table 3). Both tabw2 and WT congenic mice gained more weight when the animals were fed the HFS diet than when fed chow, but the weight gain was greater in tabw2 than WT mice (Fig. 2). The body weight differences were accounted for by an increase in fat depots (assessed by dissecting and weighing the five white fat pads) rather than in lean body mass (assessed by subtracting the weight of the five fat pads from total body weight) both on chow and HFS diet feeding (Table 3).


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Table 3. Body and fat pad weights and plasma profiles in congenic mice fed chow or a HFS diet for 10 wk beginning at 4 wk of age (male, nonfasting)

 


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Fig. 2. Body weights in response to dietary regimen. Mice were individually housed at 4 wk of age and fed either chow or high-fat and high-sucrose (HFS) diets for 10 wk. Body weights were measured daily. Squares and triangles represent B6.TH-tabw2/tabw2 (tabw2) mice and B6.TH-+/+ [wild-type (WT)] mice, respectively. Open and filled symbols represent chow and HFS diets, respectively (male, n = 4–6 for each group).

 
Hyperleptinemia, Hyperinsulinemia, and Impaired Glucose Tolerance and Glucose Uptake in Tabw2 Congenic Mice Fed HFS Diet
Obesity in tabw2 mice was accompanied by hyperleptinemia on both chow and HFS diets (Table 3). Tabw2 mice also showed mild hyperinsulinemia and mild hyperglycemia when fed the HFS diet (Table 3). In addition, upon HFS diet feeding, tabw2 mice exhibited impaired glucose tolerance indicating some degree of insulin resistance in these mice (Fig. 3). Because glucose uptake is the first step of glucose metabolism activated by insulin in the peripheral tissues, we assessed glucose uptake in tabw2 mice fed the HFS diet to identify potential alterations in peripheral insulin action. Uptake of 2-DG, a glucose analog that is transported into cells and phosphorylated but not metabolized, was examined in vivo in epididymal fat. Whereas 2-DG uptake was similar between WT and tabw2 mice in the basal state, it was attenuated in tabw2 mice in response to insulin (Fig. 4).



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Fig. 3. Glucose tolerance tests at 14–16 wk of age in tabw2 and WT mice fed either chow or HFS diets beginning at 4 wk of age. Mice were fasted overnight and injected intraperitoneally with glucose (1mg/g body wt) in saline. Blood was obtained at times of 0, 30, 60, and 120 min. Squares and triangles represent tabw2 mice and WT mice, respectively. Open and filled symbols represent chow and HFS diets, respectively (male, n = 6–8 for each group).

 


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Fig. 4. In vivo 2-deoxy-D-glucose 1, 2-[3H] (N) (2-DG) uptake in epididymal fat in WT (A) and tabw2 (B) mice fed a HFS diet (male, 14 wk, n = 4–5). Mice were fasted overnight and injected with a bolus of 2-DG in saline without (W/O) or with insulin (0.1 units), and tissue was harvested 30 min after the injection. 2-DG uptake is expressed as counts per minute (CPM) normalized by tissue protein content. *P < 0.05.

 
Refinement of the Tabw2 Map Position
To fine map tabw2, tabw2 mice were crossed to WT mice, and the F1 generation was intercrossed. The resulting F2 progeny were fed the HFS diet to take advantage of the larger phenotypic difference observed in the parental strains on the HFS diet compared with chow. In an earlier stage of the study, after collecting 70 female and 79 male F2 progeny, we observed a more distinctive segregation of obesity in F2 males than in females (not shown). We, therefore, continued the study using only male F2 progeny. A total of 430 F2 male progeny was collected and phenotyped for body weight, individual white fat pad weights, sum of the white fat pad weights, carcass weight, and brown fat pad weight. All F2 animals were first genotyped for 6 SSLP markers spanning the congenic region (D6Mit273, -93, -29, -102, -108, and -339). F2 animals homozygous for TH alleles at all six marker loci exhibited significantly higher body weights and sums of white fat pad weights than heterozygous mice or mice homozygous for B6 alleles, indicating that the tabw2 QTL acts recessively (Fig. 5). F2 animals that were recombinant within the interval were further genotyped with 16 additional SSLP markers including D6Mit188, -209, -320, -246, -5, -210, -129, -162, -321, -228, -248, -40, -263, -362, -100, and -284. QTL mapping analysis (45) was used to refine the tabw2 map position. All obesity-related phenotypes were highly correlated in the 430 F2 animals (appendix a). There was also a significant negative correlation of all phenotypes with litter size (appendix a). For this reason, litter size was introduced as a covariate in the regression analysis, and the corresponding LOD scores are presented in Table 4. Because the phenotypes were highly correlated, we conducted a principal component analysis. The analysis showed that 91.9% of the variation for all traits is summarized by the first principal component. Therefore, we used the first principal component as a new phenotype in the main scan analysis, and the resulting LOD score and PPD plots are presented in Fig. 6. The tabw2 QTL was confirmed with high significance within the congenic interval, and the 95% confidence interval placed tabw2 in a ~15-cM region extending distally from the marker D6Mit102 located at 38.5 cM on Chr 6 (MGD) (Fig. 6).



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Fig. 5. Allelic effects on body weight and fat pad weights. Body weight (A), sum of 5 dissected white fat pad weights [inguinal, epididymal, mesenteric, retroperitoneal (including perirenal), and subscapular] (B), and brown fat pad weight (C) in nonrecombinant F2 male progeny from an intercross of F1 (B6.TH-tabw2/tabw2 x B6.TH-+/+) mice for the markers spanning the congenic region (D6Mit273, -93, -29, -102, -108, and -339). B and T represent B6 and TH like alleles, respectively. *P < 0.05 vs. B/B; **P < 0.001 vs. B/B and B/T; ***P < 0.0001 vs. B/B and B/T.

 

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APPENDIX A Pairwise Correlation Matrix for the Measured Phenotypes of 430 Male F2 Mice from an Intercross of F1 (B6.TH-tabw2/tabw2 x B6. TH-+/+) Mice

 

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Table 4. Results from the stepwise regression analysis for all phenotypes in 430 F2 male mice from an intercross of F1 (B6.TH-tabw2/tabw2 x B6.TH-+/+) mice

 


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Fig. 6. Logarithm of odds (LOD) scores and posterior probability density (PPD) curves for the principal component determined in 430 F2 male mice from an intercross of F1 (B6.TH-tabw2/tabw2 x B6.TH-+/+) mice. The physical distance of a ~15-cM region extending distally from the marker D6Mit102 is 24.7 Mb (Ensemble). The horizontal shaded line above the x-axis represents the 95% confidence interval for this quantitative trait locus.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
In the present study, we mapped a new obesity QTL, tabw2, to Chr 6 in the TH mouse model of Type 2 diabetes with obesity (Fig. 1). The existence of tabw2 was confirmed by construction of a congenic line of mice, which is a useful strategy for dissecting complex traits and studying the phenotypic effects of QTL individually (5, 34). The tabw2 effect on body weight was accompanied by hyperleptinemia and was exaggerated by feeding a HFS diet (Table 3 and Fig. 2).

Leptin is a hormone secreted from adipose tissue, and the amount of leptin produced is strongly correlated with the lipid content of adipocytes (25). Therefore, leptin is thought to play a role as a negative feedback signal in response to increased fat storage (16, 25, 44). Through binding to its receptor in the hypothalamus, leptin can regulate food intake and energy expenditure by stimulating the effects of anorexogenic neuropeptides, such as melanocyte-stimulating hormone and corticotropin-releasing hormone, and reducing those of orexigenic neuropeptides, such as neuropeptide Y (16). Because obesity is typically associated with high leptin levels, the existence of leptin resistance in obese states has been suggested (33). The sum of the five fat pad weights and plasma leptin levels were increased ~1.9- and 2.8-fold, respectively, in tabw2 congenic mice compared with WT controls fed chow (Table 3). Increased plasma leptin levels may indicate leptin resistance and the existence of intrinsic defects in leptin signaling or leptin transport associated with tabw2. Alternatively, tabw2 may directly attenuate energy expenditure or promote adipogenesis and lipogenesis causing fat mass augmentation, which in turn would increase plasma leptin levels as a negative regulatory mechanism and lead to a downregulation of the cellular response to leptin caused by prolonged stimulation. The latter hypothesis might also apply to the leptin resistance shown in WT mice fed a HFS diet compared with chow (Table 3) (55, 65).

When fed a HFS diet, tabw2 congenic mice developed insulin resistance as evidenced by a mild hyperinsulinemia, impaired glucose tolerance, and reduced 2-DG uptake in epididymal fat in response to insulin administration, phenotypes not observed in WT mice fed the same diet (Table 3 and Figs. 3 and 4). It has been known that high adiposity is negatively associated with whole body insulin sensitivity in healthy individuals (8), and weight gain substantially increases the risk for Type 2 diabetes among overweight adults (39). Because tabw2 mice fed chow did not develop insulin resistance, the observed characteristics of insulin resistance in tabw2 mice fed the HFS diet may be a consequence of the increased adiposity. The adipose mass of tabw2 mice fed the HFS diet may reach a threshold for abnormal insulin action. Studies examining double congenic mice carrying both tabw2 and the Chr 19 QTL for diabetes [tannid1 (20)] fed a HFS diet will provide an opportunity to further investigate the link between obesity and the onset of diabetes in TH mice.

The tabw2 effect on adiposity was not as distinctive in female congenic mice compared with males (appendix b). Gender dimorphism for obesity susceptibility has been reported in mouse strains including in the obesity-prone KK strain (59) and in LG/J x SM/J recombinant strains (9). Indeed, Taylor et al. (59) mapped a QTL [obesity QTL 5 (Obq5)] strongly linked to female adiposity in KK mice on Chr 9. Although the causes of gender dimorphisms in adiposity remain unknown, Taylor et al. speculated that Obq5 may be a gene whose effect is influenced by sex steroid balance, based on the prior knowledge regarding the cause of gender dimorphism for diabetes in mice, as discussed earlier. This could also be the case for tabw2 but remains to be tested when the molecular basis of tabw2 is identified.


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APPENDIX B Body and Fat Pad Weights and Plasma Profiles in Congenic Mice Fed Chow or a High HFS Diet for 10 Wk Beginning at 4 Wk of Age (Female, Nonfasting)

 
Dissection of the complex diabesity trait of TH mice into single components such as the tabw2 congenic line should allow for refined mapping of individual QTL by reducing the confounding influences of additional loci present in the parental TH strain. Surprisingly, straightforward recombination mapping as one would carry out for a single locus did not prove to be feasible, and QTL mapping techniques had to be employed. However, the QTL analysis provided no evidence for additional QTL residing in the same genomic interval.

The observed values reflecting the percentage of the variance in body weight or fat pad weight explained by tabw2 in the intercross of the congenic mice with WT mice and in the BC population of F1 (B6 x TH) x TH mice are similar to those reported for other obesity QTLs found in independent mouse crosses (27, 58, 60, 65). Importantly, in the intercross of the congenic mice with WT mice, these values were not substantially increased as intuitively one might expect, where only tabw2 is segregating and, therefore, it should explain most of the variance. Additional environmental and perhaps stochastic developmental influences must play a role. For example, as observed in other studies (41, 63), we have found a significant influence of litter size on body weight and fat accumulation, presumably due to preweaning food competition in large litters. Such influences of early nutrition on adult body weight appear to be specific for some genetic backgrounds, such as tabw2, because an effect of litter size on measures of adiposity has not been observed in an intercross population of (LG/J x SM/J) F2 mice (23). We hypothesize that, at least, tabw2 as an obesity locus determines the potential to accumulate body fat, but how much body fat is actually deposited would then depend on the environment, i.e., how much food is available or how much energy is expended.

We refined the map position of tabw2 to a ~15-cM region (95% confidence interval) extending distally from the marker D6Mit102. In this region of mouse Chr 6, several obesity QTLs have previously been reported in other independent crosses of mice. These include macronutrient intake for carbohydrate (Minc2), which is responsible for preferring carbohydrate to fat in a cross of B6 and CAST/EiJ mice (48), body weight QTL (Bwq2), which is linked to body weight in a cross of B6 and KK-Ay (56), and Obq14, which is linked to fat pad weight in a cross of NZO and SM mice (60). Bwq2 has been suggested as a genetic modifier of Ay (57). Whether allelic variants at the tabw2 locus are responsible for the phenotypes linked to these QTLs could be determined through a haplotype analysis comparing the TH genotypes with those of the other susceptible and resistant strains (36) or by identification of the molecular bases of these loci.

The region for tabw2 identified in the mouse is syntenic to human Chr 3p26–24, 3p14, 3q13, and 3q21–24 and to rat Chr 4q34–42. Previously, this region has also been implicated in human obesity through either gene association or linkage studies (48). Human ghrelin (Ghr) and peroxisome proliferator-activated receptor-{gamma} genes both map to Chr 3p25, and significant associations of these genes with obesity have been demonstrated in tall obese children (22) and in a Mexican American population (11), respectively. Through sib-pair studies, significant linkage to Chr 3q22 for BMI has been reported (68), and several suggestive linkages for BMI or abdominal fat have been reported for Chr 3p26–24 (18, 40, 68).

Several candidates for the tabw2 locus suggest themselves in the congenic region: Rab7 (a member of the RAS oncogene family), thyrotropin-releasing hormone (Trh), Ghr (Ghr1; the growth hormone secretagogue receptor ligand), and Hrh1. Rab proteins belong to a superfamily of small-molecular-weight GTPases and play an essential role in intracellular vesicle trafficking (30). Rab7 protein is localized in endosomes and participates in the late endocytic pathway (14). Bao et al. (4) investigated a potential role of Rab proteins in the development of insulin resistance and hypothesized that they might be involved in glucose transporter 4 (GLUT4) vesicle trafficking and targeting. Trh is an anorexigenic neuropeptide and stimulates resting metabolic rate (15). Chronic peripheral infusion of Trh reduced body weight gain in lean rats, but obese fa/fa Zucker rats were relatively insensitive to this treatment (2). Ghr is an orexigenic gastrointestinal peptide involved in various aspects of regulating the gut-brain axis (61). Its expression in the hypothalamus and stimulation of neuropeptide Y neurons also point to Ghr's involvement in regulating energy homeostasis (12). Recently, it has been demonstrated that Ghr may play a role in determining the preference for energy source utilization, because knockout mice use more fat than other macronutrients as a fuel source when placed on a high-fat diet (67). Finally, a null mutation at Hrh1 has been reported to influence body weight gain (32). Our sequence comparison of the Hrh1 coding region, however, did not reveal differences between tabw2 and WT mice, and no differences in the amount of RT-PCR products were noted during isolation of the cDNA (not shown), making Hrh1 an unlikely candidate for the tabw2 phenotype.

Unexpectedly, positional cloning of tabw2 by standard linkage analysis does not appear to be straightforward. A possible solution is to narrow the genetic interval for tabw2 by constructing subcongenic lines (21, 46). In addition, gene expression microarray analysis may directly assist the cloning if the tabw2 allele shows altered expression levels. Alternatively, pathway analysis using the microarray data may provide positional candidate genes in combination with our relatively refined map position (52).

In summary, the tabw2 congenic line of mice is an exciting new animal model that may lead to insights into the etiology and development of obesity and its subsequent pathophysiological complications. In particular, the overt obesity observed in response to feeding a HFS diet makes tabw2 mice a very relevant animal model for the type of human obesity predominant in modern society. Subsequent studies investigating food intake and energy expenditure in these mice will elucidate the principal physiological role of tabw2. Finding the gene(s) responsible for obesity in tabw2 mice may lead to the discovery of the corresponding genes in humans and provide entry points to biochemical and regulatory pathways that may also be affected in humans.


    GRANTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
This study was supported by American Heart Association Grant 0235345N (J. H. Kim) and National Institute of Diabetes and Digestive and Kidney Diseases Grant DK-46977 (J. K. Naggert). The Jackson Laboratory institutional shared services were supported by National Cancer Institute Cancer Center Grant CA-34196.


    ACKNOWLEDGMENTS
 
We thank Drs. Edward H. Leiter at The Jackson Laboratory and Brynn Voy at the Oak Ridge National Laboratory for the valuable reviews of the manuscript.


    FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: J. H. Kim, The Univ. of Tennessee, 1215 W. Cumberland Ave., JHB 229, Knoxville, TN 37996-1920 (E-mail: jhkim{at}utk.edu).

10.1152/physiolgenomics.00197.2004


    REFERENCES
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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
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