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Inflammatory pathway genes belong to major targets of persistent organic pollutants in adipose cells.

BACKGROUND: Epidemiological studies emphasize the possible role of persistent organic pollutants (POPs) in obesity and the metabolic syndrome. These pollutants are stored in adipose tissue (AT).

OBJECTIVES: Our aim was to study the effects of POPs on human adipose cells and rodent AT.

METHODS: Using human multipotent adipose-derived stem cells, we carried out large-scale gene expression analysis to identify the major pathways modified by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), polychlorinated biphenyl (PCB) congener 126 (PCB-126), and PCB-153 and to evaluate their toxic effects. The effects of TCDD on gene expression and AT histology were also assessed in mice.

RESULTS: The most significantly regulated genes in both precursor cells and adipocytes were those involved in the inflammatory/immune response, cancer, and metabolism pathways. Interestingly, the fold induction and the number of modulated genes were higher in precursors than in adipocytes, suggesting that the former could be more sensitive to the effect of pollutants. When cells were treated with combinations of pollutants, the effects of the AhR ligands TCDD and PCB-126 were dominant compared with those of the non-dioxin-like PCB-153. The effects of AhR ligands were reduced by the AhR antagonist [alpha]-naphthoflavone. The regulation of inflammatory pathway was observed in wild-type AT but not in AhR-knockout mice.

CONCLUSIONS: Both in vitro and in vivo studies showed that adipose cells were targets of AhR ligands and suggest that inflammation is one of the main regulated pathways. These observations suggest a possible contribution of pollutants to low-grade AT inflammation that accompanies the pathogenesis of metabolic diseases.

KEY WORDS: adipocytes, adipose tissue, inflammation, obesity, PCB, persistent organic pollutants, TCDD. Environ Health Perspect 120:508-514 (2012). [Online 19 January 2012]

Several recent epidemiological studies have emphasized the possible role of environmental pollutants in obesity and the metabolic syndrome. The serum concentrations of persistent organic pollutants (POPs) such as polychlorinated dibenzo-p-dioxins and dibenzofurans(PCDD/Fs), polychlorinated biphenyls (PCBs); and organochlorine pesticides are reported to be associated with body mass index, elevated triglycerides, glucose intolerance, and cardiovascular diseases (Airaksinen et al. 2011; Alonso-Magdalena et al. 2011; Ha et al. 2007; Uemura et al. 2009). Molecular and cellular mechanisms of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the most potent compound of PCDD/Fs, are well characterized. TCDD was shown to induce xenobiotie-metabolizing enzymes as well as other genes involved in cell proliferation, differentiation, apoptosis, and inflammation (Haarmann-Stemmann et al. 2009). Most TCDD effects are mediated by its binding to the awl hydrocarbon receptor (AhR), leading to AhR nuclear translocation, heterodimerization with aryl hydrocarbon receptor nuclear translocator (ARNT), and binding to xenobiotic responsive elements present in the promoters of target genes (Barouki et al. 2007; Puga cc al. 2009). The mechanisms of action of other dioxins, furans, and certain PCBs such as PCB-126 are thought to be similar to those of TCDD, albeit with different potencies (Denison et al. 2002). In contrast, the mechanisms of action of other PCBs such as PCB-153 are poorly understood.

Adipose tissue (AT) is particularly relevant for the assessment of POP effects. POPS are lipophilic compounds stored primarily in the fat mass, which accounts for their long half-life (approximately 10 years). Pharmacokinetic studies suggest that this is the case for TODD, particularly at low environmentally relevant doses (Emond et al. 2006). Furthermore, kinetic studies indicate that fat.mass is among the major determinants of POP apparent half-lives in humans (Milbrath et al. 2009). Although this storage function may be protective to a certain extent, it also leads to a chronic low-grade internal exposure of humans to these toxicants (Kim et al. 2010). Furthermore, AT has important endocrine and metabolic physiological functions and could itself be a privileged target of these pollutants. Metabolic diseases and obesity are accompanied by a major alteration of AT functions such as a state of chronic low-grade inflammation in which adipocytes and other AT cells (e.g., endothelial cells, immune cells, preadipocytes) play a role by producing inflammation-related mediators (Goldberg 2009). These inflammatory molecules contribute to marked. perturbation of paracrine dialogues between those cells, which contributes to altered AT biology, promoting, for example, local inflammation and metabolic alteration such as insulin resistance as well as increased remodeling and fibrosis (Dalmas et al. 2011). Stienstra et al. (2011) have suggested that the resulting deregulation in the secretion of some inflammatory mediators in the systemic circulation could contribute to numerous obesity comorbidities, such as cancers. Hence, it is critical to assess the effect of POPs on AT. Some studies reported antiadipogenic and proinflammatory effects of TCDD and the dioxin-like PCB-77 on mouse 3T3-L1 cells (a mouse embryonic fibroblast cell line) (Arsenescu et al. 2008; Wen et al. 2007) or mouse embryo fibroblasts (Alexander et al. 1998). TCDD was also found to impair insulin action, signaling, and secretion in insulin-sensitive cells such as beta cells and adipocytes (Hsu et al. 2010; Nishiumi et al. 2010; Piaggi et al. 2007). Limited studies have been devoted to human adipocytes.

Our aim was to evaluate the effects of AhR ligands on adipocyte functions. We first identified human multipotent adipose-derived stern (hMADS) cells as an appropriate model to study the effect of pollutants on human adipocytes (Rodriguez et al. 2005a, 2005b; Zaragosi et al. 2006). We carried out large-scale gene expression analyses to target the major pathways and genes that were altered by pollutants. We focused our studies on TCDD, PCB-126 (the most potent of the dioxin-like PCBs), and PCB-153 (the most abundant of the non-dioxin-like PCBs). We studied the effects of these POPs on both precursor cells and mature adipocytes. We also characterized the effect of dioxin on AT in wild-type (WT) and AhR-knockout (KO) mice.

Materials and Methods

Cell culture. Cell culture media were purchased from Invitrogen (Cergy-Pontoise, France), human fibroblast growth factor 2 (hFGF2) from Peprotech (London, UK), and all the other products from Sigma-Aldrich (Saint Quentin Fallavier, France).

The establishment and characterization of hMADS cells have been described previously (Rodriguez et al. 2005a). Proliferating cells were seeded in Dulbecco's modified Eagle's medium (DMEM) with 10% fetal calf serum, 2.5 ng/mL hFGF2, 10 mM HEPES buffer, 50 U/mL penicillin, and 50 [micro]g/mL streptomycin and kept at 37 [degree]C in 5% C[O.sub.2]. hFGF2 was removed when cells reached confluence. The next day, cells were incubated in differentiation medium: DMEM/Ham's F-12 medium containing 10 [micro]g/mL transferrin, 5 [micro]g/mL insulin, 0.2 nM triiodothyronine, 100 [micro]M 3-isobutyl-1-methylxanthine (IBMX), 1 [micro]M dexamethasone (DEX), and 0.5 [micro]g/mL rosiglitazone. At day 3, DEX and IBMX were omitted from the differential medium until day 10. Differentiation (i.e., neutral lipid accumulation) was assessed by Oil Red O staining as described elsewhere (Rodriguez et al. 2005a). The experiments were carried out between days 11 and 13 while the cells were treated for 48 hr with 25 nM TCDD, 1 i.tM PCB-126, or 10 [micro]M PCB-153, all dissolved in dimethyl sulfoxide (DMSO). For the 48-hr treatment of precursor cells, the proliferating cells were treated at confluence by the same compounds, at the same concentrations.

Animals. Homozygotic WT mice (C57BL/6) were purchased from Charles River Laboratories (Arbresle, France), and the inactivation of the AhR gene was performed to produce AhR-KO mice as described else-where (Fernandez-Salguero et al. 1995). Mice were maintained on a standard 12-hr light/dark cycle with ad libitum access to standard chow and water. Seven-to 8-week-old male mice were treated with a single intraperitoneal injection of 10 [micro]g/kg TCDD dissolved in corn oil (Sigma-Aldrich) or vehicle control (corn oil). At 48 hr or 72 hr after intraperitoneal injection, the mice were killed by asphyxiation with [CO.sub.2]. This dose of TCDD was selected because previous studies in rodents suggested that it led to AT concentrations that were within the range of those used in our in vitro studies (Wang et al. 2000). The animals were treated humanely and with regard for alleviation of suffering.

Determination of mRNA Levels. Total RNA was extracted from undifferentiated or in vitro differentiated adipocytes with the RNeasy Total RNA Mini Kit (Qiagen, Courtaboeuf, France) and from mouse AT with RNeasy Lipid Tissue Mini Kit (Qiagen). Total RNA concentration was assessed spec-trophotometrically with a NanoDrop spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and their quality was determined with the Agilent 2100 Bioanalyzer (Agilent Technologies, Massy, France). Total RNA (1 [micro]g) was reverse transcribed using a high-capacity cDNA reverse transcription kit (Applied Biosystems, Courtaboeuf, France). Real-time polymerase chain reactions (PCRs) were conducted with ABsolute[TM] QPCR SYBR.[R] Green ROX mix (Thermo Electron SAS, Courtaboeuf, France) on an Applied Biosystems 7900HT Fast Real-Time PCR System (Applied Biosystems). mRNA values were normalized to the expression level of a reference gene. We tested hypoxanthine-guanine-phosphoribosyltransferase (HPRT), TATA box binding protein (TBP), and ribosomal protein L13A. HPRT had the most stable expression level among different conditions in precursor cells and mouse AT, and TBP had the most stable expression level in adipocytes. Primer sequences are available upon request.

Immunohistochemistry. Epididymal AT biopsies were fixed overnight at 4 [degree]C in 4% paraformaldehyde and then processed for standard paraffin embedding. Sections of 5 [micro]m were stained as described below and observed under a Zeiss 20 Axiostar Plus microscope (Zeiss, Oberkochen, Germany). Digital images were captured by tri-CCD camera (DXC-390P; Sony, Paris, France).

Adipocyte diameter. Slides were stained with hematoxylin and eosin. Adipocyte diameters were measured using Perfectlmage software, version 7.4 (Claravision, Massy, France). We estimated the diameter of all adjacent adipocytes from each of four separate photographs of the same slide in parenchyma. These measurements were performed at 10x magnification, with 200-300 adipocyte diameters measured for each biopsy to calculate the mean and SE for each sample.

Macrophages accumulation. Anti-mouse F4/80 antibody (AbD Serotec, Dusseldorf, Germany) was used to detect macrophages (Khazen et al. 2005). Immunohistochemical detection was performed with the avidin-biotin peroxidase method as described previously (Cancello et al. 2006). Slides were counterstained with Mayer's hematoxylin. Adipocytes and F4/80-positive cells were counted in 10 different randomly chosen areas in each processed slide at 40x magnification. The number of F4/80 cells per 100 adipocytes was considered the number of infiltrating AT macrophages.

Cytokine measurements. Cytokine concentrations in supernatants of cultured undifferentiated cells and adipocytes were determined by Luminex assay with a custom kit, Human Adipocyte Multiplex Immunoassay Kit FOUR PLEX (Millipore, Saint Quentin-en-Yvelines, France) according to the manufacturer's protocols using the Luminex xMAP Multiplexing Technology platform (Hopital Saint Antoine, Paris, France).

Sample preparation for microarray analyses. Total RNA (200 ng) from each sample (each condition in quadruplicate) was amplified and transcribed into fluorescent cRNA with the Low RNA Input Linear Amplification Kit (Agilent Technologies). Cyanine-5 dye was incorporated into the control (DMSO treated) sample, whereas samples treated with TCDD, PCB-126, or PCB-153 were labeled with cyanine-3 dye. Samples were then hybridized to Agilent whole human genome microarrays (Whole Human Genome Microarray Kit, 4x44K). Sample preparation, hybridization, and microarray washing were performed according to the manufacturer's recommendations. Arrays were scanned with a GenePix 4000B scanner (Axon Instruments-Molecular Devices, Sunnyvale, CA, USA).

Statistical analysis. For all microarrays, after loess normalization of log-transformed microarray data with the use of the Goulphar package (version 1.1.3; (Lemoine et al. 2006), gene lists were filtered to identify genes common to all microarrays (i.e., 100% retrieval). Differential gene expression, using a 5% false discovery rate, was assessed with the use of the Significance Analysis of Microarrays (SAM; version 3.02) procedure ( The functional profiling of gene expression data was determined using FunNet (, which was described elsewhere (Prifti et al. 2008). The complete data set is publicly available at the National Center for Biotechnology Information Gene Expression Omnibus ( through series accession number GSE32026 and is also available on request from the authors.

For quantitative reverse transcriptase (qRT) PCR gene expression and cytokine measurements, Kruskal-Wallis and/or Mann-Whitney U-test using StatView 5.0 (SAS Institute Inc., Cary, NC, USA) was performed. Results are presented as the mean [+ or -] SE. p-Values < 0.05 were considered significant.


hMADS, a valuable human adipocyte model to evaluate pollutant toxicity. hMADS exhibit features of stem cells, that is, a high ability to self-renew and the capacity to differentiate into different lineages, including adipocytes [see Supplemental Material, Figure 1 (]. The differentiated cells exhibit the unique characteristics of human adipocytes, unlike rodent preadipocyte cell lines, for example, hMADS can secrete leptin and adiponectin. Furthermore, cytochrome P450 1131 (CYP1B1), a constitutively expressed CYP1 family member in extrahepatic tissues, is potently induced by TCDD in both precursor and differentiated cells [see Supplemental Material, Tables 1, 2 (]. AhR is also expressed in these cells. Thus, this cell line appears to be an adequate model for the assessment of the toxic effect of pollutants on human undifferentiated and differentiated adipocytes.

Large-scale studies and major gene regulation by TCDD, PCB-126, or PCB-153. Preliminary studies allowed us to determine the amount of pollutants to be used in in vitro experiments. We chose the amount that resulted in a statistically significant induction of selected target genes without inducing cellular toxicity [measured by the MTT [3-(4,5-dimethylthiazol-2-y1)-2,5-diphenyltetrazolium bromide] assay; data not shown]. Gene expression was monitored by large-scale microarray analysis comparing treated and untreated cells. The sets of genes differentially modulated by each pollutant were determined by SAM with a false discovery rate of 5%. In precursor cells, the expression of 236, 1,344, and 792 genes was up-regulated and that of 17,158, and 104 genes was down-regulated by TCDD, PCB-126, and PCB-153, respectively. In adipocytes, the expression of 477, 117, and 441 genes was up-regulated and that of 153, 13, and 399 genes was down-regulated by TCDD, PCB-126, and PCB-153, respectively (data not shown, available upon request). To identify the enrichment in functional groups in up-and down-regulated gene lists, we used the FunNet tool. According to KEGG (Kyoto Encyclopedia of Genes and Genomes; (Kanehisa and Goto 2000; Kanehisa et al. 2011) pathways, the inflammatory response was strongly induced by all the pollutants in precursor cells. Chemokine signaling pathway, cytokine-cytokine receptor interaction, and the JAK-STAT signaling pathway represented more than 50% of up-regulated transcripts in TCDD-treated and PCB-126-treated cells (Figure 1 A, B). Other pathways, such as cell adhesion, metabolism, and cancer pathways, were also modulated by all pollutants (Figure 1 A-C).


Up-regulated transcripts

Phenylalanine metabolism                10.5%
O-Glycan biosynthesis                   10.5%
p53 signaling pathway                   15.8%
NOD-like receptor signaling pathway     15.8%
JAK-STAT signaling pathway              26.3%
Chemokine signaling pathway             26.3%
Cytokine-cytokine receptor interaction  57.9%

Down-regulated transcripts

Notch signaling pathway                   50%
Androgen and estrogen metabolism          50%
C21-steroid hormone metabolism            50%

Transcriptional domain coverage (%)


Up-regulated transcripts

Type 2 diabetes mellitus                15.2%
ECM-receptor interaction                17.4%
TGF-[beta] singnaling pathway           19.6%
JAK-STAT signaling pathway              32.6%
Cytokine-cytokine receptor interaction  43.5%

Down-regulated transcripts

Circadian rhythm, mammal               15.4%
Dilated cardiomyopathy                 23.1%
Small cell lung cancer                 23.1%
Pancreatic cancer                      23.1%
Colorectal cancer                      23.1%
ECM-receptor cancer                    23.1%
ECM-receptor interaction               23.1%
Renal cell carcinoma                   30.8%
Hypertrophic cardiomyopathy(HCM)       30.8%
Focall adhesion                        53.8%

Transcriptional domain coverage (%)


Up-regulated transcripts

Protein export                                          4.9%
Arrhythmogenic right ventricular cardiomyopathy (ARVC) 12.2%
Dilated cardiomyopathy                                 14.6%
Hypertrophic cardiomyopathy(HCM)                       14.6%
ECM-receptor interaction                               14.6%
Lysosome                                               17.1%
Focall adhesion                                        24.4%
Regulation of action cytoskeleton                      24.4%
Cytokine-cytokine receptor interaction                 34.1%

Down-regulated transcripts

Thyroid cancer                                         18.2%
Starch and sucrose metabolism                          18.2%
ECM-receptor interaction                               27.3%
Bladder cancer                                         27.3%
Small cell lung cancer                                 36.4%
TGF-[beta] singnaling pathway                          36.4%
Focall adhesion                                        45.5%
Pathyways in cancer                                    54.5%

Transcriptional domain coverage (%)


Up-regulated transcripts

Thyroid cancer                          8.3%
Pathogenic Escherichia coli infection  11.1%
Drug metabolism, cytochrome P450       11.1%
Adherens junction                      13.9%
Glycolysis/gluconeogenesis             13.9%
Prostate cancer                        13.9%
ECM-receptor interaction               16.7%
Small cell lung cancer                 19.4%
Focall adhesion                        27.8%
Leukocyte transendothelial migration   30.6%
Pathways in cancer                     47.2%

Down-regulated transcripts

Neuroactive ligand-recetor interaction 57.1%
Calcium signaling pathway              57.1%

Transcriptional domain coverage (%)


Up-regulated transcripts

Viral myocarditis                      60%
Leukocyte transendothelial migration   80%

Down-regulated transcripts

Porphyrin and chlorophyll metabolism   50%
Endometrial cancer                     50%
mTOR signaling pathway                 50%

Transcriptional domain coverage (%)


Up-regulated transcripts

Notch signaling pathway                 12.1%
Dorso-ventral axis formation            12.1%
Thyroid cancer                          12.1%
Endometrial cancer                      12.1%
Pathogenic Escherichia coli infection   12.1%
p53 signaling pathway                   15.2%
Prostate cancer                         15.2%
Pancreatic cancer                       15.2%
Ribosome                                18.2%
Small cell lung cancer                  21.2%
Leukocyte transendothelial migration    42.4%

Down-regulated transcripts

Melanoma                                14.3%
Glioma                                  14.3%
Renal cell carcinoma                    14.3%
mTOR signaling pathway                  14.3%
Glycolysis/gloconeogenesis              14.3%
Non-small cell lung cancer              17.9%
Endometrial cancer                      17.9%
Progesterone-mediated oocyte maturation 17.9%
Ribosome                                17.9%
Prostate cancer                         21.4%
Systemic lupus erythematosus              25%
Insulin signaling pathway               32.1%

Transcriptional domain coverage (%)

In adipocytes, results suggested that cancerrelated pathways were modulated by pollutants (Figure 1D-F); in particular, those involved in leukocyte transendothelial migration pathway were up-regulated by all three compounds. Because leukocyte migration from blood to tissues is important for the inflammatory response, this is in agreement with the increased inflammation observed in AT after pollutant treatment.

Determination of effects of pollutants by qRT-PCR. We first validated the microarray observation by qRT-PCR by measuring the expression of genes involved in metabolism [CYP1B1, CYP19A1, fatty acid binding protein 4 (FABP4), NAD(P)H dehydrogenase, quinone 1 (NQ01)], cell adhesion or migration [intercellular adhesion molecule 1 (ICAM1), transforming growth factor, beta-induced (TGFBI), thrombospondin 1 (THBS1), vascular cell adhesion molecule 1 (VCAM1)], inflammation [chemokine (C-X-C motif) ligand 12 (CXCL12), inter-leukin 1[beta] (ILIB), 1L8, plasminogen activator inhibitor 2 (PAI2), signal transducer and activator of transcription 1 (STAT1), tumor necrosis factor receptor superfamily, member 11b (TNFRSF11B)], and cancer pathways [insulin-like growth factor binding protein 3 (IGFBP3), prostaglandin-endoperoxide synthase 2 (PTGS2), tribbles homolog 3 (TRIB3) wingless-type MMTV integration site family, member 5A (WNT5A)]. Both precursors and adipocytes were treated with TCDD, PCB-126, or PCB-153. Several mRNAs, such as those encoding CYP1B1, IL8, neuronal pentraxin 1 (NP7X1), PAI2, PTGS2, NQ01, STAT1, TGFBI, THBSI, TNFRSF11B, and WNT5A, were induced in both types of cells by the AhR ligands TCDD and PCB-126, and the fold induction of expression was generally higher in precursors than in adipocytes [see Supplemental Material, Tables 1 and 2 (]. The level of transcripts encoding CYP19A1, FABP4, ICAM1, IGFBP3, IL1B, TRIB3, or VCANI1 was modulated only in precursors. We also assessed the expression level of AHR mRNA because it was previously found to decrease during adipogenesis in 3T3-L1 cells, resulting in the loss of functional response to xenobiotics (Shimba et al. 1998). In hMADS, AHR expression was similar at the two cell stages and was significantly decreased by the treatment with AhR ligand in precursors (see Supplemental Material, Table 1). Thus, the greater sensitivity of precursor cells did not seem to be related to AHR expression level.

PCB-153 modulated the expression of some of the selected genes in precursors but not in adipocytes [see Supplemental Material, Tables 1 and 2 (]. The effect of PCB-153 in precursors was similar to that of AhR ligands for CYP19A1, ICAM1, PTGS2, TNFRSF11B, and VCAM1 and different for CXCL12, MB, IGFBP3, NPTX1, STAT1, THBS1, and WNT5A. In adipocytes, no regulation was observed.

Pro-and anti-inflammatory effects of TCDD, PCB-126 and PCB-153. We further characterized the gene family of the inflammatory network by measuring the expression of additional pro-and anti-inflammatory genes involved in obesity-related low-grade inflammation in AT. We tested the regulation of the proinflammatory genes CD14, IL6, IL8, monocyte chemoattractant protein 1 (MCP1), CC12, PAH, and tumor necrosis factor-alpha (TNFa) and the anti-inflammatory genes CD163, interleukin 1 receptor antagonist (ILIra), and IL10, all reported to code for secreted proteins that are up-regulated in obesity (Fain 2010). In precursors and adipo-cytes, TNF[alpha] and IL10 mRNA were undetectable. IL1ra and PAD mRNA were increased, whereas CD14 mRNA was decreased, by TCDD and PCB-153 only in precursors (Figure 2A). MCP1 and IL8 mRNAs were increased by AIR ligands in both types of cells (Figure 2A, B). Thus, AhR ligands regulated the expression of genes encoding pro-and anti-inflammatory cytokines involved in the regulation of obesity-associated inflammation. In contrast, the pattern of gene regulation by PCB-153 was different from that of the other POPs: it elicited a decrease in the expression of CD14, IL6, MCP1, and PAIL genes and an increase in 'Lira only in precursor cells (Figure 2A). Because of its modest effects, we did not further pursue the characterization of PCB-153 effects.

To evaluate whether gene expression correlates with the corresponding proteins, we analyzed the secretion of IL1[beta], IL6, IL8, and MCP1 in supernatants of cultured precursors and adipocytes (Figure 2C). The secretion level of cytokines was higher in precursors than in adipocytes, and IL8 was significantly increased by a 48-hr treatment with TCDD or PCB-126. ILl[beta] secretion was undetectable. IL6 and MCP1 secretion were not significantly changed by TCDD or PCB-126 treatment.

The involvement of AhR in the inflammatory response was evaluated with an antagonist of TCDD, [alpha]-naphthoflavone ([alpha]-NF). The induction of CYP1B1, a prototypical AhR target gene and of proinflammatory genes by TCDD or PCB-126 were significantly decreased when cells were pretreated with [alpha]-NF (Table 1), suggesting that these xenohiotics could regulate the inflammatory pathway in adipose cells via the AhR.

Table 1. Role of AhR in pro-and anti-inflammatory gene regulation in
hMADS precursors and adipocytes: fold induction after treatment (mean
[+ or -] SE).

Gene        DMSO     [alpha]-NF     TCDD      TCDD+[alpha]-NF


CYP1B1      1.01 [+     2.01 [+ or   9.29 [+    3.70 [+ or -]
                 or         -]0.21        or            0.65*
             -]0.10                   -]1.42

IL1B        1.04 [+     1.84 [+ or  11.09 [+    3.04 [+ or -]
              or -]         -]0.24     or -]            0.36*
               0.18                     1.11

IL8         1.04 [+     1.64 [+ or    4.10 +     1.93 + 0.38*
                 or         -]0.33      0.82

MCP1        1.01 [+  1.16 [+ or -]   2.62 [+       1.32 [+ or
                 or           0.14        or          -]0.21*
             -]0.09                   -]0.13

PAI2        1.03 [+     2.01 [+ or  21.18 [+    2.92 [+ or -]
                 or         -]0.39     or -]            0.45*
             -]0.17                     4.40

PTGS2       1.00 [+     1.43 [+ or   6.75 [+       2.29 [+ or
                 or         -]0.07        or          -]0.10*
             -]0.03                   -]1.29

IL1ra       0.97 [+    1.54 + 0.18   2.58 [+       1.01 [+ or
                 or                       or          -]0.04*
             -]0.29                   -]0.66


CYP1B1      1.03 [+     0.72 [+ or   7.16 [+       1.89 [+ or
                 or         -]0.09     or -]          -]0.11*
             -]0.17                     1.67

IL1B        1.04 [+     0.93 [+ or    1.00 +        1.21+0.18
                 or         -]0.07      0.19

IL8         1.02 [+     0.81 [+ or   1.81 [+       0.93 [+ or
              or -]         -]0.16        or          -]0.11*
               0.14                   -]0.20

MCP1        1.07 [+     0.92 [+ or   1.88 [+    1.12 [+ or -]
                 or         -]0.07        or            0.16*
             -]0.29                   -]0.26

PAI2        1.01 [+  0.55 [+ or -]   3.93 [+       1.64 [+ or
                 or           0.16        or          -]0.33*
             -]0.09                   -]0.44

PTGS2       1.03 [+    0.84 + 0.10   1.63 [+    1.00 [+ or -]
                 or                       or             0.12
             -]0.19                   -]0.38

ILIra       1.00 [+     1.09 [+ or   0.96 [+      1.23 + 0.12
                 or         -]0.25        or
             -]0.05                   -]0.00

Gene        PCB-126     PCB-126+[alpha]-NF


CYP1B1      9.67 [+ or   4.61 [+ or -]0.97*

IL1B        8.93 [+ or   3.14 [+ or -]0.27*

IL8             4.18 +  2.09 [+ or -] 0.13s

MCP1        2.53 [+ or   1.33 [+ or -]0.24*

PAI2          14.98 [+   4.29 [+ or -]0.78*
             or -]3.77

PTGS2       5.78 [+ or   2.69 [+ or -]0.31*

IL1ra       2.21 [+ or   1.26 [+ or -]0.13*


CYP1B1      6.94 [+ or  2.70 [+ or -] 0.60"

IL1B        1.31 [+ or    1.28 [+ or -]0.16

IL8         2.26 [+ or    1.25 [+ or -]0.29

MCP1        1.57 [+ or   1.16 [+ or -] 0.16

PAI2        4.83 [+ or  2.30 [+ or -] 0.66*

PTGS2       2.07 [+ or   1.14 [+ or -]0.02*

ILIra           1.14 +    1.35 [+ or -]0.31

Gene expression was normalized by HPRT expression in undifferentiated
cells and by TBP expression in adipocytes.
* p < 0.05, For TCDD-treated or PCB-126-treated cells without versus
with pretreatment with [alpha]-NF, by Mann-Whitney U-test.

AhR-dependent regulation of inflammatory genes in mouse AT Because of the importance of inflammation in the pathogenesis of obesity and metabolic disease, we studied the effect of AhR ligands in vivo on the epididymal AT of WT and AhR-KO mice. C57BL/6 male mice were treated with 10 [micro]g/kg TCDD, a nonacutely toxic dose (Chapman and Schiller 1985) used in other similar studies (Schlezinger et al. 2010), and the expression of proinflammatory (Figure 3A) and anti-inflammatory (Figure 3B) markers was assessed. The expression of CypIbi and Noi genes that are typical AhR targets was induced in AT of TCDD-treated WT mice. Gene expression of the proinflammatory cytokines Illb and Ptgs2 and of the anti-inflammatory cvtokine III0 was also increased in TCDD-treated animals. This modulation could not be observed in AhR-K0 mice.

Because the local inflammation observed in the AT of obese subjects was generally correlated with the increased infiltration of macrophages and leukocytes (Dalmas et al. 2011), we examined mouse AT by immunohistology. Specific staining with F4/80 antibody showed an increased number of macrophages in TCDD-treated animals (Figure 4A, B). Because adipocyte hypertrophy is a factor determining macrophage infiltration in AT (Weisberg et al. 2003), we assessed the relationship between adipocyte diameter in TCDD-treated and corn-oil-treated mice. We found no difference in adipocyte size between these two conditions (Figure 4C).

These in vivo data are in agreement with the results obtained using hMADS cells. Indeed, TCDD was able to regulate the expression of genes encoding pro-and anti-inflammatory cytokines via AhR and to increase macrophage infiltration in AT.


Our results show that the hMADS cell line is a valuable model for the study of POP toxicity on human precursor and adipocytes because these cells respond to several pollutants at different differentiation stages. Observations made on these cells were confirmed in vivo by comparing AT from WT and AhR-KO mice. Some of our observations are in line with those made in human studies correlating POP levels with metabolic diseases (Airaksinen et al. 2011; Ha et al. 2007; Uemura et al. 2009). However, although the dioxin doses we used are common and nonacutely toxic, this exposure regimen is different from that of humans who are continuously exposed to very low levels of contaminants. Thus, although our functional data appear to have relevance to human observations, the difference in exposure should be kept in mind.

A nonsupervised analysis of the microarray data after treatment of precursors and adipocytes with POPs showed that transcripts for proteins involved in inflammation were among the most robustly regulated. To validate this results, we assessed the modulation of ICAM1 (Brake et al. 2006), MQO1 (Palming et al. 2007), IGFBP3 (Chan et al. 2005), and THBS1 (Varma et al. 2008) by POPs because, in addition to their role in cell adhesion, xenobiotic metabolism, angiogenesis, and cancer, these genes were reported to be related to adiposity, insulin resistance, and inflammation. We showed that AhR ligands were able to up-regulate the expression of these aforementioned genes. These data are in agreement with the in vivo metabolic disruption associated with dioxin-like POP and other POP levels (Airaksinen et al. 2011; Uemura a al. 2009) and may partly explain the growing evidence on the relationship between obesity and cancer (Harvey a al. 2011). Such associations could be also explained by insulin signaling impairment (Hsu et al. 2010; Nishiumi et al. 2010; Stienstra et al. 2011). However, the regulation of metabolic pathways appeared to be less prominent than that of the inflammatory pathway in our studies.

Obesity-associated inflammatory genes such as IL1B, IL1ra, IL8, and PTGS2 (Goldberg 2009; Stienstra et al. 2011) were also increased by AhR ligands in hMADS cells. These results are in agreement with a recent study conducted on in vitro differentiated adipocytes from human bone marrow cells (Arsenescu et al. 2008; Li et al. 2008). We also showed that TCDD and PCB-126 gene regulation was mediated by AhR because it was potently inhibited by the AhR antagonist [alpha]-NF. In AT of TCDD-treated mice, we observed a similar up-regulation of transcripts for several inflammatory mediators (Mb, Il10, Ptgs2) only in WT mice. Thus, our study suggests that the inflammatory pathway could be induced by TCDD via AhR. The modulation of gene expression was accompanied by an increased infiltration of macrophages and confirmed the existence of a TCDD-induced inflammatory state of AT. In animal models of obesity, a relationship was shown between adipose cell size and the concentration of macrophages in the stroma vascular fraction of AT (Weisberg et al. 2003). Such results were not found in the present study, suggesting that fat cell hypertrophy was not the sole determinant of macrophage recruitment into AT. Interestingly, a recent study found an association between small adipose cells and inflammation in moderately obese, weight-stable individuals (McLaughlin et al. 2010), suggesting that the association between adipocyte diameter and inflammation is more complex.

An important point revealed by our studies is that precursor cells appeared to be more sensitive than adipocytes to certain effects of AhR ligands. These data are in agreement with Fain (2010), who compared the release and expression of adipokines by nonfat versus fat cells and showed that the nonfat cells, containing adipose precursors, were more inflammatory than were fat cells. Several possible mechanisms could account for such observations. It is possible that adipocytes are less sensitive to AhR ligands because of their lipid compartment, which could divert these pollutants from their protein targets. Although we cannot exclude this possibility, it seems unlikely because the induction of CYP1B1, a prototypical AhR target gene, appears to be similar at both cell differentiation stages. Alternatively, there may be stage specificity of AHR transcriptional effects, highlighting the relevance of precursor cells in the mechanisms of pollutant-elicited AT toxicity.

PCB-153 is a non-AhR ligand that was shown to display some toxicity in other systems, notably as a cancer promoter (Knerr and Schrenk 2006), but the genornic biomarkers of PCB-153 exposure are not well identified. In hMADS, this pollutant displayed a moderate effect on differentiated cells. Interestingly, in undifferentiated cells, genes involved in cancer/inflammatory pathways such as CXCL12, IGFBP3, IL1B, and WNT5A were down-regulated by PCB-153, whereas they were up-regulated by AhR ligands. These observations highlight the critical role of AhR ligands in the modulation of inflammatory cytokines in adipocytes.

In conclusion, both our in vitro and in vivo studies suggest that adipocytes and their precursors are targets of POPs and that one of the main pathways that these pollutants trigger is inflammation. In view of the importance of this pathway in the pathogenicity of metabolic diseases, these observations indicate a possible mechanism of pollutant contribution to the development of these diseases.


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Min Ji Kim, (1) Veronique Pelloux, (2), (3) Erwan Guyot, (1) Joan Tordjman, (2), (3) Linh-Chi Bui, (1) Aline Chevallier, (1) Claude Forest, (1) Chantal Benelli, (1) Karine Clement, (2), (3) and Robert Barouki (1), (4)

(1.) INSERM UMR-S 747, Universite Paris Descartes, Centre Universitaire des Saints-Peres, Paris, France; (2.) INSERM U872, Nutriomique team 7, Universite Pierre et Marie Curie-Paris 6, Centre de Recherche des Cordeliers, UMR S 872, Paris, France; (3.) Assistance Publique-Hopitaux de Paris, Institute of Cardiometabolism and Nutrition, Pitie-Salpetriere, Paris, France; (4.) Assistance Publique-Hopitaux de Paris, Hopital Necker-Enfants Malades, Paris, France Address correspondence to R. Barouki, Universite Paris Descartes, Centre Universitaire des Saints-Peres, 45 rue des Saints Peres, 75270 Paris Cedex 06, France. Telephone: 33(0)142862075. Fax: 33(0)142863868. E-mail:

Supplemental Material is available online (

This program was supported by the Programme National de Recheche sur I' Alimentation (PNRA) Adipotox program (ANR-06-PNRA-002) of the National Agency of Research, Institut National de. la Sante et de la Recherche Medicate (INSEAM), Universite Paris Descartes, Universite Pierre et Marie-Curie, and Conseil Regional d'Ile de France. L.-C.B. was supported by a fellowship from the Fondation pour la Recherche medicate and the Ligue contre le Cancer. Human multipotent adipose-derived stem cells were provided by the laboratory of C. Dani (University of Nice Sophia Antipolis) under the conditions of a material transfer agreement [Centre National de la Recherche Scientifique (CNRS)] with INSERM special purpose vehicle collaboration action (SPV CA) 25203.

The authors declare they have no actual or potential competing financial interests.

Received 30 July 2011: accepted 19 January 2012.
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Title Annotation:Research
Author:Ji Kim, Min; Pelloux, Veronique; Guyot, Erwan; Tordjman, Joan; Buui, Linh-Chi; Chevallier, Aline; Fo
Publication:Environmental Health Perspectives
Article Type:Report
Geographic Code:4EUFR
Date:Apr 1, 2012
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