Abstract
Gestational diabetes mellitus (GDM) is a significant risk factor for the type 2 diabetes epidemic in many populations. Maternal adipose tissue plays a central role in the pathophysiology of GDM. Thus, the aim of this study was to determine the effect of GDM on the proteome of adipose tissue. Omental adipose tissue was obtained at the time of term Caesarean section from women with normal glucose tolerance (NGT) or GDM. 2D-difference gel electrophoresis (DIGE), followed by mass spectrometry, was used to identify protein spots (n=6 patients per group). Western blotting was used for confirmation of six of the spot differences (n=6 patients per group). We found 14 proteins that were differentially expressed between NGT and GDM adipose tissue (≥1.4-fold, P<0.05). GDM was associated with an up-regulation of four proteins: collagen alpha-2(VI) chain (CO6A2 (COL6A2)), fibrinogen beta chain (FIBB (FGB)), lumican (LUM) and S100A9. On the other hand, a total of ten proteins were found to be down-regulated in adipose tissue from GDM women. These were alpha-1-antitrypsin (AIAT (SERPINA1)), annexin A5 (ANXA5), fatty acid-binding protein, adipocyte (FABP4), glutathione S-transferase P (GSTP (GSTP1)), heat-shock protein beta-1 (HSP27 (HSPB1)), lactate dehydrogenase B chain (LDHB), perilipin-1 (PLIN1), peroxiredoxin-6 (PRX6 (PRDX6)), selenium-binding protein 1 (SBP1) and vinculin (VINC (VCL)). In conclusion, proteomic analysis of omental fat reveals differential expression of several proteins in GDM patients and NGT pregnant women. This study revealed differences in expression of proteins that are involved in inflammation, lipid and glucose metabolism and oxidative stress and added further evidence to support the role of visceral adiposity in the pathogenesis of GDM.
Introduction
The prevalence of type 2 diabetes is increasing rapidly. Alarmingly, it is predicted that ∼350 million people worldwide will be diagnosed with type 2 diabetes by the year 2030, double the current number (Wild et al. 2004). Gestational diabetes mellitus (GDM), defined as any degree of glucose intolerance with onset or first recognition during pregnancy (Kuhl 1998), affects up to 18% of all pregnancies (Pregnancy 2010). Like type 2 diabetes, the prevalence rate of GDM has also increased dramatically in the last decade (Darios et al. 2008, Laws & Sullivan 2009). Importantly, it is considered an important driver for the type 2 diabetes epidemic in many populations. In a recently published retrospective cohort study, the risk of developing diabetes was 9.6 times greater for patients with GDM, with the cumulative risk of developing type 2 diabetes for GDM patients being 25% at 15 years post-diagnosis (Lee et al. 2007). Thus, increasing our understanding of the pathophysiology of GDM is essential in order to provide the best opportunity for early treatment to prevent onset or progression of the disease.
During human pregnancy, a number of metabolic changes occur in maternal adipose tissue that are essential for fetal growth and development. Early pregnancy is associated with adipose tissue accretion. By contrast, the later part of normal human pregnancy is characterised by maternal hyperinsulinaemia and insulin resistance, an adaptation that is required to meet the needs of the growing fetus (Buchanan & Xiang 2005). In women with GDM, however, peripheral insulin resistance is even more pronounced (Colomiere et al. 2010), leading to increases in the circulating concentrations of fatty acids and lipids (Metzger et al. 1980). Importantly, infants of women with GDM often display dyslipidaemia and have increased fat deposition even when they are average weight for gestational age (Catalano et al. 2003). This is clinically significant as these infants are at increased risk of later metabolic disease, including obesity, diabetes, cardiovascular disease and certain cancers (Catalano et al. 2003). The impact that these maternal metabolic disturbances have on community health, health economics and human capital is substantial (Chu et al. 2008). Thus, effective control of the current diabetes and obesity epidemics may well have to begin with the appropriate management of the intrauterine environment.
Human adipose tissue is divided in two depots, the subcutaneous and the omental fat. In addition to differences in the cellular composition, human omental and subcutaneous adipose tissues also display distinct differences in metabolic and biochemical properties. It has been shown that omental adipose tissue is strongly associated with an adverse metabolic risk profile such as insulin resistance and type 2 diabetes (Fox et al. 2007). It is thought that omental adipose tissue may contribute to these diseases because of its direct access to the liver through the portal vein. For example, excess free fatty acid release from the omental adipose tissue interferes with liver metabolism and contributes to the development of hyperinsulinaemia and hypertriglyceridaemia. In addition, subcutaneous and omental adipose tissues are characterised by differences in the expression of genes and proteins related to glucose and lipid metabolism, lipid transport and cellular stress and inflammation (Linder et al. 2004, Vohl et al. 2004, Perez-Perez et al. 2009, Peinado et al. 2012, Montes-Nieto et al. 2013, Perrini et al. 2013). GDM is considered a pre-diabetic state, offering the opportunity to study abnormalities that may appear very early on in type 2 diabetes. Given that omental adipose tissue plays a central role in insulin resistance and type 2 diabetes, we considered it of interest to determine the effect of GDM on the protein expression profiles of omental adipose tissue. In this study, we used 2D-difference gel electrophoresis (2D-DIGE) to identify novel proteins associated with GDM in omental adipose tissues.
Subjects and methods
Tissue collection
Approval for this study was obtained from the Mercy Hospital for Women's Research and Ethics Committee and informed consent was obtained from all participating subjects. Human omental adipose tissue was obtained from 12 pregnant women with normal glucose tolerance (NGT) and from 12 women with insulin-treated GDM. To negate the putative effects of labour-induced changes in the expression of analytes, all samples were collected at the time of term Caesarean section before the onset of labour. Indications for Caesarean section included repeat Caesarean section or breech presentation. Women with any adverse underlying medical condition (i.e. including asthma, pre-existing diabetes and pre-eclampsia) were excluded. Women with GDM were diagnosed according to the criteria of the Australasian Diabetes in Pregnancy Society (ADIPS) by a fasting venous plasma glucose concentration of ≥5.5 mmol/l glucose and/or ≥8.0 mmol/l glucose 2 h after a 75 g oral glucose load at ∼28 weeks of gestation. All women with GDM were prescribed insulin in addition to dietary management. All pregnant women were screened for GDM, and women participating in the normal group had a negative screen. The baseline characteristics for all patients used in this study are outlined in Table 1. Adipose tissue was obtained within 10 min of delivery and thoroughly washed in ice-cold PBS to remove blood. Dissected fragments were stored at −80 °C until assayed as detailed below.
Characteristics of the study group used for the 2D-DIGE analysis. Values represent mean±s.e.m.
| NGT (n=6) | GDM (n=6) | P value | |
|---|---|---|---|
| Maternal age (years) | 31.5±1.2 | 32.0±1.8 | NS |
| Maternal BMI at ∼12 weeks (kg/m2) | 34.8±1.0 | 34.9±1.4 | NS |
| Maternal BMI at delivery (kg/m2) | 38.8±2.0 | 38.0±2.2 | NS |
| Gestational age at birth (weeks) | 38.8±0.2 | 38.0±0.4 | NS |
| Maternal insulin at delivery (IU/ml) | 22.0±2.1 | 25.7±4.2 | NS |
| Fetal birth weight (g) | 3399±63 | 3193±303 | NS |
| Fetal gender | 3 F; 3 M | 3 F; 3 M | |
| Gravida | 3.3±0.6 | 4.2±0.7 | NS |
| Parity | 2.5±0.3 | 2.6±0.2 | NS |
| Maternal OGTT at ∼28 weeks of gestation | |||
| Fasting plasma OGTT (mmol/l) | 4.4±0.1 | 5.5±0.2 | <0.05 |
| 1-h plasma OGTT (mmol/l) | 8.3±0.3 | 10.4±0.9 | <0.05 |
| 2-h plasma OGTT (mmol/l) | 6.3±0.5 | 8.8±0.4 | <0.05 |
| Maternal OGTT at ∼6 weeks postpartum | |||
| Fasting plasma OGTT (mmol/l) | ND | 5.0±0.2 | |
| 1-h plasma OGTT (mmol/l) | ND | 6.8±0.7 | |
| 2-h plasma OGTT (mmol/l) | ND | 5.5±0.7 |
NS, not significant; ND, not done; OGTT, oral glucose tolerance test; F, females; M, males.
Sample preparation and DIGE labelling
For the 2D-DIGE studies, omental adipose tissue was obtained from six NGT women and six women with GDM. Baseline characteristics for these populations are outlined in Table 1. Samples were homogenised into DIGE lysis buffer (30 mM Tris, 7 M urea, 2 M thiourea and 4% CHAPS). Proteins were extracted during 1 h at 4 °C and lysates clarified by centrifugation at 25 000 g at 4 °C for 20 min. The supernatant was precipitated with acetone, and protein extracts were then prepared following general guidelines recommended for subsequent DIGE labelling (Oliva et al. 2012). Briefly, proteins were precipitated using the 2D clean-up kit (GE Healthcare, Uppsala, Sweden) and then buffer exchanged against DIGE lysis buffer using Vivaspin 2, 3 kDa concentrators (Sartorius Stedim, Goettingen, Germany) until conductivity could be reduced to below 200 μS/cm and the pH was adjusted to ∼8.5.
For 2D-gel electrophoresis, 25 μg of each sample was labelled with 200 pmol Cy3 (NGT samples) or Cy5 (GDM samples) as detailed earlier. Additionally, an internal standard consisting of equal amounts of each omental sample was also labelled with Cy2 (Alban et al. 2003). Three labelled protein samples (one Cy2-labelled pooled internal control, one Cy3-labelled NGT sample and one Cy5-labelled GDM sample; NGT and GDM samples were paired randomly) were then combined and used for 2D gel electrophoresis analysis as detailed below. To avoid labelling bias arising from the varying fluorescence properties of gels at different wavelengths, samples from the same group were run twice in different gels but with the opposite dye-labelling pattern.
2D gel electrophoresis and imaging
Immobiline Dry-Strips (GE Healthcare, Piscataway, NJ, USA; 13 cm, pH 4–7 NL) were rehydrated overnight at room temperature in DeStreak solution (GE Healthcare) and 0.05% IPG buffer non-linear (4–7 NL). Isoelectric focusing (IEF) was performed for a total of 25 000 Vh at 20 °C. IPG strips were then equilibrated in equilibration buffer (50 mM Tris–HCl, 6 M urea, 30% glycerol and 2% SDS) supplemented with 1% dithiothreitol to maintain the fully reduced state of proteins followed by alkylation with 2.5% iodoacetamide. Proteins were separated on 10.5–14% Criterion Tris–HCl gels (Bio-Rad Laboratories) at 15 mA/gel for 60 min, 30 mA/gel for 2 h and 45 mA/gel for 1 h at room temperature until the bromophenol blue dye-front had run off the bottom of the gels.
CyDye DIGE Fluor-labelled protein gels were scanned at 100 μm using a Typhoon Trio 9100 (GE Healthcare). The emission filters were Green 532 nm (Cy3) and Red 633 nm (Cy5). Gels were automatically aligned and relative protein quantification across lean and obese samples was performed using the Progenesis SameSpots software 3.3.3420.25059 (Nonlinear Dynamics, Newcastle upon Tyne, UK). Normalisation was performed using the software algorithm. The Cy2-labelled pooled internal standard on every gel allowed accurate relative quantitation of protein spot features across different gels. Student's t-test was used to calculate significant differences in relative abundances of protein spot features in omental adipose tissue obtained from GDM and NGT pregnant women.
The 2D gels were fixed in 50% methanol and 3% phosphoric acid and then stained with Colloidal Coomassie stain and gel spots of interest excised using a gel pen. Gel spots were then transferred into water for short-term storage at 4 °C. The water was subsequently removed and discarded from each gel plug. Gel pieces were destained by four sequential washes in destaining solution (10 mM ammonium bicarbonate (AmBic) and 50% acetonitrile (ACN)), followed by two washes in 100% ACN and then air dried at room temperature. The gel pieces were rehydrated by adding 10 μl of sequencing grade trypsin (0.02 μg/μl (Promega) in 10 mM AmBic) and left to incubate overnight at 37 °C. Digestion was quenched by addition of 2 μl of 2% formic acid (FA).
MALDI-mass spectrometry and protein identification
Digest supernatants (3 μl) were applied to a Bruker Biosciences (Bremen, Germany) Anchorchip MALDI target, prepared with α-cyano-4-hydroxycinnamic acid (CHCA) (see the thin layer affinity method for CHCA in the Bruker Anchorchip manual). After 3 min, this solution was removed and the spots were air dried overnight at room temperature. MALDI-mass spectrometry (MS) was performed on a Bruker Microflex MALDI-TOF mass spectrometer (Bruker Daltonics, Bremen, Germany). Peak lists were generated using Flexanalysis (Bruker) and were calibrated by utilising trypsin autolytic peptides. Biotools software (Bruker) and the Mascot search engine were used to interrogate the SwissProt database (Release: July 2010, 517802 sequences; 182492287 residues) and proteins were identified by peptide mass fingerprinting (PMF). Search parameters were as follows: Taxonomy: Human; MS Tolerance: 100 ppm, Missing Cleavages: ≤1; Enzyme: Trypsin; Fixed Modifications: Carbamidomethylation; Variable Modifications: Oxidation (M). Identifications with Mascot expect probability values <0.05 were then manually verified by examination of spectra and/or resubmission of peak lists to Mascot. A conservative approach to protein identification was implemented and based on a number of criteria other than these scores. These included theoretical and experimental Mr and pI being in accord, experimental peptide mass accuracy variation across the mass range and repeatability of identification across different gels. If multiple members of a protein family were identified, those with the highest ranked hit were selected.
Western blotting
For validation studies, omental adipose tissue was obtained from six NGT and six GDM women other than the six patients whose samples were used in the 2D-DIGE. Baseline characteristics for these populations are outlined in Table 2. For protein detection in western blot analysis, adipose tissue was homogenised in radioimmuno precipitation assay buffer (1% SDS, 0.25% sodium deoxycholate, 1% Nonidet P-40, 150 mM NaCl and 50 mM Tris–HCl, pH 7.4) supplemented with protease inhibitors (1 mM EDTA, 0.5 mM phenylmethylsulfonyl fluoride, 10 μg/ml aprotinin and 5 μg/ml leupeptin). Cellular debris and lipids were eliminated by centrifugation of the solubilised samples at 25 000 g for 30 min at 4 °C. Protein concentration was determined by the BCA Protein Assay (Pierce, Rockford, IL, USA).
Characteristics of the study group used for the western blot analysis. Values represent mean±s.e.m.
| NGT (n=6) | GDM (n=6) | P value | |
|---|---|---|---|
| Maternal age (years) | 35.5±1.6 | 36.0±2.4 | NS |
| Maternal BMI at ∼12 weeks (kg/m2) | 35.3±1.8 | 34.0±1.5 | NS |
| Maternal BMI at delivery (kg/m2) | 40.6±3.0 | 37.0±1.6 | NS |
| Maternal insulin at delivery (IU/ml) | 24.7±2.2 | 23.2±3.3 | NS |
| Gestational age at birth (weeks) | 38.5±0.2 | 38.6±0.2 | NS |
| Fetal birth weight (g) | 3602±265 | 3498±218 | NS |
| Fetal gender | 4 F; 2 M | 4 F; 3 M | |
| Gravida | 3.8±0.8 | 3.2±0.3 | NS |
| Parity | 3.0±0.7 | 2.2±0.2 | NS |
| Maternal OGTT at ∼28 weeks gestation | |||
| Fasting plasma OGTT (mmol/l) | 4.7±0.2 | 5.3±0.3 | <0.05 |
| 1-h plasma OGTT (mmol/l) | 7.8±0.3 | 10.3±0.7 | <0.05 |
| 2-h plasma OGTT (mmol/l) | 6.2±0.4 | 8.8±0.7 | <0.05 |
| Maternal OGTT at ∼6 weeks postpartum | |||
| Fasting plasma OGTT (mmol/l) | ND | 5.0±0.2 | |
| 1-h plasma OGTT (mmol/l) | ND | 6.5±0.6 | |
| 2-h plasma OGTT (mmol/l) | ND | 5.5±0.6 |
NS, not significant; ND, not done; OGTT, oral glucose tolerance test; F, females; M, males.
Assessment of protein expression was analysed by western blotting as described previously (Colomiere et al. 2009). Whole cell lysate blots were incubated with 1 μg/ml rabbit polyclonal anti-lumican (LUM; SAB1401232; Sigma), 1 μg/ml rabbit polyclonal anti-PRX6 (P0058; Sigma), 1 μg/ml rabbit polyclonal anti-S100A9 (HPA004193; Sigma), 1 μg/ml rabbit polyclonal anti-HSP27 (SAB4501457; Sigma), 1 μg/ml mouse monoclonal anti-ANXA5 (WH0000308M1; Sigma), 0.1 μg/ml rabbit polyclonal anti-fibrinogen β-chain (HPA001900; Sigma) and mouse monoclonal anti-β-actin (A5316; Sigma) diluted in blocking buffer (5% skim milk/TBS-T (0.05%)) for 24 h at 4 °C. Membranes were viewed and analysed using the Chemi-Doc system (Bio-Rad Laboratories). Quantitative analysis of the relative density of the bands in western blots was performed using Quantity One 4.2.1 image analysis software (Bio-Rad Laboratories). Data were corrected for background and expressed as optical density (OD/mm2).
Statistical analysis
Statistical analyses were performed using a commercially available statistical software package (Statgraphics Plus version 3.1, Statistical Graphics Corp., Rockville, MD, USA). The student's t-test was used to assess statistical significance between normally distributed data; otherwise, the nonparametric Mann–Whitney U (Wilcoxon) test was used. Statistical difference was indicated by a P value of <0.05. Data are expressed as mean±s.e.m.
Results
Identification of differentially expressed proteins using 2D-DIGE
For the 2D-DIGE studies, omental adipose tissue was obtained from six NGT women and six women with GDM. Baseline characteristics for these populations are outlined in Table 1. Of note, there was no difference in maternal age or BMI between the two groups. Proteins isolated from omental adipose tissue obtained from women with NGT (n=6) and GDM (n=6) were displayed and compared using 2D-DIGE. An internal standard consisting of a pool of the different samples was Cy2 labelled and included in each gel. After 2D-DIGE, the Cy2, Cy3 and Cy5 channels were individually imaged from each of the six gels using mutually exclusive excitation and emission wavelengths, and image analysis was performed with Progenesis SameSpots software. A master map was created and ∼840 spots common to all gels were detected. See Fig. 1A for a master map. In this image, the NGT samples are labelled with Cy3 (green) and the GDM samples are labelled with Cy5 (red).
2D-DIGE analysis of omental adipose tissue obtained from women with NGT (Cy3, green) and GDM (Cy5, red). (A) Spot map corresponding to the mixed internal standard, which is common to all the gels analysed. pH 4–7 immobilised pH gradient strips were used for isoelectric focusing and 10.5–14% SDS–PAGE for the second dimension. (B) Numbered spots indicate proteins identified by MALDI-TOF MS. See Table 3 for a detailed listing of all proteins identified.
Citation: Journal of Endocrinology 218, 2; 10.1530/JOE-13-0010
The gels were stained with Coomassie Blue stain, and all visible protein spots were trypsin digested and submitted to MS/MS for identification: MALDI-TOF MS. A total of 116 spots were positively identified, corresponding to 61 distinct proteins. Figure 1B shows the position of these spots in the 2D-DIGE gel. Table 3 displays detailed information about the corresponding proteins identified. This table summarises the protein accession number of identified muscle proteins, Mascot scores, the number of matched peptide sequences, the percentage sequence coverage, the theoretical molecular mass, the theoretical pI value, average normalised values and fold change of individual proteins affected by GDM.
Human adipose tissue proteins identified by 2D-DIGE and MALDI-TOF MS
| Average | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spot no. | Protein name (abbreviation) | Accession number | Mascot score | % MS coverage | MW (kDa) | pI value | No. of matched peptides | NGT | GDM | Ratio | P value |
| 1 | 14-3-3 protein beta/alpha (1433B) | P31946 | 78 | 35 | 28.2 | 4.6 | 13 | 1.20 | 0.95 | −1.3 | 0.009 |
| 2 | 14-3-3 protein epsilon (1433E) | P62258 | 151 | 58 | 29.3 | 4.5 | 17 | 1.08 | 1.01 | −1.1 | 0.167 |
| 3 | 14-3-3 protein zeta/delta (1433Z) | P63104 | 159 | 55 | 27.9 | 4.6 | 16 | 1.13 | 0.93 | −1.2 | 0.013 |
| 4 | Alpha-1-antitrypsin (AIAT) | P01009 | 263 | 59 | 46.9 | 5.3 | 25 | 1.17 | 0.83 | −1.4 | 0.015 |
| 5 | Actin, aortic smooth muscle (ACTA) | P62736 | 137 | 47 | 42.4 | 5.1 | 17 | 1.15 | 0.86 | −1.3 | 0.167 |
| 6 | Actin, cytoplasmic 1 (ACTB) | P60709 | 189 | 55 | 42.1 | 5.2 | 22 | 0.98 | 1.08 | 1.1 | 0.161 |
| 7 | Actin, cytoplasmic 1 (ACTB) | P60709 | 165 | 54 | 42.1 | 5.2 | 21 | 1.11 | 1.02 | −1.1 | 0.369 |
| 8 | Actin, cytoplasmic 1 (ACTB) | P60709 | 174 | 52 | 42.1 | 5.2 | 21 | 1.15 | 1.02 | −1.1 | 0.155 |
| 9 | Actin, cytoplasmic 1 (ACTB) | P60709 | 169 | 54 | 42.1 | 5.2 | 23 | 0.98 | 1.12 | 1.1 | 0.192 |
| 10 | Actin, cytoplasmic 1 (ACTB) | P60709 | 126 | 37 | 42.1 | 5.2 | 15 | 1.00 | 1.10 | 1.1 | 0.176 |
| 11 | Actin, cytoplasmic 1 (ACTB) | P60709 | 188 | 55 | 42.1 | 5.2 | 21 | 0.86 | 0.75 | −1.2 | 0.144 |
| 12 | Serum albumin (ALBU) | P02768 | 363 | 57 | 71.3 | 5.9 | 34 | 1.16 | 1.12 | 1.0 | 0.508 |
| 13 | Serum albumin (ALBU) | P02768 | 309 | 50 | 71.3 | 5.9 | 27 | 0.84 | 0.74 | −1.1 | 0.175 |
| 14 | Serum albumin (ALBU) | P02768 | 32 | 10 | 71.3 | 5.9 | 28 | 1.16 | 1.13 | 1.0 | 0.572 |
| 15 | Serum albumin (ALBU) | P02768 | 318 | 40 | 71.3 | 5.9 | 25 | 1.02 | 0.86 | −1.2 | 0.125 |
| 16 | Serum albumin (ALBU) | P02768 | 443 | 68 | 71.3 | 5.9 | 47 | 1.27 | 1.03 | −1.2 | 0.082 |
| 17 | Aldehyde dehydrogenase, mitochondrial (ALDH2) | P05091 | 158 | 35 | 56.9 | 6.8 | 16 | 1.03 | 0.91 | −1.1 | 0.108 |
| 18 | Aldehyde dehydrogenase, mitochondrial (ALDH2) | P05091 | 185 | 43 | 56.9 | 6.8 | 22 | 1.21 | 0.89 | −1.4 | 0.006 |
| 19 | Annexin A2 (ANXA2) | P07355 | 139 | 40 | 38.8 | 8.5 | 13 | 1.16 | 1.03 | −1.1 | 0.302 |
| 20 | Annexin A5 (ANXA5) | P08758 | 153 | 58 | 36.0 | 4.8 | 18 | 1.30 | 0.86 | −1.5 | 0.042 |
| 21 | Annexin A5 (ANXA5) | P08758 | 194 | 46 | 36.0 | 4.8 | 14 | 1.11 | 0.74 | −1.5 | 0.010 |
| 22 | Annexin A5 (ANXA5) | P08758 | 265 | 67 | 36.0 | 4.8 | 24 | 1.15 | 0.84 | −1.4 | 0.031 |
| 23 | Annexin A6 (ANXA6) | P08133 | 173 | 34 | 76.2 | 5.3 | 22 | 0.97 | 0.84 | −1.2 | 0.043 |
| 24 | Annexin A6 (ANXA6) | P08133 | 255 | 39 | 76.2 | 5.3 | 28 | 1.17 | 0.84 | −1.4 | 0.004 |
| 25 | Apolipoprotein A-I (APOA1) | P02647 | 236 | 67 | 30.8 | 5.5 | 21 | 1.31 | 0.94 | −1.4 | 0.002 |
| 26 | Apolipoprotein A-I (APOA1) | P02647 | 217 | 63 | 30.8 | 5.5 | 19 | 1.02 | 1.27 | 1.2 | 0.122 |
| 27 | Apolipoprotein A-I (APOA1) | P02647 | 247 | 72 | 30.8 | 5.5 | 25 | 1.17 | 1.19 | 1.0 | 0.811 |
| 28 | Calreticulin (CALR) | P27797 | 196 | 44 | 48.3 | 4.1 | 16 | 0.94 | 1.08 | 1.2 | 0.074 |
| 29 | 60 kDa heat-shock protein, mitochondria (CH60) | P10809 | 196 | 45 | 61.2 | 5.6 | 22 | 1.34 | 1.04 | −1.3 | 0.013 |
| 30 | Chloride intracellular channel protein (CLIC1) | O00299 | 101 | 46 | 27.2 | 4.9 | 9 | 1.17 | 0.99 | −1.2 | 0.035 |
| 31 | Collagen alpha-1(VI) chain (CO6A1) | P12109 | 207 | 28 | 109.6 | 5.1 | 24 | 1.02 | 0.88 | −1.2 | 0.305 |
| 32 | Collagen alpha-1(VI) chain (CO6A1) | P12109 | 201 | 27 | 109.6 | 5.1 | 23 | 1.07 | 0.92 | −1.2 | 0.348 |
| 33 | Collagen alpha-1(VI) chain (CO6A1) | P12109 | 223 | 30 | 109.6 | 5.1 | 25 | 1.05 | 0.88 | −1.2 | 0.376 |
| 34 | Collagen alpha-1(VI) chain (CO6A1) | P12109 | 118 | 14 | 109.6 | 5.1 | 14 | 1.08 | 0.89 | −1.2 | 0.314 |
| 35 | Collagen alpha-1(VI) chain (CO6A1) | P12109 | 58 | 8 | 109.6 | 5.1 | 8 | 0.70 | 1.45 | 2.1 | 0.008 |
| 36 | Collagen alpha-1(VI) chain (CO6A1) | P12109 | 42 | 5 | 109.6 | 5.1 | 5 | 0.61 | 1.42 | 2.3 | 0.001 |
| 37 | Collagen alpha-1(VI) chain (CO6A1) | P12109 | 75 | 8 | 109.6 | 5.1 | 9 | 0.61 | 1.37 | 2.3 | 0.001 |
| 38 | Collagen alpha-2(VI) chain (CO6A2) | P12110 | 176 | 18 | 109.7 | 5.8 | 18 | 0.83 | 1.17 | 1.4 | 0.048 |
| 39 | Collagen alpha-2(VI) chain (CO6A2) | P12110 | 179 | 21 | 109.7 | 5.8 | 21 | 0.90 | 1.18 | 1.3 | 0.078 |
| 40 | Collagen alpha-2(VI) chain (CO6A2) | P12110 | 121 | 12 | 109.7 | 5.8 | 13 | 0.78 | 1.16 | 1.5 | 0.040 |
| 41 | Collagen alpha-2(VI) chain (CO6A2) | P12110 | 48 | 4 | 109.7 | 5.8 | 5 | 0.76 | 1.19 | 1.6 | 0.018 |
| 42 | Collagen alpha-2(VI) chain (CO6A2) | P12110 | 118 | 13 | 109.7 | 5.8 | 14 | 0.78 | 1.23 | 1.6 | 0.019 |
| 43 | Cytochrome b5 (CYB5) | P00167 | 81 | 49 | 15.3 | 4.7 | 5 | 1.15 | 1.08 | −1.1 | 0.245 |
| 44 | Dermatopontin (DERM) | Q07507 | 75 | 37 | 24.6 | 4.5 | 7 | 0.79 | 1.05 | 1.3 | 0.038 |
| 45 | Enoyl-CoA hydratase, mitochondrial (ECHM) | P30084 | 118 | 43 | 31.8 | 9.4 | 12 | 1.23 | 0.92 | −1.3 | 0.018 |
| 46 | EH domain-containing protein 2 (EHD2) | Q9NZN4 | 103 | 24 | 61.3 | 6.0 | 12 | 1.20 | 1.19 | 1.0 | 0.894 |
| 47 | Endoplasmin (ENPL) | P14625 | 134 | 16 | 92.7 | 4.6 | 16 | 1.01 | 0.87 | −1.2 | 0.078 |
| 48 | Endoplasmin (ENPL) | P14625 | 79 | 11 | 92.7 | 4.6 | 9 | 1.01 | 0.92 | −1.1 | 0.224 |
| 49 | Fatty acid-binding protein, adipocyte (FABP4) | P15090 | 78 | 43 | 14.8 | 7.5 | 6 | 0.99 | 0.79 | −1.3 | 0.009 |
| 50 | Fatty acid-binding protein, adipocyte (FABP4) | P15090 | 104 | 59 | 14.8 | 7.5 | 8 | 1.30 | 0.80 | −1.6 | 0.000 |
| 51 | Fatty acid-binding protein, epidermal (FABP5) | Q01469 | 106 | 65 | 15.5 | 7.5 | 10 | 1.02 | 1.02 | 1.0 | 0.936 |
| 52 | Fibrinogen beta chain (FIBB) | P02675 | 71 | 20 | 56.6 | 9.3 | 9 | 0.84 | 1.07 | 1.3 | 0.000 |
| 53 | Fibrinogen beta chain (FIBB) | P02675 | 163 | 28 | 56.6 | 9.3 | 14 | 0.63 | 1.55 | 2.5 | 0.003 |
| 54 | Fibrinogen beta chain (FIBB) | P02675 | 79 | 17 | 56.6 | 9.3 | 9 | 0.63 | 1.47 | 2.3 | 0.011 |
| 55 | Fibrinogen gamma chain (FIBG) | P02679 | 44 | 8 | 52.1 | 5.3 | 4 | 0.78 | 1.31 | 1.7 | 0.097 |
| 56 | Fibrinogen gamma chain (FIBG) | P02679 | 210 | 47 | 52.1 | 5.3 | 19 | 0.93 | 1.00 | 1.1 | 0.275 |
| 57 | Fibrinogen gamma chain (FIBG) | P02679 | 190 | 43 | 52.1 | 5.3 | 19 | 0.95 | 1.12 | 1.2 | 0.074 |
| 58 | Fibrinogen gamma chain (FIBG) | P02679 | 77 | 21 | 52.1 | 5.3 | 8 | 0.76 | 1.35 | 1.8 | 0.070 |
| 59 | Ferritin light chain (FTL) | P02792 | 165 | 58 | 20.1 | 5.4 | 13 | 1.29 | 0.98 | −1.3 | 0.181 |
| 60 | Ferritin light chain (FTL) | P02792 | 114 | 35 | 20.1 | 5.4 | 7 | 1.65 | 0.53 | −3.1 | 0.001 |
| 61 | Neutral alpha-glucosidase AB (GANAB) | Q14697 | 84 | 12 | 107.3 | 5.7 | 10 | 0.99 | 0.92 | −1.1 | 0.435 |
| 62 | Neutral alpha-glucosidase AB (GANAB) | Q14697 | 89 | 13 | 107.3 | 5.7 | 12 | 1.00 | 0.84 | −1.2 | 0.057 |
| 63 | Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-2 (GBB2) | P62879 | 134 | 47 | 38.0 | 5.6 | 15 | 1.02 | 1.00 | 1.0 | 0.637 |
| 64 | Rho GDP-dissociation inhibitor 1 (GDIR1) | P52565 | 69 | 29 | 23.3 | 4.9 | 7 | 0.96 | 1.25 | 1.3 | 0.117 |
| 65 | Glucosidase 2 subunit beta (GLU2B) | P14314 | 121 | 25 | 60.4 | 4.2 | 15 | 1.05 | 1.03 | 1.0 | 0.768 |
| 66 | Glycerol-3-phosphate dehydrogenase [NAD+], cytoplasmic (GPDA) | P21695 | 236 | 77 | 38.2 | 5.8 | 25 | 1.22 | 0.92 | −1.3 | 0.475 |
| 67 | Glutathione S-transferase P (GSTP) | P09211 | 105 | 57 | 23.6 | 5.3 | 10 | 1.21 | 0.89 | −1.4 | 0.004 |
| 68 | Haptoglobin (HPT) | P00738 | 121 | 29 | 45.9 | 6.1 | 13 | 0.98 | 0.99 | 1.0 | 0.866 |
| 69 | Heat-shock cognate 71 kDa protein (HSP7C) | P11142 | 121 | 22 | 71.1 | 5.2 | 12 | 1.05 | 0.84 | −1.3 | 0.013 |
| 70 | Heat-shock protein 27 (HSP27) | P04792 | 131 | 41 | 22.8 | 6.0 | 9 | 1.10 | 0.75 | −1.5 | 0.000 |
| 71 | Heat-shock protein beta 20 (HSP20) | O14558 | 106 | 60 | 17.2 | 6.0 | 7 | 1.17 | 0.96 | −1.2 | 0.258 |
| 72 | Keratin, type I cytoskeletal 10 (K1C10) | P13645 | 92 | 17 | 59.0 | 5.0 | 11 | 1.00 | 0.85 | −1.2 | 0.220 |
| 73 | Keratin, type I cytoskeletal 19 (K1C19) | P08727 | 163 | 44 | 44.1 | 4.9 | 21 | 1.00 | 0.96 | 1.0 | 0.770 |
| 74 | Keratin, type I cytoskeletal 19 (K1C19) | P08727 | 270 | 75 | 44.1 | 4.9 | 30 | 0.86 | 1.07 | 1.2 | 0.313 |
| 75 | Lactate dehydrogenase B chain (LDHB) | P07195 | 86 | 19 | 36.9 | 5.7 | 7 | 1.34 | 0.89 | −1.5 | 0.001 |
| 76 | Galectin-1 (LEG1) | P09382 | 149 | 77 | 15.0 | 5.2 | 10 | 0.99 | 1.14 | 1.2 | 0.023 |
| 77 | Galectin-1 (LEG1) | P09382 | 130 | 58 | 15.0 | 5.2 | 8 | 0.89 | 1.12 | 1.3 | 0.001 |
| 78 | Lumican (LUM) | P51884 | 165 | 38 | 38.7 | 6.2 | 11 | 0.86 | 1.28 | 1.5 | 0.023 |
| 79 | Lumican (LUM) | P51884 | 140 | 37 | 38.7 | 6.2 | 11 | 0.87 | 1.30 | 1.5 | 0.033 |
| 80 | Microfibril-associated glycoprotein 4 (MFAP4) | P55083 | 69 | 25 | 29.0 | 5.3 | 7 | 0.95 | 1.03 | 1.1 | 0.573 |
| 81 | Myosin light polypeptide 6 (MYL6) | P60660 | 163 | 76 | 17.1 | 4.4 | 14 | 1.10 | 1.05 | 1.0 | 0.740 |
| 82 | Protein disulfide-isomerase A1 (PDIA1) | P07237 | 252 | 42 | 57.5 | 4.6 | 22 | 0.94 | 0.89 | 1.0 | 0.321 |
| 83 | Protein disulfide-isomerase A1 (PDIA1) | P07237 | 219 | 34 | 57.5 | 4.6 | 18 | 1.11 | 1.07 | 1.0 | 0.439 |
| 84 | Protein disulfide-isomerase A1 (PDIA1) | P07237 | 114 | 16 | 57.5 | 4.6 | 9 | 1.04 | 0.99 | 1.0 | 0.352 |
| 85 | Protein disulfide-isomerase A3 (PDIA3) | P30101 | 190 | 40 | 57.1 | 5.9 | 20 | 1.11 | 1.04 | −1.1 | 0.129 |
| 86 | Perilipin-1 (PLIN1) | O60240 | 267 | 50 | 56.2 | 6.0 | 25 | 1.37 | 0.80 | −1.7 | 0.002 |
| 87 | Peroxiredoxin-2 (PRX2) | P32119 | 107 | 39 | 22.0 | 5.6 | 7 | 1.00 | 1.12 | 1.1 | 0.143 |
| 88 | Peroxiredoxin-2 (PRX2) | P32119 | 166 | 53 | 22.0 | 5.6 | 11 | 1.10 | 1.08 | 1.0 | 0.805 |
| 89 | Peroxiredoxin-6 (PRX6) | P30041 | 207 | 73 | 25.1 | 6.0 | 17 | 1.28 | 0.92 | −1.4 | 0.000 |
| 90 | S100A9 | P06702 | 82 | 43 | 13.3 | 5.7 | 6 | 0.95 | 1.37 | 1.4 | 0.012 |
| 91 | Selenium-binding protein 1 (SBP1) | Q13228 | 137 | 22 | 52.9 | 5.9 | 9 | 1.16 | 0.83 | −1.4 | 0.004 |
| 92 | Superoxide dismutase [Cu-Zn] (SODC) | P00441 | 80 | 42 | 16.2 | 5.7 | 5 | 1.23 | 0.97 | −1.3 | 0.008 |
| 93 | T-complex protein 1 subunit beta (TCPB) | P78371 | 97 | 25 | 57.8 | 6.0 | 12 | 0.86 | 0.87 | 1.0 | 0.832 |
| 94 | Transitional endoplasmic reticulum ATPase (TERA) | P55072 | 65 | 16 | 90.0 | 5 | 11 | 1.20 | 0.98 | −1.2 | 0.016 |
| 95 | Protein-glutamine gamma-glutamyltransferase (TGM2) | P21980 | 273 | 43 | 78.4 | 5 | 28 | 0.79 | 0.94 | 1.2 | 0.198 |
| 96 | Protein-glutamine gamma-glutamyltransferase (TGM2) | P21980 | 252 | 38 | 78.4 | 5 | 23 | 0.88 | 1.08 | 1.2 | 0.226 |
| 97 | Protein-glutamine gamma-glutamyltransferase (TGM2) | P21980 | 143 | 23 | 78.4 | 5 | 14 | 0.77 | 1.13 | 1.5 | 0.031 |
| 98 | Tropomyosin alpha-3 chain (TPM3) | P06753 | 74 | 34 | 32.9 | 4.5 | 13 | 1.11 | 1.23 | 1.1 | 0.223 |
| 99 | Tropomyosin alpha-4 chain (TPM4) | P67936 | 146 | 38 | 28.6 | 4.5 | 14 | 1.07 | 1.15 | 1.1 | 0.403 |
| 100 | Serotransferrin (TRFE) | P02787 | 254 | 29 | 79.3 | 7 | 22 | 1.06 | 0.88 | −1.2 | 0.198 |
| 101 | Serotransferrin (TRFE) | P02787 | 361 | 44 | 79.3 | 7 | 32 | 1.13 | 0.96 | −1.2 | 0.252 |
| 102 | Vimentin (VIME) | P08670 | 318 | 67 | 53.7 | 4.9 | 35 | 1.10 | 1.05 | −1.1 | 0.705 |
| 103 | Vimentin (VIME) | P08670 | 204 | 37 | 53.7 | 4.9 | 19 | 1.15 | 0.97 | −1.2 | 0.110 |
| 104 | Vimentin (VIME) | P08670 | 68 | 18 | 53.7 | 4.9 | 7 | 1.11 | 1.03 | −1.1 | 0.282 |
| 105 | Vimentin (VIME) | P08670 | 289 | 62 | 53.7 | 4.9 | 34 | 0.98 | 1.16 | 1.2 | 0.342 |
| 106 | Vimentin (VIME) | P08670 | 307 | 65 | 53.7 | 4.9 | 31 | 0.82 | 1.33 | 1.6 | 0.069 |
| 107 | Vimentin (VIME) | P08670 | 275 | 48 | 53.7 | 4.9 | 25 | 0.66 | 1.43 | 2.2 | 0.000 |
| 108 | Vimentin (VIME) | P08670 | 238 | 39 | 53.7 | 4.9 | 17 | 1.00 | 1.44 | 1.4 | 0.265 |
| 109 | Vimentin (VIME) | P08670 | 217 | 62 | 53.7 | 4.9 | 32 | 1.09 | 0.88 | −1.2 | 0.030 |
| 110 | Vimentin (VIME) | P08670 | 285 | 56 | 53.7 | 4.9 | 32 | 0.95 | 1.19 | 1.3 | 0.131 |
| 111 | Vimentin (VIME) | P08670 | 287 | 70 | 53.7 | 4.9 | 38 | 1.12 | 0.97 | −1.2 | 0.164 |
| 112 | Vimentin (VIME) | P08670 | 223 | 66 | 53.7 | 4.9 | 34 | 0.92 | 1.12 | 1.2 | 0.022 |
| 113 | Vimentin (VIME) | P08670 | 269 | 68 | 53.7 | 4.9 | 36 | 0.97 | 1.12 | 1.2 | 0.128 |
| 114 | Vinculin (VINC) | P18206 | 147 | 23 | 124.3 | 5.4 | 24 | 1.24 | 0.92 | −1.4 | 0.002 |
| 115 | Vinculin (VINC) | P18206 | 167 | 22 | 124.3 | 5.4 | 21 | 1.16 | 0.86 | −1.3 | 0.004 |
| 116 | Vitamin D-binding protein (VTDB) | P02774 | 142 | 23 | 54.5 | 5.3 | 12 | 1.18 | 1.13 | 1.0 | 0.588 |
Statistical analyses were performed by setting the threshold for differentially expressed proteins at >1.4-fold and P<0.05. Four proteins were significantly higher in adipose tissue obtained from GDM patients. These were collagen alpha-2(VI) chain (CO6A2 (COL6A2)), fibrinogen beta chain (FIBB), LUM and S100A9. Of note, collagen alpha-1(VI) chain (CO6A1) appeared as two separate chains (spots 31–34 and 35–37); however, only one of the chains of CO6A1 (spots 35–37) was significantly different between NGT and GDM.
Ten proteins were significantly lower in adipose tissue obtained from GDM patients. These were alpha-1-antitrypsin (AIAT (SERPINA1)), annexin A5 (ANXA5), fatty acid-binding protein, adipocyte (FABP4), glutathione S-transferase P (GSTP (GSTP1)), heat-shock protein 27 (HSP27 (HSPB1)), lactate dehydrogenase B chain (LDHB), perilipin-1 (PLIN1), peroxiredoxin-6 (PRX6), selenium-binding protein 1 (SBP1) and vinculin (VINC).
Immunoblot analysis of GDM-related changes in the omental adipose tissue proteome
Western blot analysis was used to validate representative findings from the 2D-DIGE analysis. We chose to validate six proteins: three that were up-regulated in GDM adipose tissue (FIBB, LUM and S100A9) and three that were down-regulated in GDM adipose tissue (ANXA5, PRX6 and HSP27). For these studies, we analysed an independent set of omental tissue samples (n=6 per group). Western blotting validating the overexpression of FIBB, LUM and S100A9 in GDM omental samples is shown in Fig. 2A, B, and C. Western blotting validating the down-regulation of ANXA5, PRX6 and HSP27 in GDM omental samples are shown in Fig. 2D, E, and F.
Western blot and quantitation for (A) FIBB, (B) LUM, (C) S100A9, (D) ANXA5, (E) PRX6 and (F) HSP27 in adipose tissue (n=6 patients per group). Protein expression was normalised to β-actin expression. Data are displayed as the mean±s.e.m. *P<0.05 vs GDM (Student's t-test). Representative western blot from eight patients (four GDM and four NGT patients) is also shown.
Citation: Journal of Endocrinology 218, 2; 10.1530/JOE-13-0010
Discussion
In this study, 2D-DIGE was used to identify GDM-associated changes in the expression of proteins in human adipose tissue. Using MALDI-TOF MS, 116 protein spots were identified, which corresponded to 61 distinct proteins. GDM was associated with an up-regulation of four proteins: CO6A2, FIBB, LUM and S100A9. On the other hand, a total of ten proteins were found to be down-regulated in adipose tissue from GDM women. These were AIAT, ANXA5, FABP4, GSTP, HSP27, LDHB, PLIN1, PRX6, SBP1 and VINC. Western blot analysis of six proteins (FIBB, LUM, S100A9, ANXA5, HSP27 and PRX6) confirmed the 2D-DIGE data. The possible roles of these proteins in the pathophysiology of GDM are discussed below.
There have been a number of studies that have also used 2DE to assess proteins that are increased and/or decreased in adipose tissue from subjects with obesity, insulin resistance and type 2 diabetes. For example, proteomic analysis revealed altered expression levels of several proteins that are involved in lipid and glucose metabolism, inflammation and oxidative stress processes in polycystic ovary syndrome (PCOS) omental adipose tissue in comparison with those of non-hyperandrogenic obese women (Corton et al. 2008). Differences regarding these studies and our studies are also discussed below.
Inflammation
S100A9, which belongs to the family of low-molecular weight S100 proteins, has emerged as an important pro-inflammatory mediator in acute and chronic inflammation (Roth et al. 2001, Gebhardt et al. 2006). It has been shown to have cytokine-like activities, activating receptor for advanced glycation end products (RAGE) and Toll-like receptor 4-dependent signalling cascades. S100A9 expression is increased in adipose tissue from subjects with impaired glucose tolerance (Ortega et al. 2012). Likewise, in this study, we found increased S100A9 expression in adipose tissue from women with GDM.
Fibrinogen, a 340 kDa glycoprotein consisting of three pairs of identical chains (α, β and γ), is a powerful and independent risk factor for cardiovascular diseases (Heinrich et al. 1994). It is also a positive acute-phase protein, playing a vital role in a number of physiopathological processes in the body, including inflammation (Kamath & Lip 2003). In keeping with this, we found increased expression of FIBB in adipose tissue of women with GDM.
A1AT and ANXA5 have also been implicated in inflammation. A1AT is a 52 kDa protease inhibitor belonging to the serpin superfamily (Gettins 2002). It protects tissues from enzymes of inflammatory cells, especially neutrophil elastase, and as such its circulating levels rise upon acute inflammation. ANXA5 has anti-inflammatory, anti-thrombotic and anti-apoptotic properties (Leon et al. 2006, Ewing et al. 2011); it reduces local vascular and systemic inflammation, reduces vascular remodelling and improves vascular function. In this study, 2D-DIGE revealed significantly lower A1AT and ANXA5 in adipose tissue from GDM subjects, suggesting increased inflammation in this tissue. However, in contrast to our studies, ANXA5 was found to be overexpressed in PCOS omental adipose tissue compared with those of non-hyperandrogenic women (Corton et al. 2008).
Although we did not have the samples available, it would have been of interest to assess the infiltration of M1/M2 macrophages in omental adipose tissue. Nevertheless, and in keeping with our previous studies (Darios et al. 2008), our proteomics data are suggestive of increased inflammation in omental adipose tissue GDM.
Structural proteins
The collagens are a superfamily of proteins that play a role in maintaining the integrity of various tissues. Collagens are extracellular matrix (ECM) proteins and have a triple-helical domain as their common structural element. The ECM of adipose tissue, mainly represented by collagen VI (Scherer et al. 1998), is dysfunctional in obesity and contributes to the development of the metabolic syndrome. Adipose tissue macrophages in insulin-resistant subjects are associated with collagen VI and fibrosis (Spencer et al. 2010), and CO6A3 is higher in adipose tissue of obese women (Pasarica et al. 2009). Increased collagen expression is also evident in the mesangium of diabetic rats (Abrass et al. 1988). Furthermore, in db/db obese mice, various types of collagens are overexpressed in epididymal WAT (Khan et al. 2009). Of note, obese ob/ob mice lacking the Col6a3 gene have a better metabolic profile compared with wild-type mice. They also gain less weight when fed a high-fat diet (Khan et al. 2009). These mice also have lower macrophage content in adipose tissue and decreased expression of inflammatory molecules, suggesting a role for collagen in adipose tissue inflammation. In this study, CO6A1 appeared as two separate chains (spots 31–34 and 35–37); however, only one of the chains of CO6A1 (spots 35–37) was significantly higher in GDM subjects. We also found that CO6A2 was significantly higher in GDM subjects. LUM is a leucine-rich ECM glycoprotein that belongs to the small leucine-rich proteoglycan family of proteins. It interacts with collagen to maintain the integrity and function of connective tissues and has been implicated in various processes, such as cell migration, proliferation, wound healing and inflammation (Chakravarti 2003, Nikitovic et al. 2008). There is increased transcription of LUM in glomeruli from human diabetic kidneys at different stages of nephropathy (Schaefer et al. 2001). Similarly, in this study, we found increased LUM in omental adipose tissue from GDM subjects. In summary, an increase in ECM proteins in adipose tissue from women with GDM would result in an increase in the overall rigidity of adipose tissue, likely contributing to an increase in its mechanical strength, and thus fibrosis. In addition, increased collagen and LUM expression in adipose tissue may also play a role in inflammation observed in GDM.
VINC is a structural protein involved in linkage of integrin adhesion molecules to the actin cytoskeleton. It has been implicated in a number of processes including cell adhesion, cell shape control and motility (Ziegler et al. 2006, Peng et al. 2011). In this study, we observed lower VINC expression in adipose tissue of women with GDM, suggesting that it may play a role in differentiation and survival of these cells. Other studies have reported similar decreases in VINC in the presence of diabetes (Yohannes et al. 2008).
Oxidative stress
Oxidative damage caused by reactive oxygen species (ROS) has been implicated in a number of metabolic diseases, including GDM (Lappas et al. 2011). It has been suggested that oxidative stress mediates many of the pathological consequences of diabetes in pregnancy and is importantly associated with poor fetal outcome. Removal of ROS by antioxidant enzymes plays an important part in limiting this damage, and indeed, antioxidants can prevent diabetic embryopathy in rat models of diabetes in pregnancy (Higa et al. 2012). PRX6 is a cytosolic peroxidase (Fisher 2011) that has been demonstrated to protect various tissues, such as skin, lung and cardiac muscle, against acute oxidative insults, and that has been shown to be down-regulated in diabetes (Johnson et al. 2009). HSPs are molecular chaperones that are classified into families according to their molecular weight (Ellis 1999, Hwang et al. 2009). A growing body of literature has linked chaperone-like molecules to adipogenesis, obesity and diabetes (Cherian & Abraham 1995, Kurucz et al. 2002, Young et al. 2004). HSP27 is a chaperone of the small HSP group, which has been implicated in multiple functions such as inflammation, cytoprotective stress proteins, inhibition of apoptosis, cell development, cell differentiation and signal transduction (Arrigo 2007, Salari et al. 2013). Indeed, overexpression of HSP27 mitigates cytokine-induced islet apoptosis and streptozotocin-induced diabetes (Dai et al. 2009). GSTs are also a family of antioxidant enzymes; they metabolise electrophilic aldehydes by catalysing their conjugation with glutathione. Direct antioxidant activity has also been postulated for SBP1 in analogy to other selenocysteine-containing enzymes such as thioredoxin reductase and glutathione peroxidase. Thus, the decrease in PRX6, HSP27, GSTP and SBP1 in adipose tissue from women with GDM compared with normal tissue is suggestive of increased oxidative stress. In support of our studies, 2D-DIGE also revealed that the antioxidant enzyme PRX2 was found to be down-regulated in PCOS omental fat (Corton et al. 2008).
Energy and lipid metabolism
Lactate dehydrogenase (LDH) is involved in glycolysis; it catalyses the conversion of lactate to pyruvate. In this study, we found that the β chain of LDH was decreased in adipose tissue of women with GDM. It is possibly that a deficiency in LDHB protein might contribute to impaired net lactate release from adipose tissue in response to insulin and may explain the lower plasma lactate levels that are seen in the obese women during hyperinsulinaemia (Qvisth et al. 2007).
Pregnancy is characterised by changes in maternal adiposity and thus changes in lipid metabolism. During late pregnancy, there is enhanced fat mobilisation (i.e. increased lipolysis) via hormone-sensitive lipase (HSL) to ensure fetal growth and development. PLIN1 is an important regulator of lipid storage; it acts as a protective coating from HSL, thereby blocking fat hydrolysis. Lower expression of PLIN1 in adipose tissue of women with GDM observed in this study is in keeping with studies showing that lipolysis is increased in women with GDM (Butte 2000).
We also report a decrease in FABP4 in adipose tissue from GDM women, which is consistent with others showing decreased FABP2 levels in GDM patients (Catalano et al. 2002). These data suggest the shunting of fatty acids away from adipose tissue where they are stored as fat and towards placenta where they would be oxidised as fuel or transported to fetus. Collectively, lower adipose tissue PLIN1 and FABP4 suggests the transition from lipid storage to increased lipolysis in GDM pregnancies. This may contribute to the dyslipidaemia and increased fat deposition observed in infants of women with GDM (Catalano et al. 2003).
Study limitations
There are a number of caveats that must be considered when interpreting the data obtained in this study. First, intact adipose tissue samples were used instead of studying adipocytes and stromal vascular fraction separately. Previous studies have shown that the proteome of these two fractions is different (Peinado et al. 2012). Secondly, all pregnant women diagnosed with GDM were prescribed insulin, which may be responsible for some of the changes described. Thirdly, the present results were obtained from pregnant women who were obese and thus cannot be extrapolated to non-obese pregnant women with GDM. In addition, 2DE itself is associated with several limitations; for example, only a portion of all the proteins that are present in a sample can be displayed. In this study, we only analysed proteins in the 4–7 pH range, avoiding proteins with a more alkaline or acidic pI. Nevertheless, to our knowledge, this is the first study performed with humans in which 2D-DIGE has been used to identify protein expression changes, induced by GDM, in omental adipose tissue.
Conclusions
GDM is a significant risk factor for the type 2 diabetes epidemic in many populations (Rice et al. 2012). Long-term women and infants are prone to obesity, diabetes and cardiovascular disease (Catalano et al. 2003, Lee et al. 2007). Thus, increasing our understanding of the aetiology of GDM will provide the best opportunity for early treatment to prevent onset or progression of the disease. The data obtained in this study confirm that adipose tissue from women with GDM exhibits a different expression profile from that of NGT pregnant women. We have identified a number of novel proteins in visceral adipose tissue that may be involved in the pathophysiology of GDM. This study revealed differences in expression of proteins that are involved in inflammation, lipid and glucose metabolism and oxidative stress. Additionally, a number of structural proteins were also found to be differentially regulated in adipose tissue from NGT and GDM women.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding
Dr M L was a recipient of Career Development Fellowships from National Health and Medical Research Council (NHMRC) (grant no. 454777 and 1047025). Prof. G E R was in receipt of an NHMRC Principal Research Fellowship. The work described in this manuscript was funded by project grants from NHMRC (grant no. 454310) and Diabetes Australia Research Trust (DART). Proteomic data analysis described in this work was supported by the use of the Australian Proteomics Computational Facility funded by the Australian NHMRC (grant no. 381413). Funding for the ChemiDoc system was, in part, provided by the Medical Research Foundation for Women and Babies.
Acknowledgements
The authors gratefully acknowledge the assistance of the Clinical Research Midwives Gabrielle Fleming and Astrid Tiefholz and the Obstetrics and Midwifery staff of the Mercy Hospital for Women for their co-operation.
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