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Endocrinology Vol. 143, No. 6 2106-2118
Copyright © 2002 by The Endocrine Society

INSULIN-GLUCAGON-GI PEPTIDES-DIABETES MELLITUS

Gene Expression Profile of Adipocyte Differentiation and Its Regulation by Peroxisome Proliferator-Activated Receptor-{gamma} Agonists

David L. Gerhold, Franklin Liu, Guoqiang Jiang, Zhihua Li, Jian Xu, Meiqing Lu, Jeffrey R. Sachs, Ansuman Bagchi, Arthur Fridman, Daniel J. Holder, Thomas W. Doebber, Joel Berger, Alex Elbrecht, David E. Moller and Bei B. Zhang

Departments of Molecular Endocrinology (F.L., G.J., Z.L., T.W.D., J.B., A.E., D.E.M., B.B.Z.), Pharmacology (D.L.G., J.X., M.L.), Applied Computer Science and Mathematics (J.R.S., A.B., A.F.), and Biometrics Research (D.J.H.), Merck Research Laboratories, Rahway, New Jersey 07065, and West Point, Pennsylvania 19486

Address all correspondence and requests for reprints to: Dr. Bei B. Zhang, R80W180, Merck Research Laboratories, P.O. Box 2000, 126 East Lincoln Avenue, Rahway, New Jersey 07065. E-mail: . bei_zhang{at}merck.com


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results and Discussion
 References
 
PPAR{gamma} is an adipocyte-specific nuclear hormone receptor. Agonists of PPAR{gamma}, such as thiazolidinediones (TZDs), promote adipocyte differentiation and have insulin-sensitizing effects in animals and diabetic patients. Affymetrix oligonucleotide arrays representing 6347 genes were employed to profile the gene expression responses of mature 3T3-L1 adipocytes and differentiating preadipocytes to a TZD PPAR{gamma} agonist in vitro. The expression of 579 genes was significantly up- or down-regulated by more than 1.5-fold during differentiation and/or by treatment with TZD, and these genes were organized into 32 clusters that demonstrated concerted changes in expression of genes controlling cell growth or lipid metabolism. Quantitative PCR was employed to further characterize gene expression and led to the identification of ß-catenin as a new PPAR{gamma} target gene. Both mRNA and protein levels for ß-catenin were down-regulated in 3T3-L1 adipocytes compared with fibroblasts and were further decreased by treatment of adipocytes with PPAR{gamma} agonists. Treatment of db/db mice with a PPAR{gamma} agonist also resulted in reduction of ß-catenin mRNA levels in adipose tissue. These results suggest that ß-catenin plays an important role in the regulation of adipogenesis. Thus, the transcriptional patterns revealed in this study further the understanding of adipogenesis process and the function of PPAR{gamma} activation.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results and Discussion
 References
 
INSULIN RESISTANCE and obesity are hallmarks of type 2 diabetes mellitus. As one of the targets of insulin action, adipose tissue plays an important role in maintaining whole- body energy homeostasis. Adipocytes store energy in the form of triglycerides in periods of nutritional abundance and release fatty acids when deprived of calories. Although the adipocyte has previously been viewed as a passive participant in the development of obesity, this cell type is recognized as an endocrine/paracrine organ that plays an active role in the regulation of energy balance and body composition (1). Adipocytes respond to hormonal input, integrate these with metabolic signals and release hormones to signal central and peripheral tissues. There has been rapid advancement in delineating mechanisms that control adipocyte differentiation and adipocyte-specific gene expression. A series of transcriptional factors acting in temporal sequence has been identified as potential regulators of adipocyte differentiation (2). Studies of the tissue-specific enhancer and proximal promoter region of the adipocyte P2 (aP2) gene led to the identification of binding sites for the two well characterized adipogenic transcription factors, CCAAT/enhancer binding protein-{alpha} (C/EBP{alpha}) and PPAR{gamma}. C/EBP{alpha} belongs to the family of basic leucine zipper transcriptional factors, and it is involved in terminal differentiation of adipocytes (3). Two other members of the C/EBP family, C/EBPß and C/EBP{delta}, are expressed during adipocyte differentiation before the expression of C/EBP{alpha}, and they respond to hormonal signals by activating the transcription of C/EBP{alpha} and PPAR{gamma} (4).

PPAR{gamma} is a member of a family of ligand-activated nuclear hormone receptors that includes PPAR{alpha} and PPAR{delta}. PPAR{gamma} promotes gene expression via formation of a heterodimeric DNA-binding complex with the RXR{alpha}. Two closely related forms of PPAR{gamma} exist as the results of alternative promoter usage and differential RNA splicing. These two forms differ by only 30 amino acids in the N terminus (5). Both PPAR{gamma}1 and -{gamma}2 are predominantly expressed in adipose tissue. Retroviral expression of PPAR{gamma}2 stimulates adipocyte differentiation of fibroblasts (6). Furthermore, ectopic expression of PPAR{gamma}2 and C/EBP{alpha} in cultured myoblasts results in a switch from myogenesis to adipogenesis (7). In addition, known activators of PPAR{gamma}, including 15-deoxy-{Delta}12,14-PGJ2 (8, 9) and thiazolidinedione (TZD) or non-TZD compounds (10, 11, 12), are potent stimulators of adipocyte differentiation. The critical role of PPAR{gamma} in the regulation of adipogenesis is further demonstrated in mouse models lacking the gene through genetic manipulation (13, 14, 15).

Activation of PPAR{gamma} leads to improvement of insulin sensitivity. Agonists of PPAR{gamma} are effective in reducing hyperglycemia, hyperinsulinemia, and hyperlipidemia in animal models of type 2 diabetes. These in vivo effects may be mediated by the action of TZDs on mature adipocytes, differentiating adipocytes, or both. Several PPAR{gamma} ligands of the TZD class (troglitazone, rosiglitazone, and pioglitazone) have also been developed as antidiabetic agents to ameliorate insulin resistance in patients with type 2 diabetes. Recently, a loss of function missense mutation in PPAR{gamma} was uncovered in two patients with severe insulin resistance and diabetes (16). Polymorphisms of PPAR{gamma}2 Pro12Ala have been shown to have an impact on adiposity and diabetes (17). These discoveries further validated that PPAR{gamma} plays an important role in the regulation of insulin sensitivity and glucose homeostasis.

The current study was initiated to investigate the regulation of gene expression by PPAR{gamma} activators and during adipocyte differentiation. Murine 3T3-L1 cells were induced to differentiate into adipocytes, RNA samples were collected at different stages of differentiation, and gene expression patterns were obtained using high density oligonucleotide arrays. A subset of genes was examined for expression patterns in vivo to examine the predictive power of in vitro responses in the context of homeostatic modulation in vivo. The transcriptional patterns revealed in this study may further the understanding of the adipogenesis process and the function of PPAR{gamma} activation.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results and Discussion
 References
 
Materials
The thiazolidinediones, rosiglitazone ([(+/-)-5-(4-[2-(methyl-2-pyridinylamino)ethoxy]phenyl)methyl]-2,4-thiazolidinedione) and TZD (5-[4-[2-(5-methyl-2-phenyl-4-oxazoly)-2-hydroxyethoxy]benzyl]-2,4-thiazolidinedione), were used in these studies. In addition, a non-TZD PPAR{gamma} agonist, nTZD (3-chloro-4-(3-(3-phenyl-7-propylbenzofuran-6-yloxy)propylthio)phenylacetic acid), was provided by the medicinal chemistry department of Merck & Co., Inc. (Rahway, NJ).

Cell culture
3T3-L1 cells (American Type Culture Collection, Manassas, VA; passages 3–9) were grown to confluence in medium A (DMEM with 10% FCS, 100 U/ml penicillin, and 100 µg/ml streptomycin) at 37 C in 5% CO2 as previously described (18). Differentiation was induced by incubating the confluent fibroblasts with medium A supplemented with methylisobutylxanthine, dexamethasone, and insulin for 2 d, followed by another 2-d incubation with medium A supplemented with insulin. The cells were further incubated in medium A for an additional 3 d to complete the adipocyte conversion. RNA samples were prepared from fibroblasts, preadipocytes treated with or without the indicated ligands in the differentiation medium for 6 or 48 h, or fully differentiated adipocytes in medium A with or without ligands for 6 or 48 h. The compounds were dissolved in dimethylsulfoxide (DMSO), and the final concentration of DMSO in the medium for control and treated cells was maintained at 0.1%.

In vivo animal studies
Male db/db mice (10- to 11-wk-old C57BL/KFJ mice, The Jackson Laboratory, Bar Harbor, ME) were housed seven per cage and allowed ad libitum access to ground Purina rodent chow (Ralston Purina Co., St. Louis, MO) and water. Lean animals were age-matched heterozygous mice maintained in the same manner. The db/db mice were treated daily by gavage with vehicle (0.25% carboxymethylcellulose) with or without PPAR{gamma} agonists for 11 d. Liver and epididymal fat pads were removed and frozen in liquid N2 for RNA extraction.

RNA preparation
Cells or tissues were extracted with TRIzol (Life Technologies, Inc., Gaithersburg, MD), and total RNA was isolated from each sample according to the manufacturer’s instructions. Total RNA was reprecipitated using RNAmate (Biochain, San Leandro, CA), and mRNA was isolated using oligo(deoxythymidine)-decorated latex beads (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions.

Hybridization and staining
Hybridization samples were prepared according to Affymetrix instructions as previously described (19). Briefly, a primer encoding the T7 RNA polymerase promoter linked to oligo(deoxythymidine)17 was used to prime double-stranded cDNA synthesis from each mRNA sample using SuperScript II ribonuclease H- reverse transcriptase (Life Technologies, Inc.). Each double-stranded cDNA sample was purified by adsorption to silica (QIAquick kit, QIAGEN) according to the manufacturer’s instructions, then in vitro transcribed using T7 RNA polymerase (MEGAscript T7 kit, Ambion, Inc., Austin, TX), incorporating biotin-UTP and biotin-CTP (Enzo Biochemicals, Inc., New York, NY) into the resulting copy RNA (cRNA). These cRNA transcripts were purified using RNeasy (QIAGEN) and quantitated by measuring absorption at 260 nm/280 nm. Five-microgram mRNA samples typically yielded between 30 and 150 µg purified cRNA. cRNA samples were fragmented at 95 C for 35 min in 10 mM MgCl2 to a mean size of approximately 50–100 nucleotides and added to hybridization buffer. Ten-microgram aliquots of each sample were hybridized to sets of four mouse Mu6800 A, B, C, and D arrays for 16 h at 45 C. Microarrays were washed, stained with streptavidin/R-phycoerythrin, and scanned with a dedicated instrument to capture a fluorescence image (Molecular Dynamics, Inc., Sunnyvale, CA). Each treatment was represented by two replicate samples on two sets of four Mu6800 A, B, C, and D arrays.

Microarray data analysis
For each probeset an index of gene expression was calculated and analyzed using the SAFER algorithm (20). The hierarchical clustering algorithm used was the UPGMA (unweighted pair group method with arithmetic mean; a version of the average linkage clustering algorithm) in Spotfire 6.2 (Spotfire, Inc., Somerville, MA).

Quantitative RT-PCR
Amplification of each target cDNA was performed with TaqMan PCR reagent kits in the ABI PRISM 7700 sequence detection system according to the protocols provided by the manufacturer (PE Applied Biosystems, Foster City, CA). The primer/probe sets shown in Table 1Go were used for the amplification step.


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  		Table 1. Primer/probe sets

 
Western blot analysis
3T3-L1 fibroblasts or adipocytes were incubated in DMEM plus 10% FBS in the presence of vehicle (DMSO) or 10 µM TZD for 24 h. The cells were then rinsed three times with cold PBS and lysed in lysis buffer [20 mM HEPES (pH 7.4), 1% Triton X-100, 2 mM EGTA, 2 mM dithiothreitol, 50 mM ß-glycerol phosphate, 1 mM sodium vanadate, 50 mM NaF, 10% glycerol, 1 mM phenylmethylsulfonylfluoride, and protease inhibitor cocktail]. Whole cell lysates containing equal amounts of crude proteins were separated in precast 4–20% gradient NuPAGE SDS-PAGE gels (Invitrogen, San Diego, CA) according to the manufacturer’s instructions. The proteins were then transferred to a polyvinylidene difluoride membrane, and probed with a rabbit polyclonal antiserum against the N terminus of ß-catenin (Upstate Biotechnology, Inc., Lake Placid, NY). Detection was performed with the ECF Western blotting kit (Amersham Pharmacia Biotech, Piscataway, NJ) by scanning with a Storm gel and blot imaging system (Molecular Dynamics, Inc.).


   Results and Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
References
 
Global survey of gene expression during 3T3-L1 cell differentiation and effects of PPAR{gamma} agonist
In recent years there have been a number of microarray studies describing gene expression in the 3T3-L1 cell system (21) and in human adipocytes (22). The earlier reports focused on gene changes during adipocyte differentiation. In this study we designed the experiments to elucidate the responses of differentiated adipocytes and differentiating adipocytes to a PPAR{gamma} agonist as well as the differentiation process itself. Murine 3T3-L1 fibroblasts were induced to differentiate by treatment with insulin (0.8 µM), dexamethasone (0.25 µM), and isobutylmethylxanthine (400 µM). Preadipocytes are defined as fibroblasts treated with the differentiation cocktail for 6 h. Another set of cells was allowed to undergo the 7-d differentiation protocol and develop into fully differentiated adipocytes with large triglyceride droplets comprising most of each cell. The effects of PPAR{gamma} agonist (TZD) were studied on both cell types, preadipocytes and mature adipocytes. A short time frame of treatment (6 h) was chosen to identify predominantly direct transcriptional responses. RNA samples were prepared from the following five groups of cells: fibroblasts, preadipocytes (treated with differentiation cocktail for 6 h), preadipocytes treated with TZD, mature adipocytes, and mature adipocytes treated with TZD for 6 h.

In addition to unique experimental design, we applied a novel statistical and clustering paradigm to analyze the data from this study. For each probeset an index of gene expression was calculated and analyzed using the SAFER algorithm (20). The SAFER gene expression index is a robust and resistant measure of gene expression that is an alternative to the average difference calculated by the Affymetrix analysis software and the model-based expression index proposed by Li and Wong (23). Like the procedure used by Li and Wong, the procedure for calculating the SAFER gene index involves both between-array normalization and an adjustment for probe-specific biases. Differences in the mean level of the gene expression index between experimental conditions (e.g. adipocytes vs. preadipocytes) were assessed using a gene-specific ANOVA model. The model facilitated estimation of ratios comparing the experimental conditions and calculation of P values testing whether the ratios are different from 1 (i.e. a ratio of 1 implies no change between the means for the experimental conditions). By fitting a separate ANOVA model for each gene, differences were assessed using an error term that included biological variability between samples and did not assume that this variability was the same for all genes.

The 6347 genes were first filtered to find only the "interesting" genes. These genes were defined as those that changed reliably and consistently for at least one of the five ratios: preadipocyte/fibroblast, preadipocyte plus TZD/preadipocytes, adipocytes/fibroblast, adipocytes/preadipocytes, and adipocytes plus TZD/adipocytes. For a change to be reliable and consistent it needed to fulfill three criteria: 1) the corresponding ratio was more than 1.5 (or <2/3 = 1/1.5); 2) the P value of the ratio was less than 0.01; and 3) the mean expression value of the gene in at least one of the two samples was more than 40. The ratio limit of 1.5-fold up- or down-regulation ensured that the change was substantial. The P value limit ensured that the change was unlikely to be due to noise alone. The lower threshold of 40 was used to filter out genes that may be expressed at levels that are below the limit that can reliably be detected by the assay. This threshold has been determined by looking at the expression indexes of negative controls. This filtering reduced the number of genes considered to 579.

The numbers of gene expression changes are indicated in Table 2Go. These data indicate profound differences between preadipocytes and fibroblasts (86 genes altered) and by TZD treatment of mature adipocytes (71 genes altered), and even greater differences between adipocytes and fibroblasts (453 genes altered). Only 4 genes were regulated by TZD treatment of preadipocytes. In most of these pairwise comparisons, more genes were down-regulated than up-regulated.


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  		Table 2. Overview of numbers of genes up-regulated (bold) or down-regulated (italic) between pairs of treated samples

 
Coordinated regulation of genes controlling cell growth and differentiation
To further analyze the gene expression pattern, the 579 genes were clustered using the UPGMA clustering algorithm based on the five ratios described above. Let gene X have the five ratios (as listed above) Xi, i = 1, 2, ... 5, and let xi = log(Xi), and similarly for genes Y and yi. To be consistent with the method for filtering genes, the distance metric used was the following modified Euclidean distance:



In this equation, H is the Heaviside step function satisfying H(x) = 0 if x < = 0 and H(x) = 1 if x > 0, max(Xi) is the maximum (numerator Xi, denominator Xi) with numerator and denominator being the original SAFER data for the individual conditions (e.g. fibroblast, adipocyte) being compared in Xi, p(xi) is the P value generated by the SAFER algorithm, sgn(x) represents the sign function where sgn(x) = 1 if x> = 0 and sgn(x) = -1 if x < 0, and k is a dendrogram tuning parameter. The dendrogram tuning parameter was tested and shown to have no impact on cluster membership over at least 4 orders of magnitude. Its only purpose is to make it easier to visually identify the clusters from the dendrogram. Figure 1AGo uses a k of 40.



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  		Figure 1. Cluster analysis of gene expression profile. Heat map of ratios (A) and profiles of cluster bins (B). Columns represent ratios as follows: 1) preadipocytes/fibroblasts, 2) preadipocytes plus TZD/preadipocytes, 3) adipocytes/fibroblasts, 4) adipocytes/preadipocytes, and 5) adipocytes plus TZD/adipocytes. Heat map A displays values of log2(ratio), with green representing -3, and red +3. The tree represents the clustering dendrogram from the UPGMA version of average linkage clustering as described in the text. The profiles indicate whether genes in a given cluster are significantly up-regulated, down-regulated, or not changed (NC), and this analysis was performed for each ratio (i.e. for each column 1, 2, ... 5). The definitions of up, down, and NC correspond to bins described in the text. The sixth heat map column represents cluster number (from 1–32 from top to bottom; see text), which matches the correspondingly numbered and colored profile in the bin profiles plot.

 
To further ensure that the chosen genes and resulting clusters were significant, we tested the independence of the columns using the {chi}2 test of independence. Each gene was placed into 1 of 34 = 81 buckets. A bucket is a four-tuple of bins, where a bin means reliable and consistent (as defined mathematically above) up vs. down vs. no change categorization of the processed expression values. The 4 bins correspond to the 4 ratio columns with the largest number of changed genes. (As indicated in Table 2Go, only 4 of 579 genes were up- or down-regulated in the column for preadipocytes+TZD/preadipocytes, so that column was dropped from this calculation.) The {chi}2 test compares the number of genes in each bucket to the expected number of genes that would be in that bucket if the columns were independent. The {chi}2 test yielded a P < 10-100. This small P value indicates strong evidence of a relationship between the states of the four columns when they are separated into bins as described above. Because some of the expected four-tuples were seen so rarely, the {chi}2 test might not be valid. To elucidate any effect this might have on our P value, we calculated the {chi}2 statistic 106 times using a Monte-Carlo method that independently sampled the empirical distribution of each of the 4 columns of 579 genes. This simulation also indicated that the P value is significantly less than 10-100, thus confirming the {chi}2 results.

The clustering analysis indicated that the 579 genes can be clustered into 32 distinct groups, and the clustering tree and the heat maps are shown in Fig. 1AGo. The profiles of the clustering bins are shown in Fig. 1BGo. Table 3GoGo illustrates the number of genes and representative genes in each cluster.


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  		Table 3A. Clusters of genes by the UPGMA version of the average linkage algorithm

 

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  		Table 3B. Continued

 
Murine 3T3-L1 cells differentiate into fat-laden adipocytes in response to a cocktail of adipogenic hormones. Earlier studies have shown that this conversion process occurs in two discrete steps. During an early clonal expansion phase, confluent 3T3-L1 cells proliferate and express the products of the ß and {delta} members of the CCAAT/enhancer binding protein (C/EBP) family of transcription factors (4, 24). The cells subsequently arrest mitotic growth, induce the expression of a number of other adipocyte-specific genes, including C/EBP{alpha}, and acquire the morphology of fully differentiated adipocytes (25, 26). In the current study the gene expression pattern of preadipocytes that were treated with the differentiation cocktail for 6 h was compared with that of fibroblasts to assess the early response genes in the clonal expansion phase. As shown in Fig. 1BGo and Table 3GoGo, clusters 1–4 represent genes that were down-regulated in preadipocytes compared with fibroblasts. Most of these genes are involved in the regulation of cell cycle and growth. Stromal cell-derived factor 1 is a member of the CXC group of chemokines. Stromal cell-derived factor 1 and its physiological receptor, CXCR-4, play an important regulatory role in leukocyte and hemopoietic precursor migration and pre-B cell proliferation (27). Matrix metalloproteinase 14 is expressed in stromal cells and plays a critical role in regulating tissue-invasive phenotypes in normal and neoplastic cells (28). Rapid down-regulation of these genes by the adipogenic hormonal cocktail may set the stage for cells to change from stromal vascular lineage to differentiated adipocyte lineage. Cysteine-rich protein 61 is an immediate-early gene that is rapidly activated by serum or platelet-derived growth factor in mouse 3T3 fibroblasts (29). Growth arrest-specific gene encodes a protein that is a component of negative regulatory circuit that controls cell growth suppression (30). Cell division cycle 37 is a protein kinase-targeting subunit of 90-kDa heat shock protein that binds and stabilizes cyclinin-dependent kinase 4 (31). FISP 12 is a secreted protein rich in cysteine, and it belongs to the same growth factor-induced immediate-early gene family as cysteine-rich protein 61 (32). Platelet-derived growth factor (PDGF) and its receptors are known to play important roles in cell growth (33). In addition, treatment of fibroblasts for 6 h with the adipogenic hormonal cocktail also led to the reduction of transcription factors, including activating transcription factor 1 (34), upstream binding factor (35), and zinc finger protein 148 (36). All of the genes in clusters 1–4 remain down-regulated or unchanged in adipocytes, and they appear not to be regulated by TZD in either preadipocytes or adipocytes.

Clusters 23–26 represent genes that are also rapidly down regulated in preadipocytes compared with fibroblasts and they are up regulated in fully differentiated adipocytes compared with preadipocytes. The genes in these clusters include those that are involved in cell growth control (Mnt) (37), stress response [oxidative stress-induced protein (38) and CHOP 10 (39)], protein synthesis, and lipid metabolism. CHOP 10 is a protein homologous to C/EBP proteins. It functions as an antagonist of C/EBPß, and it inhibits adipocyte differentiation (40). Adipocyte differentiation-related protein (ADRP) is a fatty acid-binding protein that specifically facilitates the uptake of long-chain fatty acids. Earlier investigation provided evidence that ADRP mRNA and protein expression in preadipocytes were stimulated by fatty acids in a time- and dose-dependent fashion (41). In the present study ADRP was transiently down-regulated in preadipocytes. Interestingly, PPAR{gamma} agonist up-regulated ADRP in preadipocytes, but not in mature adipocytes.

Clusters 6, 7, 12, 16, and 20–22 represent genes that are rapidly and transiently up-regulated in preadipocytes compared with fibroblasts. The functions of these genes encompass the regulation of cell growth, adhesion, signaling, and metabolism. Consistent with earlier reports, C/EBPß (cluster 21) was up-regulated in preadipocytes, but remained unchanged comparing differentiated adipocytes with fibroblasts or with treatment of TZD.

Clusters 10 (171 genes), 13 (53 genes), and 15 (45 genes) contain genes that are unchanged in preadipocytes. These genes were down-regulated in fully differentiated adipocytes compared with fibroblasts or preadipocytes, and they were not subject to regulation by TZD. Most of these genes have functions in the control of cell growth, apoptosis, adhesion, cellular structure, and stress response.

Clusters 17 and 18 contain genes that were unchanged during adipocyte differentiation. However, the 12 genes in cluster 17 were up-regulated in adipocytes by 6-h treatment with TZD, whereas the 11 genes in cluster 18 were down-regulated by TZD in adipocytes. These genes have functions in regulating cell cycle and growth.

Regulation of genes controlling adipocyte phenotype and lipid metabolism
Large portions of genes in clusters 26–32 are adipocyte specific or involved in metabolism. The profile of gene expression of cluster 27 (29 genes) is exemplified by PPAR{gamma}: unchanged in preadipocytes, up-regulated in fully differentiated adipocytes, and down-regulated in adipocytes by TZD. PPAR{gamma} is the master gene for adipogenesis (6, 7). The profile of PPAR{gamma} expression in this study is consistent with the earlier observations (42, 43). The other two genes in this cluster encode two adipocyte-secreted proteins: adipsin and angiotensinogen. Adipsin is a serine protease that is secreted by adipocytes into the circulation. It is deficient in several animal models of obesity. Adipsin can activate the alternative pathway of complement, and it may play an important role in the regulation of systemic energy balance in vivo (44). Angiotensinogen is the precursor of angiotensin II. Adipose tissue is an important source of angiotensinogen, and a complete functional renin-angiotensin system exists in human and rodent adipose tissues (45, 46). In obese patients the involvement of angiotensin II as a consequence of increased plasma angiotensinogen secreted from adipose tissue has been proposed in the development of hypertension. There are several lines of evidence supporting the idea that angiotensinogen (via angiotensin II) plays an important role in the development of adipose tissue (45, 47). First, angiotensin II stimulates the production and release of prostacyclin from adipocytes in vitro, which, in turn, promotes the differentiation of precursor cells into adipocytes. Second, both angiotensin II and (carba)prostacyclin promote the formation of new fat cells ex vivo and in vivo. Lastly, angiotensinogen knockout mice exhibit an impairment of adipose tissue development. These data are consistent with an autocrine/paracrine mechanism, implicating angiotensinogen system in adipose tissue development. After TZD treatment of adipocytes, the down-regulation of PPAR{gamma}, adipsin, and angiotensinogen may provide a feedback loop to regulate the adipogenesis/differentiation program.

Clusters 28, 29, and 30 contain genes that are up-regulated in adipocytes compared with fibroblasts and/or preadipocytes. These genes are not regulated by the 6-h TZD treatment of either preadipocytes or adipocytes. The majority of the genes in these clusters are involved in lipid metabolism, and it is meaningful that these genes are regulated in a coordinated fashion. The adipocyte complement-related protein of 30 kDa, also known as adiponectin, was cloned as a novel serum protein secreted by adipocytes and is similar to complement protein C1q (48). The circulating level of adiponectin is reduced in obesity and type 2 diabetes and is correlated with insulin resistance and hyperinsulinemia (49, 50). PPAR{gamma} ligands increase expression and plasma concentrations of this protein (51, 52). This protein has also been shown to enhance hepatic insulin action (53), reverse insulin resistance associated with both lipoatrophy and obesity (54), and increase fatty acid oxidation in muscle and cause weight loss in mice (55).

A number of transcription factors are up-regulated in adipocytes during the differentiation process, including signal transducers and activators of transcription (STATs) 1, 3, and 5. The expression of STATs during differentiation is PPAR dependent (56), and the STAT1-binding site has been identified in the promoter of PPAR{gamma}2 (57). In the same cluster there are other genes involved in the control of cell growth and cycle, including vascular endothelial growth factor and IGF. A large number of other genes encoding proteins/enzymes involved in lipid, carbohydrate, and amino acid metabolism are also cosegregated in this cluster.

Two adjacent clusters (no. 31 and 32) contain two genes that are well studied in adipocytes: stearoyl-coenzyme A desaturase (58) and adipocyte protein aP2 (59).

Assessment of gene expression profiles with quantitative PCR analysis
The expression of a subset of the genes during differentiation and in response to treatment with PPAR{gamma} agonists was further characterized using quantitative PCR (TaqMan) in a separate study. 3T3-L1 fibroblast were induced to differentiate with the hormonal cocktail in the presence or absence of PPAR{gamma} agonists for 6 or 48 h. Another set of cells was fully differentiated into mature adipocytes and subsequently treated with the PPAR{gamma} agonist for 6 or 48 h. A TZD and novel non-TZD PPAR agonists with distinct chemical structures were used in the study to ascertain that the effect of the compounds are mediated by PPAR{gamma} activation. The changes in gene expression revealed by this analysis are grouped into five distinct profiles (see Fig. 2GoGo, A–E) as follows: 1) rapidly and transiently induced during adipocyte differentiation, down-regulated by PPAR{gamma} agonists in adipocytes (e.g. N10 nuclear hormone binding receptor, thrombomodulin, and fibronectin); 2) rapidly and transiently reduced during adipocyte differentiation, down-regulated by PPAR{gamma} agonists in adipocytes (e.g. PDGF receptor and PDGF-inducible gene); 3) induced during adipogenesis and further increased by PPAR{gamma} agonists (e.g. aP2, CD36, c-Cbl-associated protein, and G0/G1 switch gene); 4) unchanged during differentiation and induced by PPAR{gamma} agonists (e.g. 17 ß-hydroxysteroid dehydrogenase type IV and caspase 8); and 5) induced in adipocytes, unchanged or down-regulated by PPAR{gamma} agonists (e.g. lipoprotein lipase, IGF-binding protein 4, C/EBP{alpha}, orphan nuclear receptor RVR, and ß3-adrenergic receptor). Overall, there is good agreement between the profiles of gene expression delineated by microarray technology or quantitative PCR analysis.



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  		Figure 2A. Effect of differentiation and PPAR{gamma} agonists on gene expression in 3T3-L1 cells. 3T3-L1 fibroblasts were induced to undergo differentiation process with the hormonal cocktail as described in the absence or presence of PPAR{gamma} agonists for 6 and 48 h. Fully differentiated adipocytes were treated with or without PPAR{gamma} agonists for 6 and 48 h. RNA samples were prepared from cells at the indicated time points after treatment and analyzed using quantitative PCR for the indicated genes. Values shown are the mean ± SEM of three determinations. {square}, DMSO control; {blacksquare}, cells treated with TZD; , cells treated with nTZD.

 


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  		Figure 2B. Continued.

 
ß-Catenin is a PPAR{gamma} response gene in vitro and in vivo
Adipocytes are derived from mesodermal stem cells that have the potential to differentiate into a variety of cell types (60). The adipogenesis process is control by a number of master genes (3). The Wnt signal transduction pathway is involved in many differentiation events during embryonic development and can lead to tumor formation after aberrant activation of its components. Wnts are a family of secreted proteins that function in paracrine and autocrine fashions and regulate developmental processes. The signal pathway of Wnt involves a complex network of protein and regulatory factors (61, 62). The cytoplasmic component ß-catenin is central to the transmission of Wnt signals to the nucleus. In the absence of Wnt, ß-catenin is phosphorylated and subsequently degraded in proteosomes. In the presence of Wnt, phosphorylation and degradation of ß-catenin are blocked, thereby allowing the translocation of ß-catenin into the nucleus, association with T cell factor/lymphoid enhancing factor transcription factors, and activation of Wnt target genes (61, 62). Recently, it has been shown that Wnt signaling, probably mediated by Wnt-10b, is a molecular switch that governs adipogenesis (63). Several lines of experimental data suggest that Wnt signaling maintains preadipocytes in an undifferentiated state through inhibition of the adipogenic transcription factors C/EBP{alpha} and PPAR{gamma}: 1) preadipocytes differentiated into adipocytes when Wnt signaling is inhibited in these cells; 2) overexpression of Wnt-1 in 3T3-L1 fibroblasts prevented adipogenesis, and such an effect of Wnt could be attenuated by overexpression of C/EBP{alpha} and PPAR{gamma} in cells; and 3) disruption of Wnt signaling also causes trans-differentiation of myoblasts into adipocytes in vitro (63).

In the current study the ß-catenin mRNA level was significantly reduced in 3T3-L1 adipocytes compared with fibroblasts (Fig. 3AGo). Treatment of the adipocytes with a TZD or a non-TZD PPAR{gamma} agonist for 6 h further reduced the expression of ß-catenin in adipocytes. Western blot analysis demonstrated similar changes in the protein levels of ß-catenin (Fig. 3bGo). To investigate the effect of PPAR{gamma} agonist on ß-catenin expression in vivo, male db/db mice with overt hyperglycemia were treated with rosiglitazone at a dose of 10 mg/kg·d via daily oral gavage. After 11 d of treatment, samples of white adipose tissue (epididymal) and plasma were obtained (24 h after the last dose of rosiglitazone). The values for glucose, triglycerides, and body weight in rosiglitazone-treated mice vs. db/db mice treated only with vehicle and lean control mice are shown in Table 4Go. Rosiglitazone treatment was associated with significant reductions in hyperglycemia and hypertriglyceridemia levels and a gain in body weight in db/db mice.



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  		Figure 3. ß-Catenin is a target gene of PPAR{gamma} in vitro and in vivo. The levels of ß-catenin mRNA expression were quantified by quantitative real-time PCR. A, 3T3-L1 adipocytes were treated for 6 h with TZD and nTZD. RNA samples were prepared from the indicated cells. The levels of ß-catenin mRNA expression are expressed relative to the amount of mRNA found in fibroblasts. Values shown are the mean ± SEM of three determinations. #, P < 0.05 comparing adipocytes with fibroblasts. *, P < 0.05 comparing control adipocytes with adipocytes treated with TZD or nTZD. B, 3T3-L1 fibroblasts, adipocytes, and adipocytes treated with TZD for 24 h were lysed, and ß-cantenin protein levels were determined in the lysates by Western blot analysis. C, Rosiglitazone (rosi) was administered to db/db mice for 11 d. Total RNA was isolated from epididymal white adipose tissue and liver of the lean controls, vehicle-treated db/db controls, and db/db treated with rosi. The levels of ß-catenin mRNA expression are expressed relative to the amount of mRNA found in the lean controls. The data are shown as the mean ± SEM of seven individual samples from each treatment group. *, P < 0.05 comparing data to lean controls by t test.

 

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  		Table 4. In vivo effects of rosiglitazone treatment in db/db mice

 
Measurement of adipose tissue ß-catenin mRNA by quantitative RT-PCR revealed that ß-catenin mRNA levels in vehicle-treated db/db control mice were similar to those in lean control mice (Fig. 3CGo). TZD2 treatment of db/db mice was associated with a significant reduction in mean ß-catenin mRNA levels in adipose tissue, but not in liver. As a control, we also examined the effect of rosiglitazone treatment on the expression of long-chain fatty acyl-coenzyme A synthase, a key enzyme in fatty acid synthesis. In contrast to ß-catenin, long-chain fatty acyl-coenzyme A synthase mRNA levels were significantly induced in adipose tissue and liver after in vivo rosiglitazone treatment (data not shown). These data suggest that ß-catenin is a new target gene of PPAR{gamma}. The down-regulation of ß-catenin by two different PPAR{gamma} agonists in 3T3-L1 adipocytes and by rosiglitazone in vivo may serve to dampen the antiadipogenic effect of Wnt signaling, thereby maintaining adipocytes in the differentiated states.

In summary, the current study examined the gene expression profiles during 3T3-L1 adipocyte differentiation and the effect of PPAR{gamma} activation during the differentiation process. In recent years there have been a number of microarray studies describing gene expression in the 3T3-L1 cell system (21) and in human adipocytes (22). The effect of PPAR{gamma} agonists on gene expression was also examined in the Zucker diabetic fatty rat model (64). In this study we applied the novel ratiometric clustering method to convert a massive amount of DNA chip data into meaningful information. The transcriptional patterns revealed in this study provide new insights that will further the understanding of function of PPAR{gamma} activation in cells and in animals.


   Acknowledgments
 


   Footnotes
 
Abbreviations: ADRP, Adipocyte differentiation-related protein; aP2, adipocyte P2; C/EBP{alpha}, CCAAT/enhancer binding protein-{alpha}; DMSO, dimethylsulfoxide; nTZD, 3-chloro-4-(3-(3-phenyl-7-propylbenzofuran-6-yloxy)propylthio)phenylacetic acid; PDGF, platelet-derived growth factor; STAT, signal transducer and activator of transcription; TZD, thiazolidinedione; UPGMA, unweighted pair group method with arithmetic mean.

Received December 5, 2001.

Accepted for publication February 14, 2002.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
 References
 

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