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*Nutrition
*Obesity
The Journal of Clinical Endocrinology & Metabolism Vol. 89, No. 3 1379-1384
Copyright © 2004 by The Endocrine Society

Molecular Determinants of Human Adipose Tissue: Differences between Visceral and Subcutaneous Compartments in Obese Women

V. Giusti, M. Suter, C. Verdumo, R. C. Gaillard, P. Burckhardt and F. P. Pralong

Departments of Internal Medicine (V.G., C.V., P.B.) and Surgery (M.S.) and Division of Endocrinology, Diabetology, and Metabolism (R.C.G., F.P.P.), Centre Hospitalier Universitaire Vaudois, Lausanne, 1011 Switzerland

Address all correspondence and requests for reprints to: Vittorio Giusti, M.D., Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland. E-mail: vittorio.giusti{at}chuv.hospvd.ch.


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Discussion
 References
 
The adipose tissue is playing an important role in the development of human obesity and its related comorbidities, but little is known about the mechanisms governing its differentiation and proliferation. In this work, we studied the expression of transcription factors involved in fat storage and metabolic regulations in adipose tissue of 50 well-characterized obese women. In multivariate analyses, 80% of c enhancer binding protein {alpha} (cEBP{alpha}), c and a sterol regulatory element binding protein 1 (c and a SREBP1), and retinoid X receptor (RXR{alpha}) levels in sc adipose tissue (SAT) could be explained by other transcription factors. In addition, RXR{alpha} was the major determinant of peroxisome proliferator and activated receptor-{gamma}1 variability in SAT, with the two factors being involved in the determination of the variability of insulin resistance. In contrast, the levels of all these transcription factors, together with various phenotypic and biological characteristics of the patients, seemed to participate only marginally in the regulation of visceral adipose tissue activity. In similar multivariate analyses, they could explain only a minor part of the variability of cEBP{alpha}, c and a SREBP1, or RXR{alpha}, suggesting the involvement of other regulators. Overall, our results demonstrate a different regulation of visceral adipose tissue and SAT and a different role of both tissues in insulin resistance and lipid storage.


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Discussion
 References
 
MATURE ADIPOSE CELLS represent the major component of adipose tissue. In addition to their role in the storage and mobilization of triglycerides (1, 2), the adipocytes have recently become increasingly recognized because true endocrine cells, capable of synthesizing and releasing a variety of peptide and nonpeptide compounds (3, 4, 5, 6). These endocrine characteristics probably represent a fundamental prerequisite to the existence of functional cross-talks between adipocytes or between the entire adipose tissue and other organs such as the brain, muscle, liver, and pancreas (7, 8, 9, 10). These interactions are probably crucial to the overall regulation of energy storage. Because this is a function ultimately assumed by adipocytes, it has been suggested that the adipose tissue may play an important pathophysiological role in various metabolic disorders (11). Therefore, a better understanding of the function of this tissue in clinical conditions such as obesity, diabetes mellitus, or the metabolic syndrome might lead to the identification of novel therapeutic targets.

In this perspective, a cascade of transcription factors acting cooperatively to promote the adipocyte differentiation program has been identified. The differentiation of adipogenic precursor cells into mature adipocytes is a complex phenomenon, characterized by the carefully ordered expression of adipocyte-specific genes triggered by a set of interacting transcription factors (12, 13, 14). During this process, the adipose tissue exhibits the remarkable capacity to alter dramatically its structure as well as its function. This plasticity underlies the heterogeneous nature of human adipose tissue: indeed, its sc and visceral compartments present significant differences in morphology, physiology, metabolic activity, and hormonal sensitivity (11, 15, 16, 17).

The peroxisome proliferator and activated receptors (PPARs) are among the most important transcription factors involved in the process of adipocyte differentiation. They probably play crucial roles in the induction of adipose- specific genes and in the phenotypic manifestations of mature adipose tissue (18, 19, 20, 21, 22). However, the specific roles played by PPARs, and notably PPAR{gamma}s, in the regulation of adipogenesis in the two different types of adipose tissue remains to be clearly delineated. In addition, the activation, the maintenance and the modulation of the expression of PPAR{gamma}s are controlled through a variety of other transcription factors, adding to the complexity of these mechanisms. Indeed, it has been shown that the enhancer binding protein {alpha} (cEBP{alpha}), the a and c sterol regulatory element binding protein 1 (aSREBP1, cSREBP1), the retinoid X receptor {alpha} (RXR{alpha}) and the PPAR-{gamma} coactivator 1 (PGC1) all contribute to the differentiation of preadipocytes as well as to the regulation of the function of mature adipocytes (1, 2, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33).

We reported recently that the levels of expression of PPAR{gamma}2 are more than 20-fold those of PPAR{gamma}1, in both sc (SAT) and visceral (VAT) adipose tissue of severely obese women (34). However, only PPAR{gamma}1 was regulated differentially between the two types of tissues, its levels being higher in SAT than in VAT. In the present study, we assessed the relative expression of other known transcription factors, coactivators, or modulators previously implicated in the cascade of events that induce and maintain the expression of PPAR{gamma} (2, 27, 35, 36, 37, 38). These data now allow us to better delineate the molecular mechanisms controlling the expression of PPAR{gamma}, thus providing a better insight into the proliferation, differentiation, and maturation of human adipose tissue (11).


   Patients and Methods
Top
 Abstract
 Introduction
 Patients and Methods
Results
 Discussion
 References
 
Patients and phenotyping

Our cohort of obese patients has already been described in a recent paper from our group (34). It consists of 50 obese women [body mass index (BMI) > 35 kg/m2] of Caucasian origin referred consecutively over a period of 24 months for weight reduction surgery. As before, the use of oral glucose or lipid-lowering agents, weight-reduction therapies, or antihypertensive drugs constituted exclusion criteria. In addition, all patients provided informed consent, and the study was approved by the institutional review board.

Preoperative phenotyping included measurement of: weight, height, BMI, and waist and hip circumference as previously reported (39). Preoperative biochemical work-up included measurements of fasting blood glucose (Ecoline 100 Merck, KGaA, Darmstadt, Germany), total cholesterol (Roche CHOD-PAP, Roche Molecular Biochemicals Systems, GmbH, Mannheim, Germany), high-density lipoprotein cholesterol (HDL-C plus, second generation, Roche Diagnostic GmbH, Mannheim, Germany) and triglycerides (TG GPO-PAP, Roche Diagnostic) using an automatic Hitachi 917 Roche apparatus. Low-density lipoprotein cholesterol was then calculated by the Friedwald’s formula. Insulin (Adaltis Insulin, code 10624, Casalecchio di Reno, BO, Italy) and leptin (Linco, St. Charles, MO) were measured by RIA, whereas HbA1c (Hemoglobin A1c variant, Bio-Rad, Laboratories GmbH, München, Germany) was measured by HPLC. Insulin resistance was calculated using the homeostasis model assessment (HOMA) equation [(insulin x glucose)/22.5] (40).

Biopsies and RNA preparation

The collection of adipose tissue samples and the extraction of total RNA were also previously described (41). Briefly, 5 cm3 of VAT was obtained perioperatively at the level of the omentum, and another 5 cm3 of SAT was taken at the level of the umbilical fold. Tissue samples were placed on ice in the operating room and total RNA extracted the same day: 1 g of adipose tissue was homogenized in 8 ml of a solution containing guanidinium thiocyanate (4 M, Fluka) and ß-mercaptoéthanol (1,2 x 10-7 M) using a Polytron homogenizer. Samples were heated for 2 min at 37 C to liquefy the lipids, shaken vigorously, passed several times through a 21G needle to disrupt the top layer of cells and shear genomic DNA, and centrifuged at 10,000 rpm to separate fat from the rest of the solution. The lower aqueous phase was transferred onto a cesium chloride cushion (5.7 M) and submitted to ultracentrifugation at 35,000 rpm overnight. The resulting pellet was resuspended into sodium acetate [0.3 M (pH 6.0)], then ethanol (100%) precipitated, washed, and finally resuspended into diethylpyrocarbonate-treated water and stored at -80 C until use.

Quality of total RNA was assessed using a commercially available kit (RNA 6000 LabChip kit, Agilent Technologies, Meyrin, Switzerland) and an Agilent 2100 bioanalyzer. Quantification was achieved by measuring light absorbency at 260 nm. In cases in which either total RNA quality or quantity was not sufficient to allow further analysis, the extraction procedure was repeated, using frozen tissue samples.

Semiquantitative RT-PCR

The relative expression of cEBP{alpha}, cSREBP1, aSREBP1, RXR{alpha}, and PGC1 was assessed by real-time quantitative RT-PCR, using the LightCycler technology (Roche Diagnostics, Rotkreuz, Switzerland) with SYBR green detection. Reverse transcription was performed with random primers, and all the primer pairs used in the various PCR are listed in Table 1Go. For each product, a standard curve was created with serial dilutions of a PCR fragment cloned into pGEM-T (pGEM-T easy Vector system I, Promega, Madison, WI), achieving a sensitivity of 10 copies/tube. Different dilutions of the samples were tested in preliminary experiments to ensure that quantification would be performed within the linear part of this standard curve. After this test, all samples were quantified in at least two different runs. The interassay percent coefficient of variation was between 5 and 15%, and a third run was performed for samples with an interassay coefficient of variation greater than 10%. For quantification purposes, cEBP{alpha}, cSREBP1, aSREBP1, RXR{alpha}, and PGC1 mRNA levels were always reported to the levels of ß2-microglobulin, a constitutively expressed gene.


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  		TABLE 1. Sequences of the various PCR primers

 
Data analysis

Normalization of the expression of the various transcription factors was achieved by calculating for each sample the ratios of cEBP{alpha}, cSREBP1, aSREBP1, RXR{alpha}, or PGC1 over ß2-microglobulin, respectively. For comparisons between VAT and SAT expression of individual transcription factors, a value of 100% was attributed to the levels measured in VAT, and SAT levels were expressed as a percent of these VAT levels. All results are reported as means ± SEM, and potential differences existing between VAT and SAT were assessed by Student’s t test.

Then potential correlations between the expression in VAT or SAT of each transcription factor and the phenotype of the patients were evaluated by univariate Spearman’s correlation analysis. Differences were considered significant when P < 0.05. The second step of our analysis consisted in correlating the levels of each of these transcription factors with those of PPAR{gamma}1 and PPAR{gamma}2, using our recently reported data on PPAR{gamma}1 and PPAR{gamma}2 expression in the same SAT and VAT samples (34). Next, a stepwise regression analysis was performed to identify significant links between the transcription factors evaluated in the present study and the levels of expression of PPAR{gamma}1 and PPAR{gamma}2. For this analysis, mRNA levels of PPAR{gamma}s were used as the dependent variables. The various biological parameters and anthropometric measures recorded for all the patients, as well as the mRNA levels of the other transcription factors evaluated, were introduced in the model as the independent variables.

Then a further stepwise regression analysis was performed to identify the factors involved in the modulation of the expression of cEBP{alpha}, cSREBP1, aSREBP1, RXR{alpha}, and PGC1, respectively. To do this, each of these transcription factors was introduced sequentially as the dependent variable in the statistical model, whereas all the other factors (including PPAR{gamma}1 and PPAR{gamma}2) together with anthropometric and biological data were used as the independent variables. Finally, the potential correlations between the various biological parameters or anthropometric measurements and the expression of transcription factors were evaluated. The R2 values presented in all stepwise regression analyses refer to all variables included in the multivariate models. All analyses were performed using the Jmp 4 statistical package (SAS Institute, Cary, NC).


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Discussion
 References
 
The detailed phenotype of our patients has already been reported (34). The mean age of patients was 38 ± 1.2 yr (range 21–55 yr), they were all markedly obese (mean BMI 46 ± 0.9 kg/m2, range 36.8–64.9),and their mean fasting insulin levels were 30.8 ± 3.9 UI/liter (range 9.9–193). Of these 50 patients, five had a diagnosis of diabetes mellitus and were treated with insulin. In addition, 15 patients suffered from dyslipidemia. Figure 1Go displays the relative expression of cEBP{alpha}, cSREBP1, aSREBP1, RXR{alpha}, and PGC1 between the SAT and VAT of these patients. Among these, only cEBP{alpha} and cSREBP1 were differentially regulated, both being expressed at significantly higher levels in SAT than VAT: cEBP{alpha} levels in SAT amounted to 154 ± 17% those in VAT (P < 0.01), whereas cSREBP1 levels in SAT were 183 ± 25% those measured in VAT (P < 0.0001).



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  		FIG. 1. Messenger RNA levels of RXR{alpha}, PGC1, cEBP{alpha}, aSREBP1, and cSREBP1 in VAT and SAT of obese women, demonstrating the differential expression of cEBP{alpha} and cSREBP1 between both tissues. Results are expressed as percent of VAT levels (means ± SEM). **, P < 0.01.

 
In univariate analysis, no significant correlation was found between any of the transcription factors studied and the various phenotypic characteristics of the patients (data not shown). In contrast, a similar univariate analysis performed between PPAR{gamma}1 and RXR{alpha} levels in SAT demonstrates a highly significantly positive correlation between the two transcription factors (Fig. 2Go, P < 0.0001). Also in univariate analysis, SAT levels of PPAR{gamma}2 were positively correlated with VAT levels of RXR{alpha} (r = 0.3576, P < 0.02, data not shown), and PPAR{gamma}2 in VAT was highly significantly correlated with cEBP{alpha} (r = 0.3918, P < 0.001, data not shown).



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  		FIG. 2. Results of univariate Spearman’s analysis, demonstrating a positive correlation between PPAR{gamma}1 and RXR{alpha} mRNA levels in SAT. Results are normalized for the expression of ß2-microglobulin (ADU, arbitrary densitometric units).

 
Table 2Go summarizes the results of the stepwise regression analyses performed with PPAR{gamma}1 or PPAR{gamma}2 as the dependent variables. These results confirm data obtained in univariate analysis, showing that the levels of RXR{alpha} in SAT accounted for 60% of the variability of PPAR{gamma}1 expression (P < 0.00001). Other significant determinants of SAT PPAR{gamma}1 expression were VAT levels of aSREBP1 and PPAR{gamma}1 itself. Consistently, the major determinant of PPAR{gamma}1 expression in VAT was the SAT levels of PPAR{gamma}1 that accounted for 24% of its variability (P < 0.001). In a similar model, the expression of cEBP{alpha} explained 15% of the variability of PPAR{gamma}2 in VAT (P < 0.01). Finally, the VAT levels of RXR{alpha} could explain 13% of the variability of PPAR{gamma}2 expression in SAT (P < 0.02).


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  		TABLE 2. Results of stepwise regression analyses, illustrating the parameters significantly linked to PPAR{gamma}1 or PPAR{gamma}2 expression VAT and SAT

 
Table 3Go summarizes the results of different stepwise regression analyses, using each transcription factor as the dependent variable. Of note, only the two most important factors identified by the statistical model are displayed in this table for clarity. These data demonstrate that for all transcription factors except PGC1, more than half of the variability of their expression in SAT could be accounted for by a single other factor (in italics and boldface) involved in the adipogenic process. In addition, and this was true for all factors except PGC1, the variables taken into consideration allowed to explain between 80 and 90% of their respective variability in SAT (data not shown). In contrast, a much smaller portion of the variability of expression of the same factors in VAT could be explained by the same analysis.


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  		TABLE 3. Stepwise regression analyses, demonstrating that the different transcription factors are regulated differentially in VAT and SAT

 
Next, similar stepwise regression analyses were performed using different biological determinants of glucose metabolism as the dependent variables (HbA1c, fasting plasma insulin, and glucose levels, HOMA index). Table 4Go summarizes these results. These data show that visceral cSREBP1 expression is a significant determinant of the variability of both HbA1c (P < 0.01) and fasting plasma glucose (P < 0.05). Furthermore, 23% of the variability of fasting plasma insulin levels could be attributed to SAT levels of RXR{alpha} (together with triglyceride levels) and 24% of the variability of the HOMA index could be attributed to SAT levels of PPAR{gamma}1 (also together with triglyceride levels).


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  		TABLE 4. Stepwise regression analyses, demonstrating the significant determinants of the parameters of glucose metabolism and of abdominal and peripheral adipose tissue distribution

 
As anticipated, there was a significant positive correlation between plasma leptin levels and the BMI of our patients (r = 0.4665, P < 0.001). A final stepwise regression analysis using either waist or hip circumference as the dependent variable showed that PPAR{gamma}1 levels in VAT and SAT together with cSREBP1 levels in VAT accounted for 29% of waist circumference variability (P < 0.005). In contrast, 28% of hip circumference variability was explained by plasma leptin, triglycerides, and aSREBP1 levels in VAT (P < 0.05, Table 4Go).


   Discussion
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
References
 
In recent years, it has become increasingly recognized that the adipose tissue plays an important pathophysiological role in the development of obesity and its related comorbidities (11, 42, 43, 44, 45, 46). The aim of the present study was to evaluate, in the largest reported cohort of obese women (34), the potential role played by a number of transcription factors in pathologic fat mass storage and insulin resistance. To assess molecular differences between VAT and SAT function in human obesity, we first compared the relative expression, in both types of tissue, of transcription factors previously implicated in adipocyte differentiation and proliferation. Thus, we could show that cEBP{alpha} and cSREBP1 are significantly more expressed in SAT than in VAT of obese women, confirming previous studies performed on smaller numbers of patients (25, 26, 47).

More importantly, we were also able to correlate the levels of expression of PPAR{gamma}1 and PPAR{gamma}2 in VAT and SAT with those of other transcription factors acting upstream of PPARs. This correlation was rendered possible by the fact that the levels of expression of PPAR{gamma}s in VAT and SAT recently reported by our group (34) were determined in the same tissue samples as those used in the present study. Our data now suggest that the regulation of the expression of PPAR{gamma}1 and PPAR{gamma}2 is different in VAT than in SAT. Indeed, the parameters significantly associated with PPAR{gamma} variability are different in both SAT and VAT and for the two isoforms of PPAR{gamma}: the expression of PPAR{gamma}1 is controlled in a major way by sc factors, whereas PPAR{gamma}2 is significantly linked to visceral factors. In particular, 60% of the variability of PPAR{gamma}1 expression in SAT could be explained by the levels of expression of RXR{alpha}.

A striking finding of this study is that in our model, the major part of the variability of the factors studied in SAT can be accounted for by the levels of expression of other transcription factors. Moreover, for most of them, over 80% of the variability of their expression could be explained by two other factors. This observation represents a strong indication that in human SAT, the factors under scrutiny here may be functionally related, possibly acting in a cascade of transcriptional events as suggested mainly in animal models (28, 29, 32, 33, 37, 48, 49, 50). In contrast, only a minor portion of the variability of their expression in VAT could be explained by our statistical model, taking into account biological and anthropometric data, together with other transcription factors. This in turn suggests that other variables that were not included in this analysis may possibly be playing an important part in their expression. These factors could well be endocrine or paracrine agents such as sex steroid hormones, glucocorticoids or resistin, and adiponectin (9, 26, 31, 51, 52, 53).

In our patients, the two important variables regulating VAT and SAT expression of many transcription factors were RXR{alpha} and cSREBP1. This underlines the functional importance of RXR{alpha} and cSREBP1 in adipose tissue of obese women. It also implicates that in humans as well, they are acting upstream of many other transcription factors including PPAR{gamma}1 and PPAR{gamma}2 (48). Interestingly, RXR{alpha} and cSREBP1 were strong determinants of several variables of glucose metabolism: the VAT levels of cSREBP1 were significantly linked to the variability of HbA1c and fasting glucose, whereas the SAT levels of RXR{alpha} and PPAR{gamma}1, together with triglyceride levels, were the major determinants of the variability of the HOMA index and insulin. These data on cSREBP1 are entirely consistent with the available literature, which suggests that it is implicated in the regulation of glucose (24, 30, 54) and lipid metabolism (55, 56). Therefore, they suggest that this is true for humans as well.

Previous studies regarding adipose tissue metabolism have identified a cascade of events promoting the proliferation and differentiation of adipocytes (37). This cascade involves a number of transcription factors, including PPAR{gamma}s, in these molecular events. Unfortunately, most of the data compiling many transcription factors are derived from animal models and moreover, there are very few studies addressing the potential differences existing between the visceral and sc adipose tissues. In this perspective, the present results represent one of the first attempts to study the coordinate regulation of the molecular aspects of adipogenesis in human obesity. Our data suggest that the two different types of adipose tissue, visceral and sc, play different roles in the pathogenesis of fat storage and metabolic disease in obese women.

The bulk of data presented here, together with the available literature (20, 38), would be consistent with the following hypothesis: in VAT, the expression of PPAR{gamma}2 is controlled by local transcription factors (RXR{alpha}, aSREBP1, and cSREBP1) promoting fat storage in adipocytes. Subsequently, given the limited storage capacity of VAT, RXR{alpha} induces the expression of PPAR{gamma}2 in SAT to increase this overall capacity. This hypothesis is further supported by the observation that leptin, which is strongly correlated to sc depots, was an important determinant of PPAR{gamma}2 variability in SAT in our patients.

In addition, our data also indicate that the signal to promote energy storage may occur in VAT. This is suggested by the observation that in VAT, only the smallest part of the variability of the transcription factors evaluated could be explained by our model. Thus, other metabolic and hormonal factors are involved in the control and modulation of adipogenesis in this tissue. In this respect, hormones of the hypothalamo-pituitary-adrenal axis may deserve a particular consideration (4).

Finally, our data suggest that PPAR{gamma}2 could rather be involved in the process of fat mass storage than in the development of metabolic comorbidities. In contrast, PPAR{gamma}1 seems to play a more important role in metabolic processes. This hypothesis is supported by the following observations: PPAR{gamma}1 in SAT participates to the variability of the HOMA index; RXR{alpha} in SAT, the most important determinant of PPAR{gamma}1, is also an important determinant of insulin levels; PPAR{gamma}1 is the major determinant of waist circumference, a good index of abdominal obesity and its related comorbidities. Together, our data therefore suggest that PPAR{gamma}1 in SAT participates to the generation of insulin resistance. Our previous data showing a higher expression of PPAR{gamma}1 in SAT as well as a significant correlation between these levels and circulating insulin concentrations agree with this hypothesis (34), and so does the existence of regional differences in the response of preadipocytes to PPAR{gamma}s and RXR{alpha} agonists (21).

Overall, the present results suggest that VAT and SAT play different roles in the pathogenesis of human obesity and metabolic disease. In addition, they demonstrate an important role for RXR{alpha} and cSREBP1 in the control of both VAT and SAT function. However, it should be stressed at this point that statistical correlations do not demonstrate causality and that the theoretical models proposed based upon our observations should be tested in better-controlled systems, such as in vitro or animal models.


   Footnotes
 
Abbreviations: aSREBP1, cSREBP1, a and c Sterol regulatory element binding protein 1; BMI, body mass index; cEBP{alpha}, enhancer binding protein {alpha}; HOMA, homeostasis model assessment; PGC1, PPAR-{gamma} coactivator 1; PPAR, peroxisome proliferator and activated receptor; RXR{alpha}, retinoid X receptor {alpha}; SAT, sc adipose tissue; VAT, visceral adipose tissue.

Received August 29, 2003.

Accepted December 16, 2003.


   References
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 

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