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Human omental and subcutaneous adipose tissue exhibit specific lipidomic signatures Mariona Jové,* José María Moreno-Navarrete,† Reinald Pamplona,* Wifredo Ricart,† Manuel Portero-Otín,* and José Manuel Fernández-Real†,1 *Nutrició i Envelliment (NUTREN)-Nutrigenomics, Institut de Recerca Biomèdica de Lleida (IRBLLEIDA)-UdL, Científic i Tecnològic Agroalimentari de Lleida (PCiTAL), Lleida, Spain; and † Department of Diabetes, Endocrinology and Nutrition, Institut d’Investigació Biomèdica de Girona (IdIBGi), Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBERobn) and Instituto de Salud Carlos III (ISCIII), Girona, Spain Despite their differential effects on human metabolic pathophysiology, the differences in omental and subcutaneous lipidomes are largely unknown. To explore this field, liquid chromatography coupled with mass spectrometry was used for lipidome analyses of adipose tissue samples (visceral and subcutaneous) selected from a group of obese subjects (nⴝ38). Transcriptomics and in vitro studies in adipocytes were used to confirm the pathways affected by location. The analyses revealed the existence of obesityrelated specific lipidome signatures in each of these locations, attributed to selective enrichment of specific triglycerides, glycerophospholipids, and sphingolipids, because these were not observed in adipose tissues from nonobese individuals. The changes were compatible with subcutaneous enrichment in pathways involved in adipogenesis, triacylglyceride synthesis, and lipid droplet formation, as well as increased ␣-oxidation. Marked differences between omental and subcutaneous depots in obese individuals were seen in the association of lipid species with metabolic traits (body mass index and insulin sensitivity). Targeted studies also revealed increased cholesterol (⌬56%) and cholesterol epoxide (⌬34%) concentrations in omental adi-

ABSTRACT

Abbreviations: ACSL1, acyl-CoA synthetase; AdipoQ, adiponectin; AKAP, A-kinase anchor protein; BMI, body mass index; CIDEC/FSP27, fat-specific protein of 27 kDa; DGAT1, diacylglycerol O-acyltransferase 1; DM, differentiation medium; DMEM, Dulbecco’s modified Eagle’s medium; DXM, dexamethasone; FABP4, fatty acid-binding protein; FBS, fetal bovine serum; GLUT4, glucose transporter type 4; GPEtn, glycerophosphoethanolamine; HODE, hydroxyoctadecadienoic acid; HPCL2, 2-hydroxyphytanoyl-CoA lyase; LD, lipid droplet; MS, mass spectrometry; MS/MS, tandem mass spectrometry; Q-TOF, quadrupole time of flight; PCA, principal component analysis; PEDF, pigment epithelium-derived factor; PEMT, phosphatidylethanolamine N-methyltransferase; PGC1␣, peroxisome proliferator-activated receptor ␥ coactivator 1␣; PHYH, phytanoyl-CoA hydroxylase; PLIN1, perilipin 1; PLS-DA, partial least squares discrimination analysis; PM, preadipocyte medium; PNPLA2, patatin-like phospholipase domain containing 2; PPAR␥, peroxisome proliferator-activated receptor ␥; SAM, signification analysis of metabolites; TG, tryglyceride 0892-6638/14/0028-1071 © FASEB

pose tissue. In view of the effects of cholesterol epoxide, which induced enhanced expression of adipocyte differentiation and ␣-oxidation genes in human omental adipocytes, a novel role for cholesterol epoxide as a signaling molecule for differentiation is proposed. In summary, in obesity, adipose tissue exhibits a locationspecific differential lipid profile that may contribute to explaining part of its distinct pathogenic role.—Jové, M., Moreno-Navarrete, J. M., Pamplona, R., Ricart, W., Portero-Otín, M., Fernández-Real, J. M. Human omental and subcutaneous adipose tissue exhibit specific lipidomic signatures. FASEB J. 28, 1071–1081 (2014). www.fasebj.org Key Words: insulin resistance 䡠 metabolism 䡠 systems biology White adipose tissue is functionally and anatomically divided into two distinct classes, the subcutaneous and the omental fat depots. Many epidemiologic studies have shown that the size of the omental depot, but not that of the subcutaneous adipose tissue, is closely associated with a higher risk of obesity-related metabolic complications, such as insulin resistance, type 2 diabetes, dyslipidemia, and cardiovascular disease. For instance, in the Framingham Heart Study, the omental and subcutaneous adipose tissue volumes of 3001 subjects were investigated in relation to several metabolic risk factors, with the conclusion that the omental adipose tissue is a unique, pathogenic fat depot (1). However, the underlying mechanism for this association is not completely understood. The distinctive biological properties of omental fat contribute to the increased burden of metabolic disease associated with central obesity (2). For instance, metabolites arising from omental tissue, but not those from subcutaneous tissue, have direct access to the liver through the portal 1 Correspondence: Section of Diabetes, Endocrinology and Nutrition, Hospital of Girona “Dr Josep Trueta,” Carretera de França s/n, 17007, Girona, Spain. E-mail: [email protected] doi: 10.1096/fj.13-234419 This article includes supplemental data. Please visit http:// www.fasebj.org to obtain this information.

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vein, and some data confirmed excessive free fatty acid release from the omental adipose tissue in central obesity. These fatty acids might interfere with liver metabolism and contribute to the development of disturbances such as glucose intolerance, hyperinsulinemia, and hypertriglyceridemia (3). On the other hand, anatomical differences in lipolysis have been repeatedly demonstrated, and the underlying mechanisms point to the level of sensitivity of the major lipolysis-regulating hormones: lipolytic action of catecholamines is decreased in subcutaneous but increased in omental adipose tissue, whereas the antilipolytic effect of insulin and prostaglandins is much less pronounced in omental than in subcutaneous fat cells (3). Moreover, the basal lipolytic rate is higher in subcutaneous than in omental adipocytes. Other studies have reported that the insulin-stimulated glucose uptake is higher in omental than in subcutaneous adipose tissue (4) and that the metabolic activity in omental adipose tissue is consistently higher than that in the subcutaneous depot, both in obese and lean individuals. Systems biology-based approaches (such as genomics, transcriptomics, proteomics, and metabolomics) allow global characterization, at a molecular level, of complex global biological systems, and these could be applied to solve the basis of pathogenic differences between these two fat depots. In this sense, transcriptomics have been applied to study the functional differences between omental and subcutaneous adipose tissue at the molecular level. These studies disclosed differentially expressed genes mainly related to pathways involved in adipogenesis and lipid metabolism both in tissues (5, 6) and in derived cells (7). Differentially expressed proteins that hinted at higher metabolic activity of the omental tissue were shown with proteomics. These included proteins involved in lipid metabolism, oxidation-reduction, and lipid transport (8). Furthermore, recent data show that these differences are also present when stem cells within these tissues are evaluated (9). Unlike genes and proteins, metabolites serve as direct signatures of biochemical activity, being therefore easier to correlate with the phenotype. In this context, metabolomics and its derivative lipidomics have become powerful approaches that have been widely adopted for clinical diagnosis (10, 11), opening a window to mechanistically investigate how biochemistry relates to clinical phenotype (12). Lipid metabolism has been studied in different diseases (such as atherosclerosis, diabetes, and insulin resistance), in which lipid metabolism has a key role (10, 11, 13), but, to date, only a few lipidomic studies investigating the difference between omental and subcutaneous human adipose tissue have been reported (14). To overcome these limitations in knowledge, in the present work we demonstrate the existence of specific lipidome signatures of the human omental and subcutaneous adipose tissue, mainly attributed to selective enrichment in specific triglycerides (TGs). More1072

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over, some glycerophospolipids and sphingolipids also differentiated the two types of adipose tissue. Surprisingly, lipid peroxidation-derived molecules were also detected and quantified, contributing to differences between these two adipose tissue depots.

MATERIALS AND METHODS Subject recruitment Samples from 76 adipose tissues (38 visceral and 38 subcutaneous) from a group of morbidly obese Caucasian men (n⫽8) and women (n⫽30) with different degrees of insulin sensitivity were analyzed. Adipose tissue from nonobese individuals [median body mass index (BMI) 23.8⫾2.6, n⫽6 –7 women] were used for evaluation of the potential role of adipose tissue hypertrophy on the location-induced differences in lipid composition. Participants were recruited at the endocrinology service of the hospital Universitari Dr. Josep Trueta (Girona, Spain). All subjects confirmed that their body weight had been stable for ⱖ3 mo before the study and gave written informed consent after the purpose, nature, and potential risks of the study were explained to them. The study was approved by the hospital ethics committee. Adipose tissue samples were obtained from subcutaneous and visceral depots during elective surgical procedures (cholecystectomy, surgery for abdominal hernia, and gastric bypass surgery), washed, fragmented, and immediately flashfrozen in liquid nitrogen before being stored at ⫺80°C. Anthropometric characteristics and analytical determinations are shown in Supplemental Table S1. Insulin action was measured using a euglycemic-hyperinsulinemic clamp (15). After an overnight fast, 2 catheters were inserted into an antecubital vein, one for each arm, used to administer constant infusions of glucose and insulin. A 2-h euglycemichyperinsulinemic clamp was initiated by a 2-step primed infusion of insulin (80 mU/m2/min for 5 min and 60 mU/m2/min for 5 min), immediately followed by a continuous infusion of insulin at a rate of 40 mU/m2/min (regular insulin; Actrapid, Novo Nordisk, Princeton, NJ, USA). Glucose infusion began at minute 4 at an initial perfusion rate of 2mg/kg/min, which was then adjusted to maintain the plasma glucose concentration at 4.9⫺5.5 mM. Blood samples were collected every 5 min for determination of plasma glucose and insulin. Quadrupole time-of-flight (Q-TOF)-based lipidome analysis From the whole cohort, 14 randomly selected paired adipose tissue samples were processed for full lipidomic analysis. In brief, 20 volumes (v/w) of cold methanol (containing 1 ␮M butylated hydroxytoluene) were added to 35⫺50 mg of adipose tissue and homogenized with a Polytron device at 4°C. Then, 20 volumes of lysis buffer (consisting 180 mM KCl, 5 mM EDTA, and 1 mM diethylenetriaminepentaacetic acid; pH 7.3) and 40 volumes of chloroform containing representative internal standard class lipids (Supplemental Table S2 and ref. 16) were added. After separation and evaporation, the organic phases were evaporated using a SpeedVac (Thermo Fisher Scientific, Barcelona, Spain), resuspended in chloroformmethanol (1:3, v/v) and processed chromatographically as described previously (17) by using a 1290 series HPLC system coupled to a 6520 electrospray ionization-Q-TOF) tandem mass spectrometry (MS/MS) system (Agilent Technologies, Santa Clara, CA, USA). This method allows the orthogonal characterization [based on exact mass (⬍10 ppm) and on

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retention time] of lipids. When combined with internal standards, this strategy is useful for attributing potential identities with low uncertainty (17). Data were collected in both positive and negative electrospray ionization-Q-TOF operated in full-scan mode at 100⫺3000 m/z in an extended dynamic range (2 GHz), using N2 as nebulizer gas (5 L/min, 300°C). The capillary voltage was 3500 V with a scan rate of 1 scan/s. In selected molecules, mass spectrometry (MS) analyses were complemented with MS/MS as described previously (3). Targeted lipidomic analysis was performed to search for the following oxidative stress-related markers: 10-hydroxydocosahexaenoic (HODE), 17-HODE, 8-isoprostaglandin F2␣, 13-HODE, 9-HODE, HODE-cholesteryl ester, hydroxyeicosatetraenoic acid, 1-palmitoyl2-glutaryl-sn-glycero-3-phosphatidylcholine; 1-O-hexadecanoyl-2O-(9-carboxyoctanoyl)-sn-glycero-3-phosphatidylcholine, 4-hydroxynonenal, 10-nitrooleate, cholesterol-5␣,6␣-epoxide, 5-cholesten-3␤-ol-7-one, 7␤-hydroxycholesterol, 22-hydroxycholesterol, resolvin D1, cholesteryl linoleate hydroperoxide, and cholesteryl linoleate. The amount of oxysterols was normalized to cholesterol content. Among those, only 5-cholesten-3␤-ol-7-one was detected (m/z 401.3414, [M⫹H]⫹) and normalized to cholesterol (m/z 369.3487, [M⫹H]⫹⫹[⫺H2O]).

Gene expression was assessed by real-time PCR using an LightCycler 480 Real-Time PCR System (Roche Diagnostics S.L., Barcelona, Spain), using TaqMan technology suitable for relative genetic expression quantification. Primer sets used (all human) were PPAR␥ (Hs01115513_m1), FABP4 (Hs01086177_m1), AdipoQ (Hs00605917_m1), glucose transporter type 4 (GLUT4; Hs00168966_m1), patatin-like phospholipase domain containing 2 (PNPLA2; Hs00386101_m1), diacylglycerol O-acyltransferase 1 (DGAT1; Hs01017541_m1), 2-hydroxyphytanoyl-CoA lyase (HPCL2, Hs00202126_m1), phytanoyl-CoA hydroxylase (PHYH; Hs00196483_m1), acyl-CoA synthetase (ACSL1; Hs00960561_m1), phosphatidylethanolamine N-methyltransferase (PEMT; Hs00540979_m1), pigment epithelium-derived factor (PEDF; Hs01106937_m1), and A-kinase anchor protein (AKAP; Hs00177481_m1). Human perilipin 1 (PLIN1; forward 5=AAGTTGAAGCTTGAGGAGCGAGG-3= and reverse 5=GCTCGCGATGGGAACGCTGA-3=), fat-specific protein of 27 kDa (CIDEC/FSP27; forward 5=-GAGGTCCAACGCAGTCCAGCTG-3= and reverse 5=-GTACGCACTGACACATGCCTGGAG-3=), peroxisome proliferator-activated receptor ␥ coactivator 1␣ (PGC1␣; forward 5=-GCAATTGAAGAGCGCCGTGTGA-3= and reverse 5=-CTGTCTCCATCATCCCGCAGAT-3=) were measured using SYBR Green technology.

Human preadipocyte differentiation

Statistics

Isolated human subcutaneous and visceral preadipocytes from obese subjects (BMI⬎30 kg/m2; Zen-Bio Inc., Research Triangle Park, NC, USA) were cultured as described previously (18). In brief, isolated human subcutaneous preadipocytes were cultured (⬃40,000 cells/cm2) with preadipocyte medium (PM; Zen-Bio Inc.) composed of Dulbecco’s modified Eagle’s medium (DMEM)/nutrient mix F-12 medium (1:1, v/v), HEPES, fetal bovine serum (FBS), penicillin, and streptomycin in a humidified 37°C incubator with 5% CO2. At 24 h after plating, cells were checked for confluence (d 0), and differentiation was induced using differentiation medium (DM; Zen-Bio) composed of PM, human insulin, dexamethasone (DXM), isobutylmethylxanthine, and a peroxisome proliferator-activated receptor ␥ (PPAR␥) agonist (rosiglitazone). After 7 d (d 7), DM was replaced with fresh adipocyte medium (Zen-Bio) composed of DMEM/ nutrient mix F-12 medium (1:1, v/v), HEPES, FBS, biotin, pantothenate, human insulin, DXM, penicillin, streptomycin, and amphotericin. At 14 d after the initiation of differentiation, cells appeared rounded with large lipid droplets (LDs) apparent in the cytoplasm. Cells were then considered mature adipocytes, harvested, and stored at ⫺80°C for RNA extraction to study gene expression levels. The experiment was performed in triplicate for each sample. The differentiation was monitored with the adiponectin (AdipoQ) and fatty acid- binding protein (FABP4) gene expression. 5,6-␣Epoxycholesterol (1, 5, and 10 ␮M) and cholesterol (1, 5, and 10 ␮M) were added during adipocyte differentiation in each medium change (at d 0 and at d 7). The selection of these concentrations was performed according to previous studies (19 –22). Chloroform (0.1%) was used as a vehicle control.

Only common mass spectra features (found in ⱖ75% of the samples of omental or subcutaneous samples) were taken into account to correct for individual bias. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and hierarchical clustering analysis were performed using MassHunter Mass Profiler Professional (Agilent Technologies) after transformation of chromatographic results to CEF format. The masses representing significant differences by a paired Student’s t test (fold changeⱖ2, P⬍0.05) were searched against the Lipid Metabolites and Pathways Strategy (Lipid MAPS) database (National Institute of General Medical Sciences, Bethesda, MD, USA; ref. 23). For correlation analyses and for significance analysis of metabolites (SAM), we used the 50% more abundant features, comprising a total of 720 molecular features, from which 159 were putatively identified according to exact molecular weight and retention time, examined using the MetaboAnalyst platform (24). Other statistic calculations were performed using SPSS (SPSS, Chicago, IL, USA). The normality of the distribution of variables was checked by the KolmogorovSmirnov test. The correlation between different parameters was evaluated by the Pearson correlation coefficient. Differences in the amounts of molecules between omental and subcutaneous depots were analyzed by the paired Student’s t test with Benjamini-Hochberg multiple testing correction. A level of P ⬍ 0.05 was selected as the point of minimal statistical significance in every comparison.

RNA purification and gene expression analysis RNA was prepared from these samples using an RNeasy Lipid Tissue Mini Kit (Qiagen; Izasa SA, Barcelona, Spain). The integrity of each RNA sample was checked by an Agilent Bioanalyzer (Agilent Technologies). Total RNA was quantified by use of a spectrophotometer (GeneQuant; GE Healthcare, Piscataway, NJ, USA) and reverse transcribed to cDNA using a High Capacity cDNA Archive Kit (Applied Biosystems Inc., Madrid, Spain) according to the manufacturer’s protocol. LIPIDOMIC SIGNATURES OF ADIPOSE TISSUE

RESULTS Differences between omental and subcutaneous adipose tissue lipidomes Using a nontargeted approach, we detected a total of 17,738 ionic species (considering both positive and negative ionizations). As the hierarchical clustering analysis shows (Fig. 1A), the samples were well separated according to location, with some outliers (subject 1). To further explore the differences between locations, multivariate analyses were used. As Fig. 1B shows, 1073

A

B

PCA

PLS-DA

D

C Subcutaneous Omental

Glycerolipids Sphingolipids Glycerophospholipids Free fatty acids

100

Increased in Subcutaneous

90 4 2 0

Increased in Omental

G Fre ly ce e f ro att ho y s a G ph cids l o Spyce lipi hi rol ds ng ip ol ids i G Fre S pi ly e ce f te ds ro att rol ho y s s a G ph cids l o Spyce lipi hi rol ds ng ip ol ids i St pids er ol s

% abundance

110

0

10

20

30

Number of differential molecular species

Figure 1. Specific lipidomic profiles for omental and subcutaneous adipose tissue. A) Heat map representation of hierarchical clustering of molecular features (see main text for definition) found in each sample of both omental and subcutaneous depots. Each line of this graphic represents an accurate mass ordered by retention time, colored by its abundance intensity normalized to internal standard and baselining to median/mean across the samples. Scale from ⫺9 (blue) to 9 (red) represents this normalized abundance in arbitrary units. B) Tridimensional PCA and PLS-DA graphs demonstrating a differentiation effect of adipose tissue location on lipidomic profiles. Blue spots represent samples from omental adipose tissue, and red spots represent samples from subcutaneous adipose tissue. Same number means same individual. x, principal component 1; y, principal component 2; z, principal component 3. C) Relative abundance of lipid families identified in the 50% most abundant molecules. Data are means ⫾ sem. D) Families of lipids affected (P⬍0.05) by adipose tissue location.

with use of either PCA or PLS-DA, the location of the adipose tissue is the factor that best explains the variability of the samples. Accuracies of resulting models after PLS-DA were 96⫺100% (Supplemental Table S3), demonstrating that a specific lipidome signature is present for both omental and subcutaneous adipose tissue. The lipids identified in the 50% more abundant fraction (based on MS ionization) comprise a total of 159 lipids. As expected, most of them (97%; Fig. 1C) belonged to the glycerolipid family (comprising TGs and diacylglycerides). The other lipids were sphingolipids and glycerophospholipids, and only a minor amount of free fatty acids and sterols were detected. Paired statistics of the whole lipidome revealed that 222 lipid species were different between omental and subcutaneous adipose tissue. Among these, we identified, by using an orthogonal approach (comparison of retention times with those of authentic standards) and MS/MS, a total number of 31 lipids. These lipids were free fatty acids, glycerophospholipids, sphingolipids, and glycerolipids, the latter being the most affected (Table 1 and Fig. 1D). After SAM (using a ␦ value of 2.4, which allowed a 0.002 false discovery rate), a total of 38 differential lipids were detected (Supplemental Fig. S2). We also performed a correlational analysis using the 50% most abundant molecules described above 1074

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(Supplemental Fig. S3). Globally, the differences in the correlation analysis were not sharp, because we could see similar networks of associations between lipids in both adipose tissue depots, suggesting similar regulation of these molecules in both fractions. However, when insulin resistance-related traits [BMI, clamp value, and C-reactive protein (CRP)] were correlated with lipid species, sharp differences between omental and subcutaneous depots were seen (Fig. 2). Specifically, the molecules that best correlated with BMI, clamp value, and CRP were different in omental and subcutaneous adipose tissue (Table 2). To know the potential influence of hypertrophy of adipose tissue depot lipidomic signatures, adipose tissues from lean subjects were also studied. As shown in Supplemental Fig. S1, the samples did not cluster as well as those from obese individuals, and only one species, compatible with TG(16:1/18:1/20:0), was significantly different between the anatomic regions (Supplemental Fig. S1). Differences between omental and subcutaneous lipid peroxidation Because of the importance of oxidative stress in the metabolic syndrome (25), we focused on several lipoxi-

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TABLE 1. Differential lipid species found between subcutaneous and omental adipose tissue Family

Free fatty acidsa Glycerophospholipidsb

Sphingolipidsa Glycerolipidsb

Compound

Regulation

P

13,16-Docosadienoic acid 8-11-14-Heptadecatrienoic acid Docosanedioic acid GPEtn(37:0) GPEtn(25:0) GPEtn(38:0) GPEtn(36:0) GPGro(34:0) Lactosylceramide (d18:1/12:0) SM(d18:1/20:0) DG(44:6) DG(33:1) DG(38:5) DG(35:2) DG(34:1) DG(40:5) DG(38:4) DG(38:4) DG(32:2) TG(50:0) TG(59:2) TG(58:2) TG(60:3) TG(64:4) TG(51:0) TG(65:1) TG(65:0) TG(55:8) TG(54:5) TG(54:3) TG(58:6)

Increased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Increased Increased Increased Increased Increased Increased Increased Decreased Decreased Decreased Decreased Decreased

0.048 0.048 0.029 0.0071 0.040 0.0079 0.0026 0.024 0.020 0.019 0.0054 0.026 0.0071 0.024 0.022 0.024 0.048 0.023 0.020 0.015 0.023 0.038 0.0062 0.048 0.041 0.015 0.014 0.033 0.038 0.024 0.038

Regulation is with respect to subcutaneous adipose tissue; values of P determined by paired Student’s t test with Benjamini-Hochberg correction for false discovery rate. DG, diacylglyceride; GPEtn, glycerophosphoethanolamine; GPGro, glycerophosphoglycerol; SM, sphingomyelin; TG, triglyceride. a Identification based on exact mass and retention time. bIdentification based on exact mass, retention time, and MS/MS spectrum.

dative products, by using a targeted lipidomic approach. Among them, we only detected one oxysterol, the cholesterol-5␣,6␣-epoxide. The levels of unesterified cholesterol and this epoxide in omental adipose tissue were higher than those in subcutaneous adipose tissue (Fig. 3A). Furthermore, although a fair to good correlation existed between the cholesterol epoxide levels in both depots (Fig. 3B), the location of adipose tissue has a strong modifying role in the relationships between cholesterol epoxide concentrations and other lipid components (Fig. 3C). Effect of location in adipocyte differentiation and ␣-oxidation gene expression Several transcripts codifying for enzymes potentially behind lipidomic differences were analyzed in a larger set of individuals for further validation of the lipidomicsuggested changes These transcriptomic analyses revealed higher levels of different genes involved in adipogenesis (PEMT, AdipoQ, PEDF, PPAR␥, PGC1␣, GLUT4, and PLIN1) in the subcutaneous vs. omental locations, as well as genes involved in TG synthesis and LIPIDOMIC SIGNATURES OF ADIPOSE TISSUE

LD formation (FSP27, ACSL1, DGAT1, and PPAR␥; Table 3). On the contrary, decreased levels of AKAP, a lipolysis-related gene, were found in subcutaneous adipose tissue. Finally, the expression of 2 genes related to the ␣-oxidation pathway (PHYH and HPCL2) was increased in the subcutaneous depot (Table 3). Effect of cholesterol-5␣,6␣-epoxide on adipocyte differentiation and ␣-oxidation pathway in cell cultures Because cholesterol-5␣,6␣-epoxide was increased in omental tissue, we hypothesized that this compound may have a pathophysiological role. We first studied the effect of cholesterol-5␣,6␣-epoxide on differentiation of isolated omental and subcutaneous adipocytes. The analyses of several genes involved in adipogenesis and adipocyte differentiation revealed that, although subcutaneous adipocytes had increased levels of differentiation markers (Fig. 4A), omental adipocytes were more sensitive to the prodifferentiation effects provoked by cholesterol-5␣,6␣-epoxide incubation (Fig. 4B⫺I). Finally, the cholesterol-5␣,6␣-epoxide affected 1075

BMI sc

Clamp value om

sc

om

Docosadienoic acid Hexadecatrienoic acid GPEtn(35:0).1 GPEtn(35:0) .2 GPEtn(36:0 ) GPEtn(36:1) GPEtn(36:2) GPEtn(37:0) GPEtn(38:1 ) GPEtn(38:1).1 GPEtn(38:1).2 GPEtn(38:2) GPEtn(38:4) GPEtn(39:0) GPEtn(O-18:0/O-18:0) GPEtn(O-38:0) GPEtnNMe(34:1) GPEtnNMe(36:2) GPEtnNMe(36:4) GPEtnNMe2(34:1) GPGro(34:0) GPGro(36:2) GPSer(36:1) GPSer(O-16:0/O-16:0) N-Glycoloylganglioside GM2 N-Palmitoylsphingosine N-Stearoyl-D-sphingosine SM(d18:0/16:0) SM(d18:0/18:0) SM(d18:1/16:0) SM(d18:1/18:0).1 SM(d18:1/18:0).2 Cer(d18:0/18:0) Cer(d18:0/24:0) Ceramide (d18:1/20:0).1 Ceramide (d18:1/20:0) .2 Cerebroside A CerP(d18:1/26:0).1 CerP(d18:1/26:0).2 GlcCer(d18:0/18:0) GlcCer(d18:0/20:0) DG(32:0).1 DG(32:0).2 DG(32:0).3 DG(32:1).1 DG(32:1).2 DG(32:2) DG(33:1).1 DG(33:1).2 DG(34:0) .3 DG(34:0).1 DG(34:0).2 DG(34:2).1 DG(34:2).2 DG(34:2).3 DG(34:2).4 DG(34:3).1 DG(34:3).2 DG(35:1) DG(36:0).1 DG(36:0).2 DG(36:2) DG(36:3) DG(36:4) DG(38:4).1 DG(38:4).2 DG(38:5).1 DG(38:5).2 DG(40:5) DG(44:6) DG(44:7) DG(44:9) TG(43:3) TG(46:0).1 TG(46:0).2 TG(48:0) TG(48:1).1 TG(48:1).2 TG(48:3).1 TG(48:3).2 TG(49:0) TG(49:1) TG(49:3) TG(50:1).1 TG(50:1).2 TG(50:2).1 TG(50:2).2 TG(50:3) TG(51:0) TG(51:3).1 TG(51:3).2 TG(51:3).3 TG(52:1) TG(52:2) TG(52:3) TG(52:4) TG(52:5) TG(53:0) TG(53:1) TG(53:5) TG(54:0) TG(55:4) TG(55:5) TG(55:8) TG(56:0).1 TG(56:0).2 TG(56:2) TG(56:4) TG(56:5) TG(56:7).1 TG(56:7).2 TG(56:7).3 TG(57:7) TG(58:1) TG(58:2).1 TG(58:2).2 TG(58:5).1 TG(58:5).2 TG(58:6).1 TG(58:6).2 TG(58:8) TG(59:2).1 TG(59:2).2 TG(59:3) TG(59:4) TG(59:5) TG(60:3).1 TG(60:3).2 TG(60:3).3 TG(60:4) TG(60:5) TG(60:8).1 TG(60:8).2 TG(60:9) TG(61:0) TG(61:1) TG(61:3) TG(61:4) TG(62:1) TG(62:2) TG(62:4).1 TG(62:4).2 TG(62:5) TG(64:1).1 TG(64:1).2 TG(64:6) TG(64:7) TG(65:0) TG(65:1) TG(65:2).1 TG(65:2).2 TG(65:4) 15:1 Cholesteryl ester 16:0 Cholesteryl ester 18:1 Cholesteryl ester 20:2 Cholesteryl ester 20:3 Cholesteryl ester 20:4 Cholesteryl ester 5beta-Cholestane-3alpha7alpha2426-tetrol

CRP sc

om Free fatty acids

Glycerolphospholipids

Sphingolipids

Glycerolipids

Sterols

Figure 2. Effects of adipose tissue location on lipidomic correlations between the identified 50% more abundant lipid species and insulin resistance-related traits (BMI, clamp value, and CRP). After selection of the 50% most abundant lipids and those with a putative identity, correlational analysis was performed using Pearson correlation tests. Color key shows the range of Pearson correlation coefficient values from green to red. n ⫽ 14/group. sc, subcutaneous; om, omental.

the ␣-oxidation related genes differentially, increasing their transcription in omental adipocytes significantly while decreasing it in the subcutaneous adipocytes (Fig. 4J, K). These changes are specific for cholesterol-5␣,6␣epoxide, because cholesterol addition under the same conditions did not produce similar effects (Fig. 4).

DISCUSSION In the present work, we demonstrate that there are specific lipidomic patterns for omental and subcutane1076

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ous adipose tissue. To overcome the potential limitation of a relatively low number of individuals analyzed in the mass spectrometric approach, we studied a larger set of individuals (n⫽38) using transcriptomic assays. Thus, the results obtained by the combined use of lipidomics and transcriptomics validates the reported conclusions both qualitatively (using independent techniques) and quantitatively (increasing the number of studied individuals). The lipidomic untargeted analysis also revealed that, taking into account the 50% most abundant metabolites (and specifically those with a putative identifica-

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TABLE 2. Lipid species that best correlate with BMI, CRP, and CLAMP Location

Parameter

Omental

BMI

CRP

Clamp value

Subcutaneous

BMI

CRP

Clamp value

Name

Correlation

P

TG(64:1) Docosadienoic acid TG(61:4) Cer(d18:0/18:0) TG(58:6) DG(34:2) TG(49:3) TG(60:3) DG(38:4) DG(40:5) DG(44:7) GPEtn(38:4) SM(d18:0/16:0) TG(55:4) TG(62:5) 18:1 cholesteryl ester SM(d18:0/16:0) TG(62:1) GPEtn(38:1) TG(65:2) TG(62:5) TG(60:8) DG(36:2) Cer(d18:0/18:0)

⫺0.7126 ⫺0.69843 ⫺0.66648 ⫺0.66029 0.68838 ⫺0.65583 ⫺0.64876 ⫺0.60829 ⫺0.60421 ⫺0.52654 ⫺0.51484 ⫺0.51416 ⫺0.81238 ⫺0.68139 0.62528 0.60113 ⫺0.62803 ⫺0.60869 ⫺0.59231 ⫺0.57662 ⫺0.75965 ⫺0.59521 ⫺0.57204 0.56877

0.0042345 0.0054621 0.0092444 0.010166 0.0064884 0.010874 0.012073 0.02099 0.022109 0.053068 0.059586 0.059979 0.00041475 0.0072869 0.016789 0.022982 0.016173 0.020883 0.025626 0.030884 0.0016208 0.024732 0.032557 0.033796

TG, triglyceride; Cer, ceramide; DG, diacylglyceride; SM, sphingomyelin; GPEtn, glycerophosphoethanolamine.

C

l ta en O m

Su bc ut an eo us

0.0

0.05 0.00

Color key

-0.5 0 0.5 1

Value

Cholesterol 5α, 6α Epoxide Omental Subcutaneous

LIPIDOMIC SIGNATURES OF ADIPOSE TISSUE

Omental Cholesterol 5α, 6α Epoxide

0.5

l

1.0

0.10

ta

1.5

0.15

0.14 0.12 0.10 0.08 0.06

en

2.0

m

Cholesterol (MS counts)

2.5

adipocyte LDs, which are dynamic functional organelles related to vesicle trafficking and cell signaling and fundamental for lipid homeostasis (27, 28). Of note was also the divergent relationships of the same lipid species with clamp-measured insulin sensitivity according to the location of the fat depot (Fig. 2 and Table 2). This finding suggests that location is

B ***

O

**

Su bc ut an eo us

A

Cholesterol epoxide/cholesterol (MS counts)

tion), we found basically (⬃97%) lipids belonging to the glycerolipid family, followed by glycerophospholipids, sphingolipids, sterols, and, finally, free fatty acids. These results agree with 99% of lipid composition in adipose tissue of healthy adults being composed of TGs, with cholesterol (0.3%) and phospholipids (0.1%) making minor contributions (26). TGs are located in

0.05

0.10

0.15

Subcutaneous Cholesterol 5α, 6α Epoxide

Figure 3. Effects of adipose tissue location on cholesterol oxidation. A) Levels of cholesterol and cholesterol-5␣,6␣-epoxide in subcutaneous and omental adipose tissue fractions. Data are means ⫾ sem. ; **P ⬍ 0.01, ***P ⬍ 0.001. B) Correlation analysis of cholesterol-5␣,6␣-epoxide in omental and subcutaneous adipose tissue. Pearson correlation coefficient is 0.5983, R2 ⫽ 0.3579, and P ⫽ 0.023. C) Differences in cholesterol-5␣,6␣-epoxide lipid correlations in omental and subcutaneous adipose tissue. n ⫽ 14/group. 1077

TABLE 3. Comparison of gene expression according to adipose tissue origin (38 paired adipose tissue samples) Gene

AdipoQ PEDF PPAR␥ GLUT4 PLIN1 FSP27 PGC1␣ AKAP PEMT PHYH HPCL2 ACSL1 DGAT1

Subcutaneous

Omental

P

2.45 ⫾ 0.82 1.16 ⫾ 0.37 0.0425 ⫾ 0.022 0.044 ⫾ 0.024 1.81 ⫾ 0.72 2.03 ⫾ 1.09 0.0026 ⫾ 0.0017 0.024 ⫾ 0.012 0.0082 ⫾ 0.0066 0.091 ⫾ 0.024 0.0236 ⫾ 0.0069 0.268 ⫾ 0.139 0.201 ⫾ 0.098

1.72 ⫾ 0.87 0.77 ⫾ 0.27 0.0268 ⫾ 0.023 0.034 ⫾ 0.024 1.34 ⫾ 0.57 1.21 ⫾ 1.01 0.0034 ⫾ 0.0021 0.039 ⫾ 0.02 0.0032 ⫾ 0.0030 0.077 ⫾ 0.021 0.0179 ⫾ 0.0060 0.206 ⫾ 0.149 0.138 ⫾ 0.083

⬍0.0001 ⬍0.0001 ⬍0.0001 0.03 0.01 0.004 0.06 ⬍0.0001 ⬍0.0001 0.004 0.001 ⬍0.0001 0.005

Values are means ⫾ sem (arbitrary units); values of P determined by paired Student’s t test with Benjamini-Hochberg correction for false discovery rate.

more important than the nature of lipid species in their association with metabolic traits. Few studies comparing the fatty acid composition between different depots of subcutaneous adipose tissue and also between omental and subcutaneous fractions have been published (29). Thus, as far as we know, this is the first study focusing on whole and native TG and other lipid molecules. Part of the subcutaneous signature consisted of increased TG concentrations, suggesting higher accumulation of glycerolipids in subcutaneous LDs. This was confirmed by higher transcription levels of the genes directly involved in TG and LD formation in the subcutaneous fat depot. A closer look at the species that show a locationspecific distribution reveals that, although the number of carbons did not show major differences (the average length of the acyl chain in TGs increased in omental lipids was 19.3, whereas that of those that were decreased was 19.0), the number of unsaturations per acyl chains was strongly dependent on tissue location. Thus, this parameter was less than 0.6 in the TGs showing increased concentrations in omental tissue and ⬎1.4 in the TGs showing decreased concentrations in omental tissue. It may be hypothesized that selective lipolytic and synthetic mechanisms operate in each tissue depot. In agreement with this hypothesis, early reports indicated that desaturase activities were strongly location dependent in ruminants (30). More recent data in humans revealed that desaturase enzymes, regulating the number of unsaturations in fatty acids, show a location-specific profile (31), in a close relationship with insulin resistance. Further, in an imaging study, it has been reported recently that the levels of omental tissue are directly related to their TG saturation (i.e., the higher omental tissue content, the lower the unsaturation of their TG depots; ref. 32), reinforcing results showing location-specific unsaturation of TG in animal models (33). In addition to specific changes in lipid synthesis and/or degradation, a close relationship with adipocyte mean size has been invoked for the degree of unsaturation in their depots (33). 1078

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It is known that glycerophosphoethanolamines (GPEtns) are among the most abundant glycerophospholipids in LD fractions. Furthermore, an association between their levels and adipocyte differentiation has been described (28). Thus, the higher level of GPEtns in subcutaneous vs. omental fat depots could be related to a more dynamic state in adipose tissue buildup. Again, the transcriptomic analysis revealed higher expression of genes involved in adipocyte differentiation in the subcutaneous depot, reinforcing this hypothesis. Taken together, these results suggest that adipocytes from subcutaneous fat have a higher level of differentiation than omental adipocytes, as suggested by previous data (34). Sphingolipids have been implicated in numerous intra- and extracellular signaling processes as both signaling molecules and secondary messengers (35). Sphingolipid metabolism is altered in adipose tissue in obesity/diabetes and atherosclerosis, possibly increasing the metabolic and cardiovascular risk (36). Previous studies in obese mice demonstrated that the levels of sphingomyelin and ceramide in these mice were lower in adipose tissue but higher in plasma than those in control animals, suggesting net export of these lipids to plasma (37). Thus, although the mechanism behind this transport is obscure (ABCG1 is implicated, among others; ref. 38), the lower levels of sphingolipids in omental fraction could be hypothetically attributed to higher export of these molecules into the plasma. Nonetheless, the changes in plasma concentrations can be derived from dietary absorption or specific changes in the secretion of lipoproteins, among other mechanisms. We also found differences in 3 free fatty acids. Among them, the levels of 8,11,14-heptadecatrienoic acid (17:3) were decreased in the omental fraction. Recent data have implied that nonclassic ␣-oxidation takes place in the differentiation pathways of adipocytes in vitro (39, 40). The finding of changes in a fatty acid derived from these pathways, together with the transcriptomic results, favors down-regulation of ␣-oxidation in the omental depot. Reinforcing the differences in metabolic activities

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JOVÉ ET AL.

AdipoQ

Omental Subcutaneous

140 130 120 110 100 90

c c a

200

*100

****

0 0 5 10 0 5 10 Epoxycholesterol (µM) Cholesterol (µM)

****

D

ip o PP Q A R γ D G A T1 H PC L PH 2 YH A C SL 1 PE M G T LU T FA 4 B P PN 4 PL A 2

Ad

250 200 150 100 50 0

200

d

150

*** 100 0

5

100

b a

50 0

0

5

% Change (vs CTL)

b

5

10

Cholesterol (µM)

Epoxycholesterol (µM)

I

% Change (vs CTL)

b

150

180 a 160 ***140 120 100 80 10 0

0

10

d b

a

d

300

d

**200

****

100

0 5 10 Epoxycholesterol (µM)

0

0

5

10

Cholesterol (µM)

0

5

100

*** 5

0

10

d

0

c

****

5

a

d

150

**

* 100 50 0

5

0

10

Epoxycholesterol (µM)

Omental

0

5

0

10

0

10

Cholesterol (µM)

5

10

Cholesterol (µM)

140

150

d

c

c

100

c

120

a

** 50

100 0

5

0

10

c

0

5

10

Cholesterol (µM)

PPARγ 200

200 b

150 100

b

a

0

5

b

150 ***100

50

50 0

10

0

5

10

Cholesterol (µM)

Epoxycholesterol (µM)

K

200

****

GLUT4

0

Cholesterol (µM)

c

100

Epoxycholesterol (µM)

10

HPCL2 130 120 110 100 90 80

5

H

50 0

0

a

200

***

80

10

d a

d

160

Cholesterol (µM)

150

J

PNPLA2 180 160 140 120 100 80

b

DGAT1 130 120 110 100 90 80

Epoxycholesterol (µM)

% Change (vs CTL)

% Change (vs CTL)

G

PEMT 200

****

50

Epoxycholesterol (µM)

F

c

a

300

d d

E

ACLS1

% Change (vs CTL)

0.2 0.1 0.0

% Change (vs CTL)

2

FABP4 180 160 140 120 100 80

Epoxycholesterol (µM)

% Change (vs CTL)

****

% Change (vs CTL)

4

****

C

300

% Change (vs CTL)

6

% Change (vs CTL)

B

Relative gene expression (A.U.)

A

PHYH 130 120 110 100 90 80

b a

d

a

** 100

a

0

200 150 50

5

10

Epoxycholesterol (µM)

0

0

5

10

Cholesterol (µM)

Subcutaneous

Figure 4. Effects of cholesterol-5␣,6␣-epoxide and cholesterol on adipocytes gene expression. A) Subcutaneous adipocytes express higher basal levels of AdipoQ, ACSL1, and FABP4 genes. BⴚI) Cholesterol-5␣,6␣-epoxide incubation induced higher levels of adipogenesis-related gene expression in omental than in subcutaneous depot, whereas cholesterol induced an inverse response: AdipoQ (B), FABP4 (C), ACSL1 (D), GLUT4 (E), PEMT (F), DGAT4 (G), PPAR␥ (H), PNPLA2 (I). J, K) Cholesterol5␣,6␣-epoxide incubation specifically induced increased transcription levels of ␣-oxidation genes HPCL2 (J) and PHYH (K) in the omental but not the subcutaneous depot. The transcriptional profile differs from that induced by cholesterol. Data are means ⫾ sem; n ⫽ 3/group. A.U., arbitrary units; CTL, control. Different letters indicate statistical differences (P⬍0.05) between different concentrations of epoxide or cholesterol. *P ⬍ 0.05, **P ⬍ 0.01, ***P ⬍ 0.001, ****P ⬍ 0.0001 with reference to adipose tissue location by post hoc analyses after 2-way analysis of variance.

between these two fat depots, we found for the first time a location-specific difference in the accumulation of an oxysterol in these tissues. Previous data, demonstrating both rheological changes in adipocyte membranes (41) and a potential role of oxysterols as liver X-receptor ligands (42), reinforced the importance of these findings. Thus, theoretically, this oxysterol could act differentially in these fat depots, contributing either to their differential metabolism (43) or to their differential rates of differentiation from pluripotent cells (44). Furthermore, recent data showing that oxysterols could contribute to endoplasmic reticulum stress (45) could explain the present differences for this pathogenic pathway between different fat depots (46). Furthermore, cell culture experiments revealed that although omental adipocyte express lower levels of genes involved in adipogenesis and LD formation, they are more susceptible to differentiation induced by cholesterol-5␣,6␣-epoxide. This finding suggests that the higher levels of this oxysterol in omental depot could be a cellular response to their resistance LIPIDOMIC SIGNATURES OF ADIPOSE TISSUE

to differentiation. Notably, in vitro data show that the cholesterol-5␣,6␣-epoxide could be also involved in the regulation of the ␣-oxidation pathway. We acknowledge that, because of its low stability, the effects reported could be also produced by cholesterol-5␣,6␣epoxide-derived species, but the comparison with cholesterol incubation reinforces the specificity of these responses. Finally, supporting the dynamic nature of adipose tissue and the complex interplay between adipose tissue physiology and its lipid composition, results obtained for adipose tissue from nonobese individuals show that the location-induced differences were much lower in nonproliferating/noninflamed/nonhypertrophic status (i.e., nonobese). In summary, the lipidomes from human adipose tissues from obese subjects are markedly different, depending on their subcutaneous or omental origin. Changes in lipid composition are related mainly to differences in adipocyte differentiation, LD metabolism, and ␣-oxidation. Increased cholesterol epoxide concentra1079

tions in omental tissue could be related to its relative resistance to differentiation, as suggested by in vitro experiments. Work performed in the Institut de Recerca Biomèdica de Lleida-Científic i Tecnològic Agroalimentari de Lleida (IRBLLEIDA-PCiTAL) was supported by the Spanish Ministry of Science and Innovation (INNPRONTA Program, BFU2009-11879/BFI and AGL2006-1243) the Autonomous Government of Catalunya (2009SGR-735, ACC1O program), and the Spanish Ministry of Health (FIS 08-1843 and 1101532]. This work was also supported by the COST B-35 Action, Instituto Tomás Pascual para la Nutrición y Salud, and partially supported by research grants from the Ministerio de Educación y Ciencia (SAF2011-0214). Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y la Nutrición (CIBERobn) is an initiative from the Instituto de Salud Carlos III (Girona, Spain). M.P.-O. and J.M.F.-R. designed the experiment; M.J. and J.M.M-N. performed research; R.P. and W.R. analyzed data; M.J., M.P.-O., and J.M.F.-R. wrote the paper. The authors declare no conflicts of interest.

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