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Molecular & Cellular Proteomics 7:378-393, 2008.
© 2008 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Integration of DIGE and Bioinformatics Analyses Reveals a Role of the Antiobesity Agent Tungstate in Redox and Energy Homeostasis Pathways in Brown Adipose Tissue ^*,S

Sílvia Barceló-Batllori^,, Susana G. Kalko^¶, Yaiza Esteban, Sílvia Moreno, María C. Carmona and Ramon Gomis^,||

From the Laboratory of Experimental Diabetes and Obesity, Endocrinology and Diabetes Unit, and ^¶ Bioinformatics Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, E-08036 Barcelona, Catalonia, Spain

	ABSTRACT

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Our previous results demonstrated that tungstate decreased weight gain and adiposity in obese rats through increased thermogenesis and lipid oxidation, suggesting that brown adipose tissue was one of the targets of its antiobesity effect. To identify potential targets of tungstate, we used DIGE to compare brown adipose tissue protein extracts from the following experimental groups: untreated lean, tungstate-treated lean, untreated obese, and tungstate-treated obese rats. To distinguish direct targets of tungstate action from those that are secondary to body weight loss, we also included in the analysis an additional group consisting of obese rats that lose weight by caloric restriction. Hierarchical clustering of analysis of variance and t test contrasts clearly separated the different experimental groups. DIGE analysis identified 20 proteins as tungstate obesity direct targets involved in Krebs cycle, glycolysis, lipolysis and fatty acid oxidation, electron transport, and redox. Protein oxidation was decreased by tungstate treatment, confirming a role in redox processes; however, palmitate oxidation, as a measure of fatty acid β-oxidation, was not altered by tungstate, thus questioning its putative function in fatty acid oxidation. Protein network analyses using Ingenuity Pathways Analysis highlighted peroxisome proliferator-activated receptor coactivator 1 (PGC-1) as a potential target. We confirmed by real time PCR that indeed tungstate up-regulates PGC-1, and its major target, uncoupling protein 1, was also increased as shown by Western blot. These results illustrate the utility of proteomics and bioinformatics approaches to identify targets of obesity therapies and suggest that in brown adipose tissue tungstate modulates redox processes and increases energy dissipation through uncoupling and PGC-1 up-regulation, thus contributing to its overall antiobesity effect.

Obesity is a complex disease characterized mainly by an increase in body fat mass that results from an imbalance between energy intake and expenditure. Energy homeostasis regulation is complex and involves many molecules, genes, and different tissues. A simplified view of this topic is that the central nervous system, and specifically the hypothalamus, regulates and integrates food intake and energy expenditure, whereas the peripheral tissues such as liver, muscle, and adipose tissues are responsible for fat and carbohydrate metabolism, storage, and thermogenesis (3). Current therapeutic approaches are directed to the modulation of these pathways leading to a negative energy balance and consequently body weight and fat loss (4).

A promising new potential therapy for the treatment of obesity may be sodium tungstate. Recently we described that oral administration of tungstate reduced body weight gain and adiposity without affecting food intake and without any major side effects in cafeteria diet-induced obese rats (5). Additionally the treatment ameliorated dyslipemia and insulin resistance in these animals. The effects appear to be mediated, at least in part, by an increase in whole body energy dissipation and by changes in the expression of genes involved in lipid oxidation and mitochondrial uncoupling in adipose tissues. White adipose tissue (WAT)¹ weight and morphology were also dramatically reduced and altered, respectively, observations that led us to identify targets of tungstate in this tissue by a proteomics approach (6). Classical two-dimensional (2D) electrophoresis coupled to peptide mass fingerprinting allowed us to demonstrate that tungstate treatment reverted the expression changes of 70% of the proteins modified in obesity in WAT. Moreover the results suggested that tungstate could modulate cellular structure, metabolism, redox, and signaling processes in WAT.

Another tissue of interest concerning the antiobesity effect of tungstate was brown adipose tissue (BAT). Although BAT weight was not altered in obese rats treated with tungstate, we observed changes in mitochondrial uncoupling gene expression (5). On the other hand, in small mammals BAT is one of the organs with a higher rate of energy consumption and is involved in lipid metabolism and energy dissipation via heat generation, namely thermogenesis; thus it is a major player in energy homeostasis (7). Considering all these observations, it is conceivable that BAT may be a major site of tungstate action in obesity, and consequently we decided to identify its targets.

DIGE technology has recently been implemented as a quantitative alternative to conventional 2D electrophoresis (8). The main advantage is that samples are labeled in different resolvable fluorescent dyes (Cy2, Cy3, and Cy5), thus increasing sensitivity up to picogram levels and a dynamic range of 5 orders of magnitude. In addition, samples can be multiplexed so that two different samples can be run in the same gel together with an internal standard. This methodology permits the inclusion of a larger number of samples and conditions in the experimental design and normalization of results relative to the standard, hence reducing intergel variation and false positives (9). This confers robust quantitative statistical analyses resulting in highly confident data with biological significance. Here we took advantage of this technology to design a complex approach to identify antiobesity-specific direct targets of tungstate in BAT. This strategy consisted of defining several experimental groups of obese and lean rats that would allow us to specifically pick up proteins implicated in obesity and regulated directly by tungstate action. The combination of DIGE technology and bioinformatics analyses revealed that tungstate modulated oxidative stress and thermogenic pathways and identified peroxisome proliferator-activated receptor (PPAR) coactivator 1 (PGC-1) as a key target of the tungstate antiobesity effect in BAT.

	EXPERIMENTAL PROCEDURES

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
All animal procedures were conducted in accordance with principles of laboratory animal care (European Community and local government guidelines) and approved by the Animal Research Committee of the University of Barcelona. Diet-induced obesity and tungstate treatment in male Wistar rats were done essentially as described previously (5). Male Wistar rats (IFFA CREDO, L'Arbresse, France) weighing 200–240 g were individually caged, subjected to a 12-h light, 12-h dark cycle, and randomly divided into two dietary sets. Control lean animals were fed a standard chow diet (UL) during 30 days. Obesity was induced by feeding animals during 30 days with a cafeteria diet containing standard chow and a daily intake of cookies, liver pâté, bacon, and whole milk supplemented with 333 g/liter sucrose and 10g/liter mineral and vitamin complex (Gevral; Cynamid Ibérica, Madrid, Spain) (5). The diet composition was 12.7% proteins, 38.7% lipids, and 36.1% carbohydrates. This diet resulted in 65% of the energy being derived from lipids, whereas for the standard diet the energy derived from lipids was only 8%. All food items were weighed daily and presented in excess. Food spillage was also collected and weighed. Daily caloric intake was calculated by multiplying the consumption of each item in the diet by its caloric density provided by the manufacturer. After the initial 30-day diet period, sodium tungstate was administered during 25 days in drinking fluids (2 mg/ml in distilled water and milk) to both lean (treated lean (TL)) and obese animals (treated obese (TO)) being fed chow and cafeteria diet, respectively. Some obese animals continued on the cafeteria diet until the end of the treatment period (untreated obese (UO)). An additional group of obese animals were calorie-restricted (after the initial 1-month obesity induction) by removing milk from their diet and feeding them half the amount of bacon and biscuits than obese rats (the other foods were kept at the same proportion; caloric restriction-treated obese (CRO)). Animal body weight and food and liquid intake were measured daily and analyzed by ANOVA. The day prior to sacrifice animals were fasted overnight and then anesthetized by inhalation of isoflurane, and interscapular BAT and other tissues were removed, snap frozen in liquid nitrogen, and kept at –80 °C until use. Animals were then killed by administration of an excess of isoflurane followed by decapitation. Experimental procedures for cafeteria diet and animal treatments require that each animal is housed in an individual cage to monitor food and drink intake. As a consequence of this, there is a limitation on the number of animals that can be used within the same set of experiments. Because the calorie-restricted obese group had not been characterized previously, because of the higher variability of the diet-induced obesity model compared with standard chow-fed lean animals, and because of the importance of tungstate treatment in obese animals for this study, we decided to use a greater number of animals for the obesity groups (three conditions, n = 6 each) than for the lean groups (two conditions, n = 3 each). DIGE experiments were designed to contain the minimum number of tissue replicates necessary to achieve statistical significance, and validation studies (Western blot, palmitate oxidation, and RT-PCR) included the same replicates used for DIGE analyses plus additional animals.

Immunohistochemistry—
A small fraction of BAT was formalin-fixed for immunohistochemistry. Briefly tissue specimens were dehydrated, embedded in paraffin, and cut into 4-µm-thick sections. Adipose tissue sections were stained with hematoxylin and eosin following standard protocols.

Sample Preparation for DIGE Analyses—
BAT from UL (n = 3), TL (n = 3), UO (n = 4), TO (n = 4), and CRO (n = 4) was homogenized in a buffer containing 7 m urea, 2 m thiourea, 2% (w/v) CHAPS, 65 mm DTT, 0.5% (v/v) IPG buffer, and 10 mm sodium orthovanadate. Proteins were extracted during 30 min at 4 °C, and lysates were clarified by centrifugation at 14,000 rpm at 4 °C for 30 min. The interface between the low density lipid layer and the insoluble pellet was carefully collected and centrifuged again. Protein extracts were then prepared following general guidelines recommended for subsequent DIGE labeling. Briefly proteins were precipitated using the 2D clean-up kit (GE Healthcare) and resuspended in a buffer containing 30 mm Tris, 7 m urea, 2 m thiourea, 4% CHAPS, pH adjusted to 8.5, and finally protein content was quantified using the RC DC protein assay kit (Bio-Rad). Sample extraction and homogeneity were checked by visualization of Coomassie Blue-stained proteins separated by 10% SDS-PAGE. The concentration of all samples was adjusted to 7 µg/µl.

2D DIGE and Image Analyses—
Samples were minimally labeled with Cy3 or Cy5 fluorescent dyes (50 µg of protein/400 pmol of dye) during 30 min at 4 °C following the manufacturer's instructions (GE Healthcare). To minimize system and inherent biological variation, half of the samples from each group were labeled with Cy3, and the other half of the samples were labeled with Cy5. An internal standard was prepared by mixing equal amounts of all samples analyzed and was labeled with Cy2 fluorescent dye. Sample multiplexing was also randomized (Table I) to produce unbiased results. IPG strips (pH 3–10, 17 cm, GE Healthcare) were cup-loaded with 50 µg of each Cy2-, Cy3- and Cy5-labeled sample in a buffer containing 7 m urea, 2 m thiourea, 2% (w/v) CHAPS, 65 mm DTT, and 1% (v/v) IPG buffer. Isoelectric focusing was carried out in a Protean IEF cell (Bio-Rad) at 62 kV-h in different phases as follows: 10 min at 50 V, 1-h ramp up to 500 V, 1 h at 500 V, 2-h ramp up to 1000 V, 10-h ramp up to 10,000 V, and 30 min at 10,000 V. Second dimension SDS-PAGE was run by overlaying the strips on 10% isocratic Laemmli gels (24 x 20 cm), which were cast in low fluorescence glass plates, on an Ettan DALT VI system. Gels were run at 20 °C at a constant power of 2.5 watts/gel during 30 min followed by 17 watts/gel until the bromphenol blue tracking front had run off the gel. Fluorescence images of the gels were acquired on a Typhoon 9400 scanner (GE Healthcare). Cy2, Cy3, and Cy5 images for each gel were scanned at 488/520-, 532/580-, and 633/670-nm excitation/emission wavelengths, respectively, at 100-µm resolution, thus obtaining a total of 27 images (9 x 3).

Protein Digestion, Mass Spectrometry, and Protein Identification—
The same gels used for DIGE analyses were used as preparative gels and were silver-stained using an MS-compatible protocol (11). Molecular weight and pI calibration using 2D standards (Bio-Rad) was done in small format minigels (12), and then data were transferred by image analyses to the preparative gels. Proteins were excised from the different gels (n = 9), silver destained, and in-gel digested with trypsin at 37 °C overnight (Promega). Peptide extraction was performed, and ZipTip concentrating and desalting was done as described previously (6, 13). Peptides were analyzed in a Voyager DE Perspective instrument (Applied Biosystems, Foster City, CA) in the reflector/delayed extraction mode as described previously (6). Data Explorer version 4.2 (Applied Biosystems) was used for spectra analyses and generating peak picking lists. Peaks were calibrated externally using a standard peptide mixture (Bruker) and internally using trypsin autolysis peptides. The peak list was exported to an Excel data sheet, and peak intensity was used to select from 100 up to a maxim of 250 peaks (increasing in 50) for peptide mass fingerprinting. Trypsin, keratin, and matrix-derived peaks were removed when they were the most intense peptides in the spectra using contaminant database list from PeakErazor (Lighthouse data, Odense, Denmark) and Aldente (www.expasy.org/tools/aldente/). Proteins were identified by peptide mass fingerprinting using Aldente software (versions 01/12/2005 and 24/05/2006, ExPASy) and protein databases Swiss-Prot and TrEMBL releases 49 and 50.2, restricted to mammalian taxonomy containing 54,384 and 172,240 sequence entries, respectively, for each software version and database release. Searches were performed using a mass tolerance of 50 ppm, a single trypsin missed cleavage, iodoacetamide as the modification for cysteine, and methionine oxidation as a variable modification. Proteins were considered as identified only when they had a positive score (p < 1e–06), they had a molecular weight/pI similar to the experimental values found from the 2D gels, the following non-homologous protein had a score of at least 2 orders lower than the first hit, and the most abundant peptides in the spectra were assigned as the identified protein. If peptides matched to multiple members of a protein family, the highest scoring rank was reported as the identified protein. Similarly if peptides matched different isoforms of the same protein, the highest scored isoform was reported. When the highest score matched the same protein from several species (or a difference of 1 order was present), the taxonomy Rattus norvegicus was selected because this was the origin of the sample; otherwise the first species identity was reported. Searches that did not fulfill the criteria described above were further analyzed by MS/MS using a MALDI-TOF/TOF 4700 Proteomics Analyzer (Applied Biosystems). Data Explorer version 4.2. (Applied Biosystems) was used for spectra analyses and generating peak picking lists. Peaks were calibrated externally using a standard peptide mixture (Bruker) and internally using trypsin autolysis peptides. The peak list was exported to an Excel data sheet, and peak intensity was used to select the most intense peaks (up to 350 fragment ions). Peptide masses from MS analyses and their fragments obtained from MS/MS spectra were combined and submitted to Sequence Query Mascot software from Matrix Science. Searches were performed using Swiss-Prot 52.1 as the database and mammals as taxonomy (50,864 sequences), 0.07-Da error for peptide mass tolerance (MS), 0.8 Da for fragment mass tolerance (MS/MS), a single trypsin missed cleavage, and iodoacetamide as the modification for cysteine. Proteins were considered identified when (a) the first hit was the same as that identified by Aldente and had a Mascot score above 25, (b) the peptide MS/MS had a ion score of at least 5, (c) a minimum of 10 ions were matched to the precursor ion from the candidate protein, and the highest peaks of the MS/MS spectra were assigned, and (d) the protein had a molecular weight/pI similar to the experimental values found from the 2D gels. This threshold was selected and used as a confirmation of the previous identification with Aldente when the score was p < 1e–06 and the following non-homologous protein had a score of at least 1 order lower than the first hit. In addition, selected peptides from proteins already identified by PMF were also fragmented to confirm and validate previous results.

Bioinformatics Analyses—
The relative expression values of the proteins identified as significant by statistical analysis (ANOVA or t test) for the 18 samples were considered for hierarchical clustering analysis (14). Heat map representation of hierarchical clustering analysis done in both components, proteins and samples, may reveal important correlations. Indeed clustering revealed that one of the samples from the TO group was an outlier (not shown). Furthermore examination of body weight evolution revealed that this animal had an anomalous behavior, and therefore it was removed from the subsequent data analyses.

UniProt codes of proteins that were identified as direct targets of tungstate action in obesity and their p values (t test) were submitted as "Focus proteins" to the Ingenuity Pathways Analysis (Ingenuity Systems) server to discover and explore relevant biological networks. When entries were from species other than R. norvegicus the corresponding homologous rat entry was searched in the UniProt/Swiss-Prot database and submitted to Ingenuity.

Western Blot—
For Western blot from 2D gels, samples were homogenized in 7 m urea, 2 m thiourea, 2% (w/v) CHAPS, 65 mm DTT, 0.5% (v/v) IPG buffer, and 10 mm sodium orthovanadate and quantified, and 100 µg were loaded on IPG strips (7 cm, pH 3–10) and run on a Protean IEF cell at 7300 V-h. The second dimension was run as described previously (12) in 10% acrylamide gels.

For conventional SDS-PAGE, samples were homogenized in a buffer containing 50 mm Tris-HCl (pH 7.5), 150 mm NaCl, 1% (v/v) Triton X-100, phosphatase inhibitors (10 mm sodium phosphate, 10 mm sodium fluoride, and 1 mm sodium orthovanadate), and protease inhibitor mixture (Sigma). Proteins were quantified, separated by conventional SDS-PAGE, and transferred to PVDF membranes. For semiquantitative Western blot, increasing amounts of BAT protein extract (10, 20, and 30 µg) were loaded onto the gel to produce a standard curve. The antibodies used for Western blotting were monoclonal anti-mouse OXPHOS Detection kit containing mitochondrial subunits I, II, and III (1:250; Mitosciences, Eugene, OR), polyclonal anti-rabbit medium-chain fatty acyl-CoA dehydrogenase (MCAD) (1:500, Alexis), polyclonal anti-rabbit uncoupling protein 1 (UCP-1) (1:400, a gift from Dr. F. Villarroya), monoclonal anti-mouse hsp70 (biotin-conjugated, 1:1000, Stressgen), streptavidin-horseradish peroxidase (1:1000, Caltag), and anti-mouse and -rabbit horseradish peroxidase (1:1000 to 1:5000, GE Healthcare). Chemiluminescence was quantified using an LAS3000 apparatus (Fujifilm) and Image Gauge 4.0 software (Science Lab, Fujifilm). Student's t test was used to determine significance (p < 0.05).

Protein Oxidation—
Samples were homogenized in a buffer containing 50 mm Tris-HCl (pH 7.5), 150 mm NaCl, 1% (v/v) Triton X-100, phosphatase inhibitors, protease inhibitor mixture (Sigma), and 50 µm DTT. Proteins were quantified by the Bradford assay, and protein oxidation was determined using OxyBlot protein oxidation detection kit (Chemicon). Briefly SDS was added to a final concentration of 6%, and two aliquots of 50 µg of protein were separated; one was used as a negative control, and the other was treated with 2,4-dinitrophenylhydrazine at room temperature for 15 min to yield the DNP-hydrazone derivative. A positive control was included consisting of oxidized albumin. Proteins were separated on a polyacrylamide gel (10%) as described above and transferred to PVDF membranes. Total protein was stained with Red Ponceau, and oxidized proteins were detected by chemiluminescence after incubation with anti-rabbit DNP antibody (1:150) and goat anti-rabbit horseradish peroxidase as the secondary antibody.

Fatty Acid Oxidation—
Palmitate oxidation rates were measured as an indicator of fatty acid oxidation as described previously (15). Briefly small fragments of BAT were homogenized, and protein was quantified and incubated for 30 min at 37 °C in oxidation buffer containing substrate [1-¹⁴C]palmitic acid (200 µm, 200 µCi/ml, Amersham Biosciences) and cofactors ATP (5 mm), NAD⁺ (1 mm), cytochrome c (25 µm), coenzyme A (0.1 mm), l-carnitine (0.5 mm), and l-malate (0.5 mm). The reaction was stopped by addition of perchloric acid. Palmitate oxidation rates were calculated from the sum of ¹⁴CO₂ and [¹⁴C]perchloric acid-soluble products and expressed in picomoles of palmitate/minute/milligram of protein.

Real Time PCR—
Total RNA was extracted from BAT using the RNeasy Lipid tissue kit (Qiagen Sciences), and DNase I was used to remove genomic contamination. One microgram of total RNA was reverse transcribed in a buffer solution containing 25 nm MgCl₂, 100 nm Tris (pH 8.3), 500 mm KCl, 39 units/ml RNAguard (GE Healthcare), 200 units/ml Moloney murine leukemia virus reverse transcriptase (Invitrogen), 10 mm deoxynucleotide triphosphates, and random hexamer primers (d(N₆)5'-PO₄; GE Healthcare). Pgc-1 was amplified by RT-PCR from synthesized cDNA (16 ng) using Power SYBR Green PCR Master Mix and ABI Prism 7900HT Sequence Detection System (Applied Biosystems). Pgc-1 primers were 5'-GAGCCGAGATAAAGCCAAACA-3' and 3'-GCGCAGGCGGTCATTG-5'. A standard curve was generated from four serial dilutions of liver synthesized cDNA. Samples were analyzed in triplicate, negative controls were included, and PCR products were verified using dissociation curve analysis immediately after RT-PCR. Results were analyzed using SDS2.1 software (Applied Biosystems) and normalized to 18S as the housekeeping gene (18 S rRNA predesigned TaqMan probe and primers Applied Biosystems), and statistics were analyzed by Student's t test (p < 0.05).

	RESULTS

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Experimental Design for the Identification of Direct Targets of Tungstate Antiobesity Action in BAT—
Induction of obesity by cafeteria diet during 1 month resulted in a 19% increase in body weight in relation to standard diet. Tungstate treatment of obese rats (2 g/liter, 25 days) reduced their body weight by 17%. These results were similar to our previous data where both cafeteria model and tungstate effect were characterized (5). To identify specific targets of tungstate related to its antiobesity effect, we originally designed a strategy consisting of finding proteins that were modified by tungstate treatment in obese rats and that were not altered when lean rats were treated, thus selecting tungstate obesity targets. However, and because obese treated animals dramatically lose weight, this comparison would reveal tungstate-induced direct changes and those secondary to body weight loss, both of which are undistinguishable using this approach. To overcome this limitation, a strategy was designed to include an additional control consisting of obese animals that lost weight by means other than tungstate treatment. Accordingly the study design consisted of five groups of animals: UL, TL, UO, TO, and CRO. Criteria for obesity induction and tungstate treatment effectiveness are described in our previous studies (5). Fig. 1A shows the body weight gain evolution of the different experimental groups over the treatment period. Calorie-restricted obese rats showed a decrease in their body weight gain by 51% compared with UO on cafeteria diet, similar to tungstate treatment (59%) (UO versus TO/CRO, p < 0.001 by ANOVA; TO versus CRO, n.s.). Cumulative caloric intake was decreased in the caloric restriction group (supplemental Fig. 1) but remained unchanged in the tungstate-treated obese rats as shown previously (5).

View larger version (40K): [in this window] [in a new window]   FIG. 1. Effect of tungstate treatment and caloric restriction on diet-induced obese rats. Obese rats were untreated (UO), tungstate-treated (TO) or calorie-restricted (CRO) following an initial 30-day induction of obesity. Lean rats were either untreated (UL) or tungstate-treated (TL). A, body weight gain evolution over the treatment period (mean ± S.E.; n = 3 lean groups; n = 6 obese groups; **, p < 0.01 by ANOVA. B, representative images of hematoxylin/eosin-stained BAT sections. C, hierarchical cluster analyses of the different samples based on the expression pattern of statistically significant spots (ANOVA, p < 0.01) from the DIGE analyses performed as indicated under "Experimental Procedures" and in Table I.

DIGE Analyses—
BAT protein homogenates from the different animal groups were prepared; minimally labeled with Cy2, Cy3, and Cy5; and processed for DIGE analysis as described under "Experimental Procedures" and in Table I. To minimize system and inherent biological variation, samples were labeled and multiplexed following these guidelines: (a) half of the samples from each group were either Cy3- or Cy5-labeled, (b) two samples from the same experimental group were never included in the same gel, (c) samples from each group were contrasted with the maximal number of different groups, and (d) samples from the same group were run twice in different gels but with the opposite dye labeling pattern. An internal standard consisting of a pool of the different samples was Cy2-labeled and included in each gel.

Protein spots were detected automatically using the software DeCyder. To allow detection of low abundance proteins, we originally set up the initial number of spots as 1600, and from this initial point, the software detected 1625 ± 88 spots (mean ± S.D., n = 27 gel images). DeCyder software does not allow manual spot detection to reduce user-subjective manipulation. This is generally considered an improvement and advantage over other 2D analysis programs where this kind of operation is feasible; nevertheless in certain circumstances it can be a drawback. For instance, we found that highly abundant spots were often detected as multiple spots, thus complicating the quantification and analysis of these proteins. To overcome this limitation, we performed a parallel analysis setting the initial number of spots as 600, resulting in a total of 555 ± 39 spots detected, and indeed, abundant proteins were mostly perceived as a single spot. Intragel analysis yielded normalized values for each sample with respect to the Cy2 standard and direct comparisons between groups. Matching between the different gels was done by means of internal standards using the BVA module, and only spots present in 21 of the 27 gel images were considered suitable for analysis. Thus, 1027 and 330 spots were matched across the different gels and analyzed in the 1600- and 600-spot analyses, respectively.

DIGE analyses rendered 27 spots that exhibited statistically significant expression changes across all groups (ANOVA, p < 0.01). Despite the small number of spots considered, hierarchical clustering analysis of their expression values clearly separated the different experimental groups with two major branches, lean and obese phenotypes (Fig. 1C). In addition, individual obese samples were correctly clustered within their corresponding group, and it was remarkable that the TO group showed major similarity to the lean group, coincident with what we observed in the histological analysis of BAT tissue (Fig. 1B). The UO animals were more distant from the lean animals as expected. Analysis of t test contrast between the different conditions also correctly segregated the different experimental groups (supplemental Fig. 2). The correct "protein signature" of the samples gave us confidence on the whole experiment, and it was a good starting point for the identification of direct targets of tungstate action.

Identification of Direct Targets of Tungstate Antiobesity Action—
Targets of tungstate action in obesity were selected as those spots that were modified by the treatment in obese rats but not in lean (p < 0.05 TO versus UO and n.s. TL versus UL). Direct targets were distinguished from targets secondary to body weight loss as those modified by tungstate treatment in obese rats and that were also different between tungstate-treated and calorie-restricted obese groups (p < 0.05 TO versus UO and p < 0.05 TO versus CRO). Fig. 2 shows a diagram of the selection criteria used for the identification of direct targets of tungstate in obesity. Spots fulfilling these conditions were identified by MALDI-TOF MS as described under "Experimental Procedures" and are shown in a representative image of a 2D gel from BAT homogenates (Fig. 3). The majority of spots contained only single proteins, but in some cases MS analyses indicated a protein mixture. Conversely multiple spots flagged the same protein identity, thus indicating the existence of posttranslational modifications or different isoforms. Proteins, their calculated and experimental molecular weight/pI in the gels, PMF identification parameters, and the ratio between TO and UO expression levels are listed in Table II. Additional details on protein identification by PMF and MS/MS and spectra data are shown for each hit as supplemental data (supplemental Fig. 3). Proteins were classified by functionality and included tricarboxylic acid-Krebs cycle and lipolysis and fatty acid oxidation groups that were both up- and down-regulated. Glycolysis and redox groups were remarkably up-regulated, and in contrast the electron transport group was generally down-regulated.

View larger version (39K): [in this window] [in a new window]   FIG. 2. Schematic selection criteria for the identification of tungstate direct targets in obesity. First spots significantly different between TO and UO were selected (p < 0.05 TO versus UO), and from these only those that were not modified in lean rats were considered (not significant TL versus UL). Then direct targets were distinguished from those secondary to body weight reduction by including from the previous selection only those spots different between TO and CRO (p < 0.05 TO versus CRO).

View larger version (113K): [in this window] [in a new window]   FIG. 3. Two-dimensional gel electrophoresis representative image of brown adipose tissue protein homogenate labeled with Cy2. Proteins were extracted as described and separated in pH 3–10 IPG strips for the first dimension and 10% acrylamide for the second dimension. The image was acquired on a Typhoon 9400 scanner at 633/670-nm excitation/emission wavelengths. Dots represent spots detected by DeCyder software, and proteins identified as tungstate targets are indicated. Table II lists the protein abbreviations and corresponding names.

View larger version (48K): [in this window] [in a new window]   FIG. 4. Expression analyses of tungstate targets using DeCyder software. Direct targets of tungstate were selected as those spots modified in obese but not in lean rats and that were different from calorie-restricted obese rats (p < 0.05 TO versus UO, n.s. TL versus UL, and p < 0.05 TO versus CRO). The figures show the standard abundance of the spots (y axis) for the different conditions: UL, TL, UO, TO, and CRO (x axis). A, proteins up-regulated by tungstate in obese rats. B, proteins down-regulated by tungstate in obese rats. TKT (C) and TPM (D) were identified in two different spots, and tungstate-induced expression changes were different in each spot. Representative 2D gel images and the relative expression ratio TO/UO for the different isoforms are shown for C and D. Table II lists the protein abbreviations and corresponding names

Another classification of the identified proteins could be according to their cellular distribution. It was noticeable that 63% of the proteins are located in mitochondria; however, this was not implausible considering that brown adipose tissue is very rich in mitochondria (7). The question arises whether tungstate could modulate mitochondriogenesis rather than regulate protein expression. To clarify this, we analyzed expression levels of some mitochondrial subunits as mitochondrial markers. Western blot analyses (supplemental Fig. 4) showed no changes between UO and TO BAT homogenates, thus suggesting a direct effect of tungstate on protein expression rather than on the number of mitochondria.

Tungstate Modulates Redox Processes but Does Not Alter Lipid Oxidation—
One of the major group of proteins affected by tungstate included chaperones and proteins involved in redox and antioxidant regulation processes. GRP75/hsp70 was one of these proteins and was identified as several spots, although only the most acidic spot was significantly increased. We validated this result by 2D Western blotting and indeed observed that only the most acidic spot was significantly increased by tungstate, although the next spot had a tendency to increase (Fig. 5A).

View larger version (45K): [in this window] [in a new window]   FIG. 5. Tungstate modulates proteins involved in redox and antioxidant processes and protein oxidation. A, representative image of hsp70 Western blot, 2D DIGE image, and quantification. The three different spots identified as the same protein are indicated by arrowheads and their pI. The standard abundance of the different spots (y axis) is also shown for the five different experimental conditions: UL, TL, UO, TO, and CRO (x axis). B, carbonyl groups from BAT homogenates, indicators of protein oxidation, were derivatized to 2,4-dinitrophenylhydrazone, then separated by SDS-PAGE, and detected by Western blot with anti-DNP antibody. Results were quantified by normalizing optical density from all bands in the OxyBlot to the Ponceau staining and are expressed as a ratio (mean ± S.E.; n = 4; **, p < 0.01, t test). Error bars represent the S.E. of the mean.

Another important group of proteins regulated by tungstate was that involved in lipolysis and fatty acid oxidation. Because some of the proteins were up-regulated whereas others were decreased, we wanted to assess the overall contribution of these pathways. Thus, palmitate oxidation was measured in BAT homogenates as an indicator of maximal fatty acid oxidation capacity. Palmitate oxidation was reduced in obese compared with lean animals as described previously (15); however, tungstate treatment did not alter lipid oxidation in obese rats (Fig. 6A). In addition, expression of MCAD, a mitochondrial enzyme that catalyzes the first step in the β-oxidation of fatty acids, was not altered by tungstate (Fig. 6B). These results suggest that although protein expression of some specific protein isoforms implicated in lipolysis and fatty acid oxidation (i.e. ACDV and THIM) was reduced by the treatment, on the whole, tungstate did not modify fatty acid oxidation capacity.

View larger version (25K): [in this window] [in a new window]   FIG. 6. Tungstate does not alter medium chain fatty acid oxidation. A, palmitate oxidation was measured as an indicator of fatty acid oxidation in homogenates of BAT in UO and TO rats (n = 4; mean ± S.E.; *, p < 0.05, t test). B, semiquantitative Western blot of MCAD. Homogenates were processed by Western blotting as described under "Experimental Procedures," and the linear regression was used to quantify the differences between bands. C, optical density values obtained from the Image Gauge 4.0 software were extrapolated from the linear regression (D) and expressed as the mean ± S.E. (n = 4, Student's t test). prot, protein. Error bars represent the S.E. of the mean.

View larger version (34K): [in this window] [in a new window]   FIG. 7. Proteome network delivered from the Ingenuity server after merging the two best ranked networks. Focus proteins, direct targets of tungstate, are in gray scale, according to statistical significance ranking. Direct interactions, requiring direct physical contact between nodes, appear as a solid line, whereas indirect interactions, without direct physical contact, appear as a dotted line. Known interactions between two members of the different networks are shown as a bold line, showing the central role of PPARGC1A (PGC-1) transcription regulator protein.

View larger version (25K): [in this window] [in a new window]   FIG. 8. Tungstate increases PGC-1 and UCP-1 expression. A, RNA was extracted from BAT and reversed transcribed, and Pgc-1 was amplified by real time PCR. 18S was used to normalize results (mean ± S.E.; n = 5; *, p < 0.05, t test). B, semiquantitative Western blot of UCP-1. Homogenates were processed by Western blotting as described under "Experimental Procedures," and the linear regression was used to quantify the differences between bands. C, optical density values obtained from the Image Gauge 4.0 software were extrapolated from the linear regression (D) and expressed as the mean ± S.E. (n = 4; *, p < 0.05, Student's t test). Error bars represent the S.E. of the mean.

	DISCUSSION

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

In BAT, the transcriptional co-activator PGC-1 is increased during brown adipocyte differentiation, regulates several aspects of mitochondrial biogenesis and function, and also controls energy dissipation and expression of UCP-1 (21). The thermogenic capacity specific to BAT results uniquely from the expression of UCP-1 in the mitochondrial membrane, which uncouples the electrochemical proton gradient from ATP synthesis during respiration (22). Thus UCP-1 facilitates the re-entry of protons, reduces ADP phosphorylation, and decreases ATP energy storage and as a consequence increases energy expenditure as heat. Our proteomics approach demonstrates that tungstate treatment to obese rats increased PGC-1 and UCP-1 expression and concomitantly reduced ATP synthase, consequently resulting in increased energy dissipation in BAT, which contributes to the overall reduction in energy balance and body weight loss (5). PGC-1 has also been associated with mitochondrial biogenesis and brown adipocyte differentiation. However, we measured mitochondrial subunit expression as a marker of mitochondria number and did not find an increase upon tungstate treatment. Remarkably a recent study demonstrates that although function and thermogenic activity are indeed regulated by this co-activator brown fat differentiation and mitochondriogenesis were unaffected (28). This suggests that indeed tungstate could alter PGC-1 expression and BAT function without affecting mitochondrial biogenesis.

An alternative function of UCP proteins, including UCP-1, is to reduce ROS generated during mitochondrial respiration (7, 22). Oxidative stress results from the imbalance between the production of ROS and its elimination by antioxidant defense mechanisms. In obesity, oxidative stress can result from increased fatty acids in tissues and plasma, hyperleptinemia, hyperglycemia, a deficient antioxidant system, or an increased production of free radicals (19). Additionally recent studies found an increase in protein oxidation and carbonylation in adipose tissue of obese C57Bl/6J mice (29) in agreement with our present results in cafeteria Wistar obese rats. A major finding of this study was that tungstate could reduce ROS in BAT from obese animals as demonstrated by a reduction of protein oxidation and carbonylation and an increased expression of proteins that regulate redox status. Thus tungstate may reduce ROS in obese rats by increasing expression of proteins that reduce oxidative stress such as UCP-1, heat shock proteins (30, 31), transketolase (18), catalase, and ALDH (17). Remarkably our previous proteomics study on WAT also pointed to a role of tungstate in reducing ROS in obesity, and actually we found that HSPs and ALDH were also increased by tungstate, thus reinforcing this hypothesis. Additional evidence in this direction is provided by the increase in ACON and G3P by tungstate because both proteins have been associated with decreased ROS levels (32). Recent studies have also suggested a potential role of ROS in regulating adipocyte differentiation (33). It is noteworthy that administration of different antioxidants decreased body weight and adipose tissue of experimental models of obesity (34, 35). What is more, the antioxidant resveratrol protects mice from diet-induced obesity and insulin resistance by interacting with PGC-1 and its regulator SIRT1 (36). Whether tungstate-induced fat and body weight reduction is a consequence of its ROS reducing potential as well as the potential role of antioxidants in preventing and ameliorating the obesity phenotype remains and ought to be further explored.

DIGE analyses suggested that tungstate regulated proteins involved in glycolysis and fatty acid β-oxidation and lipolysis. Glycolysis seemed to be up-regulated because increases in G3P and the majority of Krebs cycle components were observed upon tungstate treatment. In rodents, glucose can also be an important fuel for BAT in vivo. Under physiological insulin stimulation, BAT can reach 10% of the total glucose turnover rate in the rat, and a number of metabolic genes are induced (7). Thus it is conceivable that tungstate, an insulin mimetic in models of diabetes mellitus (37, 38), may also stimulate glycolysis similarly to insulin. Besides increased glycolysis would provide an additional source of ATP to compensate for the reduced mitochondrial supply of ATP during thermogenesis. Concerning the effect of tungstate on fat oxidation and lipolysis, our results seemed ambiguous. Whereas proteomics analyses rendered up- and down-regulation of proteins involved in these pathways, with down-regulated proteins the majority, palmitate oxidation as a measure of β-oxidation was unaltered. Several explanations may account for this discrepancy. First, protein expression does not always correlate with activity. Second, posttranslational modifications usually alter function and also can modify the expression pattern of proteins in 2D gels; in fact the identified forms of ACADV and THIM had an experimental pI that was different from the calculated pI, suggesting posttranslational modifications. Third and most important, the rate-limiting step of fatty acid oxidation is carnitine palmitoyltransferase I/II system, which facilitates the transport of fatty acyl-CoA into the mitochondria (39); consequently alterations in other enzymes in the same pathway will not affect overall fatty acid oxidation rate. On the other hand, histological analyses indicated that most of the lipid droplets in obese brown adipocytes had already been diminished by tungstate treatment. For instance we had observed that after 5 days of treatment palmitate oxidation was increased in BAT from obese animals³ thus suggesting that at the end of the treatment period lipids had already been oxidized. It is worth mentioning that long term ectopic expression of UCP-1 in WAT increased the relative contribution of glycolytic pathways to total ATP, and lipolysis and β-oxidation were not affected, whereas lipid content was reduced by 30% (40). The authors suggested a down-regulation of fat synthesis. Because we observed a similar pathway pattern and phenotype in tungstate-treated BAT, as well as an increase in UCP-1 protein levels, we may speculate a contribution of decreased fat synthesis on tungstate action. On the other hand, tungstate activates lipolytic gene expression (carnitine palmitoyltransferase I and lipoprotein lipase) in WAT (5). Thus, it seems that BAT contributes to the tungstate antiobesity effect by increasing glycolysis and thermogenesis and that WAT contributes by increasing lipolysis.

Interestingly many of the proteins identified here as modulated by tungstate in BAT have also been identified in proteomics analyses of adipogenesis in cell models. These included lipid and carbohydrate metabolic enzymes and heat shock proteins/chaperones and were examined in human adipose-derived stem cells (41) and murine 3T3-L1 fibroblasts (42), both white adipocyte cell models. Proteomics studies on brown adipocyte differentiation have not been performed so far. However, brown adipogenesis is similar to white adipogenesis as both cell types require common transcription factors such as PPAR and CCAAT/enhancer-binding proteins (43). On the other hand, different factors are implicated in the switch between white and brown adipocyte differentiation, such as the relative contribution of different CCAAT/enhancer-binding protein isoforms, FOXC2 transcription factor, co-activator retinoblastoma protein (44), or the recently discovered zinc finger protein PRDM16 (45). The potential regulation of these factors by tungstate is currently unknown. Although a parallelism exists between our proteomics study and those on adipogenesis, the effect of tungstate on white and brown adipocyte differentiation and cellular plasticity must be thoroughly studied and is currently under investigation.

Finally the question remains as to what is the mechanism of PCG-1 and protein regulation by tungstate. This compound has long been known to inhibit phosphatases (46, 47) and can also modulate the phosphorylation state of different kinases such as erk1/2, p38, or p70S6K (38, 48, 49). Interestingly our results point to a potential effect on protein phosphorylation. For instance GRP75/hsp70 and CH60 appear as several spots, and only the most acidic spot was significantly increased. Consistent with our observation, both GRP75 and CH60 have been described to be phosphorylated, and also the different phosphorylated forms were identified by 2D gel electrophoresis (50, 51). Transketolase, the rate-limiting enzyme in the non-oxidative branch of the pentose phosphate pathway (18), was also identified as two different spots in our study, and different phosphorylation sites have been described in vitro. This was in agreement with the different forms identified here with identical mass but different pI. Then tungstate may actually modify some proteins identified here by phosphorylation. On the other hand, a new prospect on protein oxidation emerges from our results. Protein carbonylation and oxidation can indeed regulate protein activity, and tungstate can modify these posttranslational modifications; nevertheless the contribution of these modifications to the tungstate effect found here needs further investigation. Regardless it seems clear that tungstate can regulate phosphorylation, oxidation, and perhaps additional posttranslational modifications. Phosphorylation has been long known to regulate transcription factor activity. For instance, cAMP-response element-binding protein transcription factor phosphorylation is essential for PGC-1 gene expression (21), and PGC-1 phosphorylation by p38 is also required for UCP-1 transcription and expression (52). The possibility of tungstate-induced p38 phosphorylation contributing to UCP-1 expression is a tempting hypothesis.

Sodium tungstate (Na₂WO₄) is a salt of tungsten and is present as a trace element in the form of oxoanions in metals in nature. Although no data exist on its availability in the human diet, it seems unlikely to have a natural contribution to human metabolism. In rats and dogs, administered sodium tungstate has a rather low toxicity as demonstrated by its oral LD₅₀ value (13–20 times higher than therapeutic dose), high bioavailability, and total plasma clearance and half-life elimination rate (53–55). In addition, antiobesity effects of tungstate occur without any adverse effects such as gastrointestinal discomfort, the main side effect described for vanadate, which is also an insulin mimetic (5, 53). Toxicity data in humans have only been reported upon occupational and environmental exposure (56). Nevertheless tungstate as an obesity therapy has satisfactorily passed phase I clinical trials, and the second phase trials will start shortly.⁴ It is noteworthy that chromium derivatives, transition metals with insulin mimetic actions, attenuate body weight gain and increase insulin sensitivity in clinical trials in subjects with type 2 diabetes (57). Despite the fact that the mechanism of action of tungstate on the insulin cascade (48, 49) may diverge from that of chromium (20), collectively these data create expectation on the therapeutic use of tungstate in obesity.

In summary, the findings of this study suggest that the antiobesity effect of tungstate results, at least in part, from the reduction of redox status and increased energy dissipation through up-regulation of PGC-1 and possibly modulation of posttranslational modifications. This opens new directions in the search for mechanisms of action of this attractive therapy for the treatment of obesity. Furthermore these results highlight the value of DIGE technology and bioinformatics tools to discover new targets for fighting against obesity and encourage its application to other pathologies important for human health.

	ACKNOWLEDGMENTS

 
We thank Dr. Francesc Canals and Nuria Colomé from the Proteomics Unit of Hospital de la Vall d'Hebron for scanning Cys fluorochromes and helpful suggestions on DIGE experimental design and analyses and Dr. Josep M. Estanyol from the Proteomics Unit of SSR University of Barcelona and Dr. Eliandre Oliveira and Antonia Odena from the Proteomics Unit of Parc Científic de Barcelona for MALDI-TOF MS and MS/MS spectra acquisition. Proteomics Units are members of Spanish ProteoRed. We also acknowledge Dr. Helena Corominola for helping in animal studies experimental design and Dr. Rosa Gasa for helpful suggestions.

	FOOTNOTES

 
Received, May 2, 2007, and in revised form, October 24, 2007.

Published, MCP Papers in Press, November 13, 2007, DOI 10.1074/mcp.M700198-MCP200

¹ The abbreviations used are: WAT, white adipose tissue; BAT, brown adipose tissue (interscapular); BVA, biological variation analysis; CRO, caloric restriction-treated obese rats; 2D, two-dimensional; MCAD, medium-chain fatty acyl-CoA dehydrogenase; PGC-1, peroxisome proliferator-activated receptor (PPAR) coactivator 1; ROS, reactive oxygen species; TL, tungstate-treated lean rats; TO, tungstate-treated obese rats; UCP, uncoupling protein; UL, untreated lean rats; UO, untreated obese rats; ANOVA, analysis of variance; PMF, peptide mass fingerprinting; DNP, 2,4-dinitrophenyl; n.s., not significant; ALDH, aldehyde dehydrogenase; ACON, aconitase; G3P, glyceraldehyde-3-phosphate dehydrogenase; ACADV, acyl-CoA dehydrogenase very-long-chain mitochondrial; THIM, 3-ketoacyl-CoA thiolase mitochondrial; HCDH, short-chain 3-hydroxyacyl-CoA dehydrogenase mitochondrial; TKT, transketolase; TPM, tropomyosin.

² I. Canals, M. C. Carmona, M. Amigó, A. Bortolozzi, F. Artigas, and R. Gomis, manuscript submitted.

³ S. Barceló-Batllori and N. Palau, unpublished results.

⁴ I. Canals, personal communication.

^* This work was supported by the Spanish Ministerio de Sanidad y Consumo (Grants Redes C03/08, G03/028, PIO20483, PIO42553, and REDIMET RD06/0015/0000). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

^S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.

To whom correspondence may be addressed: Laboratory of Experimental of Diabetes and Obesity, IDIBAPS, C/ Villarroel 170, E-08036 Barcelona, Spain. Tel.: 34-93-2275400, ext. 2910; Fax: 34-93-4516638; E-mail: sbarcelo{at}clinic.ub.es

^|| To whom correspondence may be addressed: Endocrinology and Diabetes Unit, Hospital Clinic, C/ Villarroel 170, E-08036 Barcelona, Spain. Tel.: 34-93-2279846; Fax: 34-93-4516638; E-mail: rgomis{at}clinic.ub.es

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