Lpin1 in human visceral and subcutaneous adipose tissue: similar ...

Am J Physiol Endocrinol Metab 299: E308 –E317, 2010. First published June 8, 2010; doi:10.1152/ajpendo.00699.2009.

Lpin1 in human visceral and subcutaneous adipose tissue: similar levels but different associations with lipogenic and lipolytic genes Merce Miranda,1,2 Xavier Escoté,1,2 María J. Alcaide,3 Esther Solano,1 Victòria Ceperuelo-Mallafré,1,2 Pilar Hernández,4 Martin Wabitsch,5 and Joan Vendrell1,2 1

Endocrinology and Diabetes Unit, Hospital Universitari de Tarragona Joan XXIII, IISPV, Universitat Rovira i Virgili; Centro de Investigacion Biomedica en Red de Diabetes y Enfermedades Metabólicas Asociadas; 3Surgery Service, Hospital Universitari de Tarragona Joan XXIII; 4Statistics Department, IISPV, Tarragona, Spain; and 5Sektion Pädiatrische Endokrinologie u Diabetologie, Interdisziplinäre Adipositasambulanz, Universität Klinik für Kinder und Jugendmedizin, Ulm, Germany 2

Submitted 30 November 2009; accepted in final form 3 June 2010

Miranda M, Escoté X, Alcaide MJ, Solano E, CeperueloMallafré V, Hernández P, Wabitsch M, Vendrell J. Lpin1 in human visceral and subcutaneous adipose tissue: similar levels but different associations with lipogenic and lipolytic genes. Am J Physiol Endocrinol Metab 299: E308 –E317, 2010. First published June 8, 2010; doi:10.1152/ajpendo.00699.2009.—LPIN1 is a gene with important effects on lipidic and metabolic homeostasis. Human subcutaneous LPIN1 expression levels in adipose tissue are related with a better metabolic profile, including insulin sensitivity markers. However, there are few data on the regulation of LPIN1 in visceral adipose tissue (VAT). Our aim was to perform a cross-sectional analysis of VAT compared with subcutaneous (SAT) LPIN1 expression in a well-characterized obese cohort, its relation with the expression of genes involved in lipid metabolism, and the in vitro response to lipogenic and lipolytic stimuli. A downregulation of total LPIN1 mRNA expression in subjects with obesity was found in VAT similarly to that in SAT. Despite similar total LPIN1 mRNA levels in SAT and VAT, a close relationship with clinical parameters and with many lipogenic and lipolytic genes was observed primarily in SAT depot. As shown in the in vitro analysis, the low-grade proinflammatory environment and the insulin resistance associated with obesity may contribute to downregulate LPIN1 in adipose tissue, leading to a worse metabolic profile. lipin 1 gene; insulin resistance; obesity

are characterized by a loss of synchrony in triglyceride lipolysis and biosynthesis within adipocytes in response to fed (predominantly carbohydrate oxidation) and fasting (predominantly lipid oxidation) conditions. Adipose tissue helps maintain the metabolic homeostasis by buffering the flux of fatty acids in circulation in the postprandial period (28). Dysfunction of adipose tissue is in part responsible for the hyperlipidemia that leads to lipid accumulation in other tissues (mainly muscle and liver) and also contributes to insulin resistance (28). Peroxisome proliferator-activated receptor (PPAR)␥ is a critical factor for the maintenance of whole body insulin sensitivity since it promotes adipose tissue expansion and increases the lipid-buffering capacity of peripheral organs (18). Lipin 1 gene (LPIN1) plays a role in lipid metabolism and in nuclear receptor coactivation (reviewed in Refs. 26 and 27). In fact, previous studies have linked LPIN1 to the maintenance of

OBESITY AND INSULIN RESISTANCE

Address for reprint requests and other correspondence: M. Miranda: Unitat de Recerca, Hospital Universitari Joan XXIII, C/Dr. Mallafré Guasch, 4, 43007 Tarragona, Spain (e-mail: [email protected]). E308

human metabolic homeostasis (4, 19, 30, 35, 42). It is known that LPIN1 is needed for the development of mature adipocytes (24), and mouse models that overexpress LPIN1 in adipose tissue develop obesity but also higher insulin sensitivity (25). The molecular function of LPIN1 has been identified as the phosphatidic acid phosphatase that produces 1,2-diacylglycerol, directly relating LPIN1 with the synthesis of triglycerides in the adipocyte (9). In addition, it functions as a transcriptional coactivator in the liver with an important role in lipid homeostasis and metabolism by inducing the expression of the nuclear receptor PPAR␣ and via direct physical interactions with PPAR␣ and other factors (7). LPIN1 gene expression levels have been found downregulated in subcutaneous adipose tissue (SAT) of obese subjects (19, 30, 35, 42). Differences of LPIN1 mRNA expression in the different human abdominal adipose depots, SAT or visceral adipose tissue (VAT), have not been found, at least not in women (2, 35). To date, there are few data that analyze whether the findings described in SAT may also be reproducible in the VAT depot, which in turn is considered a worse cardiovascular surrogate marker. A recent report showed a negative association between VAT LPIN1 and body mass index (BMI) in a Chinese cohort (2). Two primary LPIN1 isoforms, LPIN1␣ and LPIN1␤, are generated through alternative mRNA splicing of an internal exon within the LPIN1 gene (23). Recently, a new isoform, LPIN1␥, has been described in cDNA derived from human fetal brain mRNA (10). Subcutaneous abdominal fat contains more fat mass than intra-abdominal (visceral) fat, and its fatty acids add significantly to peripheral insulin sensitivity (1). However, visceral fat is another important fat depot influencing insulin sensitivity (15). Differences in the SAT and VAT profiles may also be crucial for metabolic homeostasis. We have conducted a crosssectional analysis in a well-characterized cohort to assess the relationship between SAT and VAT LPIN1 mRNA levels and obesity. We further constructed a hierarchical cluster analysis to explore LPIN1 expression in relation with genes involved in lipogenesis and lipolysis. Moreover, in vitro analysis of the LPIN1 response to lipogenic and lipolytic stimuli in SimpsonGolabi-Behmel syndrome (SGBS) adipocyte cells may help to understand these relationships. MATERIALS AND METHODS

Selection of patients. A cohort of 62 subjects was recruited at the Hospital Universitari Joan XXIII (Tarragona, Spain). All were of Caucasian origin and reported that their body weight had been stable

0193-1849/10 Copyright © 2010 the American Physiological Society

http://www.ajpendo.org

E309

HUMAN VISCERAL ADIPOSE LPIN1 EXPRESSION

for ⱖ3 mo before the study. They had no systemic disease other than obesity, and all had been free of any infections in the month before the study. Exclusion criteria were liver or renal diseases, malignancy, inflammatory chronic disease, type 2 diabetes mellitus, and morbid obesity (defined by a BMI ⬎40 kg/m2). VAT and SAT samples were obtained from all 62 subjects during abdominal elective surgical procedures for benign pathologies (cholecystectomy or surgery for abdominal hernia). The Hospital Universitari Ethics Committee approved the study, and informed consent was obtained from all participants. Subjects were classified by their BMI according to the World Health Organization criteria (40). Using these criteria, there were 19 nonobese, 28 overweight, and 15 nonseverely obese subjects (Table 1). Subjects were matched for age and sex. The metabolic syndrome (MetS) was defined according to the International Diabetes Federation guidelines (International Diabetes Federation, IDF Consensus Worldwide Definition of the Metabolic Syndrome, 2006: http://www.idf.org/webdata/docs/IDF_Meta_def_ final.pdf). Anthropometric measurements. Height was measured to the nearest 0.5 cm and body weight to the nearest 0.1 kg. BMI was calculated as weight (kg) divided by height squared (m2). Waist circumference was measured midway between the lowest rib margin and the iliac crest. Collection and processing of samples. All patients had fasted overnight ⱖ12 h before undergoing the surgical procedure. Blood samples were collected before the surgical procedure from the antecubital vein: 20 ml of blood with EDTA (1 mg/ml) and 10 ml of blood in silicone tubes. Fifteen milliliters of collected blood was used for the separation of plasma. Plasma and serum samples were stored at ⫺80°C until analytical measurements were performed. Adipose tissue samples were collected, washed in 1⫻ PBS, immediately frozen in liquid N2, and stored at ⫺80°C. Analytical methods. Glucose, cholesterol, and triglyceride plasma levels were determined in an Advia 1200 autoanalyzer (Siemens, Munich, Germany) using the standard enzyme methods. High-density lipoprotein cholesterol was quantified after precipitation with polyethylene glycol at room temperature (PEG-6000). Plasma insulin was determined by radioimmunoassay (Coat-A-Count insulin; Diagnostic Products, Los Angeles, CA). Sensitivity was 2.6 ␮IU/ml, and intraand interassay coefficients of variation (CV) were ⬍5%. Plasma glycerol levels were analyzed using a free glycerol determination kit and a quantitative enzymatic determination assay (Sigma-Aldrich, St.

Louis, MO). Intra- and interassay CV were ⬍6% and ⬍9.1%, respectively. Nonesterified free fatty acid serum levels were determined in an Advia 1200 autoanalyzer (Siemens) using an enzymatic method developed by Wako Chemicals (Neuss, Germany). Assay sensitivity was 0.01 meq/l, and the inter- and intra-assay CV were ⬍8%. The degree of insulin resistance was determined by the homeostasis model assessment of insulin resistance (HOMA-IR) as [glucose (mmol/l) ⫻ insulin (␮IU/l)/22.5] (17). Levels of soluble interleukin-6 (sIL-6) were measured by the highly sensitive quantitative sandwich enzyme immunoassay technique with the Human IL-6 Quantikine HS ELISA kit (R & D Systems, Oxon, UK). The sensitivity of the assay was 0.039 pg/ml. Intra- and interassay CV were ⬍9.8 and ⬍11.2%, respectively. Gene expression-relative quantification. Between 400 and 500 mg of frozen adipose tissue was homogenized with an Ultra-Turrax 8 (Ika, Staufen, Germany). Total RNA was extracted by using an RNeasy Lipid Tissue Midi Kit (Qiagen Science, Hilden, Germany), and total RNA was treated with 55 U RNase-free DNase (Qiagen), duly following the manufacturer’s instructions. Two-hundred fifty nanograms of total RNA was transcribed to cDNA using a High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor (Applied Biosystems, Foster City, CA) in a final volume of 20 ␮l. About 2 ng of cDNA per gene was used in real-time PCR quantification analysis, which was performed with duplicates on a 7900HT Fast Real-Time PCR System using Taqman Low-Density Arrays (Applied Biosystems). SDS software 2.3 and RQ Manager 1.2 (Applied Biosystems) were used to analyze the results, and all data were normalized with the expression of cyclophilin A (PPIA). For LPIN1␣ and LPIN1␤ gene expression quantification, ⬃20 ng of cDNA per gene was used in real-time PCR quantification analysis, which was performed on a 7900HT Fast Real-Time PCR System by using Fast SYBR Green Master Mix (Applied Biosystems). Results are expressed relative to PPIA expression levels and were analyzed with the comparative threshold cycle (CT) method (2⫺⌬⌬CT). Primers used were forward LPIN1, CAAGATGATATTCCTGAGGAA; reverse LPIN1␣, AAGGACTGGGAGTGGGTGA; reverse LPIN1␤, AAGACTGTGGAGGGCAAGAA; forward PPIA, CAAATGCTGGACCCAACACAA; and reverse PPIA, GCCTCCACAATATTCATGCCTTCTT. In vitro reagents. Reagents for Western blot, human tumor necrosis factor-␣ (TNF␣), and isoproterenol were supplied by Sigma-Aldrich.

Table 1. Characteristics of the population Lean (n ⫽ 19)

BMI Age, yr [means (SD)] Sex Male Female BMI, kg/m2 [median (IQR)] Waist, cm [median (IQR)] Cholesterol, mM [means (SD)] HDL cholesterol, mM [means (SD)] Triglycerides, mM [median (IQR)] Glycerol, mM [median (IQR)] NEFA, mM [means (SD)] Glucose, mM [means (SD)] Insulin, ␮IU/ml [median (IQR)] HOMA-IR [median (IQR)] sIL-6, pg/ml [median (IQR)] SBP, mmHg [median (IQR)] DBP, mmHg [median (IQR)]

Overweight (n ⫽ 28)

⬍25 51.7 (16.0)

25.0–29.9 57.1 (15.0)

13 (68.4%) 6 (31.6%) 23.6 (22.1–24.2) 83.0 (79.0–90.0) 5.2 (1.2) 1.5 (0.5) 1.0 (0.7–1.6) 157.5 (116.9–216.9) 997.5 (718.0) 4.8 (0.7) 3.4 (2.1–6.7) 0.75 (0.54–1.83) 1.4 (1.1–2.5) 120 (120–127) 70 (60–80)

16 (57.1%) 12 (42.9%) 27.2 (26.5–27.9)* 97.0 (90.5–100.0)* 4.9 (1.0) 1.3 (0.3) 1.1 (0.8–1.5) 161.3 (114.5–210.5) 742.8 (253.7) 5.5 (0.5)* 4.0 (2.8–7.2) 1.01 (0.52–2.09) 1.0 (0.7–2.2) 130 (121–140) 70 (70–80)

Obese (n ⫽ 15)

ⱖ30 57.4 (12.8) 9 (60.0%) 6 (40.0%) 32.1 (30.8–33.6)*‡ 107.0 (100.0–117.2)*‡ 5.2 (0.8) 1.4 (0.3) 1.0 (0.7–1.3) 147.6 (122.7–269.5) 824.6 (271.6) 5.6 (0.5)* 6.6 (4.5–16.5)† 1.60 (1.19–4.79)† 2.5 (1.4–5.2) § 145 (130–160)*§ 80 (78–90)†

BMI, body mass index; IQR, interquartile range; NEFA, nonesterified fatty acids; HOMA-IR, homeostasis model assessment of insulin resistance; sIL-6, soluble interleukin-6; SBP, systolic blood pressure; DBP, diastolic blood pressure. Differences vs. lean: *P ⬍ 0.001; †P ⬍ 0.05 vs. overweight; ‡P ⬍ 0.001; §P ⬍ 0.05. AJP-Endocrinol Metab • VOL

299 • AUGUST 2010 •

www.ajpendo.org

E310


Human insulin was purchased from Novo Nordisk (Bagsværd, Denmark). Media for cell culture were supplied by Gibco (Invitrogen, Carlsbad, CA), except for preadipocyte and adipocyte differentiation media, which were from Advancell (Barcelona, Spain). Cell culture and differentiation. SGBS cells are established as an adequate in vitro model for studying human fat cell biology (21, 36, 38). They were seeded at 3 ⫻ 104 cells/cm2 in preadipocyte medium (DMEM-Ham’s F-12 supplemented with FBS and antibiotics). Confluent SGBS preadipocytes were induced to differentiate to mature adipocytes in adipocyte differentiation medium [adipocyte medium (see below) plus 0.25 mM isobutylmethylxanthine and a PPAR␥ agonist]. To differentiate cells, two series of 3 days of differentiation induction with adipocyte differentiation medium were carried out, followed by replacement of 50% of the medium every 2 days for 10 days with adipocyte medium (preadipocyte medium supplemented with dexamethasone and insulin) until day 18. Mature adipocytes were left 24 h in DMEM-F-12 without serum and then stimulated for 24 h with the corresponding stimulus. Each experiment was performed in triplicates or more with different passage of the cells. Western blot analysis. Antibodies used were a rabbit anti-human lipin-1 antibody (8), a kind gift from Dr. S. Siniossoglou (Cambridge Institute for Medical Research, Cambridge, UK), and a mouse antihuman ␤-actin (Sigma-Aldrich). Western blots were assayed as described previously (20). Results are expressed relative to ␤-actin protein levels. Statistical analysis. Statistical analysis was performed by using the Statistical Package for the Social Sciences software version 15 (SPSS, Chicago, IL). For clinical and anthropometrical variables, normal distributed data are expressed as mean value (SD), and for variables with no Gaussian distribution, values are expressed as median (interquartil range). For statistical analysis of expression variables that did not have a Gaussian distribution, values were logarithmically transformed or analyzed by nonparametrical tests. Differences in clinical variables, laboratory parameters, or expression variables between groups were compared by using ANOVA with a post hoc Scheffe correction. Differences between depots were analyzed by nonparametric paired-samples test (Wilcoxon test). Interactions between factors as well as the effects of covariates and covariate interactions with factors were assessed by General Lineal Model Univariate Analysis. Correction for confounding and interacting variables was performed using a stepwise multiple linear regression analysis. Results are expressed as multiple correlation coefficient (r). Hierarchical clustering was performed using Gene Cluster software version 2.11 (Michael B. Eisen, Lawrence Berkeley National Laboratory, Berkeley, CA). The Spearman correlation, based on gene expression levels, was used as a measurement of similarity with no gene preselected to allocate gene expression pattern into groups by using the average linkage method. Results were shown graphically using Tree View version 1.60 (Michael B. Eisen). Statistical differences in doses and time response experiments were analyzed with a general linear model repeated-measures test. Statistical significance occurred if a computed two-tailed probability value was ⬍0.050. RESULTS

Total LPIN1 gene expression in both adipose depots. We analyzed 62 apparently healthy subjects with a range of BMI between normality and obesity (Table 1). Overweight and obese subjects had significantly higher fasting glucose levels, and obese subjects had increased plasma insulin and sIL-6 levels, HOMA-IR index, and blood pressure. We quantified total LPIN1 gene expression in the SAT and VAT of these subjects. Overweight and obese subjects had a significant reduction of LPIN1 levels in both adipose depots AJP-Endocrinol Metab • VOL

(Fig. 1A). SAT and VAT LPIN1 mRNA levels were similar (P ⫽ 0.799) and positively correlated (r ⫽ 0.607, P ⬍ 0.001). We also aimed to assess lipin-1 protein content. Unfortunately, the size of the biopsies was limited, and hence, we performed the analysis only in a small part of the cohort. We showed lower levels in subjects with overweight and obesity compared with lean subjects, with significance only in SAT (n ⫽ 5 for SAT and n ⫽ 3 for VAT/group; Fig. 1B). The fact that there were no significant differences in VAT may be due to the small sample size. Thus, VAT LPIN1 mRNA (and probably protein) levels are comparable with those in SAT and are also decreased in human obesity. We examined LPIN1 adipose tissue gene expression depending on the presence of MetS in our study population. There were 17 subjects that met the criteria for MetS. LPIN1 was found to be significantly downregulated in SAT in subjects with MetS (P ⬍ 0.001) and tended to be lower in VAT (P ⬍ 0.054). Nevertheless, when adjusted by BMI and HOMA-IR, this association with MetS was lost, suggesting that insulin resistance and obesity were the actual effectors. Therefore, VAT LPIN1 transcript levels showed a weaker decrease compared with SAT in subjects with MetS. LPIN1␣ and LPIN1␤ gene expression in both adipose depots. Real-time relative quantification of the expression of the previously described isoforms LPIN1␣ and LPIN1␤ was made by using SYBR Green, since there were no commercial TaqMan gene expression assays. We showed higher LPIN1␣ transcript levels in VAT compared with SAT in overweight and obese subjects (P ⫽ 0.023 and P ⫽ 0.041; Fig. 1C), whereas there were no significant differences between depots for LPIN1␤ mRNA levels. There was no difference among lean, overweight, and obese subjects in either SAT or VAT LPIN1␣ and in SAT LPIN1␤ transcript levels. On the contrary, obese subjects showed lower levels of LPIN1␤ in VAT compared with lean subjects (Fig. 1D). Regarding LPIN1 ␣- and ␤-isoforms, they do not fully mimic the decrease observed for LPIN1 in obesity. When we assessed correlations with LPIN1␣ and LPIN1␤, we showed that total LPIN1 positively correlated with both isoforms. Nevertheless, associations between isoforms were deeper than between total LPIN1 and the isoforms (Table 2). Correlation between total LPIN1 and analytical variables in both adipose depots. We performed a correlation analysis of SAT and VAT LPIN1 levels with the main anthropometrical and metabolic variables described in Table 2. We found a negative correlation between SAT LPIN1 and BMI, waist circumference, fasting triglycerides, glucose, serum insulin levels, HOMA-IR, and systolic blood pressure, whereas VAT LPIN1 only correlated negatively with BMI, waist, and insulin. Regression analysis controlling for age and sex showed that SAT LPIN1 expression, when considered as a dependent variable, was negatively determined by HOMA-IR and plasma triglycerides (r ⫽ 0.703, P ⬍ 0.001; excluded variables: BMI, systolic blood pressure) and that when considering VAT LPIN1 expression as a dependent variable in the model, it was inversely determined by BMI (r ⫽ 0.610, P ⬍ 0.001; excluded variable: insulin). Correlations between anthropometrical and metabolic variables and LPIN1 isoforms did not fully confirm that of total LPIN1. Negative associations were found between BMI and waist for SAT LPIN1␣ (despite no differences being shown for

299 • AUGUST 2010 •

www.ajpendo.org


E311

Fig. 1. Lipin-1 gene (LPIN1) levels in human adipose tissue. LPIN1 mRNA levels in obesity relative to controls in both adipose depots. Data are expressed as median and interquartile range (n ⫽ 19 lean, 28 overweight, and 15 obese). A: total LPIN1. C: LPIN1␣. D: LPIN1␤. Lipin-1 protein levels in obesity relative to controls in both adipose depots. Data are expressed as means ⫾ SD (n ⫽ 3–5). B: a representative Western blot in subcutaneous adipose tissue (SAT) is shown. Differences vs. lean: *P ⬍ 0.05, **P ⬍ 0.001. Differences vs. SAT: ¶P ⬍ 0.05. VAT, visceral adipose tissue.

LPIN1␣ among obesity groups) and VAT LPIN1␤, and we showed negative associations with serum insulin levels and HOMA-IR for LPIN1␣ and VAT LPIN1␤. We showed no significant associations between SAT LPIN1␤ and anthropometrical and metabolic variables (Table 2). Thus, VAT LPIN1 levels are negatively related to BMI, whereas SAT LPIN1 levels, despite being similar to those in VAT, are negatively related to insulin resistance. Correlation between LPIN1 and lipid metabolism gene expression levels in both adipose depots. Because SAT LPIN1 has been found to be positively correlated with PPAR␣ and PPAR␥ and with other genes with key roles in lipid metabolism, we analyzed expression of genes related to lipid metabolism in both depots. In both adipose depots we observed a positive correlation with acetyl-CoA synthetase 2 (ACSS2), which catalyzes the activation of acetate for use in lipid synthesis and energy generation; with diacylglycerol O-acyltransferase 1 (DGAT1), which synthesizes triglycerides; and with acetyl-CoA carboxylase-␣ (ACC1), which catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, the rate-limiting step in fatty acid synthesis. The PPAR␥ target phosphenolpyruvate carboxykinase 1 (PCK1) gene, which catalyzes the formation of phosphoenolpyruvate from oxaloacetate, generAJP-Endocrinol Metab • VOL

ally considered to be the pace-setting step in both gluconeogenesis and glyceroneogenesis, and fatty acid transport genes (CD36, FABP4) also showed a positive correlation with total LPIN1, albeit only in SAT (Table 2). SAT LPIN1 also had positive correlations with phosphodiesterase 3B (PDE3B), an important regulator of cAMP-mediated responses; with perilipin (PLIN), which is involved in the inhibition of lipolysis; and with triglyceride lipases [adipose triglyceride lipase (ATGL), hormone-sensitive lipase (HSL)]. In contrast, glycerol kinase (GK) did not exhibit a significant correlation with LPIN1 expression. When we analyzed associations between lipid metabolism genes and LPIN1 isoforms (␣ and ␤), most significant associations were lost (Table 2). In SAT, both LPIN1␣ and -␤ positively correlated with ACSS2, whereas in VAT, both isoforms correlated with PPAR␣. SAT LPIN1␤ also correlated with DGAT1 and HSL. Additionally, the expression patterns of these genes were analyzed by a hierarchical cluster analysis to study the relationship of LPIN1 to PPAR transcription factors and the other genes (Fig. 2). The Spearman correlation coefficient (SCC) of the expression values was calculated for all pairwise combinations. The algorithm identified the pair of arrays with the

299 • AUGUST 2010 •

www.ajpendo.org

E312


Table 2. LPIN1 gene expression correlations SAT

VAT LPIN1 SAT LPIN1a VAT LPIN1a SAT LPIN1␤ VAT LPIN1␤ BMI, kg/m2 Waist, cm Triglycerides, mM Glucose, mM Insulin, IU/ml HOMA-IR SBP, mmHg DBP, mmHg ACSS2 DGAT1 ACC1 GK PCK1 CD36 FABP4 PDE3B PLIN ATGL HSL PPARa PPAR␥

LPIN1

LPIN1a

0.615 (0.001) 0.519 (⬍0.001) 0.471 (⬍0.001) 0.320 (0.011) 0.361 (0.004) ⫺0.484 (⬍0.001) ⫺0.525 (⬍0.001) ⫺0.378 (0.003) ⫺0.305 (0.025) ⫺0.441 (0.002) ⫺0.431 (0.003) ⫺0.415 (0.005) ⫺0.200 (0.194) 0.611 (⬍0.001) 0.581 (⬍0.001) 0.567 (⬍0.001) ⫺0.030 (0.817) 0.325 (0.010) 0.283 (0.026) 0.595 (⬍0.001) 0.528 (⬍0.001) 0.423 (⬍0.001) 0.583 (⬍0.001) 0.465 (⬍0.001) 0.727 (⬍0.001) 0.568 (⬍0.001)

0.482 (⬍0.001) 0.813 (⬍0.001) 0.519 (⬍0.001) ⫺0.366 (0.003) ⫺0.345 (0.008) ⫺0.189 (0.151) ⫺0.223 (0.105) ⫺0.425 (0.003) ⫺0,416 (0.005) ⫺0.180 (0.241) ⫺0.305 (0.044) 0.429 (0.001) 0.240 (0.061) 0.224 (0.080) ⫺0.075 (0.562) 0.060 (0.645) ⫺0.019 (0.884) 0.168 (0.193) 0.075 (0.561) 0.178 (0.165) 0.171 (0.184) 0.243 (0.057) 0.153 (0.234) 0.023 (0.860)

VAT LPIN1␤

LPIN1

LPIN1a

LPIN1␤

0.482 (⬍0.001) ⫺0.249 (0.051) ⫺0.189 (0.154) ⫺0.190 (0.149) ⫺0.140 (0.314) ⫺0.222 (0.134) ⫺0.174 (0.258) ⫺0.049 (0.753) ⫺0.249 (0.103) 0.391 (0.002) 0.253 (0.047) 0.235 (0.066) 0.067 (0.604) 0.150 (0.245) 0.097 (0.454) 0.178 (0.167) 0.113 (0.380) 0.166 (0.197) 0.249 (0.051) 0.327 (0.010) 0.088 (0.498) 0.033 (0.799)

0.389 (0.002) 0.487 (⬍0.001) 0.289 (0.024) 0.546 (⬍0.001) ⫺0.328 (0.010) ⫺0.399 (0.002) ⫺0.246 (0.063) ⫺0.064 (0.649) ⫺0.319 (0.031) ⫺0.283 (0.066) ⫺0.197 (0.200) ⫺0.201 (0.190) 0.558 (⬍0.001) 0.281 (0.027) 0.320 (0.012) 0.014 (0.918) 0.159 (0.220) 0.059 (0.652) 0.217 (0.094) 0.143 (0.272) 0.046 (0.723) 0.193 (0.136) 0.157 (0.226) 0.807 (⬍0.001) 0.149 (0.250)

0.372 (0.003) 0.722 (⬍0.001) ⫺0.199 (0.051) ⫺0.214 (0.107) ⫺0.005 (0.968) ⫺0.135 (0.330) ⫺0.396 (0.006) ⫺0.405 (0.006) ⫺0.108 (0.487) ⫺0.091 (0.559) 0.083 (0.524) ⫺0.005 (0.970) 0.068 (0.601) 0.066 (0.616) 0.079 (0.547) ⫺0.108 (0.406) 0.011 (0.931) ⫺0.022 (0.864) ⫺0.127 (0.329) ⫺0.055 (0.672) 0.198 (0.123) 0.376 (0.003) ⫺0.046 (0.717)

⫺0.370 (0.003) ⫺0.306 (0.021) 0.045 (0.737) ⫺0.084 (0.548) ⫺0.336 (0.021) ⫺0.322 (0.033) ⫺0.095 (0.543) ⫺0.232 (0.135) 0.128 (0.330) ⫺0.009 (0.945) 0.050 (0.706) ⫺0.025 (0.851) 0.084 (0.526) ⫺0.115 (0.380) 0.027 (0.835) ⫺0.041 (0.756) ⫺0.159 (0.226) ⫺0.042 (0.748) 0.049 (0.709) 0.313 (0.015) ⫺0.091 (0.489)

SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; LPIN1, lipin-1 gene; ACSS2, acetyl-CoA synthetase; DGAT1, diacylglycerol Oacyltransferase; ACC1, acetyl-CoA carboxylase-␣; GK, glycerol kinase; PCK1, phosphoenolpyruvate carboxykinase; CD36, CD36 molecule; FABP4, fatty acid-binding protein 4; PDE3B, phosphodiesterase 3B; PLIN, perilipin; ATGL, adipose triglyceride lipase; HSL, hormone-sensitive lipase; PPARa and -␥, perosixome proliferator-activated receptor-␣ and -␥, respectively. Correlation between LPIN1 expression and its isoforms, between the population characteristics, and with adipose tissue gene expression within each adipose depot. Data are expressed as r, correlation coefficient (P value); n ⫽ 62.

highest coefficient and grouped them with a link. The algorithm proceeded in a recurring manner to build the tree structure step by step. In SAT and VAT, LPIN1 expression levels were first clustered with PPAR␣ expression levels (SCC ⫽ 0.730 in SAT and 0.778 in VAT). SAT LPIN1 then clustered with ACSS2, FABP4, and PDE3B (SCC ⫽ 0.641). Clusters

continued grouping the rest of the studied genes with SCC close to 0.5, except for GK. The correlation between VAT LPIN1-PPAR␣ and the other studied genes had a SCC ⬍0.5 (SCC ⫽ 0.172). Therefore, SAT LPIN1 transcript levels show a positive correlation with both lipogenic and lipolytic genes, as reported

Fig. 2. Correlation between genes of lipid metabolism in human adipose tissue. mRNA levels of total LPIN1 and of genes related to lipid metabolism were hierarchically clustered in SAT and VAT; Spearman correlation coefficient. GK, glycerol kinase; ACC1, acetyl-CoA carboxylase-␣; PCK1, phosphoenolpyruvate carboxykinase; PDE3B, phosphodiesterase 3B; FABP4, fatty acid-binding protein 4; PPAR␣ and -␥, peroxisome proliferatoractivated receptor-␣ and -␥, respectively; ACSS2, acetyl-CoA synthetase 2; PLIN, perilipin; DGAT1, diacylglycerol O-acyltransferase 1; CD36, CD36 molecule; HSL, hormone-sensitive lipase; ATGL, adipose triglyceride lipase.

AJP-Endocrinol Metab • VOL

299 • AUGUST 2010 •

www.ajpendo.org


E313

previously. Despite having LPIN1 mRNA levels similar to SAT, VAT LPIN1 transcript levels only show a strong positive correlation with PPAR␣. This is also reproduced when LPIN1 ␣- and ␤-isoforms are analyzed. Differential lipogenic and lipolytic mRNA expression levels in adipose tissue depots in obesity. Next, we compared expression levels of these genes in both adipose depots. ACSS2, DGAT1, ACC1, CD36, FABP4, PDE3B, ATGL, PLIN, PPAR␥ (P ⬍ 0.001), PCK1, and HSL (P ⬍ 0.01) were significantly more expressed in SAT than in VAT. On the contrary, GK and PPAR␣ mRNA levels were similar in both adipose depots. Interestingly, when we stratified the samples according to BMI groups, we observed that the differences between SAT and VAT expression pattern happened at the expense of lean and overweight subjects, whereas obese subjects had similar levels in both depots. Lean and obese subjects are shown in Fig. 3 (overweight subjects are not shown, although they also had higher mRNA expression in SAT). Conversely, total LPIN1 levels in both depots were not significantly different in any of the studied groups. Thus, LPIN1 transcript levels show similar levels between SAT and VAT regardless of the BMI group, which seems not to be a common profile in other genes involved in lipid metabolism. Effect of insulin and isoproterenol on LPIN1 expression in SGBS adipocytes. The positive correlations between LPIN1 and lipolytic genes may seem counterintuitive. Therefore, we aimed to analyze the effect of lipogenic (insulin) and lipolytic (isoproterenol) stimuli on LPIN1 expression to further understand its behavior. Insulin significantly increased LPIN1 mRNA levels in in vitro-differentiated adipocytes in a doseand time-dependent manner (Fig. 4, A and B). At the highest

Fig. 4. Effect of a lipogenic stimulus on LPIN1 expression in human adipocytes. A: dose-response effects of insulin on LPIN1 mRNA and protein expression; after 24 h of resting in a serum-free medium, Simpson-GolabiBehmel syndrome (SGBS) adipocytes (day 18 of differentiation) were treated with the described doses for 24 h. B: time course change of LPIN1 mRNA and protein expression by 1 nM insulin; 24-h serum-starved adipocytes were treated with 1 nM insulin for the indicated times. Data are normalized relative to reference levels [cyclophilin A (PPIA) for mRNA and ␤-actin for protein] and expressed as means ⫾ SD (n ⫽ 3). Representative Western blots for dose and time course are shown. *P ⬍ 0.05 vs. control.

Fig. 3. Relation between SAT and VAT gene expression in lean subjects and subjects with obesity. Results from VAT subtraction to SAT expression levels (medians) for each gene are positive for lean subjects, whereas obese subjects lost this pattern. Significance highlights differences between both depots (Wilcoxon test). *P ⬍ 0.001; #P ⬍ 0.01; ¶P ⬍ 0.05. AJP-Endocrinol Metab • VOL

dose assayed (100 nM), it seems that insulin does not further increase LPIN1 levels. Lipin-1 protein levels were also increased by insulin, mimicking the mRNA curve. In the time course study, a significant effect was shown after 16 h and after 48 h of incubation with 1 nM insulin for mRNA and protein, respectively (Fig. 4, A and B).

299 • AUGUST 2010 •

www.ajpendo.org

E314


On the other hand, treatment with isoproterenol, a ␤-adrenergic agonist, showed no effect on the relative mRNA concentrations of LPIN1 compared with nontreated adipocytes (Fig. 5A). In the time course experiments, only a slight but significant decrease in mRNA levels at 4 – 8 h was observed (Fig. 5B). Lipin-1 protein levels significantly decreased with the highest dose (100 nM; Fig. 5A), and besides a significant increase at 4 h, Lipin-1 protein levels decreased from 24 h onward by 10 nM isoproterenol (Fig. 5B). Effect of TNF␣ on LPIN1 expression. Obesity is characterized by a chronic mild inflammatory milieu (14, 39). Therefore, we examined LPIN1 response to an inflammatory stimulus. TNF␣ treatment decreased LPIN1 mRNA levels in a dose-dependent manner (Fig. 6A). Treatment with 1 ng/ml TNF␣ decreased LPIN1 levels from 4 h onward, with a partial recovery to baseline levels at 48 h (Fig. 6B). Lipin-1 protein levels showed a slight nonsignificant increase (Fig. 6, A and B). In summary, insulin increases LPIN1 levels, whereas TNF␣ decreases LPIN1 mRNA levels in a human adipocyte cell line. Thus, both insulin resistance and the chronic inflammatory milieu present in obesity could explain the decrease in LPIN1 transcript levels. DISCUSSION

Lipin-1 is a protein with important effects on lipidic and metabolic homeostasis (reviewed in Ref. 26). Recent studies in humans have established an association between LPIN1 expression levels in adipose tissue and a better metabolic profile, including insulin sensitivity markers. Usually, these studies have been conducted in subcutaneous fat depot, with very limited data on visceral fat (2). In this study, we have reproduced these analyses in VAT from a cohort of apparently healthy subjects with a range of BMI between normality and obesity. VAT, as well as SAT, LPIN1 mRNA expression is downregulated in subjects with obesity, which seems paralleled by protein levels. Despite VAT LPIN1 transcript levels being similar to those in SAT, it shows a different profile, as evidenced by weaker correlations with clinical parameters and with expression of VAT lipid metabolism genes compared with SAT. Moreover, we have analyzed LPIN1␣ and LPIN1␤ isoform expression in both depots. Transcript levels of total SAT LPIN1, LPIN1␣, and LPIN1␤ have been shown to correlate negatively with BMI (3, 4, 42) and positively with PPAR␣ (4). Despite the fact that we showed similar results for total LPIN1, our results do not fully corroborate these data regarding LPIN1 isoforms. On the contrary, Donkor et al. (4) confirmed the negative correlations with BMI and positive correlations with PPAR␣ and also lipid metabolism genes for LPIN1␣ and LPIN1␤ isoforms, at least in SAT. This is not reproduced in our cohort, where LPIN1␣ and LPIN1␤ show correlations depending on the depot. Since our study was completed, a recent report has described a new LPIN1␥ isoform that could contribute to explaining this difference (10). Clearly, further studies that confirm the LPIN1 isoform expression in human tissues are needed. Total LPIN1 mRNA levels in SAT and VAT were found positively correlated. Moreover, LPIN1 expression levels were similar in both adipose depots, consistent with previous results (2, 35). Interestingly, this is not a common profile in the other AJP-Endocrinol Metab • VOL

Fig. 5. Effect of a lipolytic stimulus on LPIN1 expression in human adipocytes. A: dose-response effects of isoproterenol on LPIN1 mRNA and protein expression; after 24 h of resting in serum-free medium, SGBS adipocytes (day 18 of differentiation) were treated with the described doses for 24 h. B: time course change of LPIN1 mRNA and protein expression by 10 nM isoproterenol; 24-h serum-starved adipocytes were treated with 10 nM isoproterenol for the indicated times. Data are normalized relative to reference levels (PPIA for mRNA and ␤-actin for protein) and expressed as means ⫾ SD (n ⫽ 3). Representative Western blots for dose and time course are shown. *P ⬍ 0.05 vs. control.

299 • AUGUST 2010 •

www.ajpendo.org


Fig. 6. Effect of an inflammatory stimulus TNF␣ on LPIN1 expression in human adipocytes. A: dose-response effects of TNF␣ on LPIN1 mRNA and protein expression; after 24 h of resting in serum-free medium, SGBS adipocytes (day 18 of differentiation) were treated with the described doses for 24 h. B: time course change of LPIN1 mRNA and protein expression by 1 nM TNF␣; 24-h serum-starved adipocytes were treated with 1 nM TNF␣ for the indicated times. Data are normalized relative to reference levels (PPIA for mRNA and ␤-actin for protein) and expressed as means ⫾ SD (n ⫽ 3). Representative Western blots for dose and time course are shown. *P ⬍ 0.05 vs. control.

studied genes involved in lipid metabolism. Thus, except for GK and PPAR␣, all other lipogenic or lipolytic mRNA levels were significantly higher in SAT than in VAT. This has been explained by an increased fat cell hyperplasia in SAT compared with VAT, with increased body weight, at least in women (5). In this sense, our results add new information regarding this differential expression pattern because these differences were observed in lean and overweight subjects but not in subjects with obesity. This observation is in line with the AJP-Endocrinol Metab • VOL

E315

hypothesis that proposes a decrease in the committed preadipocyte availability when adiposity is increased (32). Moreover, obese subjects, who gradually gain significant weight over years, may end up with VAT hyperplasia levels similar to SAT levels. Although subcutaneous fat represents about 80% of all fat, when the storage capacity of the subcutaneous depot becomes limiting, expansion of the secondary fat depots, including visceral fat, takes place (5). Thus, upon a certain degree of obesity, the gene expression pattern between subcutaneous and visceral depots would be equilibrated. Of note, LPIN1 did not accomplish this compartmental pattern, expressing similar mRNA levels in both SAT and VAT depots regardless of BMI. Thus, these data sustain the concept that SAT is essentially a benign depot that would prevent ectopic lipid accumulation, which is also supported by the fact that the transgenic mice that overexpress LPIN1 in adipose tissue are insulin sensitive despite developing obesity (24). Several studies have linked adipose tissue LPIN1 levels and insulin sensitivity in humans (4, 19, 30, 35, 42). A recent study in a Chinese cohort showed no correlation between LPIN1 mRNA levels and insulin sensitivity in SAT and VAT, although it might be due to the presence of severely obese subjects (2). In fact, we found no significant correlations in a Caucasian, severely obese cohort (data not shown). Recently, in healthy nonobese young men, LPIN1 subcutaneous expression showed a positive correlation with genes with key roles in fatty acid oxidation, including the nuclear receptor PPAR␣ (4). This finding points to a function of lipin-1 as a regulator/coactivator of the PPAR␣ activity in adipose tissue, leading to an increased fatty acid oxidation and improved metabolic homeostasis. Supporting this observation, the hierarchical clustering of our study showed that LPIN1 had the stronger association with PPAR␣ in both adipose compartments. In fact, it was the only common association for both depots. Nevertheless, we would like to mention that gene cluster analysis was dependent on the BMI group (data not shown), and thus, further studies with a greater sample size should be performed to understand this association within each group. With regard to gene expression levels of LPIN1 and other lipogenic or lipolytic markers in SAT, we found a positive correlation with genes involved in lipid oxidation, lipid synthesis, and lipolysis, as described previously (4). On the contrary, VAT LPIN1 mRNA levels correlated with ACC1, ACSS2, DGAT1, and PPAR␣. The fact that the associations in SAT were much steeper than that in VAT is curious. Several differences between these depots have been shown in the literature. For example, although SAT expresses higher levels of lipogenic and lipolytic genes (5), VAT-committed preadipocytes have lower capacities for replication, lipid accumulation, and lipogenic expression (31), whereas macrophage infiltration is more significant in VAT (11). Regarding the multiple strong positive correlations between LPIN1 transcript levels and lipolytic and lipogenic genes, it remains to be determined whether lipin-1 can directly modulate expression of these genes via its transcriptional coactivator function. Indeed, most of the studied genes are transcriptionally regulated by PPAR␥ and -␣, and the sole exception was shown by the absence of a correlation with GK, which, on the other hand, is weakly expressed in adipose tissue. Lipin-1 has been described to be an amplifier of the PGC-1␣/PPAR␣-

299 • AUGUST 2010 •

www.ajpendo.org

E316


mediated control of hepatic lipid metabolism (7) and of the C/EBP␣-PPAR␥ network, resulting in the maintenance of high levels of the specific gene expression that are required for adipogenesis and mature adipocyte functions (14a). An intriguing question is that of the positive correlations also shown with lipolytic genes, which are in agreement with previous results in SAT from healthy subjects (4). Moreover, lipin-1 transcriptional regulation could be differentially regulated according to the adipose depot or the degree of obesity (since BMI influenced the gene-clustering analysis), offering a very complex picture when mRNA gene expression is analyzed. To further understand LPIN1 regulation in obesity, we have analyzed the effect of several stimuli in in vitro-differentiated adipocytes. We showed that LPIN1 mRNA is upregulated by insulin in a dose- and time-dependent manner. This is in agreement with previous reports in which the insulin-sensitizing thiazolidinediones or harmine induced LPIN1 expression in adipose tissue in rodents and humans (6, 37, 42). However, we showed that the effects were much slighter for protein than mRNA. Moreover, insulin decreases the membrane-bound phosphatase, which probably has effects on LPIN1-mediated triglyceride synthesis, highlighting other ways of regulation of this factor (12). Thus, these experimental data warrant further mechanistic studies to understand LPIN1 regulation in all its aspects. Despite the positive association observed with many lipolytic genes, lipolytic stimuli with isoproterenol (confirmed also with 8-(4-chlorophenylthio)-cAMP and forskolin stimuli; Miranda M and Ceperuelo-Mallafre V, unpublished observations) had weak effects on LPIN1 transcript regulation. This is in contrast with the described stimulatory effect of dexamethasone [a synthetic glucocorticoid that induces lipolysis (41)] on LPIN1 gene and protein expression levels in human in vitrodifferentiated adipocytes (43) and other ␤-adrenergic agonists that increase LPIN1 transcript levels in muscle from rodents (13, 22). However, response to dexamethasone in adipocytes seems to be conducted by a specific glucocorticoid receptor that binds to a DNA sequence in the LPIN1 promoter (43). The convergence of the positive correlations shown between LPIN1 and lipolytic transcript levels and the in vitro effect, or even downregulation, by a lipolytic stimulus remains to be explained. LPIN1 mRNA levels are also repressed by inflammatory cytokines such as TNF␣ and IL-1␤ in mouse adipocytes (16, 34). This downregulation is accompanied by altered expression of PPAR␣ and PPAR␥ target genes and may result in a decrease in triglyceride synthesis in adipose tissue and, consequently, in an increase in serum free fatty acids. Moreover, LPIN1 repression by TNF␣ is performed via Janus tyrosine kinase 2 (34), a receptor-associated kinase that has been related to depression of the Akt to glucose uptake signaling axis selectively in insulin-resistant states (33). We would like to emphasize that, in human adipocytes, although TNF␣ clearly downregulated LPIN1 mRNA levels, protein levels were not significantly affected, at least during 24 h of treatment. Again, this deserves more studies to understand how an inflammatory milieu may affect lipin-1 activity. In any case, insulin resistance and the inflammatory milieu that characterize obesity could explain the decreased levels of LPIN1 mRNA in adipose tissue that may lead to dyslipidemia. Taken together, our data AJP-Endocrinol Metab • VOL

suggest further studies for mechanistic explorations to fully understand lipin-1 regulation. There are still some limitations that must be acknowledged. Since mRNA quantification may not reflect changes at protein level, we aimed to address protein levels. Unfortunately, the sample size limitation in protein analysis should be recognized. Besides, we want to draw attention to the fact that further studies are clearly required to examine the functional significance of these associations and to extend these results to protein activity or signaling pathways. In summary, LPIN1, a key enzyme involved in triglyceride synthesis, was found to be repressed in obesity in VAT depots, as described previously for SAT. Close associations with clinical parameters and with genes associated with lipid metabolism were much steeper in SAT than in VAT. Thus, despite similar levels of LPIN1 expression in both abdominal depots, VAT LPIN1 seems to have a different profile compared with SAT LPIN1. On the other hand, lipogenic and inflammatory stimuli clearly regulated LPIN1 in a human adipose cell line. Thus, the low-grade proinflammatory environment and the insulin resistance associated with obesity may contribute to downregulate LPIN1 in adipose tissue, leading to a worse metabolic profile. ACKNOWLEDGMENTS We gratefully acknowledge the invaluable collaboration of Symeon Siniossoglou (Cambridge Institute for Medical Research, Cambridge, UK), the Servei de Recursos Científics i Tècnics of Universitat Rovira i Virgili, and the technical assistance of Verònica Alba. Centro de Investigacion Biomedica en Red de Diabetes y Enfermedades Metabólicas Asociadas is an Instituto de Salud Carlos III project. GRANTS This work was supported by FIS 07/1024 and FIS 08/1195 from the Spanish Instituto de Salud Carlos III, Ministerio de Sanidad y Consumo, with the participation of the European Regional Development Fund. X. Escoté is supported by a fellowship from the JdlC programme and Grant No. JDCI20071020. DISCLOSURES No conflicts of interest, financial or otherwise, are declared by the author(s). REFERENCES 1. Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest 96: 88 –98, 1995. 2. Chang YC, Chang LY, Chang TJ, Jiang YD, Lee KC, Kuo SS, Lee WJ, Chuang LM. The associations of LPIN1 gene expression in adipose tissue with metabolic phenotypes in the Chinese population. Obesity (Silver Spring) 18: 7–12, 2010. 3. Croce MA, Eagon JC, LaRiviere LL, Korenblat KM, Klein S, Finck BN. Hepatic lipin 1beta expression is diminished in insulin-resistant obese subjects and is reactivated by marked weight loss. Diabetes 56: 2395– 2399, 2007. 4. Donkor J, Sparks LM, Xie H, Smith SR, Reue K. Adipose tissue lipin-1 expression is correlated with peroxisome proliferator-activated receptor alpha gene expression and insulin sensitivity in healthy young men. J Clin Endocrinol Metab 93: 233–239, 2008. 5. Drolet R, Richard C, Sniderman AD, Mailloux J, Fortier M, Huot C, Rhéaume C, Tchernof A. Hypertrophy and hyperplasia of abdominal adipose tissues in women. Int J Obes 32: 283–291, 2008. 6. Festuccia WT, Laplante M, Brûlé S, Houde VP, Achouba A, Lachance D, Pedrosa ML, Silva ME, Guerra-Sá R, Couet J, Arsenault M, Marette A, Deshaies Y. Depot-specific effects of the PPARg agonist rosiglitazone on adipose tissue uptake and metabolism. J Lipid Res 50: 1185–1194, 2009.

299 • AUGUST 2010 •

www.ajpendo.org

HUMAN VISCERAL ADIPOSE LPIN1 EXPRESSION 7. Finck BN, Gropler MC, Chen Z, Leone TC, Croce MA, Harris TE, Lawrence JC Jr, Kelly DP. Lipin 1 is an inducible amplifier of the hepatic PGC-1alpha/PPARalpha regulatory pathway. Cell Metab 4: 199 – 210, 2006. 8. Grimsey N, Han GS, O’Hara L, Rochford JJ, Carman GM, Siniossoglou S. Temporal and spatial regulation of the phosphatidate phosphatases lipin 1 and 2. J Biol Chem 283: 29166 –29174, 2008. 9. Han GS, Wu WI, Carman GM. The Saccharomyces cerevisiae Lipin homolog is a Mg2⫹-dependent phosphatidate phosphatase enzyme. J Biol Chem 281: 9210 –9218, 2006. 10. Han GS, Carman GM. Characterization of the human LPIN1-encoded phosphatidate phosphatase isoforms. J Biol Chem 285: 14628 –14638, 2010. 11. Harman-Boehm I, Blüher M, Redel H, Sion-Vardy N, Ovadia S, Avinoach E, Shai I, Klöting N, Stumvoll M, Bashan N, Rudich A. Macrophage infiltration into omental versus subcutaneous fat across different populations: effects of regional adiposity and the co-morbidities of obesity. J Clin Endocrinol Metab 92: 2240 –2247, 2007. 12. Harris TE, Huffman TA, Chi A, Shabanowitz J, Hunt DF, Kumar A, Lawrence JC. Insulin controls subcellular localization and multisite phosphorylation of the phosphatidic acid phosphatase, lipin 1. J Biol Chem 282: 277–286, 2007. 13. Higashida K, Higuchi M, Terada S. Potential role of lipin-1 in exerciseinduced mitochondrial biogenesis. Biochem Biophys Res Commun 374: 587–591, 2008. 14. Hotamisligil GS. Inflammation and metabolic disorders. Nature 444: 860 –867, 2006. 14a.Koh YK, Lee MY, Kim JW, Kim M, Moon JS, Lee YJ, Ahn YH, Kim KS. Lipin1 is a key factor for the maturation and maintenance of adipocytes in the regulatory network with CCAAT/enhancer-binding protein alpha and peroxisome proliferator-activated receptor gamma 2. J Biol Chem 283: 34896 –34906, 2008. 15. Lebovitz HE, Banerji MA. Point: visceral adiposity is causally related to insulin resistance. Diabetes Care 28: 2322–2325, 2005. 16. Lu B, Lu Y, Moser AH, Shigenaga JK, Grunfeld C, Feingold KR. LPS and proinflammatory cytokines decrease lipin-1 in mouse adipose tissue and 3T3-L1 adipocytes. Am J Physiol Endocrinol Metab 295: E1502– E1509, 2008. 17. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and betacell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28: 412–419, 1985. 18. Medina-Gomez G, Gray SL, Yetukuri L, Shimomura K, Virtue S, Campbell M, Curtis RK, Jimenez-Linan M, Blount M, Yeo GS, Lopez M, Seppänen-Laakso T, Ashcroft FM, Oresic M, Vidal-Puig A. PPAR gamma 2 prevents lipotoxicity by controlling adipose tissue expandability and peripheral lipid metabolism. PLoS Genet 3: e64, 2007. 19. Miranda M, Chacón MR, Gómez J, Megía A, Ceperuelo-Mallafré V, Veloso S, Saumoy M, Gallart L, Richart C, Fernández-Real JM, Vendrell J; Adipocyte Differentiation Study Group. Human subcutaneous adipose tissue LPIN1 expression in obesity, type 2 diabetes mellitus, and human immunodeficiency virus-associated lipodystrophy syndrome. Metabolism 56: 1518 –1526, 2007. 20. Miranda M, Escoté X, Ceperuelo-Mallafré V, Megía A, Caubet E, Näf S, Gómez JM, González-Clemente JM, Vicente V, Vendrell J. Relation between human LPIN1, hypoxia and endoplasmic reticulum stress genes in subcutaneous and visceral adipose tissue. Int J Obes (Lond) 34: 679 –686, 2010. 21. Newell FS, Su H, Tornqvist H, Whitehead JP, Prins JB, Hutley LJ. Characterization of the transcriptional and functional effects of fibroblast growth factor-1 on human preadipocyte differentiation. FASEB J 20: 2615–2617, 2006. 22. Pearen MA, Myers SA, Raichur S, Ryall JG, Lynch GS, Muscat GE. The orphan nuclear receptor, NOR-1, a target of beta-adrenergic signaling, regulates gene expression that controls oxidative metabolism in skeletal muscle. Endocrinology 149: 2853–2865, 2008. 23. Péterfy M, Phan J, Reue K. Alternatively spliced lipin isoforms exhibit distinct expression pattern, subcellular localization, and role in adipogenesis. J Biol Chem 280: 32883–32889, 2005.

AJP-Endocrinol Metab • VOL

E317

24. Phan J, Péterfy M, Reue K. Lipin expression preceding peroxisome proliferator-activated receptor-gamma is critical for adipogenesis in vivo and in vitro. J Biol Chem 279: 29558 –29564, 2004. 25. Phan J, Reue K. Lipin, a lipodystrophy and obesity gene. Cell Metab 1: 73–83, 2005. 26. Reue K, Dwyer JR. Lipin proteins and metabolic homeostasis. J Lipid Res 50: S109 –S114, 2009. 27. Reue K, Zhang P. The lipin protein family: dual roles in lipid biosynthesis and gene expression. FEBS Lett 582: 90 –96, 2008. 28. Rosen ED, Spiegelman BM. Adipocytes as regulators of energy balance and glucose homeostasis. Nature 444: 847–853, 2006. 29. Sharma AM, Staels B. Review: Peroxisome proliferator-activated receptor gamma and adipose tissue– understanding obesity-related changes in regulation of lipid and glucose metabolism. J Clin Endocrinol Metab 92: 386 –395, 2007. 30. Suviolahti E, Reue K, Cantor RM, Phan J, Gentile M, Naukkarinen J, Soro-Paavonen A, Oksanen L, Kaprio J, Rissanen A, Salomaa V, Kontula K, Taskinen MR, Pajukanta P, Peltonen L. Cross-species analyses implicate Lipin 1 involvement in human glucose metabolism. Hum Mol Genet 15: 377–386, 2006. 31. Tchkonia T, Tchoukalova YD, Giorgadze N, Pirtskhalava T, Karagiannides I, Forse RA, Koo A, Stevenson M, Chinnappan D, Cartwright A, Jensen MD, Kirkland JL. Abundance of two human preadipocyte subtypes with distinct capacities for replication, adipogenesis, and apoptosis varies among fat depots. Am J Physiol Endocrinol Metab 288: E267–E277, 2005. 32. Tchoukalova Y, Koutsari C, Jensen M. Committed subcutaneous preadipocytes are reduced in human obesity. Diabetologia 50: 151–157, 2005. 33. Thirone AC, JeBailey L, Bilan PJ, Klip A. Opposite effect of JAK2 on insulin-dependent activation of mitogen-activated protein kinases and Akt in muscle cells: possible target to ameliorate insulin resistance. Diabetes 55: 942–951, 2006. 34. Tsuchiya Y, Takahashi N, Yoshizaki T, Tanno S, Ohhira M, Motomura W, Tanno S, Takakusaki K, Kohgo Y, Okumura T. A Jak2 inhibitor, AG490, reverses lipin-1 suppression by TNF-alpha in 3T3-L1 adipocytes. Biochem Biophys Res Commun 382: 348 –352, 2009. 35. van Harmelen V, Rydén M, Sjölin E, Hoffstedt J. A role of lipin in human obesity and insulin resistance: relation to adipocyte glucose transport and GLUT4 expression. J Lipid Res 48: 201–206, 2007. 36. Wabitsch M, Brenner RE, Melzner I, Braun M, Moller P, Heinze E, Debatin KM, Hauner H. Characterization of a human preadipocyte cell strain with high capacity for adipose differentiation. Int J Obes 25: 8 –15, 2001. 37. Waki H, Park KW, Mitro N, Pei L, Damoiseaux R, Wilpitz DC, Reue K, Saez E, Tontonoz P. The small molecule harmine is an antidiabetic cell-type-specific regulator of PPAR␥ expression. Cell Metab 5: 357–370, 2007. 38. Weaver RE, Donnelly D, Wabitsch M, Grant PJ, Balmforth AJ. Functional expression of glucose-dependent insulinotropic polypeptide receptors is coupled to differentiation in a human adipocyte model. Int J Obes 32: 1705–1711, 2008. 39. Wellen KE, Hotamisligil GS. Inflammation, stress, and diabetes. J Clin Invest 115: 1111–1119, 2005. 40. World Health Organization. Obesity: preventing and managing the global epidemic. In: WHO Technical Report Series 894. Geneva: World Health Organization, 2000. 41. Xu C, He J, Jiang H, Zu L, Zhai W, Pu S, Xu G. Direct effect of glucocorticoids on lipolysis in adipocytes. Mol Endocrinol 23: 1161– 1170, 2009. 42. Yao-Borengasser A, Rasouli N, Varma V, Miles LM, Phanavanh B, Starks TN, Phan J, Spencer HJ 3rd, McGehee RE Jr, Reue K, Kern PA. Lipin expression is attenuated in adipose tissue of insulin-resistant human subjects and increases with peroxisome proliferator-activated receptor gamma activation. Diabetes 55: 2811–2818, 2006. 43. Zhang P, O’Loughlin L, Brindley DN, Reue K. Regulation of lipin-1 gene expression by glucocorticoids during adipogenesis. J Lipid Res 49: 1519 –1528, 2008.

299 • AUGUST 2010 •

www.ajpendo.org