Molecular and Cellular Endocrinology 370 (2013) 52–64

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Molecular and Cellular Endocrinology

journal homepage: www.elsevier .com/locate /mce

Unraveling the mode of action of an obesogen: Mechanistic analysis of the modelobesogen tributyltin in the 3T3-L1 cell line

Anna Pereira-Fernandes a,⇑, Caroline Vanparys a, Tine L.M. Hectors a, Lucia Vergauwen a,b, Dries Knapen a,b,Philippe G. Jorens c, Ronny Blust a

a Systemic Physiological and Ecotoxicological Research (SPHERE), Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerpen, Belgiumb Gamete Research Center (GRC), Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgiumc Department of Clinical Pharmacology/Clinical Toxicology, University of Antwerp, Universiteitsplein 1, 2610 Antwerpen, Belgium

a r t i c l e i n f o

Article history:Received 30 October 2012Received in revised form 12 February 2013Accepted 12 February 2013Available online 18 February 2013

Keywords:ObesogenMicroarrayTributyltinIn vitro cell system3T3-L1

0303-7207/$ - see front matter � 2013 Elsevier Irelanhttp://dx.doi.org/10.1016/j.mce.2013.02.011

⇑ Corresponding author. Tel.: +32 3 265 35 01; fax:E-mail addresses: Anna.Pereira-Fernandes@ua.ac

caroline.vanparys@ua.ac.be (C. Vanparys), tine.hectolucia.vergauwen@ua.ac.be (L. Vergauwen), dries.knphilippe.jorens@uza.be (P.G. Jorens), ronny.blust@ua.a

a b s t r a c t

Obesogenic compounds are chemicals that have an influence on obesity development. This study wasdesigned to unravel the molecular mechanisms of the model obesogen TBT, using microarray analysisin the 3T3-L1 in vitro system, and to evaluate the use of toxicogenomics for obesogen screening. Themicroarray results revealed enrichment of Gene Ontology terms involved in energy and fat metabolismafter 10 days of TBT exposure. Pathway analysis unveiled PPAR signalling pathway as the sole pathwaysignificantly enriched after 1 day and the most significantly enriched pathway after 10 days of exposure.To our knowledge, this is the first study delivering an in depth mechanistic outline of the mode of actionof TBT as an obesogen, combining effects on both cell physiological and gene expression level. Further-more, our results show that combining transcriptomics with 3T3-L1 cells is a promising tool for screeningof potential obesogenic compounds.

� 2013 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

Overweight and obesity are defined as diseases in which abnor-mal excessive body fat accumulation causes adverse effects onhealth, leading to a higher morbidity and mortality. A high-caloriediet and low physical activity are considered as the major influenc-ing factors for the development of obesity (Hill and Peters, 1998).However, the potential influence of continously increasing expo-sure to industrial compounds, food contaminants and environmen-tal pollutants on weight homeostasis has recently gained attention(Grun and Blumberg, 2009; Newbold et al., 2008). In that contextthe environmental obesogen hypothesis states that an early-lifeor long-term (chronic) exposure to environmental pollutants mighthave an influence on the development of, or susceptibility to meta-bolic diseases such as diabetes and obesity (Grun and Blumberg,2009; Hectors et al., 2011). Since the discovery of leptin and otheradipokines secreted by the adipose tissue, it is no longer consideredas solely a storage place for excessive energy. The endocrine charac-ter of this tissue (Koerner et al., 2005) has made endocrine disrupt-ing compounds (EDCs) first line candidates as potential obesogens,

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+32 3 265 34 97..be (A. Pereira-Fernandes),rs@ua.ac.be (T.L.M. Hectors),apen@ua.ac.be (D. Knapen),c.be (R. Blust).

broadening ‘endocrine disruption’ to a more ‘physiological disrup-tion’ concept including interferences with homeostatic processesand control, such as neural, endocrine and metabolic disruption(Casals-Casas and Desvergne, 2011; Gore, 2010). In the context of‘‘obesogenic’’ metabolic disruption, EDCs such as bisphenol A, mono(2-ethylhexyl) phthalate (MEHP), nonylphenol and tributyltin(TBT), have been described to contribute to differentiation or prolif-eration of adipocytes in vitro (Feige et al., 2007; Masuno et al., 2003,2005; Grun et al., 2006).

TBT is the most studied obesogenic compound up to now andcan therefore be considered a model obesogen. In utero studiesshowed that TBT increased the adiposity of mice later in life (Grunet al., 2006). When tadpoles of frogs (Xenopus leavis) were TBT ex-posed an increased adiposity has also been observed (Grun et al.,2006). Moreover in vivo obesogenic properties of TBT have beendescribed in salmonids, mice and snails (Grun et al., 2006; Janeret al., 2007; Meador et al., 2011). Additionally in vitro studiesshowed that exposure of mouse 3T3-L1 pre-adipocyte cells and hu-man and mouse mesenchymal stem cells to TBT induced differen-tiation into mature adipocytes (Grun et al., 2006; Kirchner et al.,2010; Yanik et al., 2011).

TBT is described as a high affinity agonist of peroxisome prolifer-ator activating receptor (PPAR) c and retinoid X receptor (RXR), twoimportant nuclear receptors during adipocyte differentiation (Grunet al., 2006). Recently an antagonist study confirmed the impor-tance of PPAR c during the TBT-induced adipocyte differentiation

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(Li et al., 2011). However, to our knowledge, no comprehensivemechanistic study has been performed so far to unravel the fullmode of action of this obesogenic compound in adipocytes.

In the context of metabolic disruption attention is now drawnto the development of relevant screening systems for consistenttesting of pollutants (Thayer et al., 2012). A transcriptomics ap-proach combined with single endpoint testing can therefore beinteresting considering the inherent multi-endpoint and highthroughput character of this technique, revealing possible other ef-fects of a compound on metabolic related endpoints such as insulinsensitivity. Moreover, different treatments acting by the samemode of action are known to be reflected by similar gene expres-sion profiles, enabling predictive toxicology and use for classifica-tion of compounds in the future. Therefore, in this study TBT waschosen as a model compound, to extend the knowledge on theunderlying molecular mechanisms of TBT and to evaluate the3T3-L1 cell system combined with toxicogenomics as an in vitrotool for screening of obesogens. In a first step TBT induced differen-tiation of 3T3-L1 cells was cell physiologically evaluated using lipidstaining techniques to select appropriate conditions for microarrayanalysis and to evaluate the reproducibility and stability of the3T3-L1 cell line. Thereafter microarray analyses have been per-formed on TBT treated (high and low concentration) pre-adipo-cytes, at an early and late stage of differentiation. Using thisapproach, this study evaluated for the first time, to our knowledge,the use of the 3T3-L1 cell line for screening combined with toxic-ogenomic analysis of obesogenic compounds using TBT as a modelcompound.

2. Materials and methods

All chemicals and cell culture reagents were obtained fromSigma–Aldrich (Bornem, Belgium) unless otherwise indicated.

2.1. 3T3-L1 routine cell culture

3T3-L1 mouse pre-adipocyte cells (American Type CultureCollection CRL-173�, LGC Promochem GmbH, Wesel, Germany)were maintained in T75 Nunc culture flasks in standard mediumcomposed of high glucose (4.5 g/L) Dulbecco’s modified Eagle’smedium (DMEM; Life Technologies, Merelbeke Belgium) supple-mented with 10% Heat Inactivated Newborn Calf serum (Life Tech-nologies), 100 U/mL Penicillin, 100 lg/mL Streptomycin (LifeTechnologies) and 1 mM sodium pyruvate. All cell culture experi-ments were performed in a 5% CO2 atmosphere at 37 �C. Beforethey reach confluence, cells were detached using 0.25% Trypsin/EDTA (Life Technologies). Cells were used until passage 12.

2.2. Experimental setup

Cells were seeded in 6- or 24- well plates at a density of 200,000cells/well and 50,000 cells/well respectively (confluent density)and grown for 2 days. At that time (day 0), standard mediumwas replaced by mature medium containing 10% Heat InactivatedFoetal Bovine Serum (FBS, Life Technologies) instead of NewbornCalf Serum and the test compound, Tributyltin Chloride (TBT-Cl,Acros Chemicals, Geel, Belgium), was added in a concentrationrange of 0.15–75 nM for 10 days, changing the medium every2–3 days. TBT was dissolved in DMSO with a final maximal concen-tration of 0.1%. Therefore, a solvent control (0.1% DMSO) wasincluded in each experiment. As positive control, cells were stimu-lated from day 0–2 for 48 h in mature medium containing a MDIhormonal cocktail (0.5 mM isobutyl methylxanthine, 0.25 lMdexamethasone and 10 lg/mL insulin) and from day 2–10 inmature medium containing only insulin (10 lg/mL) and changed

every 3 days. After exposure, cells were stained with Nile red(AdipoRed) and Oil Red O (24-well plates) or were harvested forRNA extraction (6-well plates).

2.3. Intracellular lipid measurements: Oil Red O and Nile red staining

After 10 days TBT exposure (day 10), the intracellular lipid con-tent was visualised by performing Oil Red O staining. In brief, cellswere washed with PBS and 10% formalin in PBS (with Ca2+ andMg2+, Life Technologies) was added for at least 1 h before fixation.Thereafter, cells were washed with 60% isopropanol and stainedduring 10 min with filtered Oil Red O solution (5 g/L (Sigma Al-drich), 60% isopropanol (Sigma Aldrich), 40% deionised water). Ex-cess stain was removed by washing the cultures 4 times withdistilled water and light microscopic pictures were taken. Addi-tionally, to quantify lipid content the commercially available fluo-rescent Nile red lipid stain (AdipoRed assay Reagent; Lonza,Walkersville, MD) was used following the manufacturer’s instruc-tions. Fluorescence was measured at an excitation and emissionwavelength of respectively 485 nm and 535 nm. Relative Fluores-cence Units (RFUs) were obtained and divided by the RFU of sol-vent treated cells to obtain a relative lipid accumulation.Furthermore, fluorescent pictures were then taken to visualisethe lipid droplets using a JuLi™ smart fluorescent cell analyser(International Medical Products, Brussels, Belgium).

2.4. Gene expression analysis using Real-time PCR

RNA extraction was performed using RNeasy kit form Qiagen(Antwerp, Belgium), according to manufacturer’s instructions.RNA purity and quality were evaluated using the NanoDrop spec-trophotometer (NanoDrop Technologies, Montchanin, DE, USA),integrity was checked using agarose gel electrophoresis. A startingamount of 1 lg RNA was transcribed to first strand cDNA accordingto Revert Aid™ H Minus First strand cDNA synthesis kit for RT-PCR(Thermo Fisher Scientific, Zellik, Belgium). Highly purified salt-free‘Oligogold’ primers (Eurogentec, Seraing, Belgium) were selectedfor the target gene adipocyte specific protein 2 (aP2; NM_024406)(FW: AGT-GGA-AAC-TTC-GAT-GAT-TAC-ATG-ATG-AA; RE: GCC-TGC-CAC-TTT-CCT-TGT-G) and household gene TATA bindingprotein (Tbp; NM_013684) (FW: ACC-CTT-CAC-CAA-TGA-CTC-CTA-TG; RE: ATG-ATG-ACG-GCA-GCA-AAT-CGC) (Hassan et al.,2007; Petersen et al., 2008). For both primer sets, primer efficien-cies exceeded 1.85. Real-time PCR reaction master mix was usedfollowing the manufacturer’s instructions (Brilliant� II SYBR� GreenQPCR mastermix, Agilent Technologies, Santa Clara, CA). Accordingto the equation of Pfaffl (2001) the expression values of the targetgene (aP2) were normalised by comparison to the household gene(Tbp), and an exposure versus solvent control relative expressionratio was calculated. Comparison of three household genes (18SrRNA, RPLP0 and Tbp) favoured Tbp because of its stable expressionduring differentiation and during exposure conditions (data notshown).

2.5. Microarray analysis

Microarray analysis was performed with the same RNA samplesas for Real-time PCR experiments. RNA was amplified and labelledusing the Low Input Quick Amplification Labelling Kit (LIQA, Agi-lent, Diegem, Belgium), according to the protocol of the manufac-turers. Briefly, starting from 200 ng of total RNA, poly-A RNA wasreverse transcribed using a poly dT-T7 primer. The resulting cDNAwas used for one round of amplification by T7 in vitro transcriptionreaction in the presence of Cyanine 3-CTP or Cyanine 5-CTP. Theamplified and labelled RNA samples were purified separately onan RNeasy purification column (Qiagen). Amplification yield and

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incorporation efficiency of the probes were verified by measuringthe RNA concentration at 260 nm, Cy3 incorporation at 550 nmand Cy5 incorporation at 650 nm using a Nanodrop spectropho-tometer. Thereafter, 825 ng of Cy3 and Cy 5 labelled RNA sampleswere co-hybridised on a 4 � 44 K whole mice genome microarray(G4846A; Agilent) for 17 h at 65 �C in a continuous rotating hybrid-isation oven. The hybridisation design was a v + 2 A-optimal de-sign, in two independent loops for each time-point (Knapenet al., 2009). Three biological replicates were measured for everycondition, each measured twice by including dye swaps. Arrayswere washed with Agilent wash buffers and acetonitrile and finallysubmersed in stabilization and drying solution (Agilent Technolo-gies) to prevent ozone-induced Cy5-degradation. Slides were sub-sequently scanned using a Genetix Personal 4100A scanner (AxonInstruments, Union City, CA, USA). The images were analysed usingthe Genepix Pro software 6.1 (Axon Instruments) for spot identifi-cation and for quantification of the fluorescent signal intensities.Statistical analysis of microarray data was performed with the Rpackage Limma (Smyth, 2004) as described in Vergauwen et al.(2010). Briefly, spots for which red or green FG < BG + 2SD on allarrays were deleted before analysis (FG: medium foregroundintensity; BG: average local background intensity calculated overthe full microarray; SD: standard deviation of local backgroundintensities). After background correction (backgroundCorrect,method ‘‘normexp’’, offset = 50) of the median intensity data, loessnormalisation (normaliseWithinArrays) was applied. Linear mod-els were fitted to intensity ratios, after which probes were rankedin order of evidence of differential expression using an empiricalBayes method (Smyth, 2004). Genes were regarded as significantlydifferentially expressed versus solvent control considering a Falsediscovery rate (FDR, padj-value) 60.05 and |log2FC| P0.75 (log2 foldchange). Microarray design details, raw data files and normaliseddata are submitted to the NCBI GEO database (nr. GSE41340;http://www.ncbi.nlm.nih.gov/geo).

2.6. Pathway/GO analysis

The functional annotation tool of the DAVID bioinformatics re-source (Huang et al., 2009) was used to identify common Biologicalprocesses and pathways as defined by Gene Ontology (GO) criteria.For this analysis, the enrichment of GO terms in the target gene list(i.e. differentially expressed genes) was compared, using Fisher’sexact test, to a background list (‘‘Agilent Mouse V2’’ as providedby DAVID) containing all genes analysed. Gene Ontology enrich-ment analysis of Biological processes was performed by considering‘‘GOTERM_BP_FAT’’, which is a selection of GO terms provided byDAVID, filtering the broadest terms to prevent overshadowing ofthe more specific terms. In this case, the specificity of a term isbased on the number of child terms to filter out the broadest termin the hierarchy. Pathway enrichment was carried out by selecting‘‘KEGG pathways’’. Venn diagrams were visualised using the webapplication tool GeneVenn (http://simbioinf.com/mcbc/applica-tions/genevenn/genevenn.htm) (Pirooznia et al., 2007).

Top 50 GO terms obtained from the DAVID software were ana-lysed by the visualisation software REVIGO (REduce and VIsualizeGO; http://revigo.irb.hr/). This software finds representative GOterms using a clustering algorithm (Supek et al., 2011). The non-redundant GO term set is visualised in a tree map where each indi-vidual rectangle corresponds to a representative GO term. Theserepresentatives are clustered together in super clusters of relatedterms (each in a specific colour). The size of the rectangles repre-sents the –log(p-value) of the GO term. Additionally the GO IDnumber and �log(p-value) of the representative GO terms areshown.

3. Results

3.1. Tributyltin induces differentiation of 3T3-L1 cells

To confirm the previously described obesogenic potential ofTBT, 3T3-L1 cells were exposed to a range of TBT concentrations(0.2–75 nM) and differentiation induction was evaluated. The hall-mark for adipose differentiation is the formation of lipid droplets.Fluorescent and light microscopic pictures were taken of 3T3-L1cells exposed for 10 days to TBT and MDI. These results visuallyconfirm TBT induced lipid accumulation and lipid droplet forma-tion (Fig. 1A and B). Lipid accumulation was quantified using Nilered staining (AdipoRed assay) at day 10 (1C). Data represent fourindependent experiments with four biological replicates each. Adose-dependent increase in lipid accumulation was measured,with a maximal induction of 3.5 ± 0.4 at the highest concentrationtested (Fig. 1C). Lipid accumulation was significantly induced start-ing from 9.38 nM TBT. The maximal induction of MDI was32.2 ± 0.8, showing the potential of TBT to induce 10% of the max-imal MDI induced differentiation.

The gene expression of the adipocyte specific marker gene, adi-pocyte specific protein 2 (aP2), was quantified during TBT induceddifferentiation. Based on the lipid accumulation results 10, 30 and50 nM TBT were selected as concentrations inducing respectivelythe lowest, middle and highest adipocyte differentiation. Moreoverthree different time-points during differentiation were included(day 1, day 4 and day 10). At every time point and concentrationtested, aP2 expression was significantly induced by TBT treatmentcompared to solvent control (Fig. 2). These gene expression resultsconfirm the TBT induced concentration and time dependent adipo-cyte differentiation.

3.2. Microarray gene expression analysis of TBT exposed cells

To unravel the molecular mechanisms that underlie TBT in-duced differentiation of adipocytes, microarray analysis using awhole mouse genome microarray was performed. Based on theReal-time PCR results of aP2 gene expression and lipid accumula-tion (Figs. 1 and 2) an early and late stage of differentiation (day1 (24 h) and day 10) and a low and high TBT concentration (10and 50 nM) were selected for microarray analysis.

Comparing the gene expression of each dose group at differenttime points, a time and concentration dependent increase in theamount of differentially expressed genes can be observed (Fig. 3).After 1 day of exposure only 3 and 37 genes were influenced by10 nM and 50 nM of TBT respectively, whereas 92 and 377 geneswere differentially expressed at day 10. MDI treatment resultedin differential expression of 399 and 540 genes at day 1 and 10respectively. These results indicate that at day 1, MDI treatmentcauses ten times more differential expression compared to themost potent TBT concentration. Interestingly, at day 1, 0%, 29%and 55% of the genes respectively affected by 10 nM, 50 nM TBTand MDI were expressed with very high significance (p-value 60.0005) going up to 46%, 66% and 67% after 10 days of treatment,indicating the robustness of the expression profiles (Fig. 3A). More-over, the percentage up regulated genes relative to the totalnumber of genes differentially expressed in that condition wasgenerally higher than the percentage down regulated genes, exceptfor 1 day MDI treatment (Fig. 3B).

To evaluate the similarity in gene expression between the twodifferent TBT concentrations and MDI, Venn diagrams were con-structed (Fig. 3C and D). Venn diagrams clearly show a large over-lap of genes between 50 nM TBT and MDI treatment (Fig. 3C andD). After 1 day of exposure already 35% of the genes differentiallyexpressed by 50 nM TBT were also affected by MDI (Fig. 3C),

Fig. 1. Effect of TBT on adipocyte differentiation in 3T3-L1 cells after 10 days of exposure. Fluorescent (A; AdipoRed staining) and light microscopic pictures (B; Oil Red O staining)were taken. Scale bar represents 50 lm. Fluorimetric quantification of lipid accumulation was performed using AdipoRed staining (C). Four independent experiments werecarried out at different passage numbers (p6–p10) with 4 biological replicates each (n = 4). Data represent means of the experiments (±stdev) lipid accumulation relative tosolvent control (DMSO) and significant differences with solvent control (DMSO) are indicated with asterisks (One Way ANOVA Dunnet’s post hoc test; �p 6 0.05; ��p 6 0.01;���p 6 0.001).

Fig. 2. Gene expression of the adipocyte specific gene adipocyte specific protein 2 (aP2)measured by Real-time PCR at different time points during differentiation. Data arerepresented as the mean fold change relative to the solvent control of 3 biologicalreplicates (mean ± stdev; n = 3). Significant differences between the conditionswere analysed for each time-point with One Way ANOVA (Dunnet’s post hoc test;�p 6 0.05; ��p 6 0.01; ���p 6 0.001).

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increasing to 56% overlap at day 10 (Fig. 3D). From those overlap-ping genes, 85% and 99% at day 1 and day 10 respectively, wereregulated in the same direction for both conditions (up or down).

A closer look at the results of day 10, revealed that the directionof the regulation of the overlapping genes influenced by TBT expo-sure (n = 87) was the same for the 2 tested TBT concentrations(data not shown).

3.3. Functional analysis of gene expression

GO (Biological processes) and pathway (KEGG) enrichmentanalysis was performed using the DAVID software. After 1 day ofTBT exposure, only a small set of genes were differentially ex-pressed and only at the highest concentration tested (50 nM) func-tional analysis was possible. Out of the 37 (day 1) and 377 (day 10)differentially expressed genes after TBT 50 nM treatment, DAVIDsoftware was able to map 36 and 365 genes respectively. More spe-cifically, 69% and 75% of all gene ID’s at day 1 and 10 respectivelywere associated to GO terms, whereas this was 46% and 42%respectively for KEGG pathways. In Tables 1 and 2, significant GOterms and KEGG pathways of the differentially expressed genesafter 1 day and 10 days of exposure to 50 nM TBT or MDI areshown. The fold change of genes belonging to the major GO termsor KEGG pathways are summarised in Table 3. TBT showed anenrichment of GO terms mainly involved in lipid metabolism (fattyacid metabolic process, fat cell differentiation, positive regulation of

Fig. 3. Representation of numbers of differentially expressed genes from microarray analysis. Number of differentially expressed genes ranked by significance (FDR) at each timepoint for each condition (A). Percentage of up- or downregulated genes relative to the total amount of differentially expressed genes per condition and time point (B). Venndiagrams of significant differentially expressed genes (FDR 6 0.05; |log2FC| P 0.75) at day 1 (C) and day 10 (D), visualised with the GeneVenn web application tool.

Table 1Gene Ontology and Pathway enrichment analysis of differentially expressed genes after 1 day of exposure. The Top 10 GO classes (Biological processes) en KEGG pathways for TBT(50 nM) and MDI are shown with their respective p-value (DAVID; Fisher exact test; p 6 0.05).

TBT 50 nM MDI

GO class p-value KEGG Pathways p-value GO class p-value KEGG Pathways p-value

GO:0006631 � fatty acidmetabolic process

3.05E�04 PPAR signallingpathway

3.18E�05 GO:0030029 � actin filament-based process

7.23E�06 ECM-receptorinteraction

1.34E�04

GO:0000303 � response tosuperoxide

1.91E�02 GO:0030036 � actincytoskeleton organisation

1.51E�05 Focal adhesion 2.66E�03

GO:0000305 � response tooxygen radical

2.10E�02 GO:0006955 � immune response 2.38E�05 Regulation of actincytoskeleton

4.53E�03

GO:0006006 � glucosemetabolic process

2.76E�02 GO:0007155 � cell adhesion 4.54E�05 MAPK signallingpathway

9.87E�03

GO:0009725 � response tohormone stimulus

3.30E�02 GO:0022610 � biologicaladhesion

4.54E�05 Cell adhesion molecules(CAMs)

1.50E�02

GO:0051384 � response toglucocorticoid stimulus

3.60E�02 GO:0030335 � positiveregulation of cell migration

9.45E�05 Hypertrophiccardiomyopathy (HCM)

1.51E�02

GO:0019318 � hexose metabolicprocess

3.89E�02 GO:0009611 � response towounding

1.07E�04 Dilated cardiomyopathy 2.41E�02

GO:0009719 � response toendogenous stimulus

4.07E�02 GO:0001525 � angiogenesis 1.20E�04 Cytokine-cytokinereceptor interaction

2.49E�02

GO:0031960 � response tocorticosteroid stimulus

4.35E�02 GO:0007517 �muscle organdevelopment

1.23E�04

GO:0006090 � pyruvatemetabolic process

4.53E�02 GO:0051272 � positiveregulation of cell motion

1.80E�04

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lipid metabolic process, acetyl-Coa metabolic process, lipid and fattyacid oxidation) and glucose metabolism (glucose, hexose, pyruvatemetabolic process) at both time points (Tables 1 and 2). Responseto hormone stimulus (GO: GO:0009725) and response to superoxide(GO:0000303) was only enriched after 1 day of exposure. Interest-ingly, uncoupling protein 2 (Ucp2) a member of the response tosuperoxide GO was upregulated at both times of exposure to TBT(Table 3). The tree map visualisation in Fig. 4 represents a cluster-ing of the most representative GO terms influenced after 10 days of

TBT exposure, revealing fatty acid metabolism as most importantcluster, followed by fat cell differentiation related GO terms. TheGO term fat cell differentiation (GO:0045444), significantly enrichedat day 10 of 50 nM TBT exposure, was represented by several inter-esting genes. Except for cyclin d1 (Ccnd1), all other genes wereupregulated, such as adipogenin (Adig), stearoyl-coenzyme Adesaturase 1 (Scd-1), chemerin (Rarres2) and adiponectin (Adipoq).

Significantly enriched KEGG pathways were mainly observedafter 10 days of exposure to 50 nM TBT and, in agreement with

Table 2Gene Ontology and Pathway enrichment analysis of differentially expressed genes after 10 days of exposure. The Top 10 GO classes (Biological processes) en KEGG pathways forTBT (50 nM) and MDI are shown with their respective p-value (DAVID; Fisher exact test; p 6 0.05).

TBT 50 nM MDI

GO class p-value KEGG Pathways p-value GO class p-value KEGG Pathways p-value

GO:0006631 � fatty acidmetabolic process

4.36E�12 PPAR signallingpathway

1.45E�17 GO:0006091 � generation ofprecursor metabolites and energy

3.28E�34 Oxidativephosphorylation

1.44E�24

GO:0055114 � oxidationreduction

5.27E�11 Fatty acidmetabolism

5.27E�10 GO:0055114 � oxidationreduction

2.04E�33 Parkinson’s disease 1.34E�21

GO:0050873 � brown fat celldifferentiation

8.29E�11 Citrate cycle (TCAcycle)

1.49E�07 GO:0022900 � electron transportchain

1.45E�22 Huntington’s disease 1.74E�18

GO:0045444 � fat celldifferentiation

1.65E�08 Pyruvatemetabolism

1.61E�06 GO:0050873 � brown fat celldifferentiation

4.75E�20 Alzheimer’s disease 6.45E�16

GO:0045834 � positiveregulation of lipid metabolicprocess

3.08E�07 Propanoatemetabolism

1.49E�05 GO:0015980 � energy derivationby oxidation of organiccompounds

2.69E�18 Citrate cycle (TCAcycle)

1.34E�14

GO:0006091 � generation ofprecursor metabolites andenergy

5.74E�07 Valine, leucine andisoleucinedegradation

1.87E�04 GO:0045333 � cellular respiration 7.30E�18 PPAR signallingpathway

6.35E�12

GO:0006084 � acetyl-CoAmetabolic process

2.08E�06 Parkinson’s disease 4.65E�04 GO:0051186 � cofactor metabolicprocess

1.06E�16 Cardiac musclecontraction

5.23E�09

GO:0006006 � glucosemetabolic process

3.05E�06 Adipocytokinesignalling pathway

5.63E�04 GO:0045444 � fat celldifferentiation

7.04E�16 Valine, leucine andisoleucinedegradation

1.02E�08

GO:0034440 � lipid oxidation 4.62E�06 Fatty acid elongationin mitochondria

1.25E�03 GO:0006732 � coenzymemetabolic process

6.52E�15 Propanoatemetabolism

1.72E�07

GO:0019395 � fatty acidoxidation

4.62E�06 Alzheimer’s disease 1.85E�03 GO:0006099 � tricarboxic acidcycle

2.11E�13 Fatty acidmetabolism

3.73E�06

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GO terms, predominantly involved in glucose and fatty acid metab-olism. Moreover, energy related KEGG pathways such as TCA cycleand pyruvate metabolism were significantly affected, revealingupregulated genes such as malate dehydrogenase (Mdh2), Succi-nateCoA ligase (Suclg1), isocitrate dehydrogenase (Idh3a), oxoglu-tarate dehydrogenase (Ogdh) (Fig. 5, Table 3). The influence ofTBT and MDI treatment on lipid metabolism based on significantlyenriched KEGG pathways is summarised in Fig. 5, indicating thetime and concentration dependence of involved genes. Other KEGGpathways enriched after 10 days of exposure by TBT and MDI wereassociated with an increase in oxidative phosphorylation (Alzeimer,Parkinson and Huntington disease) (Table 2). Adipocytokine signallingKEGG pathway was also enriched after TBT exposure. Next toadiponectin (Adipoq) and its receptor (Adipor2), other recently dis-covered adipokines (not included in Adipocytokine signalling KEGGpathway) were also induced after 10 days of TBT exposure: Adipsin(Cfd), Angiotensinogen (Agt) and Chemerin (Rarres2) (Table 3).

The only KEGG pathway significantly enriched after 1 day ofTBT (50 nM) exposure was the PPAR signalling pathway (pvalue = 3.18E�05) (Table 1). An overview of the impact of TBTand MDI treatment on the PPAR signalling pathway is depictedin Fig. 6. Remarkably, this pathway was not significantly enrichedby MDI exposure at the same time point. At that time point (1 day)MDI treatment caused an enrichment of KEGG pathways and GOterms, mainly involved in cytoskeleton remodelling (regulation ofactin cytoskeleton), adhesion (focal adhesion, cell adhesion molecules(CAMs)) and receptor interaction (ECM-receptor interaction, cyto-kine–cytokine receptor interaction). After 10 days of TBT exposurePPAR signalling remained the most significantly enriched pathway(p-value = 1.45E�17; Table 3), with an upregulation of down-stream genes of the PPAR c/RXR heterodimer (Fig. 6). These geneswere involved in lipogenenesis (e.g. Scd1/3), cholesterol metabo-lism (e.g. Nr1h3), fatty acid transport (e.g. Fabp3), adipocyte differ-entiation (e.g. Adipoq) and gluconeogenesis (e.g. Pck1). Moreover,all these downstream genes showed a higher expression after50 nM compared with 10 nM TBT treatment, indicating a possibledose dependent effect on gene expression (Supplemental Fig. 1).MDI treatment also caused a significant enrichment of the PPAR

signalling pathway at late stage differentiation (day 10), but to alesser extent than TBT exposure (Table 2).

4. Discussion

Recently the environmental obesogen hypothesis has been pos-tulated, focusing on the possible role of developmental or chroniclife time exposure to environmental synthetic compounds duringobesity development (Grun and Blumberg, 2009). Together,in vitro, in utero and in vivo studies have reported the adipogenicor obesogenic capacity of TBT in several species, including humans,making it the most studied obesogen (Grun et al., 2006; Janer et al.,2007; Meador et al., 2011; Kirchner et al., 2010; Yanik et al., 2011).The aim of this study was to unravel the molecular mode of actionof TBT, to gain insight into the possible obesogenic mechanismsunderlying the effects on adipogenesis. In this way, important reg-ulatory pathways can be revealed that can be important in mode ofaction based-screening of unknown obesogens. The TBT concentra-tions used in these experiments were comparable with averageconcentrations measured in human blood samples from Michigan,USA (2.5 � 10�8 M) (Kannan et al., 1999).

In this study, lipid accumulation and gene expression of the adi-pocyte marker gene aP2 showed that TBT induced the differentia-tion of the 3T3-L1 adipocytes in a dose dependent and timedependent manner. These results suggest the potential use of thisexperimental setup (3T3-L1 cell line and Nile red staining) as aninitial screening system for obesogenic compounds. TBT exposurewas able to induce 10% of the maximal MDI triggered lipid accu-mulation. This difference was expected, knowing that MDI is a hor-monal cocktail consisting of three compounds that induceadipocyte differentiation (Ntambi and Young-Cheul, 2000). Theuse of lipid droplet staining techniques are widely used for thedetermination of the differentiation of adipocytes. However, oftenthe quantification of the lipid accumulation is photograph-based.In this paper we show the possibility to use the fast, fluorescencebased Nile red approach, revealing a reproducible technique forfirst line obesogenicity testing.

Table 3Gene expression values of the all the genes depicted in Figs. 5 and 6 and adipokines. Data are presented as log2 FC values, with bold values indicating significant differentiallyexpressed genes (p value 6 0.05; |log2FC| P 0.75).

Genesymbol

Gene description Accesion nr Day 1 Day 10

10 nM 50 nM MDI 10 nM 50 nM MDI

Fatty acid beta oxidationAcox1 Acyl-Coenzyme A oxidase 1, palmitoyl NM_015729 0.06 0.30 �0.27 0.30 0.90 0.75Acadvl Acyl-Coenzyme A dehydrogenase, very long chain NM_017366 0.29 0.65 0.34 0.55 1.21 1.60Acadm Acetyl-Coenzyme A dehydrogenase, medium chain NM_007382 0.10 0.07 0.43 0.51 1.38 1.14Acadl Acetyl-Coenzyme A dehydrogenase, long-chain NM_007381 0.25 0.49 �0.25 0.50 0.98 0.38Hadha Hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-

Coenzyme A hydratase (trifunctional protein), alpha subunitNM_178878 �0.01 0.13 0.08 0.53 0.97 0.80

Hadh Hydroxyacyl-Coenzyme A dehydrogenase NM_008212 0.09 0.21 �0.17 0.36 0.85 0.87Acaa2 Acetyl-Coenzyme A acyltransferase 2 (mitochondrial 3-oxoacyl-Coenzyme A

thiolase)NM_177470 0.61 0.78 �0.21 0.74 1.43 1.18

Hadhb Hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A (trifunctional protein), beta subunit

NM_145558 0.09 0.06 0.14 0.57 1.56 1.51

Cpt1 Carnitine palmitoyltransferase 1 NM_013495 0.64 0.86 �0.37 0.18 0.65 �0.10Cpt2 Carnitine palmitoyltransferase 2 NM_009949 0.23 0.29 0.21 0.52 1.34 1.21

TCA cycleAco2 Aconitase 2, mitochnodrial NM_080633 0.07 �0.05 0.02 0.46 0.95 0.88Idh3a Isocitrate dehydrogenase 3 (NAD+) alpha NM_029573 �0.03 0.13 �0.1 0.73 1.51 1.66Ogdh Oxoglutarate dehydrogenase (lipoamide) NM_010956 0.12 0.10 0.14 0.28 0.93 1.16Suclg1 Succinate-CoA ligase, GDP-forming, alpha subunit NM_019879 �0.02 0.27 0.13 0.47 0.97 1.11Mdh2 Malate dehydrogenase 2, NAD (mitochondrial) NM_008617 0.19 0.02 0.00 0.55 1.08 1.36Csl Citrate synthase like NM_027945 0.05 0.06 0.28 0.55 0.84 1.55Pcx Pyruvate carboxylase NM_001162946 0.66 1.03 1.32 1.14 1.98 2.29Phdb Pyruvate dehydrogenase (lipoamide) beta NM_024221 �0.05 0.13 �0.07 0.55 1.21 1.57

Triglyceride and fatty acid synthesisAcaca Acetyl-Coenzyme A carboxylase alpha NM_133360 0.27 0.32 0.45 0.44 0.84 0.95Agpat9 1-acylglycerol-3-phosphate O-acyltransferase 9 NM_172715 0.32 0.57 �0.14 0.92 1.76 2.01Agpat3 1-acylglycerol-3-phosphate O-acyltransferase 3 NM_053014 0.16 0.30 �0.67 0.35 1.01 1.46Agpat2 1-acylglycerol-3-phosphate O-acyltransferase 2 NM_026212 0.06 0.10 �0.03 1.14 2.07 1.86Lpin1 Lipin 1 NM_001130412 �0.02 0.05 �0.04 0.45 0.74 1.36Dgat1 Diacylglycerol O-acyltransferase 1 NM_010046 0.06 0.19 0.22 0.63 1.16 1.33Dgat2 Diacylglycerol O-acyltransferase 2 NM_026384 0.09 0.30 0.06 1.35 2.47 3.82

PPAR signalling pathwayRxrg Retinoid X receptor gamma NM_009107 0.05 0.02 �0.17 0.52 1.24 1.17Scd1 Stearoyl-Coenzyme A desaturase 1 NM_009127 0.27 0.46 0.14 2.52 3.55 3.89Scd2 Stearoyl-Coenzyme A desaturase 2 NM_009128 0.19 0.40 �0.01 0.21 0.37 0.87Scd3 Stearoyl-coenzyme A desaturase 3 NM_024450 0.07 �0.05 0.06 0.66 0.96 1.3Dbi Diazepam binding inhibitor NM_001037999 0.19 0.39 �0.40 0.87 1.65 2.02Fabp3 Fatty acid binding protein 3, muscle and heart NM_010174 0.06 0.02 0.04 1.42 3.38 0.49Fabp5 Fatty acid binding protein 5, epidermal NM_010634 0.31 0.15 �0.69 1.20 1.93 2.16Cd36 CD36 antigen NM_007643 0.15 0.77 �0.16 1.98 3.77 3.64Lpl Lipoprotein lipase NM_008509 0.82 0.79 �0.45 0.57 0.63 0.54Acsl1 Acyl-CoA synthetase long-chain family member 1 NM_007981 �0.09 �0.13 �0.23 0.98 1.78 3.00Acsl3 Acyl-CoA synthetase long-chain family member 3 NM_028817 0.20 0.57 �0.13 0.53 0.92 0.17Nr1h3 Nuclear receptor subfamily 1, group H, member 3 (LXR receptor) NM_013839 0.35 0.51 �0.31 0.86 1.68 1.63Scp 2 Sterol carrier protein 2, liver NM_011327 �0.14 0.10 0.18 0.43 0.86 1.82Angptl4 Angiopoietin-like 4 NM_020581 2.41 3.17 �0.65 2.32 3.42 0.96Plin1 Perilipin 1 NM_175640 0.14 0.17 0.07 1.53 2.75 3.45Fabp4 Fatty acid binding protein 4, adipocyte NM_024406 1.11 1.84 �0.10 2.58 3.67 4.46Adipoq Adiponectin, C1Q and collagen domain containing NM_009605 0.17 0.11 0.25 1.72 2.57 4.18Sorbs1 Sorbin and SH3 domain containing 1 NM_178362 0.48 0.26 1.10 1.00 1.67 2.08Gyk glycerol kinase 0.02 �0.05 0.13 0.25 0.72 0.91Pck1 Phosphoenolpyruvate carboxykinase 1, cytosolic NM_011044 �0.02 0.02 �0.03 1.63 1.98 3.03

Uncoupling of mitochondriaUcp2 Uncoupling protein 2 NM_011671 0.51 0.91 0.77 0.45 0.83 0.3

AdipokinesAdipor2 Adiponectin receptor 2 NM_197985 0.06 0.04 0.32 0.49 1.31 1.23Cfd Complement factor D (adipsin) NM_013459 �0.04 0.05 0.09 1.64 1.26 4.77Agt Angiotensinogen NM_007428 0.19 0.34 0.02 0.97 1.45 0.32Rarres2 Retinoic acid receptor responder 2 (chemerin) NM_027852 0.41 0.82 �0.13 1.12 1.96 2.60Retn Resistin NM_001204959 0.08 0.07 0.21 0.90 0.83 3.99

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In this study the obesogenic effect of solely TBT was tested, incontrast to a combined exposure of MDI followed by the test com-pound, as used by other researchers who recently screened poten-tial obesogens (Taxvig et al., 2012). Although under physiologicalconditions adipogenic hormones such as glucocorticoids and insu-lin are present, the one-compound treatment approach was pre-ferred to obtain a clear view on the mechanisms behindobesogen specific induced differentiation.

Real time PCR experiments on different time points during dif-ferentiation show an early increase of aP2 expression after onlyone day of TBT exposure whereas longer exposure periods wereneeded for MDI. However, morphologically cells exposed for 24 hwith TBT did not show lipid droplet formation (data not shown),showing that these changes in aP2 gene expression occur beforethe phenotypic changes. Based on the aP2 Real-time PCR resultsa high (50 nM) and low (10 nM) TBT concentration, and an early

Fig. 4. Tree map visualisation of the Gene Ontology (GO) enrichment analysis of TBT exposed 3T3-L1 cells (50 nM; day 10). GO enrichment analysis (Biological processes) wasperformed using DAVID software. Top 50 GO classes were analysed by the visualisation software REVIGO (REduce and VIsualize GO; (Supek et al., 2011)). Individualrectangles correspond to representative GO terms and are clustered together in superclusters of related terms (each in a specific colour). Size of rectangles represents–log(p-value) of the GO term. GO ID number and �log(p-value) of each GO term is indicated.

Fig. 5. Metabolic processes upregulated during TBT induced differentiation, based on significant GO terms and KEGG pathways. Genes shown in this scheme determined thesignificance of GO terms and KEGG pathways. Significant upregulation of genes is indicated by a red coloured box next to the gene name, with upper and lower rowsindicating day 1 and day 10 of exposure respectively and columns indicating the different exposures.

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Fig. 6. Schematic representation of the PPAR signalling KEGG pathway. Significant upregulation of genes is indicated by a red coloured box next to the gene name, with upperand lower rows indicating day 1 and day 10 of exposure respectively and columns indicating the different exposures.

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(day 1) and late time point (day 10) during differentiation were se-lected for microarray analysis. In this way the early effects of TBTexposure on pre-adipocytes as well as the resulting effects of10 day exposure can be established. Microarray-based geneexpression results showed that the majority of genes were differ-entially expressed with a high significance, confirming the robust-ness of gene expression patterns in this cell line and suggesting thepossibility to use these cells in a toxicogenomic context. At Day 1,399 genes are differentially expressed for MDI, compared to only37 differentially expressed genes for TBT. This is not surprisingsince, MDI treatment consists of three compounds acting on differ-ent adipocyte inducing pathways: IBMX an cAMP elevating agent,dexamethasone acting on glucocorticoid receptor and insulin.

In depth analysis of microarray results on TBT exposed cells re-vealed that GO categories related to energy metabolism (TCA cycle,fat metabolism, lipid transport, etc.) and pathways such as PPARsignalling were affected at both tested time points. Interestingly,24 h exposure with MDI enriched pathways and GO terms mainlyinvolved in cytoskeleton remodelling, adhesion and receptor inter-action. This indicates an earlier effect on the expression of fat re-lated genes due to TBT exposure compared to MDI.

Due to their importance in adipocyte biology and obesity devel-opment, these processes were examined in more detail, focusingon the underlying genes responsible for this enrichment (Table 3,Figs. 5 and 6).

4.1. Regulation of adipocyte differentiation

Within the significantly enriched GO term adipocyte differentia-tion, adipogenin (Adig) expression was upregulated after 10 days ofTBT exposure and also upregulated by MDI. Adipogenin is a plasmatransmembrane protein that is upregulated during adipogenesis(Hong et al., 2005). Another gene of this GO class is Cyclin D1(Ccnd1), known to inhibit adipocyte differentiation (Fu et al.,2005a; Fu et al., 2005b). Interestingly, it was the only gene

downregulated by TBT treatment of the GO class adipocyte differen-tiation. MDI treatment did not alter the expression of Ccnd1.

4.2. Metabolic processes

4.2.1. Triglyceride metabolismGO and Pathway enrichment analysis clearly showed significant

alterations of several fat and carbohydrate metabolism processesafter TBT exposure (Fig. 5). The GO term associated with triglycer-ide synthesis was upregulated during TBT induced adipogenesis(Fig. 5A; GO:0019432 � triglyceride biosynthetic process, p-value:4.13E�04). The transcription of acyl CoA synthetases (Acsl), wasupregulated at a late stage of differentiation. Gerhold et al.(2002) also showed the upregulation of Acsl1 in adipocytes aftertreatment with PPAR c agonists. Next to their role in triglyceridesynthesis, diacylglycerol acyltransferases (DGATs) play a crucialrole in the development of lipid droplets and their transcriptionis known to be induced during 3T3-L1 differentiation (Harriset al., 2011). Another crucial gene for lipid droplet formation isperilipin (Plin1) and was upregulated after TBT exposure, reflectingon a molecular scale the cell physiological lipid droplet formation(Table 3; Fig. 5).

Next to triglyceride synthesis, the GO terms corresponding tob-oxidation of fatty acids and TCA cycle were also significantlyupregulated during TBT exposure (Tables 2 and 3; Fig. 5B and C).These results clearly confirm a previous proteomic study of New-ton et al. (2011), who showed an upregulation of b-oxidation andan increased flux through the TCA cycle during adipocyte differen-tiation. NADH and FADH2 generated during b-oxidation and TCAcycle can be used during oxidative phosphorylation (Bartlett andEaton, 2004), this pathway was significantly enriched after 10 daysof exposure with TBT (Fig. 5D; mmu00190:Oxidative phosphoryla-tion; p-value = 0.002). The energy generation required during adi-pocyte differentiation may provide a first possible explanationfor the upregulation of genes involved in b-oxidation, oxidative

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phosphorylation and TCA cycle (Fig. 5E) (Newton et al., 2011). An-other plausible explanation could be a protective mechanism of thecell facing high concentrations of triglycerides. At that moment,part of the triglycerides could be broken down by b-oxidation fol-lowed by the TCA cycle and oxidative phosphorylation generatingan excessive proton gradient over the inner mitochondrial mem-brane. Uncoupling protein 2 (Ucp2), which was upregulated duringTBT exposure, could then act as a leaking channel through themitochondrial membrane to undo the negative effects of thisexcessive proton gradient (Fig. 5F). Upregulation of these meta-bolic pathways and the Ucp2 gene was also observed during expo-sure with PPAR c agonists such as rosiglitasone and MEHP (Feigeet al., 2007; Camirand et al., 1998), indicating the possible PPARdependency of these pathways in this cell line.

4.2.2. Cholesterol regulationThe nuclear receptor Liver X receptor a (LXR a, Nr1h3) controls

lipid and cholesterol homeostasis and plays a major physiologicalrole as cholesterol sensor. LXR a is highly expressed in the adiposetissue and its expression is regulated by PPAR c (Seo et al., 2004).However, the role of this receptor during the induction of adipo-cyte differentiation is still controversial. Hummasti et al. (2004)showed that stimulation of 3T3-L1 cells with LXR agonists didnot alter the lipid accumulation of 3T3-L1 cells, whereas Juvetet al. (2003) showed opposite results. In relation to this nuclearreceptor our results show an induction of Nr1h3 transcription after10 days of TBT exposure (Table 3).

4.3. Regulation of adipokine transcription

Additional to their central role in lipid accumulation, adipocytesare also important endocrine signalling cells. Adipokines are pro-teins produced and secreted by fat cells and include molecules in-volved in lipid metabolism, insulin sensitivity, the alternativecomplement system, inflammation, vascular haemostasis, bloodpressure regulation and angiogenesis, as well as the regulation ofenergy balance (Trayhurn and Wood, 2004). Adiponectin, one ofthe most studied adipokines, has been associated with insulinresistance, type 2 diabetes and metabolic syndrome (Kadowakiet al., 2006) and has been proposed to act in an autocrine mannerto induce differentiation of adipocytes (Fu et al., 2005a; Fu et al.,2005b). In our results, TBT exposure induced the expression ofadiponectin (Adipoq, Table3), and its receptor (Adipor2, Table3)after 10 days of exposure, suggesting that TBT stimulated the auto-crine adiponectin induction of adipogenenesis. A recently de-scribed adipokine, chemerin (Rarres2), inducing adipogenesisthrough its own receptor in an autocrine manner (Roh et al.,2007), was significantly upregulated at both time-points afterTBT exposure (Table 3). Adipsin (Cfd), was upregulated after10 days of TBT exposure (Table 3) and stimulates triglyceride stor-age in adipose cells (Van Harmelen et al., 1999). It has been pro-posed that in obese patients, angiotensin II could be involved inthe development of hypertension (Ailhaud et al., 2000). Angioten-sinogen, a precursor of angiotensin II, was upregulated by TBTexposure at late stage differentiation (Table 3). Surprisingly, leptinexpression was not significantly induced in our microarray exper-iment. Also, other microarray studies with rosiglitasone treated3T3-L1 cells were not able to detect changes in expression of leptin(Feige et al., 2007). Resistin is recognised as an adipokine thatcould link obesity to insulin resistance and is known to be upreg-ulated during late adipocyte differentiation (Kawashima et al.,2003). Our results show an upregulation of resistin after 10 daysof exposure with TBT or MDI (Table 3). The upregulation of theexpression of several adipokines due to TBT exposure, indicatethe induction of a complete and physiologically relevant adipocytephenotype after TBT exposure.

4.4. Mode of action of TBT as an obesogen

Different mechanisms have been proposed for the mode of ac-tion of obesogens, such as (i) glucocorticoid transcriptional regula-tion (Sargis et al., 2010); (ii) estrogen signalling (Cooke and Naaz,2004) (iii) inhibition of Wnt signalling leading to an upregulationof PPAR c (Christodoulides et al., 2009) (iv) binding of PPAR c/RXR a heterodimer and thereby inducing adipogenic genes (Grunand Blumberg, 2006).

4.4.1. Glucocorticoid mediated transcriptional activityGlucocorticoid signalling is known to contribute to obesity

development. It is known that glucocorticoids (e.g. dexametha-sone) enhance the differentiation of adipocytes (Smas and Sul,1995). A major determinant for glucocorticoid action is the expres-sion of 11 b-hydroxysteroid dehydrogenase (11 b-HSD). One iso-form, 11 b-HSD1, acts as a reductase, converting inactivecortisone to active cortisol, while the other isoform (11 b-HSD2)catalyses the inverse reaction. Elevated 11 b-HSD1 activity hasbeen linked to obesity in humans (Rask et al., 2001) and Zucker rats(Livingstone et al., 2000). TBT has been described to inhibit 11 b-HSD2 enzymatic activity and may in this way cause an increasein cortisol levels (Atanasov et al., 2005). However our results showthat MDI treatment induced the expression of 11 b-HSD1, whereasTBT did not. 11 b-HSD2 expression remained unchanged by bothtreatments, indicating that the previously described effect of TBTon 11 b-HSD2 might be due to inhibition of the enzymatic activity,without direct effect on its transcription.

4.4.2. Estrogen signallingIn vivo studies showed that estrogen deficiency, such as in

Estrogen Receptor (ER) a, aromatase and Follicle Stimulating Hor-mone (FSH) knockout mice, is correlated with obesity development(Danilovich et al., 2000; Heine et al., 2000; Murata et al., 2002;Jones et al., 2007). In vitro studies showed that TBT inhibits thegene expression and activity of aromatase (Cooke, 2002; Saitohet al., 2001). These results are in concordance with in vivo experi-ments showing masculinisation of fish and an increase androgenproduction due to TBT exposure (McAllister and Kime, 2003).

4.4.3. Wnt signallingThe Wnt signal transduction pathway is important during the

many differentiation events during embryogenesis. Wnt proteinsare secreted and act in a autocrine or paracrine manner to regulatedevelopmental processes. The cytoplasmatic protein b-catenin iscentral to the transmission of the Wnt signals to the nucleus. Inthe absence of Wnt, b-catenin gets degraded by proteasomes.However, when Wnt is present b-catenin can be translocated tothe nucleus and activate Wnt target genes after its association withT-cell factor lymphoid enhancing transcription factors (Seidens-ticker and Behrens, 2000). Ross et al. (2000) were the first to showthe importance of Wnt signalling, and more precisely Wnt10-bduring adipocyte differentiation. Wnt signalling maintains preadi-pocytes in an undifferentiated state through inhibition of the adi-pogenic transcription factors CCAAT/enhancer binding protein a(C/EBP a) and PPAR c. When Wnt signalling is prevented in preadi-pocytes, they are induced to differentiate into adipocytes. Differen-tiation studies with PPAR c agonists have shown a downregulationof b-catenin (Gerhold et al., 2002), however our results after10 days of TBT induced differentiation showed no significant dif-ferences in gene-expression.

4.4.4. Binding on PPAR c/RXR heterodimerPPAR c, a nuclear receptor, is one of the major regulators of adi-

pose differentiation. PPARs activate the gene expression of theirtarget genes as permissive heterodimers with Retinoid X Receptors

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(RXRs) (Feige et al., 2006). PPAR c is highly expressed in the adi-pose tissue and is both necessary (Rosen, 2002) and sufficient(Tontonoz et al., 1994) for the differentiation of adipocytes. TBTis an agonistic ligand of both PPAR c and RXR, indicating the poten-tial for PPAR c or RXR mediated induction of adipogenesis. Re-cently, antagonist studies confirmed the importance of PPAR cbinding during the TBT induced differentiation (Li et al., 2011).The microarray data presented here confirm and extend previousreports showing the importance of PPAR signalling during TBT in-duced differentiation. PPAR signalling pathway was the only path-way significantly enriched after 24 h of exposure (Table 1) and wasthe most enriched pathway after 10 days of exposure (Table 2). Anumber of downstream genes of the PPAR c/RXR heterodimer, in-volved in metabolic processes such as lipid metabolism, gluconeo-genesis and adipocyte differentiation were upregulated after 1 or10 days of TBT treatment (Fig. 6).

4.4.5. Other possible mechanisms for TBT obesogenic actionRosiglitazone is known for its insulin sensitising properties, this

is a possible way through which TBT might enhance the differenti-ation of adipocytes (Hu et al., 2007). However, from our study noindications were found confirming that hypothesis, since noenrichment of the insulin signalling pathway was observed. Never-theless, the one-compound treatment, without insulin, cannot giveus full explanations about that possible mode of action.

This paper shows the high potential of the 3T3-L1 cell line forobesogenic screening, however results only address a limited win-dow of modes of action. This mouse cell line consists of unipotentpreadipocytes, which have undergone determination and caneither stay pre-adipocytes or differentiate into mature adipocytes.In that context human or mouse mesenchymal stem cells (MSCs)provide an interesting alternative model due to their true multipo-tency, making it possible to study earlier effects on commitment ofadipocyte lineage (Avram et al., 2007). Bone Morphogenetic Pro-tein-4 is known to play a role in the commitment steps of adipo-cyte differentiation and could also be an early target of TBTinduced differentiation, however due to the unipotency of the3T3-L1 cells this could not be examined in this study (Bowersand Lane, 2007). Kirchner et al. (2010) showed the possible impor-tance of epigenetic modifications during the in utero obesogenic ef-fects of TBT. Epigenetic modifications of PPAR c target genes inMSCs might favour the expression of adipogenic genes later in lifeand predisposing these cells to become adipocytes. Decherf et al.(2010) recently showed that TBT exposure during lactation causesa disruption of central metabolic signalling in the hypothalamic-pituarity-thyroid axis, responsible for the production of thyroidhormones. All these indirect mechanisms cannot be covered in thisscreening system, stressing the general importance of a multiplecell approach for screening endocrine acting compounds.

Nevertheless, the 3T3-L1 cell line is a valuable cell system sinceit is (i) the most characterised model system at the moment for thestudy of adipocyte differentiation; (ii) a ‘cell line’ making it a moreconvenient system compared to MSCs for the development of ahigh throughput screening and for comparison of obesogenic com-pounds in a systemic manner, knowing the previously describeddonor-to-donor variability and heterogeneous nature of MSCs(Phinney et al., 1999; Sen et al., 2001); (iii) a mouse cell systemmaking direct comparison to in vivo rodent experiments possible.

In conclusion, we have confirmed the obesogenic properties ofTBT using the Nile red staining. Compared to photograph basedmeasurements of adipogenesis, this technique offers a fast andlow-cost alternative, measuring whole well fluorescence giving arepresentative lipid accumulation value. This shows the high po-tential of this technique for a first screening of potential obesogens.Moreover we contributed to further unravelling of the TBT-inducedPPAR c signalling pathway, indicated by the upregulation of

important downstream genes of this nuclear receptor. Our geneexpression data revealed an upregulation of genes involved in en-ergy related pathways, lipid metabolism and adipocyte differenti-ation at the transcriptome level. Moreover adipokine expressionwas also upregulated after TBT exposure, revealing the complete-ness of this cell line for adipocyte-related pathways. These resultsindicate that combining a cell physiological staining technique (forfirst line screening) with gene expression profiling in the 3T3-L1cell line provides a good model to assess the obesogenicity of po-tential obesogenic compounds. Future research including geneexpression profiling with a set of carefully selected obesogens(e.g. acting through different mechanisms) and non-obesogeniccompounds will provide the selection of marker genes, able to dis-criminate obesogenic from non-obesogenic compounds and there-fore interesting as potential biomarkers for obesogenicity.

Funding

This work was supported by a GOA project (Endocrine disrupt-ing environmental chemicals: From accumulation to their role inthe global ‘‘neuro-endocrine’’ epidemic of obesity and its metabolicconsequences; FA020000/2/3565) of the University of Antwerp andan Agilent ACT-UR grant (Integrating transcriptomic and proteomicdata to unravel obesogenic mechanisms of action of endocrine dis-ruptive compounds in the 3T3-L1 adipocyte cell system). Pereira-Fernandes Anna acknowledges the ‘Institute for the promotion ofinnovation by science and technology (IWT)’ in Flanders (Belgium)for financial support.

Acknowledgements

The authors gratefully acknowledge Femke De Croock for tech-nical assistance.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.mce.2013.02.011.

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