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Am J Physiol Endocrinol Metab. Mar 2011; 300(3): E571–E580.
Published online Dec 28, 2010. doi:  10.1152/ajpendo.00231.2010
PMCID: PMC3279304

Pathways regulated by glucocorticoids in omental and subcutaneous human adipose tissues: a microarray study


Glucocorticoids (GC) are powerful regulators of adipocyte differentiation, metabolism, and endocrine function and promote the development of upper body obesity, especially visceral fat stores. To provide a comprehensive understanding of how GC affect adipose tissue and adipocyte function, we analyzed patterns of gene expression (HG U95 Affymetrix arrays) after culture of abdominal subcutaneous (Abd sc) and omental (Om) adipose tissues from severely obese subjects (3 F, 1 M) in the presence of insulin or insulin (7 nM) plus dexamethasone (Dex, 25 nM) for 7 days. About 20% (561 genes in Om and 569 genes in sc) of 2,803 adipose expressed genes were affected by long-term GC. While most of the genes (90%) were commonly regulated by Dex in both depots, 26 in Om and 34 in Abd sc were affected by Dex in only one depot. 60% of the commonly upregulated genes were involved in metabolic pathways and were expressed mainly in adipocytes. Dex suppressed genes in immune/inflammatory (IL-6, IL-8, and MCP-1, expressed in nonadipocytes) and proapoptotic pathways, yet induced genes related to the acute-phase response (SAA, factor D, haptoglobin, and RBP4, expressed in adipocytes) and stress/defense response. Functional classification analysis showed that Dex also induced expression levels of 22 transcription factors related to insulin action and lipogenesis (LXRα, STAT5α, SREBP1, and FoxO1) and immunity/adipogenesis (TSC22D3) while suppressing 17 transcription factors in both depots. Overall, these studies reveal the powerful effects of GC on gene networks that regulate many key functions in human adipose tissue.

obesity, particularly visceral and upper body obesity, is associated with increased risk for obesity-related comorbidities, including type 2 diabetes and cardiovascular diseases. Glucocorticoids (GC) are powerful regulators of fat deposition and distribution, as is most clearly shown by the visceral obesity associated with Cushing's syndrome. In human obesity, the prereceptor activation of cortisone to cortisol via 11β-hydroxysteroid dehydrogenase (HSD1) is upregulated in both omental (Om) and abdominal subcutaneous (Abd sc) adipose tissues (26, 34, 36), and this difference is exaggerated after culture with GC (26). The resulting increases in local cortisol generation are thought to promote fat deposition, most markedly in visceral depots but also in Abd sc adipose tissue. However, very little is known about the molecular mechanisms that mediate depot differences in the effects of GC on adipocyte function. Our previous studies of leptin and lipoprotein lipase demonstrated differences in sensitivity and responsiveness to GC effects in Om and Abd sc (18, 35). In the present study, we used microarray approaches to compare the global effects of long-term culture with GC on gene expression in Om and Abd sc tissues.

Nutritional state affects cortisol production as well as responsiveness to cortisol. Administration of GC in the overnight-fasted state (low insulin) has catabolic effects on adipocytes [increased lipolysis (13)] and does not increase circulating leptin (25). However, when administered in combination with a meal or insulin, GC increase serum leptin (24, 25). Our in vitro data using human adipose tissue organ culture are consistent with the hypothesis that insulin and GC coordinately promote fat deposition and modulate adipose gene expression after meal ingestion (18, 35). Thus, we assessed the effects of GC added in the presence of insulin in the present study. The use of adipose tissue fragments in organ culture permits assessment of GC actions on the adipocytes in intact tissue that includes adipocytes and other cell types, potentially reflecting the in vivo situation. To discern whether changes in tissue gene expression reflected alterations at the level of adipocytes, we measured gene expression in adipocyte and stromal vascular fractions prepared from fresh obtained and cultured adipose tissues.



Adipose tissues were obtained during abdominal surgeries. By medical history, all but one subject were free of diabetes, endocrine, or inflammatory diseases. One diabetic subject was included since the effects of Dex were similar in the subject. All subjects were weight stable for at least 1 mo prior to surgery. Subjects taking β-blockers, but not other antihypertensive medications, were excluded. Surgeries took place at the St. Peters Medical Center, New Brunswick, NJ, and the University of Maryland Medical Center, Baltimore, MD. All subjects gave informed consent as approved by the IRB of Rutgers University, St. Peters Hospital, and the University of Maryland at Baltimore. Subject characteristics used for microarray and follow-up studies are presented in Tables 1 and and22.

Table 1.
Characteristics of subjects used for microarray studies
Table 2.
Characteristics of subjects used for confirmation of microarray

Adipose tissue handling and culture.

Adipose tissue was either immediately frozen on dry ice in the operating room or transferred to the laboratory in Medium 199. Minced Om and Abd sc tissues were placed in organ culture in the conditions of insulin (I; 7 nM) alone or insulin plus dexamethasone (ID; 25 nM Dex) for 7 days as previously described (21). Adipocytes and stromal vascular fractions (SVF) were prepared freshly obtained (0′) or after 7-day culture with ID (21). We used 7-day culture for our study as our goal was to identify genes that were affected by Dex over the long term and our previous studies of lipoprotein lipase (LPL) and leptin (18, 35) showed that the initial levels of gene expression (in fresh tissue) were maintained from days 5 to 8 of culture, as originally shown by studies by Smith and colleagues with respect to the metabolic activity (glucose metabolism) and insulin response of the tissue (10, 40). Furthermore, during the initial days of culture (days 1–4), cytokines are high and metabolic genes drop significantly compared with the freshly obtained tissue (unpublished observation, M.-J. Lee and S. K. Fried).

RNA extraction.

Total RNA was extracted from adipose tissue, adipocytes, and SVF by use of a modified method of Chomczynski and Sacchi (9). RNA quantity and quality were assessed spectrophotometrically and by electrophoresis on 1% agarose gels.

Affymetrix microarray experiments.

All experiments were performed using the HG U95 Affymetrix human array sets. Labeling and hybridization were performed following standard Affymetrix protocols. Briefly, 10 μg of total RNA was reverse transcribed using the One-Cycle cDNA Synthesis Kit (Affymetrix) and biotin labeled using the IVT Labeling Kit (Affymetrix). The sample was cleaned, and hybridization controls were added. After washing and staining with streptavidin phycoerythrin, arrays were scanned in the Affymetrix scanner.

Microarray data analysis.

Expression data were processed as probe set summaries using the Affymetrix MAS 5.0 algorithm. The signal intensities were scaled to give an average target intensity of 500 (1.9 ± 0.2). The data were imported to BRB-Array tools for analysis (http:linus.nci.nih.gov BRB-ArrayTools.html) (38). During the importing process, we normalized the data by selecting the options, normalizing the median over entire array, and using the median array as reference. We excluded probe sets using the following filtering criteria: 50% of the absolute signals were below 50, absence calls exceeded 50% of all conditions, and their expression data values had less than a 1.5-fold change in either direction from the median value. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO series accession number GSE26123.

Data analysis.

We searched for differentially expressed genes with Dex treatment by comparing insulin vs. insulin plus Dex conditions using Class Comparison Tools (38) with a false discovery rate (FDR) of 5% within each depot. To check commonly or depot-specifically regulated genes, we compared the two lists. The commonly regulated genes were separated by whether they were up- or downregulated with Dex treatment. To understand the biological pathways, up- or downregulated gene lists were uploaded to DAVID (Database for Annotation, Visualization, and Integrated Discovery) for analysis (http://david.abcc.ncifcrf.gov/home.jsp). Data were analyzed using Functional Annotation Tools. Genes were categorized with gene ontology (GOTERM_BP_ALL) and pathway (KEGG_PATHWAY) analysis. We also used Gene Functional Classification Tools to classify genes into functionally related groups.

Verification of Dex-regulated genes.

To confirm the microarray results, we carried out real-time quantitative (RT-q)PCR in an additional nine subjects [BMI 40 ± 4.9 (23–57 kg/m2), 6 F/3 M, age 46 ± 5.3 (19–65 yr)]. Total RNA was reverse-transcribed using a Transcriptor First Strand cDNA synthesis kit (Roche), and qPCR was performed on a LightCycler 480 (Roche) with Taqman probes (ABI and Roche). Standard curves for each gene were generated using cDNA prepared from pooled RNA samples. The efficiencies were between 1.95 and 2.05. 18S rRNA and cyclophilin A (PPIA) were used as reference genes, as their expression levels were not affected by hormone treatments and BMI. Relative expression levels were calculated using LightCycler 480 relative quantification software (Roche). Data were similar regardless whether 18S or PPIA was used as a reference gene and relative expression to 18S is presented.


Data are expressed as means ± SE. The effects of depots and Dex treatment were determined by analysis of variance with repeated measures using GraphPad 5.0 software.


Most Dex-regulated genes were common between Om and Abd sc human adipose tissues.

Among 2,803 genes that passed our filtering criteria, we searched gene sets that were regulated by Dex within each depot. In Om 561 and in Abd sc 569 genes were affected by Dex treatment. Comparison of the two gene lists showed that 535 (~90%) genes were commonly regulated, whereas 26 were regulated only in Om and 34 were regulated only in Abd sc (Fig. 1). Of 535 commonly regulated genes, 280 genes were upregulated and 255 genes were downregulated.

Fig. 1.
Venn diagram of genes commonly or depot-dependently regulated by dexamethasone (Dex) in omental (Om) and abdominal subcutaneous (sc) human adipose tissues.

The majority of Dex-upregulated genes were involved in metabolic pathways.

To gain an understanding of the biological pathways, the lists of commonly up- or downregulated genes were annotated and summarized according to shared categorical data using gene ontology (GO Terms_All Pathway) and pathway analysis (KEGG_Biological Pathway). Of 17 pathways identified in Dex-upregulated genes by GO classification, 13 pathways were related to metabolism (Table 3). These include lipid (P = 3.7 × 10−8), carbohydrate (P = 4.4 × 10−7), amino acid and nitrogen compounds (P = 3.7 × 10−5), alcohol (P = 8.1 × 10−7), and cofactor (P = 2.2 × 10−5) metabolism. Other Dex-induced pathways were responses to stress (P = 1.3 × 10−5), growth regulation (P = 4.7 × 10−3), and negative regulation of apoptosis (P = 1.1 × 10−2). KEGG_BP analysis results were similar: a majority of 25 identified pathways were related to metabolism (not shown). Interestingly, the two most significant pathways identified with KEGG_BP were related to amino acid catabolism: tryptophan (P = 2.5 × 10−7) and branched-chain amino acid (BCAA) (P = 7.5 × 10−7) degradation. Gene Functional Classification produced seven clusters that were enriched in Dex-induced genes: mitochondrial proteins shared by BCAA, fatty acid, and carboxylic acid metabolism were identified with the highest gene enrichment score (GES) of 8.6, followed by metallothioneins (8.0), peptidase (2.0), signaling pathways (1.8), transmembrane proteoglycan (1.0), ATP binding/protein kinase (0.4), and DNA binding/transcription factors (0.2).

Table 3.
Pathways induced by Dex in Om and sc adipose tissues

Dex suppressed immune/inflammatory pathways.

GO analysis identified 18 pathways in Dex-suppressed genes (Table 4). The most significantly downregulated pathway was a response to stress (p = 4.6 × 10−12). Others were development (P = 9.2 × 10−11), cell proliferation (P = 2.7 × 10−7), apoptosis (P = 5.3 × 10−6), and signaling (P = 5.7 × 10−3). Interestingly, nitrogen compounds (P = 1.2 × 10−3) and carbohydrate (P = 3.1 × 10−3) metabolism pathways were also identified. KEGG_BP analysis showed similar results (not shown). Seven clusters were categorized with Gene Functional Classification: chemokines (GES, 5.1), transmembrane glycoproteins/receptors (5.0), signaling (3.7), protein kinase (0.74), and DNA binding/transcription factor (0.36).

Table 4.
Pathways suppressed by Dex in Om and sc adipose tissues

Dex increased expression of 36 genes involved in lipid metabolism.

LPL, apolipoprotein D (apoD), fatty acid synthase (FAS), fatty acid desaturase-1 (FADS1), diacylglycerol transferase-1 (DGAT1), pyruvate carboxylase (PC), fatty acid CoA ligase long-chain 1 (FACVL1), sterol-regulatory element-binding protein-1 (SREBP1), and perilipin (PLIN). We confirmed the Dex effects on LPL, apoD, and FAS expression by use of RT-qPCR (Fig. 2). Cell fractionation studies showed that these lipid metabolism genes were expressed in mature adipocytes and that in vitro culture did not alter where the genes were expressed (not shown).

Fig. 2.
Dex-induced metabolic genes in both Om and sc human adipose tissue. Dex increased lipoprotein lipase (LPL; A), apolipoprotein D (apoD; B), fatty acid synthase (FAS; C), glucose transporter-4 (GLUT4; D), phosphoenolpyruvate carboxykinase-1 (PCK1; E), and ...

Dex induced 28 genes and suppressed 18 genes involved in carbohydrate metabolism.

Upregulated genes are involved in glycolysis-TCA cycle (aldolase C, α-ketoglutarate dehydrogenase, citrate synthase, and PC), glycogen synthesis (glycogenin, glycogen synthase, and glycogen branching enzyme 1), and glucose metabolism and triglyceride synthesis [glucose transporter 4 (GLUT4), pyruvate dehydrogenase kinase isozyme 4 (PDK4), and phosphoenolpyruvate carboxykinase-1 (PCK1); Fig. 2]. GLUT4 (Fig. 3) and PCK1 were exclusively expressed in adipocytes (not shown). Nine of 18 Dex-suppressed carbohydrate metabolism genes were subcategorized as carbohydrate biosynthetic pathways, such as the hexoseamine biosynthetic pathway and extracelluar matrix protein modification. Included genes were dual-specificity tyrosine phosphorylation-regulated kinase-2 (DYRK2), glutamine-fructose-6-phosphate transaminase-1 (GFAT1), and carbohydrate (keratin sulfate gal-6) sulfotransferase-1 (CHST1).

Fig. 3.
mRNA expression levels of GLUT4, adiponectin, TNF, and IL-6 in tissue, fat cells (FC), and stromal vascular fraction (SVF) in Om and sc human adipose tissue before (freshly obtained, left) and after 7-day culture with insulin + Dex (right); n = 3.

Higher responsiveness to Dex effects in Om than in Abd sc.

RT-qPCR of genes related to carbohydrate and lipid metabolism revealed that the magnitude of Dex effect was greater in Om than in Abd sc. For example, with regard to genes related to triglyceride accumulation, Dex stimulation of FAS [5.6-fold (Om) vs. 2.9-fold (sc), P < 0.05] and LPL [7.6-fold (Om) vs. 2.7-fold (sc), P < 0.05, n = 9] were higher in Om than in Abd sc (Fig. 2). In carbohydrate metabolism pathways, the Dex induction of GLUT4 [6.8-fold (Om) vs. 3.4-fold (sc), P < 0.05] and PCK1 [6.9-fold (Om) vs. 2.9-fold (sc), P = 0.09] were also higher in Om.

Dex induced 21 genes and suppressed 18 genes involved in amino acid and nitrogen compound metabolism.

The majority of Dex-induced genes were involved in amino acid catabolism (tryptophan, arginine, tyrosine, serine, and BCAAs) and many encoded mitochondrial proteins. Hydroxyacyl-CoA dehydrogenase type II, l-3-hydroxyacyl-CoA dehydrogenase, short-chain aldehyde dehydrogenase-2 family (mitochondrial), and enoyl-CoA hydratase short-chain 1 (mitochondrial) are included in this category. Other Dex-upregulated genes were glutamine synthetase [glutamate-ammonia ligase (GLUL); Fig. 2], monoamine oxidase-A (MAOA), and amine oxidase copper containing 3 (AOC3). Nine of 18 suppressed genes were involved in synthetic pathways, such as amino acid and polyamine synthesis. Included genes were glycyl-tRNA synthase (GARS), asparagine synthase (ASNS), adenosylmethionine decarboxylase-1 (AMD1), and isoleucine-tRNA synthase (IARS).

Of the 18 Dex-downregulated pathways, four were closely related: response to stress (49 genes), inflammatory response (21 genes), response to external stimulus (34 genes), and chemotaxis (13 genes). These pathways shared a list of genes represented by chemokines [chemokine (C-X-C motif) ligand 1 (CXCL1), chemokine (C-C motif) (CCL)2, -8, -14, and 15 and interleukin-6 and -8], cytokine receptors (CXCR1, -4, and -7), and other cytokine-induced factors [TNF-induced protein-6 (TNFAIP6) and interferon-γ-inducible protein-16 and -30 (IFI16 and IFI30); Fig. 4]. These were expressed mainly in stromal fraction (Fig. 3 and not shown).

Fig. 4.
Regulation of acute-phase reactants and adipokines by Dex. Dex induced adipsin (A), complement factor 7 (C7; B), haptoglobin (C), serum amyloid A (SAA; D), leptin (E), and adiponectin (F) and suppressed IL-6 (G), IL-8 (H), and monocyte chemoattractant ...

Dex-induced immune/inflammatory genes identified as response to abiotic stimulus (Gene Ontology) or response to stress (KEGG_BP).

Several secretory proteins are included in this category: serum amyloid A (SAA), retinol-binding protein-4 (RBP4), osteopontin (SPP1), haptoglobin (HP), and components of complement (C7, C1Q, and factor D). Dex also increased leptin and adiponectin mRNA expression levels in both depots (Fig. 4). Intriguingly, many of these secretory products are expressed in adipocytes, except C7 (Fig. 3 and not shown).

Gene functional classification clustered 22 (induced) and 17 (suppressed) DNA binding/transcription factors.

Upregulated transcription factors were C/EBPα, TSC22 domain family member 3 [TSC22D3, also known as GC-induced leucine zipper (GILZ)] (Fig. 5), gata-binding protein-2 (GATA2), forkhead box O1A (FOXO1A), and sterol response element-binding protein-1 (SREBP1). The suppressed list included Jun b protooncogene, kruppel-like factor 10, and paired related homeobox 1 (PRRX1). Glucocorticoid receptor (GR) transcript levels were similar between Om and sc, expressed in both adipocyte and stromal fractions, and not affected by Dex treatment (Fig. 5).

Fig. 5.
Dex effects on DNA binding or transcription factors. Seven-day culture with Dex did not alter glucocorticoid receptor (GR; A) and chicken ovalbuman upstream transcription factor I (COUP-TFI; B) while significantly inducing GC-induced leucine zipper (GILZ; ...

Dex-downregulated development genes.

This category included genes involved in angiogenesis, cell proliferation, and differentiation. Endothelial cell growth factor 1 (ECGF1), fibroblast growth factor receptor 1 (FGFR1), transforming growth factor-β1 and -3 (TGFB1 and TGFB3), bone morphogenic protein-2 (BMP2), and growth differentiation factor 15 (GDF15) are included in the list. Several homeobox gene family members, such as H2.0-like homeobox1 (HLX), MSH homeobox homolog 1 (MSX1), and PRRX1 were also included.

Dex-downregulated signal transduction genes.

Genes in this category included receptor-mediated kinase, mitogen-activated kinase (MAPK9 and MAP4K4), and cell-to-cell signaling genes. Chemokine receptors (CXCR4), growth factors and their receptors (TGFB3, FGFR1, and TNFRSF10B) were also included in this category.

Dex suppressed the proapoptosis pathway (30 genes) while inducing negative regulators of apoptosis (10 genes).

The suppressed category included several cytokines, growth factors, and regulators of DNA damage repair such as methyl-CPG-binding domain protein-4, DNA-damage-inducible transcript 3, caspase 4, apoptosis-related cysteine peptidase (CASP10), growth arrest and DNA damage-inducible-α, and death-associated protein-6 (DAXX6). BTG family member 2, peroxiredoxin 2, and secreted frizzled-related protein-1 were identified as Dex-induced negative regulators of apoptosis.

Some genes were regulated by Dex in only one depot.

Twenty-six (6 up and 20 down) in Om and 34 genes (19 up and 15 down) in Abd sc were changed only in one depot (Supplementary Tables 1 and 2). We performed RT-qPCR on four genes in this list using independent samples (Fig. 6). SBNO2 was significantly repressed by Dex only in Om, confirming the array results. Although it did not reach statistical significance, Dex also tended to decrease SBNO2 expression in Abd sc (P = 0.06, n = 9), likely indicating a depot difference in the magnitude of the Dex effect rather than a depot difference in response for this gene. However, clear depot differences in the response to Dex were confirmed for the other three genes tested. Twinfilin, actin-binding protein homolog 2 (TWF2), was induced by Dex only in Om only, whereas fat mass and obesity gene (FTO) and inhibitor of DNA binding/differentiation 3 (ID3) were suppressed by Dex only in sc. ID3 was predominantly expressed in stromal cells, whereas the other three genes were expressed in both adipocyte and stromal fractions (not shown).

Fig. 6.
Genes regulated by Dex in a depot-dependent manner. Dex induced twinfilin 2 (TWF2; A) in Om without effect in sc tissue. Dex downregulated strawberry notch signaling 2 (SBNO2; B) in Om, confirming microarray data. Dex, however, also tended to decrease ...


Our results reinforce the pleiotropic effects of Dex (a type II GR agonist) on transcriptional profiles of human adipose tissue. As expected, we found that Dex, added in the presence of insulin, maintains the expression of genes related to insulin action and TG storage, as well as “good adipokines” such as adiponectin and leptin (10, 18), while suppressing immune/inflammatory and proapoptotic pathways. Intriguingly, Dex induced some immune/stress genes, including acute-phase reactants and components of complement cascades (innate immunity). While the majority of genes were commonly regulated in both depots, 26 in Om and 34 genes in Abd sc were regulated by Dex in only one depot. Further work is needed to determine whether any of these genes contribute to the GC promotion of visceral adiposity or depot differences in adipose tissue function.

Dex regulates many genes in glucose, amino acid, and fatty acid metabolism, promoting net storage of lipids.

Dex increased carbohydrate catabolism (glycolysis-TCA cycle) while suppressing carbohydrate synthetic pathway genes. In agreement with its effects on GLUT4, we previously found that insulin effect on glucose conversion to TG is increased after culture with insulin plus Dex (3). Similarly, Dex treatment in the presence of insulin improves insulin action on Akt/PKB phosphorylation and glucose transport in adipocytes (19, 21). Thus, in the context of development of obesity, higher levels of adipose tissue cortisol driven by higher HSD1 promote a pattern of gene expression that may contribute to the development or maintenance of obesity, especially in central depots.

Our data confirm and expand the list of GC-regulated genes in FA and TG synthesis (LPL, AGPAT2, DGAT1, FAS, FACVL1, PC, PCK1, ACC2, and SREBP1) and turnover (PLIN and ATGL), as well as transport of other lipid species (apoD). Importantly, we confirmed that these changes in gene expression occur at the level of the adipocytes. The fundamental importance of these genes in fat storage is demonstrated by studies in transgenic mice. AGPAT2 deletion or null mutations in AGPAT2 cause lipodystropy (1, 11). DGAT1 and perilipin knockout mice are resistant to diet-induced obesity (39, 41), whereas adipose overexpression of PCK1 causes obesity (16).

In contrast to its anabolic actions on lipid metabolism, Dex induced amino acid catabolism genes, especially tryptophan and BCAAs. Obesity is associated with higher circulating BCAA levels (32, 37), and surgical weight loss decreases plasma BCAA levels along with increased BCAA catabolism genes in human adipose tissue (37). Adipose tissue transcriptome studies showed a significant downregulation of mitochondrial BCAA catabolism genes in adipose tissue with increased plasma BCAA levels in monozygotic obese twins compared with lean sibling pairs (33). Alterations in GC action may contribute to impairments in BCAA catabolism associated with obesity.

Dex increases expression of genes involved in protection against a variety of stressors and promotion of cell survival.

The most significantly downregulated pathway was related to immune/inflammatory response. Genes in this category were cytokines and chemokines and their receptors. These are predominantly expressed in nonadipocytes (6, 14, 35). Thus, the effects that we observed here in organ culture likely reflect alterations in inflammatory cytokine expression by resident macrophages, lymphocytes, and preadipocytes. A novel and important finding of our study is that Dex also upregulates some immune/inflammatory genes that are categorized as response to stress or abiotic stimulus. This category includes SAA, C7, C1q, factor D, RBP4, SPP1, GPX3, and MTs. These are known to be involved in acute-phase response or innate immunity. The importance of adipose tissue in the systemic innate immune response is recognized (12, 22). Intriguingly, many of these Dex-induced immune genes are expressed in mature adipocytes, demonstrating the cell type-specific effects of Dex within adipose tissue.

Another novel finding was that Dex also upregulated negative regulators of apoptosis while downregulating proapoptosis, suggesting Dex suppression of apoptosis in human adipose tissue. Although Dex has cytotoxic effects on certain cell types, increasing evidence has shown that GC also protect a variety of cells from apoptosis, including mammary gland epithelial cells, adipocytes, and preadipocytes (23, 30, 48). The mechanisms by which GC regulate apoptosis are poorly understood. GC induction of target genes, serum and GC-inducible protein kinase-1 (SGK-1) and MKP-1 is important in GC-induced cell survival (27, 28, 46). Accordingly, we also found that Dex induced MKP-1 expression in adipose tissue. In addition, our data also suggest that Dex regulates many components of apoptosis. The prosurvival effects of Dex are important considering that adipocytes of the obese are embedded in a high cytokine milieu.

Intriguingly, Dex induced amine oxidases, such as MOA and AOC3 in both depots. These have not been recognized as GC-regulated genes in adipocytes. Amine oxidases catabolize endogenous or xenobiotic amines producing hydrogen peroxide plus aminoaldehyde (2). Hydrogen peroxide is recognized as a signaling molecule involved in cell proliferation, death, and defense response. Aminoaldehydes are involved in secondary metabolite synthesis and abiotic stress tolerance. These suggest that amine oxidases play roles in defense/stress pathways. Amine oxidase families also play other roles. Monoamine oxidases are highly expressed in endothelial and smooth muscle cells and adipocytes, and their expression is induced during adipocyte differentiation (5) and is known to stimulate adipogenesis (15). Furthermore, monoamine oxidase substrates mimic diverse insulin effects in adipocytes, including activation of glucose transport, stimulation of lipogenesis, and inhibition of lipolysis (8, 31, 45). Thus, Dex-induced amine oxidases have many potential roles in adipose tissue function, including adipogenesis and adipocyte metabolism as well as stress/defense response.

Metallothioneins (MT), low-molecular-weight cysteine-rich, stress-response and metal-binding proteins, have been implicated in metal detoxification, Zn and Cu homeostasis, scavenging of free radicals protecting cells from oxidative stress, and the acute-phase response. MTs are expressed and secreted from adipocytes, and their expression levels are induced during differentiation of rat adipocytes (43, 44). It is also notable that MT (-I and -II) knockout mice are moderately obese (4), suggesting their roles in the regulation of energy balance. Others have shown that Dex induces MT gene expression in adipocytes (44) and other cell types (20). Our finding that Dex induction of MTs in human adipose tissue is in agreement with Dex effects on defense/stress response.

Depot differences.

An additional novel finding was identification of genes that were regulated by Dex in only one depot, either visceral (Om) or Abd sc. Although the functions of these genes are not clear, ID3 is known to inhibit adipocyte differentiation (29). Thus, we speculate that the higher expression of ID3 in Om and the fact that it is significantly suppressed by Dex only in Abd sc may contribute to these depot differences in preadipocyte differentiation capacity (42).

Bujalska et al. (7) attempted to identify depot-specifically regulated GC target genes using preadipocytes (stromal cells) from Om and sc with cortisol treatment (100 nM, overnight). They identified 38 commonly and 101 depot-specifically regulated (37 in Om and 64 in sc) genes. Although many of the genes overlap between the two studies, our list contains more metabolic genes that are expressed in mature adipocytes. Furthermore, some of their “depot-specifically regulated genes” (IL-6, LPL, and leptin) are shown by us to be regulated by Dex in both depots (18, 26, 35). Differences in experimental design, i.e, cell types and the duration and concentration of GC treatment, can explain some of the differences between the two studies, as GC are known to exert cell type-, time-, and dose-dependent effects.

Limitations of the study.

This study has a number of limitations. Because we used an organ culture system, we cannot determine whether changes in gene expression are a result of direct Dex actions on the adipocytes and stromal cells, or alternatively are secondary to interactions between two fractions. We separated cell fractions to address which cell fractions Dex-regulated genes are expressed in. We did not find any alteration in the distribution of genes studied after in vitro culture. Furthermore, we found that our organ culture system, with the combination of insulin and Dex, maintains many adipose tissue gene expression levels relative to the levels in freshly obtained tissue. Thus, analysis of gene expression in an organ culture system that preserves both cell types, and therefore their paracrine interactions, is likely to be physiologically relevant. Future studies are warranted to determine the direct targets of Dex in different cell types.

We used a long-term (7-day) culture with Dex. Thus, the question of whether Dex-regulated genes are primary or secondary targets of GC cannot be addressed in our study. Nonetheless, we believe that identification of gene networks that are regulated by Dex over the long term is physiologically relevant. Short-term treatment or chromatin immunoprecipitation with GR antibody after GC treatment will identify more direct targets of GC in adipocytes.

The majority of Dex-regulated genes are common between the two depots. This might be due to the fact that we used adipose tissue from central depots (Om and Abd sc). Gluteal or other peripheral adipose tissues may respond to GC differently. Although our data support the prolipogenic actions of Dex in adipose tissue, the common effects of Dex between the two depots suggest that intrinsic depot differences in the regulation of these genes might not be the main determinant of preferential deposition of adiposity in visceral depots observed with hypercortisolemia. It is therefore likely that regional differences in fat deposition are modulated by in vivo factors (e.g., blood flow and innervation) or depot differences in the metabolism of cortisone/cortisol (26). It is important to note that the gene array used in this study included only 12,000 probes and that some depot-specific genes may not be represented. In fact omentin-1, the only known visceral specific gene (47), was not included in this array.

This study was not powered to detect potential differences in the magnitude of Dex effects on obese compared with lean individuals, in males compared with females, or in subjects with visceral compared with peripheral fat distributions. We also only used a moderately high concentration (25 nM) of Dex in the presence of insulin. This Dex concentration corresponds to a pathological or a high local concentration of cortisol. In follow-up studies, we found that Dex and cortisol have similar effects on target genes (M. J. Lee and S. K. Fried, unpublished observation), so that our results are likely to be physiologically relevant. However, the current study cannot address the possibility that some genes may differentially respond to sub-threshold or more physiological levels of Dex in a depot-dependent manner. Consistent with this idea, our early studies provided evidence for a lower sensitivity (rightward shift in the dose-response curve) for Dex stimulation of LPL activity and gene expression in Om vs. Abd sc (18). Furthermore, in RT-qPCR confirmation of array data in additional nine subjects, the magnitude of the stimulatory responses to Dex differed between the two depots. Dex induction of genes related to fat accumulation and glucose metabolism [FAS, SCD, GLUT4, LPL, and PCK1 (P < 0.05 and a trend for PCK1 with P = 0.09)] was greater in Om than in sc, similar to our previous studies (17, 18). These subtle differences may contribute to the preferential lipid deposition in visceral depots. It will be important for future studies to identify genes that may be differentially expressed between the two depots across a range of GC concentrations.

In summary, we found that Dex increased gene networks that promote lipid deposition and suppressed inflammatory pathways in both Om and Abd sc adipose tissues. Simultaneously, Dex increased carbohydrate and amino acid catabolism genes to provide carbon and energy sources for the esterification and storage of fatty acids. Our analyses also highlight several novel findings that point to the importance of GC in orchestrating a coordinated response that promotes cell survival, including stimulation of acute-phase/innate immunity, defense response, and antiapoptotic pathways. Differential sensitivities to these pleiotropic actions of GC are likely to contribute to regional variations in adipose tissue biology.


This work was supported by a pilot and feasibility grant from Mid-Atlantic Nutrition and Obesity Research Center (P30 DK-072488) and American Heart Association postdoctoral fellowship (M.-J. Lee), National Institute of Diabetes and Digestive and Kidney Diseases Grants DK-52398, DK-080448, and P30 DK-046200 (BNORC) and a research grant from Novartis (S. K. Fried).


No conflicts of interest are reported by the authors.

Supplementary Material

Supplemental Tables:


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