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Diabetologia

, Volume 59, Issue 11, pp 2393–2405 | Cite as

The epigenetic signature of systemic insulin resistance in obese women

  • Peter Arner
  • Anna-Stina Sahlqvist
  • Indranil Sinha
  • Huan Xu
  • Xiang Yao
  • Dawn Waterworth
  • Deepak Rajpal
  • A. Katrina Loomis
  • Johannes M. Freudenberg
  • Toby Johnson
  • Anders Thorell
  • Erik Näslund
  • Mikael Ryden
  • Ingrid Dahlman
Open Access
Article

Abstract

Aims/hypothesis

Insulin resistance (IR) links obesity to type 2 diabetes. The aim of this study was to explore whether white adipose tissue (WAT) epigenetic dysregulation is associated with systemic IR by genome-wide CG dinucleotide (CpG) methylation and gene expression profiling in WAT from insulin-resistant and insulin-sensitive women. A secondary aim was to determine whether the DNA methylation signature in peripheral blood mononuclear cells (PBMCs) reflects WAT methylation and, if so, can be used as a marker for systemic IR.

Methods

From 220 obese women, we selected a total of 80 individuals from either of the extreme ends of the distribution curve of HOMA-IR, an indirect measure of systemic insulin sensitivity. Genome-wide transcriptome and DNA CpG methylation profiling by array was performed on subcutaneous (SAT) and visceral (omental) adipose tissue (VAT). CpG methylation in PBMCs was assayed in the same cohort.

Results

There were 647 differentially expressed genes (false discovery rate [FDR] 10%) in SAT, all of which displayed directionally consistent associations in VAT. This suggests that IR is associated with dysregulated expression of a common set of genes in SAT and VAT. The average degree of DNA methylation did not differ between the insulin-resistant and insulin-sensitive group in any of the analysed tissues/cells. There were 223 IR-associated genes in SAT containing a total of 336 nominally significant differentially methylated sites (DMS). The 223 IR-associated genes were over-represented in pathways related to integrin cell surface interactions and insulin signalling and included COL5A1, GAB1, IRS2, PFKFB3 and PTPRJ. In VAT there were a total of 51 differentially expressed genes (FDR 10%); 18 IR-associated genes contained a total of 29 DMS.

Conclusions/interpretation

In individuals discordant for insulin sensitivity, the average DNA CpG methylation in SAT and VAT is similar, although specific genes, particularly in SAT, display significantly altered expression and DMS in IR, possibly indicating that epigenetic regulation of these genes influences metabolism.

Keywords

CpG island DNA methylation Visceral adipose tissue 

Abbreviations

CpG

CG dinucleotides

DMS

Differentially methylated sites

FDR

False discovery rate

IR

Insulin resistance

PBMC

Peripheral blood mononuclear cell

qPCR

Quantitative real-time PCR

SAT

Subcutaneous adipose tissue

TRAIL

TNF-related apoptosis-inducing ligand

UTR

Untranslated region

VAT

Visceral adipose tissue

VEGFR

Vascular endothelial growth factor receptor

WAT

White adipose tissue

Introduction

The impaired ability of insulin to induce cellular responses (i.e. insulin resistance [IR]) is a pathophysiological mechanism that links obesity to metabolic disorders such as type 2 diabetes and cardiovascular disease [1]. Both genetic and epigenetic factors are implicated in the development of systemic IR [2], which may be characterised by elevated circulating levels of insulin in the fasting state despite normal or elevated glucose levels. The association between IR and excess abdominal fat, in particular in the intra-abdominal or visceral adipose tissue (VAT) depot, is believed to be mediated by increased spontaneous hydrolysis of lipids (i.e. adipocyte lipolysis) [3]. Released NEFA can induce IR in the liver [4]. In addition, systemic IR is characterised by ectopic triacylglycerol accumulation in skeletal muscle and the liver [5]. Other pathways implicated in systemic IR include low-grade inflammation in white adipose tissue (WAT) [6].

An unfavourable intrauterine environment is associated with IR in adulthood suggesting a, possibly epigenetically regulated, metabolic memory [7]. The term ‘epigenetics’ refers to stable long-term alterations in the transcriptional potential of cells and includes histone modifications and DNA methylation, the latter occurring mainly in the context of CG dinucleotides (CpGs) [8]. In any given individual, the epigenetic profiles can differ substantially between different organs and cell types [9]. In WAT, global as well as site-specific differences in CpG methylation have been associated with obesity and type 2 diabetes [10, 11, 12]. A recent epigenome-wide association study identified one locus where CpG methylation in CD4+ T cells is significantly associated with IR [13]. However, to our knowledge, no study of genome-wide CpG methylation profiling in the organs directly implicated in the development of IR has previously been reported.

The aim of this study was to explore whether systemic IR is associated with epigenetic dysregulation of WAT, determined by genome-wide CpG methylation and gene expression profiling in subcutaneous adipose tissue (SAT) and VAT. Adipose tissue is not ideal for routine clinical examinations; therefore, a secondary aim was to determine whether the DNA methylation signature in peripheral blood mononuclear cells (PBMCs) reflects WAT methylation and may thus be used as a marker for systemic IR.

Methods

Participants and clinical evaluation

The 80 women included in this study were selected from the extremes of insulin sensitivity, as measured by HOMA-IR [14], from 220 obese women who participated in a clinical trial on the effect of bariatric surgery (ClinicalTrial.gov registration no. NCT01785134). The sample size was selected based on previous experience from transcriptome and DNA methylation profiling on WAT in relation to clinical metabolic phenotypes [10]. Of the 80 women, none had undergone any active weight-reducing attempt for at least 6 months prior to surgery. Eight women were diagnosed with hypertension, seven of which were prescribed antihypertensive treatment (ACE inhibitors, n = 3; diuretics, n = 2; calcium-channel blockers, n = 2; β-blockers, n = 5). Eleven patients were prescribed antidepressants, and one patient was taking methylphenidate for attention deficit hyperactivity disorder. Mild impaired kidney function (n = 1), obstructive sleep apnoea (n = 1), von Willebrand’s disease (n = 1) and substituted vitamin B12 deficiency (n = 1) were each diagnosed. Otherwise, participants were healthy according to medical history. All sampling and measurements were performed before or during bariatric surgery (laparoscopic gastric bypass).

Participants were investigated at 08:00 hours after an overnight fast. Anthropometric measurements were performed followed by venous blood sampling. Blood glucose and lipids were analysed at the Karolinska University hospital’s routine chemistry laboratory (Stockholm, Sweden). Plasma insulin was measured by ELISA (Mercodia, Uppsala, Sweden) as previously described [15]. Insulin sensitivity was assessed by HOMA-IR and was calculated from fasting measures of glucose and insulin as described [14]. High HOMA-IR values indicate IR. The 40 women with the highest HOMA-IR values and the 40 women with the lowest values were selected for inclusion in the present study. PBMCs were isolated in BD Vacutainer Cell Preparation tubes (Becton, Dickinson San Jose, CA, USA) and stored as pellets at −80°C for further analysis.

The study was approved by the Regional Ethics Committee in Stockholm and all participants gave their written informed consent prior to participation. The study was carried out in accordance with the principles of the Declaration of Helsinki as revised in 2008.

WAT sampling

Biopsies from the abdominal SAT depot were obtained from the surgical incision. Omental adipose tissue (visceral adipose tissue [VAT]) specimens were obtained using ultrasound scissors immediately after surgeons entered the abdominal cavity. Participants were fasted overnight and 154 mmol/l NaCl was given by i.v. infusion until adipose tissue specimens were removed. All WAT samples were rapidly rinsed in NaCl (154 mmol/l) and specimens of 300 mg unfractionated WAT were immediately frozen in liquid nitrogen and kept at −70°C for subsequent DNA and RNA preparation.

Global transcriptome assays

From high-quality total RNA we prepared and hybridised biotinylated complementary RNA to GeneChip Human Transcriptome Arrays 2.0 (HTA; Affymetrix, Santa Clara, CA, USA) as described in the electronic supplementary material (ESM) Methods. Of the 23,442 probesets annotated with a gene symbol, 5860 (25%) transcripts with the lowest mean expression and 5860 (25%) with the lowest variation in expression (i.e. SD divided by mean expression) were excluded, resulting in 11,722 probesets being taken forward for subsequent analysis of differentially expressed genes. The applied cut-off for mean expression was used to exclude a set of organ-specific genes that should not be expressed in adipose tissue according to the literature. Webgestalt (http://bioinfo.vanderbilt.edu/webgestalt/) was used to identify pathways over-represented among differentially expressed genes and differentially methylated sites (DMS) [16].

DNA methylation microarray assays

DNA extracted from SAT and VAT pieces, as well as from PBMCs, was assayed using the Infinium Human Methylation 450 (450 K) BeadChips (Illumina, San Diego, CA, USA) as described in ESM Methods [17]. BeadChip images were processed as described in ESM Methods. For differential methylation analysis, β values were converted to M values (M = Log2[β/(1 − β)]), which have a more appropriate distribution for statistical tests for comparisons between groups. Before analysis of DMS a number of filtering steps were performed resulting in 112,057 (SAT), 124,089 (VAT) and 99,462 (PBMCs) probes, respectively, being taken forward to identify DMS.

Methylation data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO; http://ncbi.nlm.nih.gov/geo, accession number GSE76399).

Validation experiments

Ten differentially expressed genes with DMS in SAT were selected for validation experiments. The genes were selected because they displayed consistent results in SAT and either VAT or PBMCs, or because they were mentioned in the Discussion. Gene expression was measured by quantitative real time-PCR (qPCR) using recommended inventoried Taqman assays from Applied Biosystems (Thermo Fisher Scientific, Waltham, MA, USA). Each sample was analysed once. Group assignment was blinded during experimentation.

Eleven DMS in SAT, in seven genes, were selected for validation by EpiTYPER (Agena Biosciences, San Diego, CA, USA), see ESM Methods for details. We were unable to design EpiTYPER assays for DMS in some differentially expressed genes validated by qPCR. We therefore selected a DMS COL4A1 for confirmation although this gene was not quantified by qPCR.

Statistical analysis

We used the Bioconductor package, Limma (https://bioconductor.org/packages/release/bioc/html/limma.html) to analyse the methylation M values to identify DMS between insulin-resistant and insulin-sensitive women, adjusting for BMI and age [18, 19, 20]. A threshold of p < 0.05 was used in the epigenetic analysis. We also used parametric analysis in Limma to compare gene expression levels (Log2) between the insulin-resistant and insulin-sensitive groups adjusting for BMI. In transcriptome analysis a thresholds false discovery rate (FDR) of 10% was used. A t test was applied to compare clinical phenotypes, average global DNA methylation and validation results (qPCR and EpiTYPER) between the insulin-resistant and insulin-sensitive groups; a χ2 test was used to compare proportions.

Results

Clinical characteristics of participants

The clinical characteristics of the included participants are detailed in Table 1. As expected from the study design, the insulin-resistant group had substantially higher HOMA-IR, fasting plasma glucose and fasting serum insulin as compared with the insulin-sensitive group. The insulin-resistant group also displayed higher body weight, BMI, waist circumference and plasma triacylglycerol concentrations. Total and HDL-cholesterol levels were similar and there was no significant difference in age when comparing the groups. Thus, the groups were representative of the insulin-resistant or insulin-sensitive state.
Table 1

Clinical characteristics of cohort

Characteristic

Insulin resistant

(n = 40)

Insulin sensitive

(n = 40)

p value

Age (years)

36.4 ± 6.3

35.7 ± 5.7

0.57

Weight (kg)

116.8 ± 16.7

110.1 ± 11.7

0.04

BMI (kg/m2)

42.7 ± 4.7

39.1 ± 3.0

8.37 × 10−5

Waist circumference (cm)

129.8 ± 11.9

122.3 ± 11.1

0.0061

fP Glucose (mmol/l)

6.0 ± 1.3

5.1 ± 0.4

9.07 × 10−5

fS Insulin (pmol/l)

127 ± 39

29 ± 8

1.29 × 10−25

HOMA-IR

5.6 ± 2.0

1.1 ± 0.3

7.11 × 10−23

fS Cholesterol (mmol/l)

4.6 ± 1.1

4.5 ± 0.9

0.64

fS HDL-cholesterol (mmol/l)

1.1 ± 0.4

1.2 ± 0.3

0.78

fS Triacylglycerols (mmol/l)

1.45 ± 0.7

1.02 ± 0.4

0.000786

Data are means ± SD; all participants are women

Groups were compared with t test

fP, fasting plasma; fS, fasting serum

Transcriptome profile in SAT and VAT

Comparison of the expression levels of 11,722 transcripts between insulin-resistant and insulin-sensitive women adjusted for BMI identified 647 differentially expressed genes in SAT (represented by 656 probesets, FDR 10% [see ESM Table 1]). Expression of ten differentially expressed genes in SAT was confirmed by qPCR; all displayed directionally consistent results between insulin-resistant and insulin-sensitive women in both microarray and qPCR analysis, of which eight genes remained nominally significant with qPCR (ESM Table 2). We compared these results with previously reported genome-wide transcriptome analyses of SAT between insulin-resistant and insulin-sensitive individuals according to HOMA-IR. Among 321 differentially expressed genes in SAT of 40 European-Americans, reported by Elbein et al (FDR 5%) [21], 26 genes overlapped with the present study, all of which displayed directionally consistent change in expression (p < 3.4 × 10−7). Among 373 differentially expressed genes in SAT (top/bottom 20%) from 323 individuals, reported by Qatanani et al [22], 19 genes overlapped with the present study and 18 of these displayed directional consistency (p < 9.6 × 10−5) (ESM Table 1).

The 647 differentially expressed genes were over-represented for a number of pathways (Table 2), including pathways related to inflammation and immunity (e.g. TNF-related apoptosis-inducing ligand [TRAIL] signalling, IL3-mediated signalling and vascular endothelial growth factor receptor [VEGFR] signalling), which is in agreement with the findings by Elbein et al [21] and Qatanani et al [22]. As expected, genes in the insulin signalling pathway were also over-represented. The 70 differentially expressed genes in the insulin signalling pathway are shown in ESM Table 3 and include IRS2, which was downregulated by 15%, and IL6R, which was upregulated by 7% in insulin-resistant women.
Table 2

Over-representation of specific gene-sets among differentially expressed genes in SAT between insulin-resistant and insulin-sensitive womena

Pathwayc

Observedb

Expectedb

Adjusted p value

TRAIL signalling pathway

73

49

0.0024

Signalling events mediated by VEGFR1 and VEGFR2

70

48

0.0024

GMCSF-mediated signalling events

70

48

0.0024

IL3-mediated signalling events

70

48

0.0024

PAR1-mediated thrombin signalling events

70

48

0.0024

S1P1 pathway

70

47

0.0024

IFN-γ pathway

70

48

0.0024

ErbB1 downstream signalling

70

47

0.0024

β1 integrin cell surface interactions

78

50

0.0024

Urokinase-type plasminogen activator and uPAR-mediated signalling

70

47

0.0024

Plasma membrane oestrogen receptor signalling

71

48

0.0024

IGF1 pathway

70

47

0.0024

Insulin pathway

70

47

0.0024

Arf6 signalling events

70

47

0.0024

aWebgestalt was used to identify over-represented gene-sets (Pathway commons) among 647 differentially expressed genes as compared with all 11,722 analysed genes using default settings

bNumber of differentially expressed genes

Arf6, ADP-ribosylation factor 6; ErbB1, epidermal growth factor receptor; GMCSF, granulocyte-macrophage colony-stimulating factor; PAR1, proteinase-activated receptor 1; S1P1, sphingosine-1-phosphate receptor; uPAR, plasminogen activated receptor urokinase type

In VAT there were 51 differentially expressed genes (represented by 52 probesets) between insulin-resistant and insulin-sensitive women at FDR 10% (Table 3). For comparison, Qatanani et al [22] reported 788 differentially expressed genes in VAT between insulin-resistant and insulin-sensitive individuals (top/bottom 20%), out of which eight genes overlapped with the 51 differentially expressed genes in the present study (i.e. GSDMB [fold changes insulin-resistant vs insulin-sensitive: 0.82], AGPAT9 [0.78], PAIP2B [0.85], CA3 [0.45], SERPINI1 [0.91], RASSF4 [1.13], MYD88 [1.09], SLCO2B1 [1.24]); all eight genes displayed directionally consistent expression in both studies (p < 4.7 × 10−3) [22] (Table 3). The 51 differentially expressed genes in VAT in our study were not over-represented for any specific pathway.
Table 3

Differentially expressed genes in VAT between insulin-resistant and insulin-sensitive women

Probeset

Gene

VAT

VATa

SAT

IR

IS

IR/IS

Adjusted p valueb

IR/IS

IS

IR/IS

Adjusted p valueb

TC09001184.hg.1

PGM5-AS1

174 (27)

219 (34)

0.79

0.002

 

127

0.87

 

TC09001281.hg.1

GKAP1

56 (4)

63 (6)

0.89

0.002

 

56

0.90

0.021

TC17002851.hg.1

GSDMB

92 (10)

112 (19)

0.82

0.0082

0.84

91

0.84

0.016

TC04000460.hg.1

AGPAT9

70 (12)

89 (18)

0.78

0.022

0.79

55

1.00

 

TC12000227.hg.1

PDE3A

109 (22)

133 (22)

0.82

0.028

 

100

0.82

0.033

TC09001585.hg.1

SCAI

62 (5)

69 (7)

0.90

0.034

 

67

0.88

0.016

TC15000030.hg.1

GOLGA8IP

195 (17)

217 (22)

0.90

0.034

 

186

0.93

0.08

TC05000782.hg.1

ARHGAP26

127 (18)

109 (13)

1.16

0.034

 

111

1.09

0.078

TC22000816.hg.1

ST13

499 (38)

558 (57)

0.89

0.035

 

537

0.89

0.016

TC20000575.hg.1

SIGLEC1

189 (30)

164 (18)

1.15

0.035

 

164

1.11

0.072

TC09000495.hg.1

ANP32B

215 (14)

237 (21)

0.91

0.038

 

244

0.92

0.019

TC15000157.hg.1

GOLGA8J

228 (24)

257 (28)

0.89

0.038

 

203

0.91

0.03

TC05000212.hg.1

ISL1

80 (10)

99 (25)

0.80

0.041

 

29

0.99

 

TC02001974.hg.1

PAIP2B

96 (10)

114 (17)

0.85

0.042

0.93

87

0.89

 

TC15002013.hg.1

TARSL2

79 (4)

86 (7)

0.92

0.043

 

83

0.94

0.078

TC05001954.hg.1

FAT2

56 (7)

49 (5)

1.14

0.043

 

76

1.11

 

TC01003789.hg.1

ST13P19

52 (5)

58 (6)

0.89

0.043

 

51

0.91

0.048

TC15002805.hg.1

ULK4P1

172 (38)

220 (50)

0.78

0.047

 

142

0.82

0.019

TC17001703.hg.1

MBTD1

106 (7)

116 (11)

0.91

0.048

 

109

0.92

0.031

TC20000926.hg.1

KCNB1

150 (20)

127 (23)

1.18

0.052

 

159

1.12

 

TC06004132.hg.1

MOCS1

162 (33)

205 (40)

0.79

0.052

 

190

0.86

0.014

TC05001714.hg.1

LOX

206 (38)

169 (29)

1.22

0.052

 

291

1.07

 

TC05001317.hg.1

CCL28

58 (4)

63 (6)

0.91

0.058

 

61

0.97

 

TC07001811.hg.1

AASS

103 (11)

118 (16)

0.88

0.059

 

95

0.85

0.021

TC08002581.hg.1

CA3

110 (59)

242 (176)

0.45

0.062

0.42

99

0.81

 

TC03000892.hg.1

SERPINI1

49 (5)

54 (6)

0.91

0.062

0.81

36

0.95

 

TC11000898.hg.1

NAALAD2

39 (7)

48 (10)

0.80

0.066

 

54

0.74

0.019

TC15000160.hg.1

ULK4P3

147 (37)

188 (43)

0.78

0.07

 

115

0.80

0.02

TC01000619.hg.1

CDKN2C

110 (18)

134 (25)

0.82

0.072

 

134

0.92

 

TC10000289.hg.1

RASSF4

142 (19)

126 (13)

1.13

0.072

1.23

153

1.12

0.059

TC19000034.hg.1

CIRBP

565 (44)

615 (54)

0.92

0.072

 

564

0.97

 

TC18000224.hg.1

PHLPP1

91 (6)

101 (9)

0.91

0.072

 

105

0.89

0.03

TC13000436.hg.1

UPF3A

177 (13)

190 (16)

0.93

0.072

 

209

0.96

 

TC04001410.hg.1

ADH1B

3013 (467)

3478 (495)

0.87

0.074

 

3236

0.80

0.017

TC15001546.hg.1

DAPK2

146 (21)

171 (26)

0.86

0.074

 

159

0.85

0.0088

TC04001305.hg.1

CXCL10

60 (57)

36 (11)

1.67

0.074

 

57

0.99

 

TC09000319.hg.1

TJP2

166 (14)

179 (14)

0.93

0.074

 

194

0.98

 

TC03000187.hg.1

MYD88

172 (16)

158 (14)

1.09

0.076

1.12

193

1.06

 

TC07001493.hg.1

GTF2IRD2P1

151 (14)

163 (15)

0.92

0.081

 

158

0.92

 

TC02002891.hg.1

ARL4C

77 (9)

69 (10)

1.12

0.081

 

61

1.09

 

TC09002904.hg.1

NIPSNAP3B

78 (20)

102 (28)

0.77

0.081

 

96

0.77

0.019

TC12001300.hg.1

ABCC9

338 (63)

391 (63)

0.86

0.082

 

630

0.75

0.0026

TC12001299.hg.1

KCNJ8

129 (11)

142 (14)

0.91

0.088

 

144

0.89

0.02

TC11000933.hg.1

CEP57

98 (7)

107 (13)

0.92

0.089

 

110

0.89

0.019

TC11000802.hg.1

SLCO2B1

264 (66)

213 (52)

1.24

0.094

1.26

217

1.19

0.087

TC02000395.hg.1

PNO1

57 (5)

53 (4)

1.08

0.094

 

71

1.06

 

TC01001043.hg.1

PHGDH

119 (15)

135 (22)

0.88

0.094

 

92

0.91

 

TC11001197.hg.1

ADAMTS15

98 (12)

88 (10)

1.11

0.094

 

110

1.28

0.021

TC18000132.hg.1

RNF125

95 (10)

109 (15)

0.88

0.094

 

104

0.95

 

TC02002086.hg.1

ANKRD20A8P

42 (6)

47 (7)

0.91

0.094

 

41

0.92

0.07

TC01001866.hg.1

ADCK3

181 (13)

201 (23)

0.90

0.095

 

192

0.93

0.071

Data are shown as average (SD) for VAT or average for SAT

aComparison with published transcriptome profile [27] on VAT from insulin-resistant vs insulin-sensitive individuals

bGene expression was compared between groups using Limma and adjusting for BMI; threshold FDR < 10%

IR, insulin-resistant; IS, insulin-sensitive

To assess possible depot-specific differences in gene expression, we overlapped the gene array data from VAT and SAT. ESM Fig. 1 a shows a histogram of the per-gene correlation between gene expression in VAT and SAT tissue samples and Fig. 1b shows a boxplot of between-sample correlation. As expected, within-participant correlation is higher than between-participant. All 51 differentially expressed genes in VAT displayed directionally consistent differences in expression in SAT between insulin-resistant and insulin-sensitive women, and 30 of these genes were significant (FDR 10%; Table 3). Conversely, of the 647 differentially expressed genes in SAT, all displayed directionally consistent differences in VAT (ESM Table 1), 209 of which were nominally significant (p ≤ 0.05). The magnitude of the difference in expression of these genes between insulin-resistant and insulin-sensitive women was comparable between VAT (median difference in expression 8.8%; range 3.8–23.9%) and SAT (median 10.7%; range 4.6–38.5%). For individual genes, the median difference in ratio of expression between insulin-resistant and insulin-sensitive women was 0.027% (range 0.005–23.0%) between adipose depots. Together, these comparisons suggest that in the present cohort, IR is associated with similar dysregulations of gene expression in the examined WAT depots.
Fig. 1

DNA methylation landscape in insulin-resistant vs insulin-sensitive women in SAT (a, d), VAT (b, e) and PBMCs (c, f). Based on Illumina annotation, 112,057 (SAT), 124,089 (VAT) and 99,462 (PBMCs) CpG probes were mapped to genome regions. We calculated the average level of DNA methylation within each of the insulin-resistant (black bars) and insulin-sensitive (white bars) groups stratified on genome region in relation to functional gene regions (a, b, d) and CpG content (d, e, f). TSS1500, within 1500 bp of transcriptional start site (TSS); TSS200, within 200 bp of TSS. Genome locations: Island, CpG island; N_Shelf, upstream CpG island shelf; N_Shore, upstream CpG island shore; S_Shore, downstream CpG island shore; S_Shelf, downstream CpG island shelf; Open_sea; other CpG regions

Global pattern of CpG methylation

The average degree of DNA methylation (i.e. the average β value for all probes remaining after filtering) was compared between the insulin-resistant and insulin-sensitive groups. There were no significant differences in either SAT (insulin-resistant 0.504 ± 0.019 [average β value ± SD]); insulin-sensitive 0.507 ± 0.013), VAT (insulin-resistant 0.483 ± 0.014; insulin-sensitive 0.477 ± 0.022) or PBMCs (insulin-resistant 0.508 ± 0.020; insulin-sensitive 0.510 ± 0.015). The average level of DNA methylation stratified by genome region in relation to CpG content and functional parts of genes is shown in Fig. 1.

DMS in SAT

Comparison of CpG methylation in SAT between insulin-resistant and insulin-sensitive women was assessed at 112,057 sites. Although none of the DMS were significant after FDR correction, 10,746 were nominally significant with median differences in methylation of 0.024 (range 4 × 10−4 to 0.092) between groups (p ≤ 0.05). These data were compared with results from other DNA methylation profiling studies on SAT applying the same 450 K platform. Nilsson et al reported, in a cohort of 56 individuals, 15,627 DMS (q < 0.15) in WAT associated with type 2 diabetes [10]; 671 of the DMS overlapped with those in the present study, of which 592 displayed directionally consistent differences in methylation in both cohorts (p < 2.7 × 10−87) (ESM Table 4) [10]. In a study of 190 men and women, Rönn et al identified 39,533 CpG sites whose methylation in WAT of women was associated with BMI. Of these BMI-associated CpG sites, 2052 overlapped with the present study and 1973 displayed directionally consistent differences in methylation (p < 1 × 10−90) (ESM Table 4) [20]. Benton et al reported 3601 DMS before vs after weight loss induced by bariatric surgery [12]. Ninety-three DMS overlapped with the present study out of which 91 sites displayed directionally consistent results between obese individuals before weight loss and insulin-resistant individuals (p < 2.7 × 10−20) (ESM Table 4). Eleven DMS were confirmed by EpiTYPER; nine displayed directionally consistent results between insulin-resistant and insulin-sensitive women in both microarray and EpiTYPER analysis, of which four remained nominally significant, and three more were close to significance (p < 0.06) (ESM Table 2). It is worth noting that, of the DMS analysed by EpiTYPER, seven had been previously reported, all of which were confirmed by the present study.

Next, we merged the 647 differentially expressed genes in SAT with the 10,746 DMS and identified 223 IR-associated genes containing a total of 336 DMS (ESM Table 5). These genes are evenly distributed in the genome, and each gene contains one or a few DMS (Fig. 2). A subset of these genes is listed in Table 4. Twenty-nine genes displayed direct, positive or negative, correlation between gene expression and methylation (ESM Table 6). Whereas CpG methylation in 5’ regions of genes has classically been associated with reduced gene expression, CpG methylation in gene bodies has been reported to stimulate gene expression [23]. It was therefore of interest to map the IR-associated DMS in relation to gene region, and relate the degree of methylation to gene expression. Among 158 DMS in 5’ regions of genes, 67 CpG sites displayed reciprocal direction of effect between gene expression and CpG methylation. Among 178 DMS in gene bodies and 3’ untranslated regions (3’UTRs), 80 CpG sites displayed a positive association between changes in DNA methylation and gene expression. Thus, there was no evidence that DNA methylation in the 5’ regions of genes preferentially repressed gene expression, nor the opposite in gene bodies.
Fig. 2

Chromosomal position of 223 IR-associated genes containing a total of 336 DMS. Inner circle shows gene expression data (blue, upregulated expression in IR; yellow, downregulated expression in IR), outer circle represents methylation data (blue, high methylation in IR; yellow, low methylation in IR)

Table 4

A subset of differentially expressed genes accompanied by DMS in SAT between insulin-resistant vs insulin-sensitive womena

Probe

Gene

Relation to gene region

DNA methylation

Gene expression

IS average

IR − IS

p value

T2Db,c [10]

BMIb,d [20]

GBPb,e [12]

IS average

IR/IS

p value

cg07251857

ALPK3

1st exon

0.546

0.026

0.022

 

0.016

 

76

0.89

2.56 × 10−3

cg06532379

ALPK3

1st exon

0.193

0.039

0.015

 

0.015

 

76

0.89

2.56 × 10−3

cg14080050

B4GALT1

Body

0.447

−0.037

0.015

 

−0.014

 

228

1.10

1.48 × 10−3

cg13858803

B4GALT1

Body

0.566

0.027

0.040

 

0.027

 

228

1.10

1.48 × 10−3

cg00300298

BCL2L1

Body

0.251

−0.037

0.038

 

−0.019

 

151

1.07

2.01 × 10−3

cg12873919

BCL2L1

Body

0.504

−0.036

0.032

   

151

1.07

2.01 × 10−3

cg03290977

C1QTNF7

Body

0.247

−0.035

0.034

 

−0.024

 

44

0.86

3.28x10−3

cg01939704

C1QTNF7

Body

0.616

0.020

0.022

   

44

0.86

3.28 × 10−3

cg07538039

C1QTNF7

Body

0.610

0.025

0.021

   

44

0.86

3.28 × 10−3

cg06097727

C1QTNF7

Body

0.547

0.035

0.043

 

0.016

 

44

0.86

3.28 × 10−3

cg24829483

C1QTNF7

5′UTR

0.633

0.039

0.034

   

44

0.86

3.28 × 10−3

cg00545229

C1QTNF7

TSS200

0.563

0.041

0.018

   

44

0.86

3.28 × 10−3

cg15372098

C3orf26

Body

0.027

−0.016

0.014

   

69

0.92

3.98 × 10−4

cg00991994

C3orf26

Body

0.401

0.055

0.039

0.067

0.035

 

69

0.92

3.98 × 10−4

cg17351376

CD248

1st exon

0.504

0.019

0.032

   

239

1.42

1.03 × 10−3

cg07145284

CD248

TSS200

0.085

0.029

0.038

 

0.018

 

239

1.42

1.03 × 10−3

cg00350296

CD248

TSS1500

0.158

0.041

0.018

 

0.022

 

239

1.42

1.03 × 10−3

cg13860849

CD248

1st exon

0.191

0.054

0.002

 

0.015

 

239

1.42

1.03 × 10−3

cg10772263

CHST3

5′UTR

0.322

0.020

0.028

 

0.025

 

113

1.17

1.31 × 10−3

cg04268405

CHST3

TSS1500

0.369

0.042

0.024

  

0.219

113

1.17

1.31 × 10−3

cg12081643

COL4A1

3′UTR

0.670

−0.042

0.008

   

530

1.17

1.34 × 10−3

cg20818806

COL4A1

Body

0.299

0.042

0.019

   

530

1.17

1.34 × 10−3

cg02658690

COL4A1

Body

0.207

0.042

0.014

  

0.218

530

1.17

1.34 × 10−3

cg10908116

COL4A1

Body

0.247

0.043

0.017

0.053

0.026

 

530

1.17

1.34 × 10−3

cg02099572

COL4A1

Body

0.140

0.047

0.005

0.056

  

530

1.17

1.34 × 10−3

cg03430597

COL5A1

Body

0.751

0.018

0.004

 

0.018

 

162

1.10

5.97 × 10−4

cg24354213

COL5A1

Body

0.601

0.027

0.023

 

0.014

 

162

1.10

5.97 × 10−4

cg14274542

COL5A1

Body

0.596

0.037

0.019

 

0.012

 

162

1.10

5.97 × 10−4

cg10765212

COL5A2

TSS200

0.129

0.021

0.047

   

246

1.20

3.25 × 10−4

cg15194531

FMNL1

Body

0.466

0.041

0.005

 

0.018

 

165

1.09

5.32 × 10−4

cg08145262

FRS2

5′UTR

0.658

0.031

0.020

 

0.020

 

155

0.93

1.64 × 10−3

cg19563525

FRS2

5′UTR

0.382

0.035

0.006

 

0.017

 

155

0.93

1.64 × 10−3

cg10227830

GAB1

Body

0.272

0.039

0.016

   

141

0.89

1.53 × 10−4

cg25911551

GAB1

Body

0.494

0.046

0.049

 

0.019

 

141

0.89

1.53 × 10−4

cg08202226

GATAD2B

TSS1500

0.793

−0.057

0.018

 

−0.029

 

282

0.94

3.97 × 10−3

cg05514401

IRS2

1st exon

0.792

0.031

0.002

 

0.028

 

242

0.85

1.24 × 10−3

cg11624345

KCNN4

Body

0.391

0.025

0.025

 

0.014

 

87

1.06

4.11 × 10−3

cg03731131

KCNN4

Body

0.378

0.032

0.039

   

87

1.06

4.11 × 10−3

cg22904711

KCNN4

Body

0.313

0.060

0.002

0.047

0.015

 

87

1.06

4.11 × 10−3

cg14616541

MYH10

Body

0.834

0.024

0.010

   

292

0.87

1.64 × 10−3

cg22588546

MYH10

Body

0.496

0.047

0.008

0.039

  

292

0.87

1.64 × 10−3

cg21542094

PFKFB3

TSS1500

0.081

−0.001

0.025

 

−0.013

 

542

0.80

5.53 × 10−5

cg00902516

PFKFB3

Body

0.739

0.020

0.019

 

0.016

 

542

0.80

5.53 × 10−5

cg03261682

PFKFB3

Body

0.780

0.028

0.006

 

0.026

 

542

0.80

5.53 × 10−5

cg05686026

PFKFB3

Body

0.683

0.045

0.001

 

0.033

 

542

0.80

5.53 × 10−5

cg03478610

PPP2R3A

5′UTR

0.871

−0.031

0.034

 

−0.014

 

91

0.93

1.49 × 10−3

cg00369142

PPP2R3A

3′UTR

0.378

0.044

0.013

0.060

0.025

 

91

0.93

1.49 × 10−3

cg11468953

PTPRJ

Body

0.519

−0.039

0.027

 

−0.020

 

139

1.18

3.07 × 10−3

cg12124589

QSOX1

Body

0.775

−0.032

0.027

 

−0.020

 

175

1.08

1.70 × 10−3

cg09505809

QSOX1

TSS1500

0.179

0.039

0.031

   

175

1.08

1.70 × 10−3

cg00971364

RBMS3

TSS200

0.043

−0.018

0.034

   

381

0.90

5.14 × 10−4

cg23537305

RBMS3

Body

0.819

0.016

0.045

 

0.017

 

381

0.90

5.14 × 10−4

cg20299414

RBMS3

Body

0.729

0.035

0.018

 

0.013

 

381

0.90

5.14 × 10−4

cg27569887

RBMS3

3′UTR

0.698

0.043

0.026

   

381

0.90

5.14 × 10−4

cg16572224

SH3PXD2B

Body

0.816

−0.049

0.002

−0.039

−0.019

 

145

1.13

4.76 × 10−3

cg05223396

SH3PXD2B

Body

0.404

0.025

0.049

   

145

1.13

4.76 × 10−3

cg09744420

STX11

Body

0.654

0.041

0.002

 

0.020

 

356

0.89

2.98 × 10−3

cg19841369

SYNE2

Body

0.159

0.028

0.044

 

0.016

 

236

0.89

2.18 × 10−3

cg16725974

SYNE2

5′UTR

0.532

0.046

0.027

0.057

0.022

 

236

0.89

2.18 × 10−3

cg23250157

SYNE2

Body

0.756

0.061

0.018

   

236

0.89

2.18 × 10−3

cg18837713

ZDHHC17

Body

0.616

0.045

0.010

 

0.027

 

161

0.94

5.15 × 10−3

aDifferentially expressed genes (10% FDR) accompanied by DMS (p < 0.05) in SAT between insulin-resistant and insulin-sensitive women. Groups were compared using Limma and adjusting for BMI (gene expression, DMS) and age (DMS). This table contains a subset of the ESM Table 4 and focuses on DMS confirmed from the literature and mentioned in the discussion

bComparison with published transcriptome profiles on SAT from insulin-resistant vs insulin-sensitive individuals

cT2D vs control

dRegression coefficient

eAfter vs before bariatric surgery and weight loss

IR, insulin-resistant; IS, insulin-sensitive; T2D, type 2 diabetes

The 223 IR-associated genes were over-represented for pathways related to integrin cell surface interactions, focal adhesion and insulin signalling (ESM Table 7). Data for the insulin signalling genes are shown in Table 5.
Table 5

Differentially expressed insulin signalling pathway genes accompanied by DMS in SAT between insulin-resistant and insulin-sensitive womena

Probe

Gene

Relation to gene region

DNA methylation

Gene expression

IR

IS

IR − IS

p value

IS

IR/IS

p value

cg17133045

AKT3

Body

0.761 (0.047)

0.735 (0.051)

0.026

0.007

144

0.92

4.38 × 10−3

cg04221461

AKT3

Body

0.524 (0.070)

0.490 (0.043)

0.034

0.002

144

0.92

4.38 × 10−3

cg08428486

BRAF

Body

0.807 (0.122)

0.833 (0.035)

−0.026

0.048

235

0.92

3.96 × 10−4

cg25204078

BRAF

TSS1500

0.771 (0.040)

0.757 (0.042)

0.014

0.034

235

0.92

3.96 × 10−4

cg06748146

HK1

Body

0.734 (0.047)

0.709 (0.044)

0.026

0.007

170

1.09

3.07 × 10−3

cg05514401

IRS2

1st exon

0.823 (0.065)

0.792 (0.048)

0.031

0.002

242

0.85

1.24 × 10−3

cg18932526

MAPK8

TSS1500

0.907 (0.099)

0.929 (0.020)

−0.022

0.020

73

0.94

3.47 × 10−3

cg19612574

MAPK8

TSS1500

0.935 (0.074)

0.950 (0.019)

−0.015

0.022

73

0.94

3.47 × 10−3

cg20994699

PDX103A

Body

0.572 (0.092)

0.544 (0.086)

0.028

0.048

100

0.82

4.48 × 10−4

cg03465562

PHKA2

Body

0.929 (0.092)

0.953 (0.019)

−0.024

0.021

167

0.91

9.80 × 10−4

DNA methylation data are expressed as average (SD); gene expression data are expressed as average

IR, insulin-resistant; IS, insulin-sensitive; TSS1500, within 1500 bp of transcriptional start site

DMS in VAT

CpG methylation in VAT was assessed at 124,089 sites. Although none of the DMS were significant after FDR correction, 10,217 were nominally significant (p ≤ 0.05) between insulin-resistant and insulin-sensitive women with median difference in methylation of 0.028 (range 0.001–0.105) (ESM Table 8). We mapped the 10,217 DMS from the present study to other DNA methylation profiling studies in VAT that used the 450 K platform. Benton et al reported 15 DMS in VAT before vs after weight loss induced by bariatric surgery, of which two CpG sites displayed nominally significant and directionally consistent results in the present study (p < 0.2) (ESM Table 8) [12]. Guenard et al listed 83 DMS in VAT associated with the metabolic syndrome [24] and, of these, none were differentially methylated in VAT between insulin-resistant and insulin-sensitive women in the present study. Finally, we compared results between SAT and VAT in the present study. Among nominally significant DMS between insulin-resistant and insulin-sensitive women, 1455 CpG sites overlapped between SAT and VAT, 1406 of which displayed directionally consistent results between depots (ESM Table 8).

Next, we merged the 51 differentially associated expressed genes in VAT with the 10,217 DMS and thus identified 18 IR-associated genes containing a total of 29 DMS (Table 6). There were three DMS in two differentially expressed genes that were common between SAT and VAT; cg14229247 (in ANP32B), and cg08400424 and cg11796181 (both in ARHGAP26) (Table 6). cg14229247 in ANP32B could not be confirmed by EpiTYPER, whereas we were unable to design assays for the DMS in ARHGAP26, leaving some uncertainty to these results (ESM Table 2). Four genes displayed direct, positive or negative, correlation between gene expression and methylation in VAT (ESM Table 6). Of the 11 DMS in the 5′ region of genes, seven CpG sites displayed an inverse association between gene expression and methylation. Among 18 DMS in gene bodies and 3′UTR regions, two CpG sites displayed coherent changes.
Table 6

Differentially expressed genes accompanied by DMS in VAT between insulin-resistant and insulin-sensitive womena

Probe

Gene

Relation to gene region

DNA methylation

Gene expression

IR

 

IS

 

IR − IS

p value

IS

IR/IS

p value

cg17174775

AASS

TSS1500

0.016

0.039

0.027

0.033

−0.011

0.0022

118

0.88

0.000121

cg09711028

ABCC9

Body

0.913

0.041

0.897

0.046

0.016

0.033

391

0.86

0.000303

cg16236108

AGPAT9

TSS200

0.062

0.042

0.076

0.04

−0.014

0.027

89

0.78

7.62 × 10−6

cg14229247b

ANP32B

TSS1500

0.04

0.048

0.048

0.046

−0.009

0.038

237

0.91

3.61 × 10−5

cg08400424b

ARHGAP26

Body

0.55

0.1

0.597

0.103

−0.047

0.028

109

1.16

2.33 × 10−5

cg05185926

ARHGAP26

3′UTR

0.712

0.101

0.75

0.121

−0.038

0.025

109

1.16

2.33 × 10−5

cg11796181b

ARHGAP26

Body

0.754

0.043

0.708

0.047

0.046

0.00017

109

1.16

2.33 × 10−5

cg12264626

CA3

TSS1500

0.183

0.054

0.162

0.095

0.021

0.036

242

0.45

0.000136

cg00908631

CDKN2C

TSS1500

0.668

0.052

0.632

0.06

0.036

0.0011

134

0.82

0.000184

cg10156302

DAPK2

Body

0.605

0.091

0.552

0.111

0.053

0.01

171

0.86

0.000235

cg23165541

DAPK2

5′UTR

0.403

0.094

0.363

0.09

0.039

0.014

171

0.86

0.000235

cg06904649

DAPK2

Body

0.767

0.037

0.744

0.043

0.022

0.043

171

0.86

0.000235

cg16151151

ISL1

Body

0.196

0.069

0.172

0.095

0.024

0.016

99

0.8

0.000045

cg17686487

ISL1

Body

0.434

0.082

0.395

0.089

0.039

0.012

99

0.8

0.000045

cg16270526

ISL1

Body

0.225

0.069

0.185

0.077

0.04

0.023

99

0.8

0.000045

cg26422022

LOX

TSS200

0.032

0.039

0.039

0.036

−0.007

0.040419

169

1.22

9.74 × 10−5

cg22836153

LOX

Body

0.057

0.038

0.065

0.033

−0.008

0.042

169

1.22

9.74 × 10−5

cg03422350

MOCS1

Body

0.722

0.068

0.68

0.071

0.042

0.031

205

0.79

9.42 × 10−5

cg10791278

MOCS1

Body

0.782

0.056

0.737

0.061

0.045

0.0016

205

0.79

9.42 × 10−5

cg06023702

PAIP2B

TSS200

0.029

0.043

0.04

0.041

−0.011

0.018

114

0.85

5.02 × 10−5

cg06241044

PAIP2B

5′UTR

0.265

0.052

0.242

0.066

0.023

0.038

114

0.85

5.02 × 10−5

cg22999327

PDE3A

Body

0.53

0.105

0.485

0.124

0.045

0.028

133

0.82

1.19 × 10−5

cg02631767

PDE3A

Body

0.875

0.056

0.857

0.058

0.018

0.048

133

0.82

1.19 × 10−5

cg04857033

PHGDH

Body

0.376

0.078

0.336

0.089

0.04

0.049

135

0.88

0.00039

cg26166935

PHLPP1

Body

0.857

0.039

0.837

0.037

0.02

0.03

101

0.91

0.000203

cg03299121

PNO1

TSS200

0.055

0.044

0.064

0.041

−0.01

0.0003

53

1.08

0.000385

cg06123940

RNF125

TSS1500

0.798

0.044

0.785

0.043

0.012

0.029

109

0.88

0.0004

cg18101249

RNF125

Body

0.082

0.049

0.089

0.037

−0.006

0.046

109

0.88

0.0004

cg13849419

TJP2

Body

0.509

0.095

0.468

0.106

0.041

0.043

179

0.93

0.000239

DNA methylation data are expressed as average (SD); gene expression data are expressed as average

aDifferentially expressed genes (10% FDR) accompanied by DMS (p < 0.05) in VAT between insulin-resistant and insulin-sensitive women. Groups were compared using Limma and adjusting for BMI (gene expression, DMS) and age (DMS)

bDMS and differentially expressed gene common to SAT and VAT

DMS in PBMCs

We investigated whether IR was associated with systemic epigenetic differences by analysing DNA methylation profiles in PBMCs. There were no significant DMS after correction for multiple testing among the 99,462 analysed CpG sites, although 2451 were nominally significant with median differences in methylation of 0.021 (range 7 × 10−5–0.130) between groups (p ≤ 0.05) (ESM Table 9). There were 268 DMS that overlapped between SAT and PBMCs, of which 109 displayed directionally consistent results (ESM Table 4). Among DMS accompanied by differential gene expression in SAT, only three CpG sites displayed significant differential methylation in a consistent direction in PBMCs: ADAMTS2 cg26694831, average difference in β value between the insulin-resistant and insulin-sensitive women in SAT −0.037 and PBMCs −0.044 (p = 0.005), respectively; FIP1L1 cg19408398, average difference in SAT 0.026 and PBMCs 0.034 (p = 0.012), respectively; SAMD4A cg06633081 average difference in SAT −0.033 and PBMCs −0.027 (p = 0.048), respectively. EpiTYPER analyses of these CpG sites in SAT were non-significant, although DMS in ADAMTS2 and FIP1L1 remained directionally consistent (ESM Table 2).

Cell-mixture-adjusted analysis of DMS

We applied a reference-free algorithm for cell-mixture adjustment to detect DMS, and compared the results with our original whole-tissue-based results [25]. There were 2669, 14,410, and 949 DMS in SAT, VAT and PBMCs, respectively, after cell-mixture adjustment. The number of DMS overlapping between the cell-mixture-adjusted analysis and our original analysis was 948 for SAT, 2059 for VAT and 380 for PBMCs; of these 943, 1999 and 379 DMS, respectively, displayed directionally consistent results (ESM Tables 1012).

Discussion

Previous studies have linked WAT CpG methylation to adiposity and type 2 diabetes. Here, for the first time we report a comprehensive analysis of IR-associated DMS and their correlation with gene expression in SAT and VAT.

VAT mass is more strongly associated with IR than SAT, as reviewed [26]. In our genome-wide transcriptome analysis, however, there were a greater number of genes that were differentially expressed in SAT than in VAT in the insulin-resistant state. Nevertheless, the majority of the IR-associated genes displayed differences in expression that were directionally consistent between SAT and VAT. Together, these data suggest that there is no depot-specific transcriptomic signature that is associated with systemic IR. In agreement with this, Klimcáková et al reported similar alterations in the two adipose depots of obese patients with unfavourable metabolic status [27]. This suggests that other factors, such as the amount of VAT or the metabolite profile, could be more important for determining the effect of VAT on IR or other metabolic disorders. We confirm that IR-associated genes in WAT are over-represented for pathways related to immune response and angiogenesis (VEGFR signalling in the present study), whereas reported over-representation of genes important for cell cycle regulation and metabolism was not observed [21, 22]. The reason for the latter discrepancy could be due to selection of study participants.

There were no global differences in DNA methylation between the insulin-resistant and insulin-sensitive women in any of the studied tissues. A number of genes in both SAT and VAT displayed differential methylation accompanied by differential gene expression in insulin-resistant as compared with insulin-sensitive women. We did not observe any significant DMS between the insulin-resistant and insulin-sensitive groups after adjustment for multiple testing in the present dataset. However, considering all nominally significant DMS in the present study (which admittedly include false-positives), the vast majority of DMS that overlap between the present study and previous studies of BMI or type 2 diabetes display directionally consistent methylation differences in the reported cohorts. Furthermore, of the DMS analysed by EpiTYPER, seven had been previously reported and they were all confirmed. This observation suggests that many DMS are real, despite not reaching formal statistical significance in the present study. Traditionally, methylation of CpG islands in promoters has been associated with repression of gene expression whereas CpG sites in gene bodies often display a positive association between methylation and expression [23]. In the present study there was no evidence that DNA methylation in the 5′ regions of genes preferentially repressed gene expression, nor the opposite in gene bodies. Interestingly, the link between transcriptional repression and DNA methylation is less clear for non-CpG island promoters (CpG-poor promoters); many active genes have methylated CpG-poor promoters [28]. Together, the above findings suggest that the relationship between CpG methylation and IR is complex, comprising many CpG sites that have a modest association with IR and a variable impact on gene expression.

There were 223 IR-associated genes with DMS in SAT that were over-represented for pathways related to integrin cell surface interactions, focal adhesion and insulin signalling. Integrins constitute a component of the extracellular matrix and previously have been implicated in adipose remodelling in conjunction with obesity and IR [29, 30]. Specific IR-associated genes with DMS are listed in Table 7, together with potential mechanisms that could explain their association with insulin sensitivity (details on CpG methylation are given in Table 4). These specific genes all have DMS that confirm previous findings, and are associated with adipose tissue and insulin signalling in the literature according to PubMatrix (http://pubmatrix.grc.nia.nih.gov/, accessed 31 August 2015).
Table 7

Selected IR-associated genes with DMS

Gene

Expression and CpG-methylation in SAT: observations from the current study

Previously reported findings of gene/protein function

GAB1

SAT CpG methylation in the gene body was inversely associated with gene expression and IR was associated with lower GAB1 expression (fold change IR vs IS: 0.89)

GAB1 is an adaptor molecule that can stimulate adipocyte glucose uptake through a GAB1/PI 3-kinase/PKB/AS160 pathway [31]

PFKFB3

SAT CpG methylation in the promoter was directly associated with gene expression, whilst CpG methylation in the gene body was inversely associated. IR was associated with lower PFKFB3 expression (fold change IR vs IS: 0.80)

PFKFB3 regulates the steady-state concentration of fructose-2,6-bisphosphate, a potent activator of a key regulatory enzyme of glycolysis. Fat cell overexpression of PFKFB3 enhances insulin sensitivity [32]

IRS2

SAT CpG methylation in the 5′ region was inversely associated with gene expression and IR was associated with lower IRS2 expression (fold change IR vs IS: 0.85)

IRS2 mediates the effects of insulin on glucose homeostasis and cell growth

PTPRJ

SAT CpG methylation in the gene body was inversely associated with gene expression and IR was associated with higher PTPRJ expression (fold change IR vs IS: 1.18)

Recently it was shown that high-fat diet fed Ptprj −/− mice displayed enhanced insulin sensitivity and improved glucose tolerance, thus establishing PTPRJ as a negative regulator of insulin signalling [33]

AS160, Akt substrate 160-KD; GAB1, growth factor receptor bound protein 2-associated binding protein 1; PFKFB3, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3; PI 3-kinase, phosphatidylinositol 3-kinase; PKB, protein kinase B; PTPRJ, protein-tyrosine phosphatases, receptor-type, J

Although, overall, the CpG methylation in PBMCs did not mirror DMS in SAT associated with IR, a few DMS accompanied by differential gene expression in SAT displayed significant differential methylation in a direction consistent with that in PBMCs. CpG methylation results for FIP1L1 and ADAMTS2 remained directionally consistent in validation experiments. FIP1L1 which encodes FIP 1-like, primarily characterised as a fusion protein (FIP1L1-PDGFRA) in hypereosinophilic disorders [34]. ADAMTS2 encodes procollagen I N-proteinase that excises the N-propeptide of type I and type II procollagens. Mutation in ADAMTS2 causes the connective tissue disease Ehlers–Danlos syndrome. None of these genes have been characterised in relation to insulin sensitivity. Neither PBMC, SAT nor VAT DNA methylation signatures could confirm the previously reported association of global leucocyte DNA methylation with IR [35]. Furthermore, a DMS in the ABCG1 gene in T cells that previously has been associated with HOMA-IR was not detected in the present study [13]. In most cases differences in both gene expression and DNA methylation between groups in the present study were small. One reason for the small differences in DNA methylation could be that DNA from adipose tissue, which contains different cell types having potentially different DNA methylation signatures, were studied. Similarly we investigated unfractionated PBMCs, and the DNA methylation pattern in subpopulations of these cells may differ [9].

There are sex differences in insulin sensitivity [36] and since we only investigated women it is unknown at present whether DNA methylation may have a different role for IR in obese men.

Conclusion

Whereas global DNA CpG methylation in adipose tissue is not associated with systemic IR, specific genes display differential expression in SAT accompanied by DMS. Such genes include GAB1, IRS2, PFKFB3, and PTPRJ. Further analysis of the function and epigenetic regulation of these genes in fat cells will help determine their potential causal role in systemic IR. CpG methylation in PBMCs does not reflect DMS in WAT, suggesting that epigenetic analyses in circulating leucocytes are not suitable for metabolic phenotyping of obese individuals.

Notes

Acknowledgements

The microarray hybridisations were done at BEA (www.bea.ki.se). We wish to thank M. Rönnholm and P. Muller for excellent technical assistance with the array assays (Karolinska Institutet, Stockholm, Sweden). We thank the Mutation Analysis Core Facility (MAF) at the Karolinska University Hospital (Stockholm, Sweden) for their support with the EpiTYPER assay, especially A.-C. Rönn. This study was supported by the SRP Diabetes program at Karolinska Institutet, CIMED, the Swedish Research Council, the Erling-Persson Family Foundation, Novo Nordisk Foundation, EASD/Eli-Lilly Foundation, the Swedish Diabetes Foundation and the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant no. 115372).

Duality of interest

ASS, HX, DW, DKR and TJ are employed by GlaxoSmithKline. XY is employed by Janssen. AKL is employed by Pfizer. All other authors declare that there is no duality of interest associated with their contribution to this manuscript.

Contribution statement

PA and MR planned the project. ID, AT, MR, EN and PA were responsible for acquisition of data. ID, IS, ASS, HX, JMF, DW, DR, AKL, TJ and XY analysed data. ID wrote the draft manuscript. All authors contributed to the interpretation of data and revision of the manuscript draft, and approved the final version. PA is the guarantor of this work.

Supplementary material

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© The Author(s) 2016

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Peter Arner
    • 1
  • Anna-Stina Sahlqvist
    • 2
  • Indranil Sinha
    • 3
  • Huan Xu
    • 4
  • Xiang Yao
    • 5
  • Dawn Waterworth
    • 6
  • Deepak Rajpal
    • 6
  • A. Katrina Loomis
    • 7
  • Johannes M. Freudenberg
    • 6
  • Toby Johnson
    • 2
  • Anders Thorell
    • 8
    • 9
  • Erik Näslund
    • 9
  • Mikael Ryden
    • 1
  • Ingrid Dahlman
    • 1
  1. 1.Department of MedicineKarolinska Institutet, Karolinska University HospitalStockholmSweden
  2. 2.GlaxoSmithKline R&DStevenageUK
  3. 3.Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
  4. 4.GlaxoSmithKline R&DResearch Triangle ParkUSA
  5. 5.Computational and Systems Biology, Discovery Sciences, Janssen Pharmaceutical, Research & DevelopmentLLCSan DiegoUSA
  6. 6.GlaxoSmithKline R&DKing of PrussiaUSA
  7. 7.Pfizer Worldwide Research and DevelopmentGrotonUSA
  8. 8.Department of SurgeryErsta HospitalStockholmSweden
  9. 9.Department of Clinical SciencesKarolinska Institutet, Danderyd HospitalDanderydSweden

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