Integration of DIGE and Bioinformatics Analyses Reveals a Role of the Antiobesity Agent Tungstate in Redox and Energy Homeostasis Pathways in Brown Adipose Tissue
2007; Elsevier BV; Volume: 7; Issue: 2 Linguagem: Inglês
10.1074/mcp.m700198-mcp200
ISSN1535-9484
AutoresSílvia Barceló-Batllori, Susana G. Kalko, Yaiza Esteban, Sílvia Moreno, Maria Carmen Carmona, Ramón Gomis,
Tópico(s)Adipokines, Inflammation, and Metabolic Diseases
ResumoOur 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 γ coactivator 1α (PGC-1α) as a potential target. We confirmed by real time PCR that indeed tungstate up-regulates PGC-1α, 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-1α up-regulation, thus contributing to its overall antiobesity effect. 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 γ coactivator 1α (PGC-1α) as a potential target. We confirmed by real time PCR that indeed tungstate up-regulates PGC-1α, 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-1α up-regulation, thus contributing to its overall antiobesity effect. Obesity has emerged as one of the major global epidemics of the 21st century and is reaching frightening proportions. According to the World Health Organization, more than one billion adults are overweight, and 300 million are clinically obese. Prevalence is growing exponentially worldwide both in developed and developing countries, and most noticeably, childhood obesity has also increased dramatically over the last decades (1James W.P.T. Haslam D.W. Obesity: preventing and managing the global epidemic.Lancet. 2005; 366: 1197-1209Abstract Full Text Full Text PDF PubMed Scopus (3487) Google Scholar, 2Odgen C.L. Carrol M.D. Curtin L.R. McDowell M.A. Tabak C.J. Flegal K.M. Prevalence of overweight and obesity in the United States, 1999–2004.J. Am. Med. Assoc. 2006; 295: 1549-1555Crossref PubMed Scopus (7371) Google Scholar). An increase in food availability together with environmental, genetic, and lifestyle changes seems to account for the development of this pandemic. Obese subjects have a decreased life quality and expectancy as well as an increased risk of developing comorbidities such as insulin resistance and type 2 diabetes, cardiovascular disease, hepatic steatosis, pulmonary and muscular pathologies, cancer, and psychological disorders among others. On the other hand, management of this epidemic and its comorbidities is a major economic burden for society. Consequently prevention, treatment, and understanding of obesity etiology ought to be urgent priorities for both scientific and government communities. 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 (3Spiegelman B.M. Flier J.S. Obesity and the regulation of energy balance.Cell. 2001; 104: 531-543Abstract Full Text Full Text PDF PubMed Scopus (1919) Google Scholar). Current therapeutic approaches are directed to the modulation of these pathways leading to a negative energy balance and consequently body weight and fat loss (4Bays H.E. Current and investigational antiobesity agents and obesity therapeutic treatment targets.Obes. Res. 2004; 12: 1197-1211Crossref PubMed Scopus (209) Google Scholar). 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 (5Claret M. Corominola H. Canals I. Saura J. Barcelo-Batllori S. Guinovart J.J. Gomis R. Tungstate decreases weight gain and adiposity in obese rats through increased thermogenesis and lipid oxidation.Endocrinology. 2005; 146: 4362-4369Crossref PubMed Scopus (47) Google Scholar). 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) 1The 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-1α, peroxisome proliferator-activated receptor (PPAR) γ coactivator 1α; 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. 1The 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-1α, peroxisome proliferator-activated receptor (PPAR) γ coactivator 1α; 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. 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 (6Barceló-Batllori S. Corominola H. Claret M. Canals I. Guinovart J.J. Gomis R. Target identification of the novel antiobesity agent tungstate in adipose tissue from obese rats.Proteomics. 2005; 5: 4927-4935Crossref PubMed Scopus (29) Google Scholar). 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 (5Claret M. Corominola H. Canals I. Saura J. Barcelo-Batllori S. Guinovart J.J. Gomis R. Tungstate decreases weight gain and adiposity in obese rats through increased thermogenesis and lipid oxidation.Endocrinology. 2005; 146: 4362-4369Crossref PubMed Scopus (47) Google Scholar). 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 (7Valverde A.M. Benito M. Lorenzo M. The brown adipose cell: a model for understanding the molecular mechanisms of insulin resistance.Acta Physiol. Scand. 2005; 183: 59-73Crossref PubMed Scopus (59) Google Scholar). 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 (8Unlu M. Morgan M.E. Minden J.S. Difference gel electrophoresis: a single gel method for detecting changes in protein extracts.Electrophoresis. 1997; 18: 2071-2077Crossref PubMed Scopus (1841) Google Scholar). 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 (9Marouga R. David S. Hawkins E. The development of the DIGE system: 2D fluorescence difference gel analysis technology.Anal. Bioanal. Chem. 2005; 382: 669-678Crossref PubMed Scopus (516) Google Scholar). 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) γ coactivator 1α (PGC-1α) as a key target of the tungstate antiobesity effect in BAT. 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 (5Claret M. Corominola H. Canals I. Saura J. Barcelo-Batllori S. Guinovart J.J. Gomis R. Tungstate decreases weight gain and adiposity in obese rats through increased thermogenesis and lipid oxidation.Endocrinology. 2005; 146: 4362-4369Crossref PubMed Scopus (47) Google Scholar). 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) (5Claret M. Corominola H. Canals I. Saura J. Barcelo-Batllori S. Guinovart J.J. Gomis R. Tungstate decreases weight gain and adiposity in obese rats through increased thermogenesis and lipid oxidation.Endocrinology. 2005; 146: 4362-4369Crossref PubMed Scopus (47) Google Scholar). 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. 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. 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. 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 × 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 × 3).Table I2D DIGE experimental designNo. of gelCy2Cy3Cy51PoolUL1TL12PoolUO1TO13PoolCRO1UL24PoolUO3CRO45PoolCRO3TL36PoolTO3UO47PoolUL3TO4aSample was an outlier and was removed (see “Experimental Procedures”).8PoolTO2CRO29PoolTL2UO2a Sample was an outlier and was removed (see “Experimental Procedures”). Open table in a new tab Image analysis was performed using DeCyder version 5.0 software (GE Healthcare) following published and the manufacturer's recommendations (9Marouga R. David S. Hawkins E. The development of the DIGE system: 2D fluorescence difference gel analysis technology.Anal. Bioanal. Chem. 2005; 382: 669-678Crossref PubMed Scopus (516) Google Scholar, 10Karp N.A. Kreil D.P. Lilley K.S. Determining a significant change in protein expression with DeCyder™ during a pair-wise comparison using two-dimensional difference gel electrophoresis.Proteomics. 2004; 4: 1421-1432Crossref PubMed Scopus (149) Google Scholar). The differential in-gel analysis module was used for intragel co-detection of samples and internal standard protein spots. Artifactual spots (dust and others) were filtered (maximum slope, <2.5; maximum peak height, <150) and removed. Analyses were done in duplicate setting the initial spot detection number at 1600 to detect low abundance spots and at 600 to detect high abundance spots as a single spot. The biological variation analysis (BVA) module was used for intergel matching of internal standard and samples across all gels and performing comparative cross-gel statistical analyses of all spots based on spot volumes (10Karp N.A. Kreil D.P. Lilley K.S. Determining a significant change in protein expression with DeCyder™ during a pair-wise comparison using two-dimensional difference gel electrophoresis.Proteomics. 2004; 4: 1421-1432Crossref PubMed Scopus (149) Google Scholar), permitting the detection of differentially expressed spots between experimental conditions (ANOVA and Student's t test, p < 0.05). Only spots present in 21 of the 27 gel images were considered. BVAs were also done from both 1600- and 600-spot differential in-gel analyses, and results were pooled. Finally matches and data quality of proteins of interest were manually checked to avoid false positives. The same gels used for DIGE analyses were used as preparative gels and were silver-stained using an MS-compatible protocol (11Shevchenko A. Wilm M. Vorm O. Mann M. Mass spectrometric sequencing of proteins from silver-stained polyacrylamide gels.Anal. Chem. 1996; 68: 850-858Crossref PubMed Scopus (7787) Google Scholar). Molecular weight and pI calibration using 2D standards (Bio-Rad) was done in small format minigels (12Felley-Bosco E. Demalte I. Barcelo S. Sanchez J.-C. Hochstrasser D.F. Schlegel W. Reymond M.A. Information transfer between large and small two-dimensional polyacrylamide gel electrophoresis.Electrophoresis. 1999; 20: 3508-3513Crossref PubMed Scopus (7) Google Scholar), 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 (6Barceló-Batllori S. Corominola H. Claret M. Canals I. Guinovart J.J. Gomis R. Target identification of the novel antiobesity agent tungstate in adipose tissue from obese rats.Proteomics. 2005; 5: 4927-4935Crossref PubMed Scopus (29) Google Scholar, 13Barceló-Batllori S. André M. Servis C. Lévy N. Takikawa O. Michetti P. Reymond M. Felley-Bosco E. Proteomic analysis of cytokine induced proteins in human intestinal epithelial cells: implications for inflammatory bowel diseases.Proteomics. 2002; 2: 551-560Crossref PubMed Scopus (103) Google Scholar). Peptides were analyzed in a Voyager DE Perspective instrument (Applied Biosystems, Foster City, CA) in the reflector/delayed extraction mode as described previously (6Barceló-Batllori S. Corominola H. Claret M. Canals I. Guinovart J.J. Gomis R. Target identification of the novel antiobesity agent tungstate in adipose tissue from obese rats.Proteomics. 2005; 5: 4927-4935Crossref PubMed Scopus (29) Google Scholar). 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. 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 (14Eisen M.B. Spellman P.T. Brown P.O. Botstein D. Cluster analysis and display of genome-wide expression patterns.Proc. Natl. Acad. Sci. U. S. A. 1998; 95: 14863-14868Crossref PubMed Scopus (13160) Google Scholar). 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. 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 (12Felley-Bosco E. Demalte I. Barcelo S. Sanchez J.-C. Hochstrasser D.F. Schlegel W. Reymond M.A. Information transfer between large and small two-dimensional polyacrylamide gel electrophoresis.Electrophoresis. 1999; 20: 3508-3513Crossref PubMed Scopus (7) Google Scholar) 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 so
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