Hepatic gene expression profiles in a long-term high-fat diet-induced obesity mouse model

Gene 340 (2004) 99 – 109 www.elsevier.com/locate/gene

Hepatic gene expression profiles in a long-term high-fat diet-induced obesity mouse model Sujong Kim a,*, Insuk Sohn b, Joon-Ik Ahn a, Ki-Hwan Lee a, Yeon Sook Lee c, Yong Sung Lee a a

Department of Biochemistry, College of Medicine, Hanyang University, 17, Haengdang-dong, Seongdong-gu, Seoul 133-791, South Korea b Department of Statistics, Graduate School, Korea University, Seoul 136-701, South Korea c Department of Food and Nutrition, Seoul National University, Seoul 151-742, South Korea Received 10 March 2004; received in revised form 17 May 2004; accepted 1 June 2004 Available online 24 July 2004 Received by T. Sekiya

Abstract To understand the molecular mechanisms underlying alterations in the pathophysiologic status of dietary obesity, we examined hepatic genes differentially expressed in a long-term high-fat intake-induced obesity mouse model. C57BL/6J male mice were fed with two kinds of diets for 12 weeks; a low-fat diet (LFD), a high-fat diet (HFD; n = 8), and the expression levels of f 10,000 transcripts in liver tissues from the two groups were assessed using cDNA microarray analysis. Twelve-week feeding with the HFD resulted in significant increase in body weight, visceral fat accumulation and circulating cholesterol concentration, compared with the LFD group. The cDNA microarray analysis revealed marked differences in the expressions of 97 hepatic genes. These genes were categorized into seven groups: 1. 2. 3. 4. 5. 6. 7.

metabolism; defense, stress, and inflammation responses; signal transduction, apoptosis, and cell cycle; transcription regulation; protein synthesis and modification; transport; and cellular adhesion, cytoskeleton and trafficking.

The expression of genes involved in fatty acid catabolism and ketone body synthesis, such as acyl – CoA oxidase1 (Acox1) and HMG – CoA lyase (Hmgcl), was significantly increased, and expression of genes involved in lipogenesis and cholesterol synthesis, such as acetyl – CoA synthetase2 (Acs2), fatty acid synthase (Fasn), and squalene epoxidase (Sqle), was drastically decreased in the HFD group. Interestingly, the genes implicated in defense and stress responses, such as glutathione S-transferases (GSTs) and heat shock proteins (Hsps), were also highly represented in the HFD group. Besides, a number of previously unappreciated regulatory molecules were changed by the HFD. These results revealed a transcriptional adaptation to long-term HFD and provided interesting information about the molecules involved in the development and maintenance of the obesity phenotype in vivo. D 2004 Elsevier B.V. All rights reserved. Keywords: cDNA microarray; Metabolism; Defense and stress response; C57BL/6J mouse

Abbreviations: HFD, high-fat diet; LFD, low-fat diet; TC, total cholesterol; TG, triglyceride; FFA, free fatty acid; Acox, acyl – CoA oxidase; Hmgcl, HMG – CoA lyase; Acs, acetyl – CoA synthetase; Fasn, fatty acid synthase; Sqle, squalene epoxidase; GST, glutathione S-transferase; Hsp, heat shock protein; PPAR, peroxisome proliferator-activated receptor; Acaa, acetyl – CoA acyltransferase; Fdft, farnesyl diphosphate farnesyl transferase; Fbp, fructose bisphosphatase; Cpt, carnitine palmitoyltransferase; SREBP, sterol regulatory element binding protein; Nrf2, nuclear factor, erythroid derived 2, like 2; ARE, antioxidant response element. * Corresponding author. Tel.: +82-2-2290-8240; fax: +82-2-2291-8397. E-mail address: [email protected] (S. Kim). 0378-1119/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.gene.2004.06.015

1. Introduction Obesity usually results from excessive energy storage over a prolonged period of time. The development of obesity is believed to be influenced by a number of factors, including genes and environment. Studies on animal models have clearly demonstrated two distinct types of obesity; the first type is genetic obesity, as seen in rodent strains such as the

100

S. Kim et al. / Gene 340 (2004) 99–109

Zucker fatty (fa/fa) rat and the leptin-deficient obese (Lepob/ Lepob) mouse, which becomes obese under various experimental conditions (Phillips et al., 1996; Zhang et al., 1994). The second type of obesity, which reflects more closely the human condition, results from a combination of genetic and environmental factors. Among environmental factors, longterm high-fat intake has been most intensively studied because of its contribution to the development of both obesity and diabetes in humans and rodents (Olefsky et al., 1974; Lin et al., 2000; Murase et al., 2001). The C57BL/6J mouse has especially been used as a human obesity model because this strain develops obesity, hyperglycemia, and hyperlipidemia when raised on a high-fat and high-sucrose diet; however, it remains lean if the fat content of the diet is limited (Lin et al., 2000). It is generally accepted that the majority of the pleiotropic effects of long-term high-fat diet (HFD) is accompanied with changes in gene expression profiles. Several genes which encode enzymes or signal mediators involved in lipid and glucose metabolism have been shown to respond to longterm HFD (Murase et al., 2001; Yu et al., 2000). For example, acyl – CoA oxidase (Acox) and uncoupling protein-2 genes have been found to be altered in livers of longterm HFD mice, accompanied with an increase in the mRNA level of sterol regulatory element binding protein1 (SREBP1), the major transcriptional regulator for lipogenic genes (Murase et al., 2001). Investigations of such changes have unraveled many insights into the molecular mechanisms of metabolic and/or endocrine adaptations to the longterm HFD; however, the results in most cases have been obtained in a ‘‘gene by gene’’ manner. In living organisms, the mechanisms are much more complex. For this reason, a global analysis of gene expression in response to changes in nutritional status appears to be essential for understanding the biological mechanisms. To obtain a more comprehensive picture of the diet-induced hepatic transcriptional adaptation in the C57BL/6J mouse, we used cDNA microarray, containing f 10,000 mouse transcripts. In the present study, comparisons of mice fed with a HFD and a low-fat diet (LFD) revealed apparent differences in the mRNA expression of 97 genes. Some of them have been known to be sensitive to nutritional status; however, the majority was newly identified. The interesting findings, which are related to metabolism, defense, and stress responses, are described in detail.

in stainless steel wire –mesh cages in a room kept at 23 F 1 jC with a 12-h light/dark cycle (light period: 8:00 –20:00 h). After acclimatization with the facility for 1 week, mice were randomly assigned to one of two different dietary groups for 12 weeks (n = 8): LFD1 and HFD2. The HFD is composed of 36% of total energy as fat, compared with 17% fat in the LFD. The diets are based on a modification of the recommendations of the American Institute of Nutrition (1993). Mice had free access to both food and distilled water, which were provided fresh every day. Food intake and body weights were recorded every day and every other day, respectively. 2.2. Sample collection and analytical methods On completion of the experiment, all mice were weighed and blood was collected via the postcaval vein from anesthetized animals into EDTA-treated blood collection tubes between 09:30 and 10:00 h after 12 h of food deprivation. Plasma was prepared by centrifugation of blood at 1000  g for 15 min at 4 jC and stored at 80 jC until analysis. Immediately after blood collection, livers were perfused in situ with ice-cold saline, and livers and adipose tissues were then removed and weighed. Three identical portions ( f 1 g) from each liver were prepared and stored at 80 jC until used for preparation of RNA and lipid and histochemistry. Plasma total cholesterol (TC) was measured with an automatic dry chemistry analyzer, Spotchem (KDK, Japan). Plasma free fatty acid (FFA) and triglyceride (TG) were measured by enzymatic and colorimetric methods, using assay kits from Roche Diagnostics (Mannheim, Germany) and Sigma Diagnostics (St. Louis, MO, USA), respectively. For liver lipid analysis, total lipids were extracted with a mixture of chloroform/methanol (Folch et al., 1957), and liver TG and TC were measured enzymatically as described above after the total lipids were dissolved in Triton X-100. 2.3. Statistical analysis Data were expressed as means F S.D. The differences among two diet groups were analyzed by t-test within the SPSS version 10.0 statistical package for Windows (Chicago, Illinois, USA). Differences were considered significant at p < 0.05. 2.4. Microarray analysis

2.1. Animal and diet

Mouse 10K cDNA microarray used in this study consisted of 10,336 spots, as previously described (Ahn et al., 2004). It included 6531 transcripts from the National

This study was conducted in conformity with the policies and procedures of the Institutional Animal Care and Use Committee of the Seoul National University (SNU). Threeweek-old C57BL/6J male mice were obtained from the SNU Animal laboratories (Seoul, Korea) and housed individually

1 Ingredient of LFD (g/kg diet) : cornstarch, 529.4; sucrose, 100; casein, 200; soybean oil, 70; cellulose, 50; AIN-93G mineral mix, 35; AIN93 vitamin mix, 10; L-cystine, 3; choline bitartrate, 2.5. 2 Ingredient of HFD (g/kg diet) : cornstarch, 419.5; sucrose, 100; casein, 200; soybean oil, 180; cellulose, 50; AIN-93G mineral mix, 35; .AIN-93 vitamin mix, 10; L-cystine, 3; choline bitartrate, 2.5.

2. Materials and methods

S. Kim et al. / Gene 340 (2004) 99–109

Institute of Aging (NIA), 1243 transcripts from the Brain Molecular Anatomy Project (BMAP), 2060 transcripts from InCyte Pharmaceuticals (Fremont, CA, USA), and yeast DNA and housekeeping genes as negative control. Total RNA was prepared from livers using Trizol (Invitrogen, Carlsbad, CA, USA). Fluorescence-labeled cDNA probes were prepared from 20 Ag of total RNA by using an amino-allyl cDNA labeling kit (Ambion, Austin, Texas, USA). Equal amounts of the RNA from six mice of each group were mixed and each sample was equally divided; one half was used to generate Cy3-labeled cDNA, the other half was used to generate Cy5-labeled cDNA for dye swapping. At least six replicates were performed, and three of these were repeated with the fluorophores reversed to eliminate false-positive results. The Cy5 and Cy3 probes were mixed, and hybridization was performed at 55 jC for 16 h, as previously described (Ahn et al., 2004). The two fluorescent images (Cy3 and Cy5) were scanned separately by a GMS 418 Array Scanner (Affymetrix, Santa Clara, CA, USA), and the image data were analyzed using ImaGene 4.2 (Biodiscovery, Santa Monica, CA, USA) and MAAS (Gaiagene, Seoul, Korea; Kim et al., 2001) software. For each hybridization, emission signal data were normalized by multiplying the Cy3 signal values by the ratio of the means of the Cy3 and Cy5 signal intensities for all spots on the array. To eliminate the unreliable data, the following criteria were adopted. 1. PCR amplification of the sequence spotted on the array was deemed acceptable only if the amplification was confirmed and a single size product was obtained. 2. Accurate printing of each spot was required, as shown by emission signal from more than 40% of the spot area. 3. The signal from the fluorophore labels had to be higher than 256 (28). In this analysis, we calculated the median value of gene expression ratio from six independently repeated microarray experiments. We used the modified t-tests, SAM method to evaluate statistical significance of changes in gene expression (Tusher et al., 2001). We took the genome-wide significance level at the SAM(d) = 0.66 and adopted a cutoff of 2.0-fold change based on our experiences. Genes showing significant differences in expression levels were classified into different functional categories, based on Gene Ontology (GO) with modifications.

101

Al of reaction mixture containing AmpliTaq DNA polymerase (0.04 U/Al, Perkin Elmer, Shelton, Connecticut), 50 mM Tris (pH 8.3), 3 mM MgCl2, 0.25 mM dNTPs, 1/50,000 dilution of SYBR Green I (Molecular Probes, Eugene, OR), and 0.25 AM appropriate sense and antisense PCR primers. The sequences of the primers were as follows: fatty acid synthase (Fasn) forward, 5V-TCC ACC TTT AAG TTG CCC TG-3V, reverse, 5V-TCT GCT CTC GTC ATG TCA CC-3V; Acox1 forward, 5V-TTA AAC ACC CAC CCA CCA AG-3V, reverse, 5V-CGA AAG CCT GGA GGT AAA GA-3V; Acox2 forward, 5V-TTC AAG GAA GCT GTG GAG AAA-3V, reverse, 5V-GGG AAG CAG GTC TAG GAA GG-3V; acetyl – CoA acyltransferase 1 (Acaa1) forward, 5V-ATG AAC TGA AGC GTC GTG G-3V, reverse, 5V-TCT GTA GCG TCC CTC TCT GG-3V; farnesyl diphosphate farnesyl transferase 1 (Fdft1) forward, 5V-TCA GAC CCA TCA TCA AGC AA-3V, reverse, 5V-ATG ACA GGT AAA TGG GCG AG-3V; squalene epoxidase (Sqle) forward, 5V-TGC ACC ACA GTT TAA ACC CA-3V, reverse, 5V-GAC TCC TTC AGG TGC TCA GG-3V; fructose bisphosphatase 2 (Fbp2) forward, 5VGTC CAT CGG AAC CAT TTT TG-3V, reverse, 5V-TCC AGC ATG AAG CAG TTG AC-3V; aldolase1 forward, 5VCTA CAA GGC TCT GAG CGA CC-3V, reverse, 5V-ACA GGA AAG TGA CCC CAG TG-3V; carnitine palmitoyltransferase1, liver (Cpt1l) forward, 5V-AGA ATC TCA TTG GCC ACC AG-3V, reverse, 5V-CAG GGT CTC ACT CTC CTT GC-3V; Cpt2 forward, 5V-CAC AAC ATC CTG TCC ACC AG-3V, reverse, 5V-CAT TGC AGC CTA TCC AGT CA-3V; peroxisome proliferator-activated receptor a (PPAR a) forward, 5V-GTG GCT GCT ATA ATT TGC TGT G-3V, reverse, 5V-GAA GGT GTC ATC TGG ATG GGT-3V; PPAR c forward, 5V-CAA GAC TAC CCT TTA AGT GAA-3V, reverse, 5V-CTA CTT TGA TCG CAC TTT GGT-3V; h-actin forward, 5V-GGG TCA GAA GGA CTC CTA TG-3V, reverse, 5V-GTA ACA ATG CCA TGT TCA AT-3V. The following cycling conditions were used: one denaturing cycle at 95 jC for 5 min, followed by 30 cycles of 95 jC for 30 s, 60 jC for 45 s, and 72 jC for 1 min. Relative RNA levels were determined by analyzing the changes in SYBR Green I fluorescence during PCR according to the manufacturer’s instructions. h-actin was amplified in parallel and the results were used for normalization. The correct size of PCR product was confirmed by electrophoresis on a 2% agarose gel stained with ethidium bromide. Purity of the amplified PCR products was determined by melting point analysis using the ICycler software.

2.5. Real Time RT-PCR 3. Results Four micrograms of total RNA was reverse-transcribed in 25 Al of reaction mixture, containing 2.5 U MuLV reverse transcriptase, 1 U RNAse inhibitor, 5 mM MgCl2, 50 mM KCl, 10 mM Tris –HCl, (pH 8.3), 2.5 AM oligo (dT) primer, and 1 mM dNTPs. The reaction mixture was heated to 42 jC for 60 min and then denatured at 85 jC for 5 min. cDNA was amplified with ICycler (BioRad, Hercules, CA, USA) in 50

3.1. Effects of high-fat diet on weight-related and biochemical parameters We measured the body weight of 4-week-old mice (20.0 F 0.2 g) just before the start of the feeding programs and then randomized eight mice each into two different

102

S. Kim et al. / Gene 340 (2004) 99–109

groups: LFD and HFD. As depicted in Table 1, 12-week feeding of C57/6J mice with the HFD resulted in significant increases in body weight (187%, p < 0.05), epididymal (182%, p < 0.05) and perirenal adipose tissue weights (150%, p < 0.05), as compared to the LFD-fed mice. Liver TG and TC were increased 2.5-fold (6.65 vs. 16.42 mg/g liver, p < 0.05) and 4.7-fold (4.88 vs. 22.85 mg/g liver, p < 0.05) in the HFD group, respectively. Plasma TC was markedly increased in the HFD mice, while plasma TG and FFA levels remained unchanged, in agreement with the previous studies, which showed that long-term HFD feeding did not elevate plasma lipids but induced fatty liver and increased parametrical adipose tissue weight (Murase et al., 2001; Han et al., 2000; Brix et al., 2002). As expected, histological analysis of the HFD mice livers revealed a distinctive pattern of fat droplets accumulation, indicating a shift of the liver toward fat storage (data not shown). 3.2. Long-term high-fat diet induced changes in gene expression The gene expression profiles of liver tissues from the LFD and HFD mice were compared. Only the genes whose mRNA levels were changed 2.0-fold or higher and detected as significant change by SAM method were designated as differentially expressed genes (Figs. 1 and 2). By these criteria, 97 genes were found to have significant changes in expression; 81 genes were overrep-

Table 1 Weight-related and biochemical parameters of the HFD and LFD mice LFD mice Final body weight, ga Body weight gain, ga Food intake, g/d Liver weight, g/100 g body weighta Epididymal adipose tissue weight, g/100 g body weighta Perirenal adipose tissue weight, g/100 g body weighta Liverc Total lipid, mg/g liver TG, mg/g liver TC, mg/g liver Plasmac TG, mM TC, mg/dl FFA, mM

HFD mice

26.79 F 1.43 6.79 F 0.87 3.94 F 0.29 4.80 F 0.13

32.75 F 1.23b 12.67 F 1.61b 3.66 F 0.23 5.40 F 0.13b

1.24 F 0.15

2.26 F 0.27b

0.5 F 0.08

0.75 F 0.12b

45.28 F 3.24 6.65 F 0.68 4.88 F 0.41

7.12 F 1.52 103 F 17.59 1.26 F 0.30

165.57 F 13.22b 16.42 F 1.13b 22.85 F 2.11b

5.24 F 0.75 141.40 F 14.95b 1.35 F 0.40

Results are means F S.D. (n = 8). FFA, free fatty acids; TG, triglyceride; TC, total cholesterol. a Mice were sacrificed after 12 weeks of feeding with each diet, and final body weight and tissue weight were determined. b p < 0.05 as compared with the LFD mice. c On the final day of experiments, blood and livers were collected after 12 h of food deprivation.

resented and 16 genes were underrepresented in liver tissues from the HFD mice, as compared with the LFD mice (Table 2). Table 3 lists the differentially expressed genes implicated in 1. 2. 3. 4. 5. 6. 7.

metabolism; defense, stress, and inflammation responses; signal transduction, apoptosis, and cell cycle; transcription regulation; protein synthesis and modification; transport; and cellular adhesion, cytoskeleton, and trafficking.

The largest numbers of genes expressed differentially in the HFD group were those involved in metabolism. This included genes encoding lipogenic enzymes such as Fasn, acetyl – CoA synthetase2 (Acs2), glycerol-3-phosphate acyltransferase, and malic enzyme, which were decreased by 2.5-, 8.5-, 2.5-, and 2.6-fold in the long-term HFD mice, respectively, compared with the LFD mice. The genes encoding enzymes of cholesterol biosynthesis, Sqle, Fdft1, NAD(P)-dependent steroid dehydrogenase-like, and sterol-C4-methyl oxidase-like were also decreased by at least 2-fold in the HFD mice. The gene for a steroidogenic enzyme, isopentenyl – diphosphate delta isomerase was similarly decreased in the HFD mice. In contrast, genes related to fatty acid h-oxidation and ketogenesis such as Acox1, Acaa1, and HMG – CoA lyase (Hmgcl) were augmented in the HFD mice. Hydroxysteroid (17-h) dehydrogenase 4, which is a multifunctional protein to catalyze the oxidation of estradiol with high preference as well as peroxisomal fatty acid h-oxidation (de Launoit and Adamski, 1999), was also increased 3.5-fold in the HFD mice. The mRNA level of CD36/FAT, which is responsible for the transport of long chain fatty acids into the muscle and adipose tissue, was up-regulated by the HFD (Coburn et al., 2000). Other genes showing the similar expression pattern included two genes of cytochrome P450 families (Cyp3a11, Cyp4a10), aldehyde dehydrogenase family 1, member B1, aldehyde dehydrogenase family 3, subfamily A2, AMP deaminase 3. Expression of a class of genes associated with carbohydrate metabolism, such as sorbitol dehydrogenase 1, Fbp2, and aldolase 1 A isoform, showed the increased patterns. Another interesting result was that several hepatic genes related to defense, stress responses, or detoxification responses, such as carbonic anhydrase 2, metallothionein 1, glutathione S-transferases (GSTs), glutamate – cysteine ligase, catalytic subunit, heat shock proteins (Hsps: Hspca, Hspcb, Hspa8), and histocompatibility 2, were expressed an average 2-fold higher levels in the HFD group. On the same way, several genes of acute-phase response or inflammatory processes, displayed increased expression in the HFD mice; such are Kallikrein B, serine proteinase inhibitor (clade A, member 3G), serine protease inhibitor 1 – 5, a-2-macroglobuline.

S. Kim et al. / Gene 340 (2004) 99–109

103

Fig. 1. Representative MA plots comparing the HFD versus the LFD groups. M represents the log ratio of the average intensities at two fluorescent dyes used to label probes and A represents averaged logarithmic intensity. Broken lines represent a 2-fold change.

Most genes which play roles in signal transduction, apoptosis, and cell cycle showed increased expression patterns: growth arrest specific 8, DNA primase, p58 subunit, small protein effect 1 of Cdc42, Wint6, serine/

threonine kinase 6, 11, and 36, proliferin related protein, and insulin-induced gene1 (Insig1). Some of the differentially expressed genes are themselves regulators of transcription, which might induce

104

S. Kim et al. / Gene 340 (2004) 99–109

Fig. 2. SAM scatter plot of observed relative difference versus the expected relative difference. The genes showing significant difference in expression between the LFD and the HFD mice were identified. Broken lines represent d = 0.66.

gene-specific transcriptional adaptation. These include Fos, c-Myc, and Max dimerization protein 5, transforming growth factor beta 1 induced transcript 4, zinc finger protein, subfamily 1A1, placenta and embryos oncofetal gene. Nuclear factor, erythroid derived 2, like 2 (Nrf2), which mediates the induction of a wide variety of ‘antioxidant response element (ARE)’-regulated genes in response to xenobiotics or oxidative environments (Pietsch et al., 2003; Ishii et al., 2000), also showed increased expression. The genes encoding transporters (lactotransferin, intestinal calcium binding protein), cell-adhesion protein (catenin src), cytoskeleton organizers (tubulin a2 and h5, GABA receptor-associated protein-like 1, destrin), and trafficking regulators (kinesin family member 23, lectin, mannose-binding 2) were also up-regulated in the HFD mice. The microtubule-based trafficking motor, dynactin 1, decreased in the HFD mice.

3.3. Confirmation of differential expression of selected genes by real-time RT-PCR analysis Changes of gene expression observed by DNA microarray analysis were further confirmed with a small set of known genes by real-time RT-PCR (SYBR Green I), using the same mice liver samples used in the above microarray hybridization. When gene expression profiles obtained by both microarray analysis and RT-PCR were compared, their patterns were very similar with regard to the direction (up- or downregulation) and degree of differences in expression (Table 4). Expression levels of Acox2, PPAR a, PPARc, Cpt1l, and Cpt2, which were not spotted on the microarray or rejected according to technical criteria of microarray, were increased by 2.2-, 2.2-, 3.4-, 2.3-, and 2.6-fold in the long-term HFD mice, respectively, when examined using real-time RT-PCR.

4. Discussion Table 2 Numbers of genes differentially expressed in long-term HFD micea Molecular function

Increase

Decrease

Total

Metabolism Defense/stress/inflammation responses Signal transduction/apoptosis/cell cycle Transcription regulation Protein synthesis and modification Transport Cellular adhesion/cytoskeleton/trafficking Unclassified Total

16 16 10 8 3 3 9 16 81

13 0 2 0 0 0 1 0 16

29 16 12 8 3 3 10 16 97

a

Data represent a summary of results of six independent hybridization. In three of six replicated experiments, the labeling of HFD and LFD samples was reversed to compensate for any nonlinearity in the emission signal intensity response curve for each fluorophore. Genes designated as differentially expressed in the HFD mice were those for which the normalized signal increased or decreased at least 2.0-fold and detected as significant change by SAM method.

The purpose of the current study was to examine the hepatic gene expression profiles in a long-term HFD-induced obesity mouse model. Although the liver plays a central role in maintaining energy balance and contributing to energy storage in the fed state, the earlier microarray approaches to diet-induced obesity have frequently been performed on adipose tissue (Boeuf et al., 2002; Lopez et al., 2003; Moraes et al., 2003). Besides, global study on hepatic transcriptional response was limited only to the early stage of HFD intake (d = 1 and 11; Gregoire et al., 2002). Because obesity is usually caused by energy imbalance over a prolonged period of time, we examined differential hepatic gene expression profiles of the long-term HFD-induced obesity model in the present study. The largest numbers of genes expressed differentially in the long-term HFD mice were those involved in metabolism (Table 3). The down-regulation of lipogenic genes and the

S. Kim et al. / Gene 340 (2004) 99–109 Table 3 Hepatic genes which were up- or down-regulated by long-term HFD Genbank ID Metabolism AI854239 BG071187 BG064680 AA209041 AW552727 AI841574 AI850456 BG069211 AI836437 BG083949 BG079889 AA261287 BG087037 BG078698 BG080183 AA028760 AA260409 BG069668 AI893900 AA518639 AA268120 AA109684 AA122925 BG087349 BG086218 BG076616 BG079850 BG081883 BG080773

Gene name acetyl – CoA synthetase 2, ADP forming dihydrolipoamide S-acetyltransferase malic enzyme, supernatant glycerol-3-phosphate acyltransferase, mitochondrial fatty acid synthase HMG – CoA synthase 1 NAD(P) dependent steroid dehydrogenase-like farnesyl diphosphate farnesyl transferase 1 squalene epoxidase sterol-C4-methyl oxidase-like isopentenyl – diphosphate delta isomerase acyl – CoA oxidase 1, palmitoyl acetyl – CoA acyltransferase 1 HMG – CoA lyase hydroxysteroid (17-beta) dehydrogenase 4 hydroxysteroid dehydrogenase-1, delta < 5>-3-beta cis-retinol/3alpha hydroxysterol short-chain dehydrogenase-like sorbitol dehydrogenase 1 fructose bisphosphatase 2 aldolase 1, A isoform cytochrome P450, family 3, subfamily a, polypeptide 11 cytochrome P450, family 4, subfamily a, polypeptide 10 carbonic anhydrase 2 aldehyde dehydrogenase 1 family, member B1 aldehyde dehydrogenase family 3, subfamily A2 glutamate dehydrogenase glutamate oxaloacetate transaminase 1, soluble quininoid dihydropteridine reductase AMP deaminase 3

Defense/Stress/Inflammation Responses BG088952 glutathione S-transferase, mu 2 BG087780 glutathione S-transferase, mu 6 BG076460 glutamate – cysteine ligase, catalytic subunit (Gclc) AA051654 metallothionein 1 BG064829 heat shock protein 1, alpha BG079631 heat shock protein 1, beta BG064886 heat shock protein 8 AI841289 heat shock protein 1B W14540 histocompatibility 2, K region AA122791 histocompatibility 2, Q region locus 7 AI846176 histocompatibility 2, T region locus 22 AW049505 histocompatibility 2, complement component factor B

Fold

SAM (d)

8.5

3.5

2.3

1.8

2.6 2.5

2.0 2.1

2.5 3.0 4.7

1.9 2.5 2.6

2.0

1.5

3.6 4.3 4.8

2.6 2.8 3.2

2.0 2.1 2.0 3.5

1.5 1.7 1.2 2.1

2.5

1.7

2.8

2.2

8.4 2.9 2.1 3.1

4.1 2.0 1.6 2.7

2.0

1.7

3.8 2.0

2.5 1.2

2.1

1.7

2.2 2.0

1.8 1.4

2.1

1.6

2.2

1.8

3.7 2.6 2.8

3.0 1.7 2.1

2.6 2.6 2.9 1.9 3.0 7.8 4.3

2.1 1.6 2.1 1.2 2.0 4.4 2.2

3.2

2.5

5.8

2.2

105

Table 3 (continued) Genbank ID

Gene name

Fold

Defense/Stress/Inflammation Responses AA245959 kallikrein B, plasma 1 C85808 serine (or cysteine) proteinase inhibitor, clade A, member 3G BG070053 serine protease inhibitor 1 – 5 BG076712 alpha-2-macroglobulin Signal Transduction/Apoptosis/Cell cycle W83881 growth arrest specific 8 AA237894 DNA primase, p58 subunit BG088653 small protein effector 1 of Cdc42 BG063059 wingless-related MMTV integration site 6 (Wnt6) BG077290 serine/threonine kinase 6 C87546 serine/threonine kinase 11 BG080768 serine/threonine kinase 36 BG077825 proliferin related protein AW047382 suppressor of cytokine signaling 1 AA237224 epidermal growth factor receptor BG080552 insulin induced gene 1 AW047187 Delta-like 1 (Drosophila) Transcription Regulation BG070196 FBJ osteosarcoma oncogene (Fos) AI836493 myelocytomatosis oncogene (c-Myc) BG087998 Max dimerization protein 5 BG084103 transforming growth factor beta 1 induced transcript 4 AI325349 Zinc finger protein, subfamily 1A, 1 BG064909 DNA segment, Chr 3, MJeffers 1 (N-Ras) BG078428 placentae and embryos oncofetal gene BG067417 nuclear factor, erythroid derived 2, like 2 (Nrf2) Protein Synthesis and Modification AA259551 eukaryotic translation elongation factor 1 alpha 1 BG078930 FK506 binding protein 4 BG086285 eukaryotic translation initiation factor 5A Transport AA458178 BI076450 AA222205

CD36 antigen/FAT lactotransferrin calcium binding protein, intestinal

Cellular Adhesion, Cytoskeleton and Trafficking C77281 catenin src(p120) BG064838 tubulin, alpha 2 BG087420 tubulin, beta 5 AI835946 GABA receptor-associated protein-like 1 BG077716 RIKEN cDNA 2310057H16 gene

SAM (d)

2.0 3.6

1.6 1.5

2.3 4.6

1.4 3.2

2.0 2.0 3.8

1.2 1.5 3.0

2.2

1.6

2.0 2.6 2.8 2.2 25.7

1.6 1.4 2.2 1.5 6.0

2.2

1.5

2.8 3.1

2.2 2.5

2.1

1.4

2.2

1.2

2.0 3.6

1.3 2.4

2.3

1.4

2.0

1.3

2.0

1.2

3.0

2.3

24.7

6.2

2.3 2.0

1.6 1.5

2.7 4.5 2.0

2.0 2.7 1.6

2.7 2.0 2.1 2.1

2.3 1.4 1.1 1.5

2.0

1.5

(continued on next page)

106

S. Kim et al. / Gene 340 (2004) 99–109

Table 3 (continued) Genbank ID

Gene name

Fold

SAM (d)

Cellular Adhesion, Cytoskeleton and Trafficking BG069974 kinesin family member 23 BG079450 epithelial protein lost in neoplasm BG073428 destrin BG087238 lectin, mannose-binding 2 AI845169 dynactin 1

2.1 2.5 2.3 2.1 2.2

1.3 2.0 1.7 1.4 1.8

Unclassified W97158 BG081554 BG067443 BG081577 BG080373 BG080860

2.0 2.6 2.0 2.5 3.2 2.3

1.5 1.6 1.3 1.9 2.6 1.5

2.6 3.8 3.1 3.5 3.6

1.8 2.4 2.3 2.9 2.7

2.3 2.5 2.1 2.0 3.7

1.8 1.6 1.6 1.4 2.8

C81126 BG075419 C76157 BG076061 BG076647 BG080498 C87836 AW556794 BG064954 BG084869

RIKEN cDNA 1200016E24 gene ESTs ESTs expressed sequence AA408877 expressed sequence AW743433 ganglioside-induced differentiation-associated-protein 1 gene-rich cluster, C8 gene hypothetical protein LOC219024 hypothetical protein LOC330836 Ly1 antibody reactive clone D5Wsu150e: DNA segment, Chr 5, Wayne State Uni. 150 RIKEN cDNA 1700124P09 gene RIKEN cDNA 1810055G02 gene RIKEN cDNA 5730544D12 gene RIKEN cDNA D030051B22 gene RIKEN cDNA A430096B05 gene

up-regulation of genes implicated in uptake and oxidation of fatty acid and ketogenesis might be associated with the physiological status in the HFD mice such as fatty liver and the increased fat accumulation (Table 1). The gene expression profiles of this study led us to hypothesize that in the HFD mice, the uptake of fatty acids into liver was augmented by increased CD36/FAT, resulting in subsequent accumulation of TG in the liver, and that hepatic TG accumulation might have driven the up-regulation of genes involved in lipid catabolism and the down-regulation of lipogenic genes by feedback mechanism. Gregoire et al. (2002) showed that gene expression of lipogenic enzymes such as Fasn and glycerol-3-phosphate acyltransferase was increased 6.5- and 2.2-fold, respectively, in the liver of the mice fed with a HFD for 1 day, but returned to baseline levels by day 11. Besides, Ferrante et al. (2001) observed increased expression of genes for fatty acid synthesis as well as genes involved in fatty acid oxidation in the genetic obese (Lepob/Lepob) mouse fed with an HFD. The discrepancies between our data and previous studies mentioned above might have been due to metabolic and/or endocrine adaptations to the long-term HFD and the different genetic backgrounds between mouse models. The down-regulation of genes involved in cholesterol synthesis was in good accord with the hypercholesterolemic status in the HFD mice because cholesterol homeostasis is maintained by a feedback mechanism involving the cholesterol molecule itself as an end-product repressor (Goldstein and Brown, 1990). This result was similar to the

one obtained from the HFD mice fed for 1 or 11 days (Gregoire et al., 2002). One of the most interesting results was that several hepatic genes related to detoxification or defense responses were expressed an average 3-fold higher in the long-term HFD group (Table 3). In particular, expressions of metallothionein 1 and GST genes were markedly increased in the HFD group. Ishii et al. (2000) showed that expression of metallothionein and GSTs are regulated by a common ‘antioxidant response element (ARE)’, and suggested that their gene products play a protective role against oxidative damage in various tissues by neutralizing ROS. In the present study, the mRNA level of Nrf2, a nuclear receptor which has been shown to play an important role in AREmediated gene expression in response to xenobiotics or oxidative environments, was also increased 3-fold in the HFD group (Pietsch et al., 2003; Ishii et al., 2000). Ferritin heavy chain, another Nrf2-responsive gene, was also slightly increased in the HFD mice [1.7-fold, SAM(d) = 1.18] (not presented because fold change < 2.0). Aldehyde dehydrogenase isotypes, classified into the metabolism category in this report, have previously been shown to play pivotal roles in the detoxification of lipid peroxidation products such as 4-hydroxynonenal (Lindahl and Petersen, 1991; Canuto et al., 1994). Therefore, 2-fold higher expression of these gene family members in the HFD mice can be explained as a Table 4 Comparison between cDNA microarray analysis and real time RT-PCR Gene fatty acid synthase (Fasn) farnesyl diphosphate farnesyl transferase 1 (Fdft1) squalene epoxidase (Sqle) acyl – Coenzyme A oxidase 1, palmitoyl (Acox1) acyl – Coenzyme A oxidase 2, branched chain (Acox2)d acetyl – CoA acyltransferase 1 (Acaa1) fructose bisphosphatase 2 (Fbp2) aldolase 1, A isoform (Aldo1) peroxisome proliferator – activated receptor a (PPAR a)d peroxisome proliferator – activated receptor g (PPAR c)d carnitine palmitoyltransferase 1, liver (cpt1l)d carnitine palmitoyltransferase 2 (cpt2)d

cDNA microarray

real time RT-PCRa

2.5 2.0

0.32 F 0.25b 0.35 F 0.21b

3.6 2.0

0.19 F 0.11c 3.7 F 1.4b

–

2.2 F 0.3b

2.1 (1.7)

3.9 F 1.4b

2.9 2.1 –

5.5 F 2.2b 4.5 F 1.4c 2.2 F 0.38b

–

3.4 F 0.13c

–

2.3 F 0.31b

–

2.6 F 0.21b

a Total RNA was isolated from livers of mice, and the relative mRNA expression levels of genes were measured by real-time RT-PCR analysis with SYBR Green I. Data are expressed as fold changes (means F S.D.), normalized to h-actin mRNA expression, where the values for the LFD mice were set at 1.00. The analyses were performed in triplicate. b p < 0.05 as compared with the LFD mice. c p < 0.001 as compared with the LFD mice. d This gene was not spotted on the cDNA microarray or excluded by the technical criteria (see Section 2.4).

S. Kim et al. / Gene 340 (2004) 99–109

defense response against lipotoxic environment, such as fat accumulation, in liver tissue. Simultaneous increases in the expression of a number of antioxidative stress genes together with their major transcription regulator in the HFD mice suggest that there appear to be global transcriptional regulations of biological defense responses to oxidative stress elevated by the long-term HFD. Previously, Gregoire et al. (2002) have shown that gene expression of GSTs, metallothionein 1, and MER5/antioxidant protein 1 was decreased in the liver of mice fed with a HFD for 1 or 11 days. The different durations of HFD feeding could very well explain the differences between the two studies. Another notable observation related to ‘defense and stress responses’ is the elevated expression of Hsps in the HFD mice (Table 3). The molecular chaperones are postulated to be responsible for repairs of misfolded or damaged proteins under highly oxidative and lipotoxic environment. Collectively, these results suggest that long-term HFD affects globally a wide range of diverse processes in the defense and stress response pathways. Genes involved in acute-phase response or inflammatory processes also displayed increased expression in the HFD mice. Acute-phase reactants have been suggested to contribute to the maintenance of chronic low-grade inflammation state involved in the progression of obesity and its associated deteriorations such as atherosclerosis and metabolic syndrome (Festa et al., 2000; Pickup and Mattock, 2003). The expression profiles of this category were in good accord with the previous observations in obese humans and animals (Pickup and Mattock, 2003). The changes in mRNAs for genes likely to influence overall cell cycle, growth, apoptosis, and signal transduction did not follow a consistent trend. Some positive regulators of cell cycle (growth arrest specific 8, DNA primase, Wnt6, and serine/threonine kinase 6), several negative regulators of cell cycle, and putative pro-apoptotic signal transducers (proliferin-related protein and serine/ threonine kinase 11) were highly expressed in the HFD mice (Table 3). On the other hand, mitogenic epidermal growth factor receptors were decreased in the HFD mice. One possible explanation for these seemingly conflicting changes in the expression of genes related to growth, cell cycle, and apoptosis is that different cells or nuclei within liver respond differently (e.g., nuclei in hepatocyte and endothelial cells). One of the most interesting findings in this category is the 2.8-fold decrease in the expression of Insig1 in the HFD mice. Insig1 is a critical component of the sterol-sensing system that regulates processing of SREBPs, the major transcription factors to regulate expression of lipogenic and cholesterogenic genes (Janowski, 2002). Therefore, the above-described changes in the expression of genes involved in lipid and cholesterol metabolism (Table 3) might partly be ascribed to the different processing of SREBPs due to changes in expression of Insig1 because gene expression of SREBPs was not significantly altered in the HFD group. Previously, Murase et al.

107

(2001) reported that C57BL/6J mice maintained on a high TG diet containing 30% (wt/wt) TG for 5 months showed an increased level of hepatic SREBP1 mRNA, compared with control mice fed with a low TG diet containing 5% (wt/wt) TG. In the liver of the mice fed with a HFD for 1 day or 11 days, however, gene expression of SREBPs was not significantly altered (Gregoire et al., 2002), which was similar to our results. The discrepancies between studies might have been due to the differences in the feeding durations and compositions of the adopted HFDs. Because the liver plays pivotal roles in lipid homeostatic response to feeding conditions such as fed, fasted, and re-fed, the hepatic expression of genes, involved in lipid metabolism, was highly nutritionally regulated at the transcriptional level (Yamamoto et al., 2004). Therefore, the expression pattern of these genes in different feeding conditions has to be examined in the future. In regard of hepatic transcriptional regulation of lipid metabolism, PPARs are of particular interest because PPARs have earlier been reported to regulate expression of genes involved in the balance between uptake, oxidation and synthesis of fatty acid, and TG secretion (Kersten, 2002). Because PPAR transcripts were not spotted on the microarray or rejected according to technical criteria, we examined mRNA levels of PPARs by real-time RT-PCR. Expression of PPARs increased by more than 2.0-fold in the HFD mice. Cpts, which are PPAR-responsive genes, also showed similar expression patterns (Table 4). The observed changes in expression of genes involved in fatty acid catabolism and ketogenesis, including Cpts, might partly be ascribed to the increased expression of PPARs (Table 3 and 4). These results were in good accord with the previous observations, which showed that HFD increased expression of PPAR a, PPAR c, and Cpts in murine or rodent liver, regardless of the feeding duration (Lee et al., 2001; Redonnet et al., 2001; Gregoire et al., 2002). In addition to metabolism, there is now compelling evidence that PPARs have a major role in the regulation of inflammation and the acute-phase response (Delerive et al., 2001). PPAR activators have been shown to exert antiinflammatory activities in various cell types by inhibiting the expression of proinflammatory genes such as cytokines, metalloproteases, and acute-phase proteins (Delerive et al., 2001). In this study, however, the long-term HFD increased mRNA levels of genes involved in acute-phase response or inflammatory processes as well as PPARs. It is not clear at present that there are PPAR-regulated transcriptional networks which integrate the pathways of metabolism, inflammation, and acute-phase responses. The physiological significance of other transcription factors, which were expressed differentially in this study, in dietary obesity is not clear at present. The targets of these factors and their interactions with other cofactors or suppressors are not known. Further studies on the regulation of these and other genes in this category are expected to discern whether a

108

S. Kim et al. / Gene 340 (2004) 99–109

common regulatory mechanism underlies gene expression in the HFD mice. In summary, in the present study, hepatic genes differentially expressed in a long-term HFD-induced obesity mouse model were examined using cDNA microarray analysis and real time RT-PCR. The microarray data indicated that the transcriptional regulation programs associated with long-term HFD are considerably complex and that a number of hither-to-uncharacterized gene expression events appear to be differentially regulated during long-term intake of HFD. These wide ranges of effects are highly expected because multiple cellular processes are regulated by nutritional factors as well as hormones. The present unbiased approach toward expression characterization implicated a number of previously unappreciated regulatory molecules for playing roles in the development and maintenance of the obesity phenotype in vivo.

Acknowledgements We thank Professor Woon Ki Paik for critical review of this manuscript.

References Ahn, J.-I., Lee, K.-H., Shin, D.-M., Shim, J.-W., Lee, J.-S., Chang, S.-Y., Lee, Y.-S., Brownstein, M.J., Lee, S.-H., Lee, Y.-S., 2004. Comprehensive transcriptome analysis of differentiation of embryonic stem cells into midbrain and hindbrain neurons. Dev. Biol. 265, 491 – 501. American Institute of Nutrition, 1993. AIN-93 purified diets for laboratory rodents: final report of the American Institute of Nutrition ad hoc writing committee on the reformulation of the AIN-76A rodent diet. J. Nutr. 123, 1939 – 1951. Boeuf, S., Keijer, J., Franssen-Van Hal, N.L.W., Klaus, S., 2002. Individual variation of adipose gene expression and identification of covariated genes by cDNA microarrays. Physiol. Genomics 11, 31 – 36. Brix, A.E., Elgavish, A., Nagy, T.R., Gower, B.A., Rhead, W.J., Wood, P.A., 2002. Evaluation of liver fatty acid oxidation in the leptin-deficient obese mouse. Mol. Genet. Metab. 75, 219 – 226. Canuto, R.A., Ferro, M., Muzio, G., Bassi, A.M., Leonarduzzi, G., Maggiora, M., Adamo, D., Poli, G., Lindahl, R., 1994. Role of aldehyde metabolizing enzymes in mediating effects of aldehyde products of lipid peroxidation in liver cells. Carcinogenesis 15, 1359 – 1364. Coburn, C.T., Knapp Jr., F.F., Febbraio, M., Beets, A.L., Silverstein, R.L., Abumrad, N.A., 2000. Defective uptake and utilization of long chain fatty acids in muscle and adipose tissues of CD36 knockout mice. J. Biol. Chem. 275, 32523 – 32529. de Launoit, Y., Adamski, J., 1999. Unique multifunctional HSD17B4 gene product: 17beta-hydroxysteroid dehydrogenase 4 and D-3-hydroxyacylcoenzyme A dehydrogenase/hydratase involved in Zellweger syndrome. J. Mol. Endocrinol. 22, 227 – 240. Delerive, P., Fruchart, J.C., Staels, B., 2001. Peroxisome proliferatoractivated receptors in inflammation control. J. Endocrinol. 169, 453 – 459. Ferrante Jr., A.W., Thearle, M., Liao, T., Leibel, R.L., 2001. Effects of leptin deficiency and short-term repletion on hepatic gene expression in genetically obese mice. Diabetes 50, 2268 – 2278. Festa, A., D’Agostino Jr., R., Howard, G., Mykkanen, L., Tracy, R.P., Haffner, S.M., 2000. Chronic subclinical inflammation as part of the

insulin resistance syndrome: the Insulin Resistance Atherosclerosis Study (IRAS). Circulation 102, 42 – 47. Folch, J., Lee, M., Stanley, G.H.S., 1957. A simple method for isolation and purification of total lipids from animal tissue. J. Biol. Chem. 226, 497 – 509. Goldstein, J.L., Brown, M.S., 1990. Regulation of mevalonate pathway. Nature 343, 425 – 430. Gregoire, F.M., Zhang, Q., Smith, S.J., Tong, C., Ross, D., Lopez, H., West, D.B., 2002. Diet-induced obesity and hepatic gene expression alterations in C57BL/6J and ICAM-1-deficient mice. Am. J. Physiol., Endocrinol Metabol. 282, E703 – E713. Han, L.K., Xu, B.J., Kimura, Y., Zheng, Y., Okuda, H., 2000. Platycodi radix affects lipid metabolism in mice with high fat diet-induced obesity. J. Nutr. 130, 2760 – 2764. Ishii, T., Itoh, K., Takahashi, S., Sato, H., Yanagawa, T., Katoh, Y., Bannai, S., Yamamoto, M., 2000. Transcription factor Nrf2 coordinately regulates a group of oxidative stress-inducible genes in macrophages. J. Biol. Chem. 275, 16023 – 16029. Janowski, B.A., 2002. The hypocholesterolemic agent LY295427 up-regulates INSIG-1, identifying the INSIG-1 protein as a mediator of cholesterol homeostasis through SREBP. Proc. Natl. Acad. Sci. U. S. A. 99, 12675 – 12680. Kersten, S., 2002. Peroxisome proliferator activated receptors and obesity. Eur. J. Pharmacol. 440, 223 – 234. Kim, J.H., Kim, H.Y., Lee, Y.S., 2001. A novel method using edge detection for signal extraction from cDNA microarray image analysis. Exp. Mol. Med. 33, 83 – 88. Lee, Y., Wang, M.Y., Kakuma, T., Wang, Z.W., Babcock, E., McCorkle, K., Higa, M., Zhou, Y.T., Unger, R.H., 2001. Liporegulation in diet-induced obesity. The antisteatotic role of hyperleptinemia. J. Biol. Chem. 276, 5629 – 5635. Lin, S., Thomas, T.C., Storlien, L.H., Huang, X.F., 2000. Development of high fat diet-induced obesity and leptin resistance in C57BI/6J mice. Int. J. Obes. Relat. Metab. Disord. 24, 639 – 646. Lindahl, R., Petersen, D.R., 1991. Lipid aldehyde oxidation as a physiological role for class 3 aldehyde dehydrogenases. Biochem. Pharmacol. 41, 1583 – 1587. Lopez, I.P., Marti, A., Milagro, F.I., Zulet Md Mde, L., Moreno-Aliaga, M.J., Martinez, J.A., De Miguel, C., 2003. DNA microarray analysis of genes differentially expressed in diet-induced (cafeteria) obese rats. Obes. Res. 11, 188 – 194. Moraes, R.C., Blondet, A., Birkenkamp-Demtroeder, K., Tirard, J., Orntoft, T.F., Gertler, A., Durand, P., Naville, D., Begeot, M., 2003. Study of the alteration of gene expression in adipose tissue of diet-induced obese mice by microarray and reverse transcription – polymerase chain reaction analyses. Endocrinology 144, 4773 – 4782. Murase, T., Mizuno, T., Omachi, T., Onizawa, K., Komine, Y., Kondo, H., Hase, T., Tokimitsu, I., 2001. Dietary diacylglycerol suppresses high fat and high sucrose diet-induced body fat accumulation in C57BL/6J mice. J. Lipid Res. 42, 372 – 378. Olefsky, J., Reaven, G.M., Farquhar, J.W., 1974. Effects of weight reduction on obesity. Studies of lipid and carbohydrate metabolism in normal and hyper-lipoproteinemic subjects. J. Clin. Invest. 53, 64 – 76. Phillips, M.S., Liu, Q., Hammond, H.A., Dugan, V., Hey, P.J., Caskey, C.J., Hess, J.F., 1996. Leptin receptor missense mutation in the fatty Zucker rat. Nat. Genet. 13, 18 – 19. Pickup, J.C., Mattock, M.B., 2003. Activation of the innate immune system as a predictor of cardiovascular mortality in Type 2 diabetes mellitus. Diabet. Med. 20, 723 – 726. Pietsch, E.C., Chan, J.Y., Torti, F.M., Torti, S.V., 2003. Nrf2 mediates the induction of ferritin H in response to xenobiotics and cancer chemopreventive dithiolethiones. J. Biol. Chem. 278, 2361 – 2369. Redonnet, A., Groubet, R., Noel-Suberville, C., Bonilla, S., Martinez, A., Higueret, P., 2001. Exposure to an obesity-inducing diet early affects the pattern of expression of peroxisome proliferator, retinoic acid, and triiodothyronine nuclear receptors in the rat. Metabolism 50, 1161 – 1167.

S. Kim et al. / Gene 340 (2004) 99–109 Tusher, V.G., Tibshirani, R., Chu, G., 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 98, 5116 – 5121. Yamamoto, T., Shimano, H., Nakagawa, Y., Ide, T., Yahagi, N., Matsuzaka, T., Nakakuki, M., Takahashi, A., Suzuki, H., Sone, H., Toyoshima, H., Sato, R., Yamada, N., 2004. SREBP-1 interacts with hepatocyte nuclear factor-4 alpha and interferes with PGC-1 recruitment to suppress hepatic gluconeogenic genes. J. Biol. Chem. 279, 12027 – 12035.

109

Yu, X.X., Mao, W., Zhong, A., Schow, P., Brush, J., Sherwood, S.W., Adams, S.H., Pan, G., 2000. Characterization of novel UCP5/ BMCP1 isoforms and differential regulation of UCP4 and UCP5 expression through dietary or temperature manipulation. FASEB J. 14, 1611 – 1618. Zhang, Y., Proenca, R., Maffei, M., Barone, M., Leopold, L., Friedman, J.M., 1994. Positional cloning of the mouse obese gene and its human homologue. Nature 372, 425 – 431.