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Logo of genesnutGenes & Nutrition
Genes Nutr. 2010 September; 5(3): 237–250.
Published online 2009 December 18. doi:  10.1007/s12263-009-0163-0
PMCID: PMC2935530

Gene expression profiles of a mouse congenic strain carrying an obesity susceptibility QTL under obesigenic diets


Genetic factors are strongly involved in the development of obesity, likely through the interactions of susceptibility genes with obesigenic environments, such as high-fat, high-sucrose (HFS) diets. Previously, we have established a mouse congenic strain on C57BL/6 J background, carrying an obesity quantitative trait locus (QTL), tabw2, derived from obese diabetic TALLYHO/JngJ mice. The tabw2 congenic mice exhibit increased adiposity and hyperleptinemia, which becomes exacerbated upon feeding HFS diets. In this study, we conducted genome-wide gene expression profiling to evaluate differentially expressed genes between tabw2 and control mice fed HFS diets, which may lead to identification of candidate genes as well as insights into the mechanisms underlying obesity mediated by tabw2. Both tabw2 congenic mice and control mice were fed HFS diets for 10 weeks beginning at 4 weeks of age, and total RNA was isolated from liver and adipose tissue. Whole-genome microarray analysis was performed and verified by real-time quantitative RT–PCR. At False Discovery Rate adjusted P < 0.05, 1026 genes were up-regulated and 308 down-regulated in liver, whereas 393 were up-regulated and 187 down-regulated in adipose tissue in tabw2 congenic mice compared to controls. Within the tabw2 QTL interval, 70 genes exhibited differential expression in either liver or adipose tissue. A comprehensive pathway analysis revealed a number of biological pathways that may be perturbed in the diet-induced obesity mediated by tabw2.

Keywords: Gene expression profiles, QTL, Congenics, Diet-induced obesity, Mice


The high prevalence of obesity in our society is currently overwhelming; approximately 1.2 billion people are overweight worldwide and among those at least 300 million people are obese [30]. The related medical complications are life-threatening diseases, including type 2 diabetes, heart disease, hypertension, and many forms of cancer [11]. The etiology of obesity is complex, involving genetic susceptibility, environmental influence, and gene-environmental interactions [23].

Animal models that share both physiologic and genetic similarity with humans have been used to minimize many difficulties encountered in carrying out obesity studies in humans [27]. Polygenic rodent models carrying natural variations have been developed and serve as valuable resources for obesity research, closely mimicking the polygenic inheritance of obesity in humans.

Previously, we have mapped a quantitative trait locus (QTL) linked to body weight on mouse chromosome 6 in a cross between C57BL/6J (B6) and obese diabetic TALLYHO/JngJ (TH) mice [9]. The TH allele was associated with higher body weights, and the QTL is named tabw2 (TALLYHO Associated Body Weight 2). Subsequently, we have constructed a congenic strain that carries a TH-derived genomic segment containing tabw2 on a B6 background. This congenic strain (tabw2 mice) exhibits increased adiposity and hyperleptinemia, and upon feeding high-fat, high-sucrose (HFS) diets, the obesity becomes exacerbated, followed by the development of insulin resistance [9].

The present study sought to investigate the genome-wide gene expression profiles in liver and adipose tissue to elucidate differentially expressed genes between tabw2 and control mice fed HFS diets. This study will identify differentially expressed genes within the congenic region, providing candidate genes for tabw2, as well as other genes involved in common pathways of obesity. The findings will contribute to understanding the gene networks underlying the diet-induced obesity mediated by tabw2.

Materials and methods

Animals and diets

The tabw2 congenic and control mice used in this study were from previously established lines [9]. Briefly, B6 female and TH male mice were crossed to yield F1 (or N1) progeny that were then backcrossed to B6 mice. The resulting N2 progeny were genotyped with flanking markers to select heterozygotes for the tabw2 QTL interval that were then again backcrossed to B6 mice. This procedure was repeated for 10 cycles of backcrossing to achieve more than 99% homogeneity [21] for the B6 genome in the congenic strain at which point two heterozygotes were intercrossed to yield offspring that were either homozygous for the TH alleles (tabw2 mice) or homozygous for the B6 alleles (control mice) (Fig. 1). Homozygous mice were then interbred to maintain the tabw2 and control mice.

Fig. 1
Construction of a congenic mouse strain carrying the obesity QTL on chromosome 6, named tabw2, derived from TALLYHO/JngJ (TH) mice in the C57BL/6J (B6) background by marker assisted backcrossing. An obese TH male mouse was crossed to a normal B6 female ...

All mice were allowed free access to food and water in a temperature and humidity controlled room with a 12-h light/dark cycle. Mice were weaned onto HFS diets (32% kcal from fat and 25% kcal from sucrose) (12266B, Research Diets, New Brunswick, NJ, USA) at 4 weeks of age. At 14 weeks of age, mice were weighed, then euthanized by CO2 asphyxiation, and liver and adipose tissue (inguinal, epididymal, retroperitoneal, perirenal, and subscapular fat pads) were collected, immediately frozen in liquid nitrogen, and stored at −80°C for RNA isolation. Statistical analysis for body weight data was conducted by ANOVA with StatView 5.0 (Abacus Concepts, Berkeley, CA). All animal studies were carried out with the approval of The University of Tennessee Animal Care and Use Committee.

RNA isolation

Total RNA was isolated from liver and white adipose (combined inguinal, epididymal, retroperitoneal, perirenal, and subscapular fat pads) tissue using RNeasy Lipid Tissue Midi Kit (75842, QIAGEN, Valencia, CA, USA) according to the manufacturer’s instructions. For adipose tissue, the entire tissue was homogenized and total RNA extracted, whereas approximately 50% of the liver was homogenized. Total RNA was further purified using RNeasy MinElute Cleanup Kit (74204, QIAGEN) for microarray analysis.

Microarray analysis

Hybridizations were performed at Genome Explorations Inc. (Memphis, TN, USA) using GeneChip® Mouse Genome 430 2.0 Array (Affymetrix, Santa Clara, CA, USA) following the standard protocol. The Mouse Genome 430 2.0 Array contains 45,000 probe sets on a single array to analyze the expression level of over 39,000 transcripts and variants from over 34,000 well-characterized mouse genes (Affymetrix). Total RNA isolated from liver and adipose tissue as described previously from 4 male tabw2 mice and 4 male control mice were used for microarray analysis, requiring 16 arrays.

The gcRMA (robust multi-array) process in Bioconductor ( was used to produce a normalized signal measure for each gene on each array. Data were examined for outliers and consistency of arrays, then statistical analysis was performed using SAS software (SAS Institute Inc., Cary, NC, USA). A mixed ANOVA model [31] for each gene tested factorial treatment effects of genotype and tissue, and used array variation as the experimental error. Genes with significant (P < 0.05) ANOVA interaction and significant pair-wise False Discovery Rate [22] were considered differentially expressed. ANOVA results were used to create volcano plots to help visualize the distribution of differential expression.

Real-time quantitative RT–PCR

Total RNA (2 μg) was reverse-transcribed with SUPERSCRIPT RT (11904-018, Invitrogen, Carlsbad, CA, USA) using oligo d(T)12–18 (18418-012, Invitrogen) as primer to synthesize first-strand cDNA in 20-μl volume according to manufacturer’s instructions. Oligonucleotide primers were synthesized (Sigma–Aldrich, St. Louis, MO, USA) using sequences obtained from Primer Bank ( or the published literature (Table 1). The PCR reaction was carried out in a 25-μl volume in 1× SYBR Green PCR core reagents (PA-112, SABiosciences, Frederick, MD, USA) containing 1 μl cDNA template diluate (1:5, v/v) and 6 pmol primers. Real-time PCR was conducted using an ABI Prism 7700 sequence detection system (Applied Biosystems, Foster City, CA, USA). For each sample, triplicate amplifications were performed and the average measurements used for data analysis. The difference in average threshold cycle ([increment]Ct) values between 36B4 gene and a specific gene was calculated for each individual. The data were then presented as relative fold-change using control mice as the reference by equation 2−([increment]Ct of tabw2 mice−[increment]Ct of control mice) [13]. If the difference was negative, the calculation was inverted and made negative, to signify over-expression in tabw2 mice. Mice measured by qRT–PCR were not the same as used in the microarray analysis to increase biological validation (n = 5, male, for each genotype).

Table 1
Primer sequences for real-time quantitative RT-PCR


Tabw2 mice fed HFS diets were significantly heavier than control mice [33.4 ± 1.2 (n = 14) vs. 28.0 ± 0.4 (n = 14) g; mean ± SEM; P = 0.0002; male; 14-week old].

Differentially expressed gene profiling overview in liver and adipose tissue from tabw2 and control mice

Using a global expression chip, we compared the levels of gene expression in liver and adipose tissue from tabw2 mice and control mice fed HFS diets. Gene expression profiles were visualized by volcano plots (Fig. 2). Overall, large differences in gene expression levels were rare between tabw2 and control mice, which can be deduced from the volcano plots clustered at the center. This may be because the only genomic difference between the tabw2 and control mice is in the congenic region.

Fig. 2
Volcano plot comparison of gene expression between control (B) and tabw2 (T) mice in liver and adipose tissue. The X-axis indicates the differential expression, plotting the fold-difference ratios on a log-2 scale. The Y-axis indicates log10 statistical ...

Of over 39,000 transcripts (hereafter referred to as genes), at a significance level of P < 0.05, 1026 genes were up-regulated and 308 down-regulated in liver, whereas 393 were up-regulated and 187 down-regulated in adipose tissue in tabw2 mice compared to control mice. When examined in each tissue for the top 50 (25 up-regulated and 25 down-regulated) genes with the largest effect of genotype (Tables 2 and and3),3), the most largely changed genes were found in adipose tissue; Sfrp5 (up-regulated in tabw2 mice) and Mup1 (down-regulated in tabw2 mice) (Table 2).

Table 2
The 50 genes with largest fold change between tabw2 and control mice in adipose tissue
Table 3
The 50 genes with largest fold change between tabw2 and control mice in liver

Differentially expressed genes located within the tabw2 QTL interval

Using congenic mice, the microarray analysis strategy has been useful in identification of QTLs [1, 28]. In an attempt to select attractive positional candidate genes for tabw2, we examined the gene expression levels located within the tabw2 congenic interval on chromosome 6, based on the hypothesis that the genetic alteration of tabw2 may cause dysregulation of the gene expression. Forty-five genes in liver and 32 genes in adipose tissue located within the congenic interval (47.0–137.3 Mb) were differentially expressed between tabw2 and control mice (Table 4); 7 genes, including Znrf2, Pole4, Isy1, Frmd4b, Tmcc1, Ccnd2, and Lrp6, appeared in both tissues. Of these 70 genes, seven (5830411G16Rik and Chast13 in liver and Pole4, Ret, C530028O21Rik, Ccnd2, and Klrd1 in adipose tissue) were present in the top 50 genes with the largest fold change between tabw2 and control mice (boldface entries Table 4).

Table 4
Differentially expressed genes between tabw2 and control mice in liver and adipose tissue (fat) that are located within the congenic interval on chromosome 6

Except for a few genes, such as Mgll, the differentially expressed genes within the congenic interval had mostly unknown connections with obesity. Monoglyceride lipase (Mgll) hydrolyzes the monoglycerides formed during the hydrolysis of triglycerides [24]. The gene expression of Mgll was increased in liver of tabw2 mice. In agreement with this, hepatic increases in protein and activity of Mgll have previously been reported in obese mice fed high-fat diets, whereas little changes in adipose tissue occurred [2].

Another interesting finding was the down-regulation of Arhgdib gene in liver of tabw2 mice. ARHGDIB (also known as Rho GDIβ or D4/Ly GDI) negatively regulates Rho small GTP-binding protein by inhibiting dissociation of GDP from Rho protein. The Arhgdib gene is usually largely expressed in hematopoietic cells and known to be involved in immune response regulation [12, 32]. In the context of immune functions, a significant decrease in the expression of the Klrd1 gene was also exhibited in adipose tissue of tabw2 mice. KLRD1 (also known as CD94) associates with a member of the NKG2 family and regulates natural killer cell functions [6].

Biochemical pathways differentially regulated in tabw2 and control mice

In order to elucidate a biochemical differentiation between tabw2 and control mice, we conducted a pathway analysis. All the differentially expressed genes were examined for known pathway networks using the Database for Annotation, Visualization, and Integration Discovery Bioinformatics Resources 2008 (DAVID) Functional Annotation Tool ( Through the biochemical pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG), 70 genes were assigned to 13 known pathways in liver, whereas 32 genes were assigned to 9 known pathways in adipose tissue with Expression Analysis Systematic Explorer (EASE) threshold of 0.1 and a minimum of 2 genes present for the corresponding pathway (Table 5).

Table 5
Biological pathways associated with differentially expressed genes between tabw2 and control mice through KEGG pathway using DAVID

While only 6 genes included in the pathways (boldface entries) were located within the tabw2 congenic region, five (Tcf3, Ccnd2, Lrp6, Wnt5b, and Ruvbl1) out of the 6 genes were involved in the Wnt signaling pathway in either liver or adipose tissue.

Multiple genes were present in pathways associated with intermediary metabolism. These include genes required for fatty acid oxidation, such as Hadhsc (mitochondrial β-oxidation), Acaa1a and Hsd17b4 (peroxisomal β-oxidation), and Cyp4a14 (microsomal ω-oxidation) and lipogenic enzymes, such as Acss2 and Acaca. Acyl-CoA synthetase short-chain family member 2 (Acss2) catalyzes the production of acetyl-CoA from CoA and acetate, producing a key molecule in multiple metabolic pathways [14, 26]. Acetyl-CoA carboxylase alpha (Acaca) catalyzes the carboxylation of acetyl-CoA to produce malonyl-CoA that is used as a building block in the de novo long-chain fatty acid synthesis [29].

Microarray validation by real-time qRT–PCR

Changes of gene expression elucidated by microarray analysis were further verified with selected genes by real-time qRT-PCR. We chose to validate 21 genes of interest from the list of genes found in the top 50 genes with the largest effect of genotype, located on the tabw2 interval, or involved in Wnt signaling or intermediary metabolism (Table 6). The qRT-PCR results from the 21 selected genes showed close agreement with microarray fold-changes (r = 0.81, P < 0.001). Few genes including Ccnd2, Lrp6, and Nfatc3 in adipose tissue and Ruvbl1 and Nlk in liver were outside the qRT-PCR confidence interval.

Table 6
Microarray vs. real-time quantitative RT-PCR (qRT-PCR) for selected genes in liver and adipose tissue (fat) from tabw2 and control mice


We applied oligonucleotide microarray analysis accompanied by real-time qRT-PCR to evaluate changes in gene expression in diet-induced obesity mediated by tabw2 QTL. By using the tabw2 congenic mice and control mice fed a HFS diet, we were able to elucidate gene networks that may be perturbed by tabw2.

Emerging evidence indicates that Wnt signaling is involved in adipogenesis, as well as in glucose and lipid metabolism [18]. In our study, we detected changes in gene expression of a Wnt member, Wnt5b, and several regulators and effectors of Wnt signaling, including Sfrp5 that prevents Wnts binding to frizzed receptors, in tabw2 mice. A large increase in gene expression levels of Sfrp5 was also previously reported in diet-induced obesity in mice [10]. Recently, the WNT5B gene has been reported to be associated with risk of type 2 diabetes in the Japanese populations [8] and Caucasian subjects [25].

Obesity is often concomitant with alterations in the rhythmic regulations of biological systems. For example, blunted diurnal variations and dampened ultradian pulsatility of circulating hormones, such as leptin and ghrelin, were observed in obese humans [7]. Gene expression of Mup, the lipocalin family, is regulated in liver by a pulsatile stimulus of growth hormone [16]. Interestingly, decreased MUP levels in urine were exhibited in obese mice [15]. Although the role of MUP in adipose tissue is unknown, we speculate that the significant decrease of the Mup1 gene expression in adipose tissue of tabw2 mice (Table 2) might reflect alterations in endocrine rhythmicity in these mice.

Given that fat mass is significantly increased in tabw2 mice, it was surprising to observe that expression of genes involved in fatty acid oxidation systems (Acaa1a, Cyp4a14, Hadhsc, and Hsd17b4) was up-regulated in liver, and expression of lipogenic genes (Acss2 and Acaca) was down-regulated in adipose tissue of tabw2 mice (Table 6). A decreased expression of lipogenic genes in adipose tissue was previously reported in obese human subjects [4, 17]. A possible reason for the paradoxical findings is that the decreased expression of lipogenic genes reflects a late and adaptive process; i. e., when the adipose tissue was sampled, the subjects were at a late stage of obesity and no longer expanding fat mass [4]. Observations in the present study do not rule out the possibility of an increase in lipogenic gene expression in adipose tissue at younger ages when the process of fat storing might be more rapid and dynamic than at 14 weeks of age.

Seventy of the differentially expressed genes were located within the congenic interval, which provides the possibility that a polymorphism/mutation in one of these genes could be responsible for the obesity phenotype attributed to tabw2. Our microarray data will assist candidate gene selections when the tabw2 interval is fine mapped.

In summary, we have provided a genome-wide overview of changes in gene expression that may contribute to diet-induced obesity mediated by tabw2. Our genomic profiling increased our understanding of dysregulated biological systems in tabw2 mice that will lead to targeted metabolic and molecular studies. These data may contribute to understanding the mechanisms of gene-by-diet interactions in the development of obesity, which potentially provides insights into mechanisms for human obesity.


This work was supported in part by American Heart Association Grants 0235345 N and 0855300E, NIH/National Institute of Diabetes and Digestive and Kidney Disease Grant 1R01DK077202-01A2, funding from the Center of Genomics and Bioinformatics, and a pilot and feasibility grant from the University of Tennessee Obesity Research Center to J.H.Kim.

Conflict of interest statement Authors declare not to have any conflict of interest.


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