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Genome-wide analysis of DNA methylation in subjects with type 1 diabetes identifies epigenetic modifications associated with proliferative diabetic retinopathy

  • Elisabet Agardh1,
  • Annika Lundstig2,
  • Alexander Perfilyev2,
  • Petr Volkov2,
  • Tove Freiburghaus2,
  • Eero Lindholm3,
  • Tina Rönn2,
  • Carl-David Agardh3 and
  • Charlotte Ling2Email author
BMC Medicine201513:182

https://doi.org/10.1186/s12916-015-0421-5

Received: 3 November 2014

Accepted: 15 July 2015

Published: 6 August 2015

Abstract

Background

Epigenetic variation has been linked to several human diseases. Proliferative diabetic retinopathy (PDR) is a major cause of vision loss in subjects with diabetes. However, studies examining the association between PDR and the genome-wide DNA methylation pattern are lacking. Our aim was to identify epigenetic modifications that associate with and predict PDR in subjects with type 1 diabetes (T1D).

Methods

DNA methylation was analyzed genome-wide in 485,577 sites in blood from cases with PDR (n = 28), controls (n = 30), and in a prospective cohort (n = 7). False discovery rate analysis was used to correct the data for multiple testing. Study participants with T1D diagnosed before 30 years of age and insulin treatment within 1 year from diagnosis were selected based on 1) subjects classified as having PDR (cases) and 2) subjects with T1D who had had diabetes for at least 10 years when blood DNA was sampled and classified as having no/mild diabetic retinopathy also after an 8.7-year follow-up (controls). DNA methylation was also analyzed in a prospective cohort including seven subjects with T1D who had no/mild diabetic retinopathy when blood samples were taken, but who developed PDR within 6.3 years (converters). The retinopathy level was classified by fundus photography.

Results

We identified differential DNA methylation of 349 CpG sites representing 233 unique genes including TNF, CHI3L1 (also known as YKL-40), CHN2, GIPR, GLRA1, GPX1, AHRR, and BCOR in cases with PDR compared with controls. The majority of these sites (79 %) showed decreased DNA methylation in cases with PDR. The Natural Killer cell-mediated cytotoxicity pathway was found to be significantly (P = 0.006) enriched among differentially methylated genes in cases with PDR. We also identified differential DNA methylation of 28 CpG sites representing 17 genes (e.g. AHRR, GIPR, GLRA1, and BCOR) with P <0.05 in the prospective cohort, which is more than expected by chance (P = 0.0096).

Conclusions

Subjects with T1D and PDR exhibit altered DNA methylation patterns in blood. Some of these epigenetic changes may predict the development of PDR, suggesting that DNA methylation may be used as a prospective marker of PDR.

Keywords

Diabetic complicationDNA methylationEpigeneticsInflammationPredictionProliferative retinopathyProspectiveType 1 diabetes

Background

Proliferative diabetic retinopathy (PDR) and macular edema are the main causes of vision loss in subjects with diabetes [1]. Subjects with type 1 diabetes (T1D) have a higher prevalence of PDR than those with type 2 diabetes (T2D) and key risk factors for development of PDR include hyperglycemia, diabetes duration, hypertension, and genetic factors [1]. The Diabetes Control and Complications Trial has shown that intensive glycemic control reduces the incidence of PDR in subjects with T1D [2]. However, after said trial, the Epidemiology of Diabetes Interventions and Complications research group continued to follow the same subjects and found that the measure of glycemic control, HbA1c, deteriorated in the previously intensively treated group of subjects, while it improved in the conventionally treated one, so that the subsequent HbA1c levels no longer differed during the 10-year follow-up period [3]. Yet the disparity in the cumulative incidence of further progression of PDR between the groups continued to increase, indicating that mechanisms underlying the progressive alterations in retinal microvessels might be long lasting [3]. It has been hypothesized that transient peaks of hyperglycemia might be an independent risk factor for progression of retinopathy in these subjects and that hyperglycemic peaks may cause persistent epigenetic changes despite subsequent normoglycemia [4]. Epigenetic modifications, such as DNA methylation and histone modifications, influence many cellular processes including regulation of gene transcription, embryonic development, X chromosome inactivation, and genomic imprinting [5]. Altered epigenetic patterns may contribute to disease development and differential DNA methylation has been found in subjects with T1D and T2D compared with non-diabetic controls [610]. Epigenetic modifications may also influence the development of vascular complications in diabetic subjects and a recent study demonstrated that differential DNA methylation was associated with diabetic nephropathy [11]. However, whether epigenetic modifications are associated with PDR in subjects with T1D remains unknown. The aim of this study was therefore to analyze the genome-wide DNA methylation pattern in blood from subjects with T1D, including 28 cases with PDR and 30 controls. Using a prospective cohort, we also tested if changes in DNA methylation can be found in subjects with T1D prior to development of PDR and thereby predict the disease.

Methods

Study participants

Study participants with T1D, diagnosed before 30 years of age and insulin treatment within 1 year from diagnosis, were selected from the regional diabetes register (Diabetes 2000) according to the following criteria: 1) subjects with T1D for at least 10 years and classified as having no/mild diabetic retinopathy with, at most, a few microaneurysms at initiation of the study (n = 37), and 2) subjects with new-formed pathological retinal vessels classified as having PDR (n = 28). All included subjects had genomic DNA isolated from blood cells available. The degree of retinopathy was verified by available fundus photographs (EA) or by record information on vitreous hemorrhage at the time of blood sampling. The degree of retinopathy was thereafter established approximately once per year by fundus photography. After an approximately 6.3-year follow-up, seven out of 37 subjects had developed PDR, forming a prospective follow-up cohort, and were classified as converters. Additionally, after an approximately 8.7-year follow-up, 30 out of 37 subjects were still without PDR and classified as controls; the 28 subjects with PDR were classified as cases. The characteristics of cases and controls are described in Table 1 and of converters and controls in Additional file 1: Table S1. The study was approved by the Regional Ethical Committee (Lund University) and conducted in accordance with the principles of the Helsinki Declaration. All study participants gave written informed consent.
Table 1

Characteristics of cases with proliferative diabetic retinopathy and controls included in the genome-wide analysis of DNA methylation in blood from subjects with type 1 diabetes

Characteristics

Controls

Cases

P value

N (male/female)

30 (15/15)

28 (17/11)

 

Age (years)*

36.1 ± 13.7

34.0 ± 8.5

1

Diabetes duration (years)

17.1 ± 9.6

21.0 ± 3.8

1 × 10−4

HbA1c (%)

7.1 ± 1.2

8.6 ± 1.6

2 × 10−4

Systolic blood pressure (mmHg)

126.7 ± 15.9

133.0 ± 21.4

0.44

Diastolic blood pressure (mmHg)

72.9 ± 9.9

77.0 ± 8.1

0.15

Data are presented a mean ± SD. P value based on Mann–Whitney two independent samples test. *Age at DNA sampling and fundus photography

Genome-wide analysis of DNA methylation

The genome-wide DNA methylation analysis was performed using the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA, USA), which contains 485,577 probes that cover 21,231 (99 %) RefSeq genes [12]. Genomic DNA (500 ng) from blood was bisulfite treated using the EZ DNA methylation kit (Zymo Research, Orange, CA, USA) and subsequently used for analysis of DNA methylation following the Infinium HD Assay Methylation Protocol (Illumina). The BeadChips’ images were captured using the Illumina iScan. All included samples showed a high quality bisulfite conversion efficiency (intensity signal >4000) and passed all GenomeStudio quality control steps based on built-in control probes for staining, hybridization, extension, and specificity. In total, 2,309 individual probes were filtered away based on mean detection P >0.01 and 85 Y chromosome probes were filtered away. The raw methylation data was exported from GenomeStudio and analyzed using Bioconductor and the lumi package. Here, β-values were converted to M-values (M=log2(β/(1-β))) [13]. Next, data was background corrected by subtracting the median M-value of built in control probes and normalized using quantile normalization [13]. The DNA methylation array data were further corrected for batch effects using COMBAT [13]. Finally, to more easily interpret the results, M-values were reconverted to β-values, which is used when describing the data and creating the figures. All identified significant CpG sites with differences in DNA methylation between PDR cases and controls were screened for cross-reactive probes in the Illumina Infinium HumanMethylation450k BeadChip [14].

Statistical analyses

We performed a principle component analysis of the genome-wide DNA methylation data and correlated the top five principle components with PDR, gender, age, duration of diabetes, and HbA1c. To identify differences in DNA methylation between cases with PDR and controls, a logistic regression model was used, including gender, age, and duration of diabetes as covariates and DNA methylation as a quantitative variable. A false discovery rate (FDR) analysis was used to correct for multiple testing and a FDR less than 5 % (q <0.05) was considered significant. Significant CpG sites were analyzed in the prospective cohort using the same statistical model and covariates, and presented if P <0.05.

Results

Differential DNA methylation of specific CpG sites in cases with PDR

To study the epigenetic basis of PDR in subjects with T1D, DNA methylation of 480,079 CpG sites was analyzed in blood from 28 cases with PDR and 30 controls using the Infinium HumanMethylation450 BeadChip. The characteristics of the cases and controls included in the genome-wide analysis of DNA methylation are described in Table 1. Age was similar between the groups but cases with PDR had longer diabetes duration and higher HbA1c levels than controls (Table 1). To examine the impact of these factors on DNA methylation, we performed a principle component analysis of the genome-wide DNA methylation data and correlated the top five principle components with PDR, gender, age, duration of diabetes, and HbA1c levels. While PDR, gender, age, and duration of diabetes showed significant correlations with one or several of the top five principle components (P <0.05; Additional file 2: Table S2); HbA1c was not significant in this analysis. Based on this result, we included gender, age, and duration of diabetes as covariates in the regression model where the association between PDR and DNA methylation was studied. After correcting for multiple testing using a FDR analysis, we identified 349 CpG sites representing 233 unique genes that were differentially methylated between cases with PDR and controls (q <0.05; Additional file 3: Table S3). We found decreased DNA methylation in 276 sites (79 %) and increased DNA methylation in 73 sites (21 %) in PDR cases. The absolute differences in DNA methylation ranged from 0.08 % to 13.5 %, representing a fold change of 0.549 to 1.231 and a 45.1 % decrease to a 23.1 % increase in methylation in PDR cases compared with controls (Fig. 1a,b and Additional file 3: Table S3). Based on a literature search, genes with known functions in retina and eye development (BCOR, GFI1, GLRA1, HDAC4, HMGA1, HTT, KLF5, LAMA4, and SHANK1) [15], inflammation (AHRR, CCL1, CD247, CD58, CD9, CXCR5, KDM1A, and SLC9A3R1) [16], diabetic complications (ACTN4, ARG1, CHI3L1, CHN2, CNP, GIPR, GPX1, KIFC1, NFE2, PEMT, RPS6KA2, TNF, TNFAIP3, and TNFAIP8) [17], and oxidative stress (AHRR, BACH2, GPX1, OXSR1, PFKFB3, and RCAN1) [18] were among the genes that showed differential DNA methylation in blood from T1D subjects with PDR (Fig. 1cf). Some of these genes are represented in several of the analyzed groups.
Fig. 1

Number of CpG sites with significantly higher (a) or lower (b) DNA methylation in cases with PDR versus controls. Genes involved in retina and eye development (c), inflammation (d), diabetic complications (e), and oxidative stress (f) were among the ones with one or more CpG sites differentially methylated between cases with PDR and controls (q <0.05); data are presented as mean ± SD. Global DNA methylation is calculated as average DNA methylation based on all CpG sites in each region on the chip and shows no differences between cases with PDR compared with controls in mean methylation value based on gene regions (g) or regions in relation to nearest CpG island (h)

We then performed a pathway analysis of the 233 genes with one or more CpG sites differentially methylated between cases with PDR and controls using WebGestalt [19]. We found the KEGG pathway “Natural Killer cell mediated cytotoxicity” to be significantly enriched in the PDR cases (adjusted P = 0.006), including eight genes: KLRD1, TNF, CD247, PIK3CD, GZMB, NFATC1, PRF1, and KIR2DL4 (Table 2).
Table 2

Pathway analysis of genes with CpG sites differentially methylated in cases with PDR versus controls

KEGG pathway: Natural Killer cell mediated cytotoxicity (P = 0.0001; Padj = 0.0063)

Total # of genes in pathway: 121

Expected # of genes: 1.48

Observed # of genes: 8

Gene symbol

Description

KLRD1

Killer cell lectin-like receptor subfamily D, member 1

TNF

Tumor necrosis factor

CD247

CD247 molecule

PIK3CD

Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit delta

GZMB

Granzyme B (granzyme 2, cytotoxic T-lymphocyte-associated serine esterase 1)

NFATC1

Nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1

PRF1

Perforin 1 (pore forming protein)

KIR2DL4

Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 4

Global DNA methylation in blood from cases with PDR and controls

To evaluate the global methylome in blood from cases with PDR and controls, we calculated the average level of DNA methylation in the two groups based on either the functional genome distribution (Fig. 1g) or the CpG content and neighborhood context (Fig. 1h). The results showed no significant differences in global DNA methylation between PDR cases and controls after FDR correction (Fig. 1g,h, Additional file 4: Table S4). Nevertheless, the average level of DNA methylation was high within the gene body, 3′UTR, and in intergenic regions (60–75 %), whereas it was lower in TSS1500 and 5′UTR (23–29 %) and lowest within TSS200 and the first exon (12–14 %; Fig. 1g). In relation to CpG islands, the average methylation was low within the CpG island (15 %), intermediate in the shore regions (43–44 %), and high in the shelf regions and open sea (73–77 %; Fig. 1h).

DNA methylation as a prospective marker of PDR

We next tested if differences in DNA methylation may be used as prospective markers of PDR in subjects with T1D. Here, we used DNA from blood from T1D subjects with no/mild diabetic retinopathy when the blood samples were taken, but who developed PDR within approximately 6 years (converters), and compared the degree of DNA methylation in these converters with methylation in control subjects who did not develop PDR (controls; Additional file 1: Table S1). In this prospective cohort there was no significant differences in diabetes duration between converters and controls, but the converters had significantly lower age and higher HbA1c levels (P <0.05; Additional file 1: Table S1) compared to the controls. We examined if any of the 349 CpG sites showing differential DNA methylation in the PDR cases versus controls (Additional file 3: Table S3) also show differential DNA methylation in the prospective cohort. We found 28 sites representing 17 unique genes including BCOR, GIPR, and GLRA1 that showed differential DNA methylation in the same direction in the prospective cohort as well as in the case–control cohort with P ≤0.05 (Table 3). This is more than expected by chance based on a χ2 test (P = 0.0096). However, the FDR analysis shows that these sites are only nominally significant in the prospective cohort (Table 3). The majority of these CpG sites (93 %) exhibit lower methylation in the converters compared with the controls.
Table 3

CpG sites showing significant differences in DNA methylation between cases with PDR and controls (FDR q <5 %, Additional file 3: Table S3), that are also nominally differentially methylated in the same direction in the converters of the prospective cohort at P <0.05

  

Location in relation to:

DNA Methylation (%)

 

Probe ID

Chr

Nearest gene

Gene region

CpG Island

Controls

Converters

Difference

P value

q value

cg26703534

5

AHRR

Body

S Shelf

63.6 ± 2.6

59.9 ± 4.1

−3.7

9.5 × 10−4

0.17

cg05575921

5

AHRR

Body

N Shore

82.4 ± 6.3

75.0 ± 8.1

−7.4

2.1 × 10−3

0.18

cg07339236

20

ATP9A

Body

Open sea

18.0 ± 2.5

15.6 ± 1.6

−2.4

0.013

0.35

cg05026884

X

BCOR

5′UTR

Island

26.5 ± 11.1

22.7 ± 4.7

−3.8

4.2 × 10−3

0.21

cg01490258

X

BCOR

5′UTR

N Shore

13.2 ± 2.9

12.4 ± 2.0

−0.8

0.010

0.33

cg23701759

X

BCOR

5′UTR

Island

34.9 ± 4.9

30.9 ± 3.7

−4.0

0.014

0.35

cg10055320

X

BCOR

5′UTR

N Shore

42.2 ± 4.4

39.2 ± 5.1

−3.0

0.029

0.50

cg00507154

19

C19orf76

1st Exon; 5′UTR

Island

70.2 ± 4.6

67.7 ± 2.6

−2.5

0.030

0.50

cg08601628

8

C8orf74

Body

Open sea

82.5 ± 3.0

80.6 ± 3.2

−1.9

0.022

0.43

cg12993916

6

DTNBP1

Body

Open sea

78.9 ± 3.0

76.8 ± 3.7

−2.1

0.015

0.35

cg03636183

19

F2RL3

Body

N Shore

68.9 ± 4.4

65.1 ± 4.7

−3.8

9.3 × 10−3

0.32

cg02942825

19

GIPR

3′UTR

S Shore

49.8 ± 3.2

48.0 ± 5.8

−1.8

0.050

0.62

cg17808910

7

GLCCI1

Body

S Shelf

73.7 ± 3.9

75.5 ± 4.6

1.8

0.048

0.62

cg26567012

5

GLRA1

3′UTR

Open sea

55.4 ± 3.8

52.0 ± 6.0

−3.4

0.030

0.50

cg01010839

10

NET1

TSS1500; Body

N Shore

71.2 ± 4.3

70.2 ± 2.2

−1.0

0.015

0.35

cg24796663

6

PHF1

TSS1500

N Shore

12.9 ± 1.8

11.3 ± 1.9

−1.6

1.5 × 10−3

0.17

cg11224624

8

PLEC1

Body

Island

2.1 ± 0.3

2.3 ± 0.3

0.2

0.033

0.52

cg22291265

19

SHANK1

Body

Open sea

7.5 ± 2.3

6.9 ± 1.3

−0.6

0.050

0.62

cg06589051

2

TGFBRAP1

TSS1500

S Shore

72.4 ± 2.0

70.2 ± 4.1

−2.2

0.040

0.61

cg08597832

8

TOP1MT

Body

N Shore

82.1 ± 2.5

79.8 ± 2.7

−2.3

0.020

0.41

cg11166303

2

TSSC1

Body

N Shore

67.8 ± 7.5

61.2 ± 8.5

−6.6

4.0 × 10−3

0.21

cg09577317

8

 

Intergenic

N Shore

83.9 ± 1.6

81.0 ± 1.2

−2.9

1.7 × 10−4

0.058

cg07948143

14

 

Intergenic

Open sea

75.5 ± 3.2

71.0 ± 4.6

−4.5

2.9 × 10−3

0.20

cg24058013

18

 

Intergenic

Island

89.8 ± 1.0

87.7 ± 1.8

−2.1

5.4 × 10−3

0.24

cg03889263

3

 

Intergenic

Open sea

20.0 ± 3.9

15.9 ± 2.6

−4.1

6.9 × 10−3

0.27

cg05546763

14

 

Intergenic

Island

82.0 ± 2.0

80.6 ± 2.3

−1.4

0.016

0.36

cg15342087

6

 

Intergenic

Open sea

81.1 ± 2.1

80.2 ± 2.2

−0.9

0.046

0.62

cg06126421

6

 

Intergenic

Open sea

69.2 ± 6.2

69.0 ± 4.4

−0.2

0.050

0.62

Data are presented a mean ± SD

Cross-reactive probes

As a quality control, we further screened the 349 sites with significant differences in DNA methylation between PDR cases and controls for cross-reactive probes in the Illumina Infinium HumanMethylation450k BeadChip [14]. Only one of the 349 probes had a perfect match elsewhere in the genome and 13 probes matched for 47–49 out of 50 bases (Additional file 5: Table S5).

Association between HbA1c levels and DNA methylation in blood

We finally tested if HbA1c was associated with differential DNA methylation of individual CpG sites in the control cohort including subjects with T1D without PDR. Gender, age, and diabetes duration were included as covariates in the linear regression model. However, after correction for multiple testing using FDR, no significant association between HbA1c and DNA methylation was found (q = 0.99). Additionally, HbA1c was only associated with methylation of 14,522 CpG sites at P <0.05, which is much less than expected by chance.

Even though HbA1c was not significantly associated with DNA methylation based on this linear regression analysis, nor based on the principle component analysis, we tested to include HbA1c in the regression model for the association between DNA methylation and PDR, as there was a significant difference in HbA1c between PDR cases and controls. Although not genome-wide significant (q = 0.08–0.38), the 349 CpG sites detected to be differentially methylated between PDR cases and controls in our main analysis (q <0.05; Additional file 3: Table S3) all still showed a strong nominal association (P = 4.0 × 10−7 to 7.6 × 10−3) after inclusion of HbA1c in the model (Additional file 6: Table S6). Thereby, our results support that adjustment for HbA1c has no major effect on the association between PDR and DNA methylation, but it makes the study less well powered since the size of our study only supports the inclusion of up to four variables in the regression analysis in order to maintain statistical power. Additionally, when including HbA1c in the model we found increased standard errors in the regression analysis of all 349 CpG sites (Additional file 6: Table S6), indeed suggesting that this additional variable makes the model less stable.

Discussion

In the present study, we demonstrate for the first time that differential DNA methylation in/near 233 unique genes is associated with PDR in a case–control cohort. In a prospective cohort, we also found epigenetic markers that can predict PDR. Our study suggests that DNA methylation in blood can be used as a predictive biomarker of PDR in subjects with T1D. However, replication studies are needed to verify our results.

Diabetic retinopathy is a common microvascular complication and, in its severe forms, the leading cause of blindness among working-aged adults in the western world [1]. In PDR, ischemia is preceding formation of new vessels, which untreated can result in vitreous hemorrhage and retinal detachment. Inflammation does also play a key role in development of microvascular diabetic complications, including PDR [17]. Subjects with T1D have a relatively high prevalence of diabetic retinopathy compared with subjects with T2D and it is well established that HbA1c levels, diabetes duration, and hypertension increase the risk for PDR [20]. Previous studies have tried to identify biomarkers that can be used to clinically predict diabetic retinopathy in subjects with diabetes [21]. However, so far, limited breakthroughs have taken place [2224]. In our study, we chose to identify markers, not for diabetic retinopathy per se but for the most severe consequence of ischemic retinopathy, i.e. PDR. Since diabetes duration and degree of non-proliferative retinopathy are risk factors for PDR development, we aimed at including control subjects with as low risk as possible. It was not possible to identify a sufficient number of control subjects with at least 10 years of diabetes who were also completely without microaneurysms, but as occasional microaneurysms may come and go we included subjects with a few microaneurysms. Using a longitudinal approach, we could conclude that 81 % (30 out of 37 subjects) of these subjects still were without PDR approximately 8 years later and they were assigned as controls. Progression to PDR occurred in seven subjects (19 %) and were assigned converters. Those subjects differed from both cases and controls by younger age and poorer metabolic control measured by HbA1c. Those characteristics are associated with a high risk for the rapid development of severe PDR, also known as florid retinopathy [25], which results in severe visual impairment or blindness if not properly treated in time. Based on our study design, we have been able not only to identify epigenetic markers associated with PDR but also predictive of the most severe form of diabetic retinopathy in T1D.

We identified 349 CpG sites representing 233 genes with altered DNA methylation levels in T1D subjects with PDR. The majority of these sites (79 %) showed decreased DNA methylation in cases with PDR. A lower methylation level may be associated with higher expression as DNA methylation has been shown to repress the binding of transcription factors to promoter regions and attract proteins such as transcriptional co-repressors and histone deacetyltransferases associated with a dense chromatin structure and inactive genes [5]. Although it would have been interesting to also study gene expression in the present case–control cohort, this was not possible since only DNA was extracted when blood was collected. Nevertheless, DNA methylation in blood can quite easily be used as a biomarker that predicts disease and the epigenetic alterations found in the present study suggest that this may be possible in subjects with T1D.

Several identified genes with altered DNA methylation in cases with PDR, such as TNF, CHI3L1, and CHN2, encode proteins with previous known function in diabetic complications and/or diabetic retinopathy [17]. TNF, which encodes tumor necrosis factor, exhibits decreased DNA methylation in cases with PDR. Tumor necrosis factor is a proinflammatory cytokine that is elevated in serum from subjects with PDR [26]. Epigenetic modifications of TNF in T1D subjects may subsequently regulate the expression of TNF and thereby contribute to inflammation in diabetic retinopathy. CHI3L1, also known as YKL-40, encodes an inflammatory glycoprotein involved in endothelial dysfunction and cardiovascular disease [27]. We found decreased DNA methylation of CHI3L1 in cases with PDR, which may contribute to increased expression of this gene. Interestingly, serum levels of YKL-40 were elevated in subjects with diabetic retinopathy [28] and ischemia was associated with higher YKL-40 mRNA levels in carotid plaque [29]. As ischemia precedes PDR, it may also affect methylation and/or expression of CHI3L1 in subjects with diabetic retinopathy. Cases with PDR also exhibited decreased DNA methylation of CHN2, which encodes chimerin 2, a protein known to affect proliferation and migration of smooth muscle cells. Interestingly, a SNP in an intron of CHN2 was recently associated with diabetic retinopathy in Chinese people with T2D, further supporting a role for this gene in the development of diabetic retinopathy [30].

The largest absolute difference in DNA methylation between PDR cases and controls was 13.5 %, which represents a fold change of 1.22. Our epigenetic data are in line with previous genetic studies where the odds ratio of most genetic variants associated with PDR has been modest [31]. However, we identified more than 200 genes with differential DNA methylation in PDR cases after correction for multiple testing and it is possible that modest changes of multiple genes together contribute to the disease phenotype. The present study is, to our knowledge, the first one to analyze DNA methylation genome-wide in subjects with PDR. However, a previous study by Bell et al. [11] also reported absolute differences in DNA methylation of approximately 10 % when DNA methylation of approximately 27,000 sites was analyzed in blood from subjects with diabetic nephropathy.

In order to assess if epigenetic modifications may be used as biomarkers that predict PDR, we analyzed DNA methylation in a prospective cohort. Differential DNA methylation may predict PDR as several epigenetic modifications identified in cases with PDR were also found in our prospective cohort. BCOR encoding BCL6 co-repressor has the most CpG sites differentially methylated in both the case–control cohort and in the prospective cohort. While these data suggest that epigenetic modifications of BCOR may be used to predict PDR, larger prospective studies are needed to further validate our findings. GIPR, encoding glucose-dependent insulinotropic polypeptide receptor, also showed decreased DNA methylation in our prospective cohort as well as in the cases with PDR, and may subsequently be used as a candidate gene that predicts PDR. Interestingly, a previous study found increased expression of the GIPR in the retinas of streptozotocin-induced diabetic rats [32]. However, whether expression of the GIPR is increased in the human retina of diabetic subjects remains unknown and will be ethically difficult to analyze. The prospective cohort also confirmed demethylation of the aryl hydrocarbon receptor repressor (AHRR) in PDR cases, a locus which has been repeatedly shown to associate with smoking [3335]. Unfortunately, we do not have access to smoking status in our study from when samples were collected and hence we cannot rule out that altered smoking habits between the groups may affect this association.

Our study supports a role of epigenetic variation in the pathogenesis of PDR. Furthermore, the heritability of PDR has been estimated to 27 % and genetic variants may thereby affect the risk for PDR [36]. However, although genome-wide association studies have been performed to identify genetic variants associated with PDR, the outcome from these studies was limited [3638]. It is possible that genetic variants interact with epigenetic variation to increase the risk for disease [39, 40]. However, further genome-wide studies are needed to dissect whether interactions between genetics and epigenetics play a role in PDR.

Conclusion

Together, our study is the first to identify epigenetic modifications in T1D subjects with PDR. Our data suggest that differential DNA methylation may contribute to the pathogenesis of PDR in subjects with T1D and that DNA methylation in blood may be used as a predictive biomarker for this severe type of diabetic retinopathy. However, replication is needed to establish whether the findings really represent a novel biomarker of retinopathy risk.

Abbreviations

FDR: 

False discovery rate

PDR: 

Proliferative diabetic retinopathy

T1D: 

Type 1 diabetes

T2D: 

Type 2 diabetes

Declarations

Acknowledgments

This work was supported by grants from the Swedish Research Council, including Dnr. 521-2010-2745, Dnr. 523-2010-1061, a Linnaeus grant (LUDC: Dnr. 349-2008-6589) and strategic research area grant (EXODIAB: Dnr. 2009–1039), ALF, the Novo Nordisk Foundation, the Swedish Diabetes foundation, the Järnhardt Foundation, the Foundation for Visually Impaired in Former Malmöhus län, the Swedish Medical Society, the Swedish Eye Foundation, the Skåne University Hospital Foundation, and Påhlsson foundation as well as equipment grants from Wallenberg KAW (2009–0243) and Lundberg Foundation (grant no. 359). We thank SCIBLU at Lund University for analyzing global DNA methylation.

Authors’ Affiliations

(1)
Department of Clinical Sciences, Ophthalmology, Lund University, Scania University Hospital
(2)
Department of Clinical Sciences, Epigenetics and Diabetes Unit, Lund University Diabetes Centre, CRC, Scania University Hospital
(3)
Department of Clinical Sciences, Endocrinology, Lund University, Scania University Hospital

References

  1. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556–64.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy. The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. N Engl J Med. 2000;342:381–9.Google Scholar
  3. White NH, Sun W, Cleary PA, Danis RP, Davis MD, Hainsworth DP, et al. Prolonged effect of intensive therapy on the risk of retinopathy complications in patients with type 1 diabetes mellitus: 10 years after the Diabetes Control and Complications Trial. Arch Ophthalmol. 2008;126:1707–15.View ArticlePubMedGoogle Scholar
  4. El-Osta A, Brasacchio D, Yao D, Pocai A, Jones PL, Roeder RG, et al. Transient high glucose causes persistent epigenetic changes and altered gene expression during subsequent normoglycemia. J Exper Med. 2008;205:2409–17.View ArticleGoogle Scholar
  5. Ling C, Groop L. Epigenetics: a molecular link between environmental factors and type 2 diabetes. Diabetes. 2009;58:2718–25.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Miao F, Smith DD, Zhang L, Min A, Feng W, Natarajan R. Lymphocytes from patients with type 1 diabetes display a distinct profile of chromatin histone H3 lysine 9 dimethylation: an epigenetic study in diabetes. Diabetes. 2008;57:3189–98.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Nitert MD, Dayeh T, Volkov P, Elgzyri T, Hall E, Nilsson E, et al. Impact of an exercise intervention on DNA methylation in skeletal muscle from first-degree relatives of patients with type 2 diabetes. Diabetes. 2012;61:3322–32.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Rakyan VK, Beyan H, Down TA, Hawa MI, Maslau S, Aden D, et al. Identification of type 1 diabetes-associated DNA methylation variable positions that precede disease diagnosis. PLoS Genet. 2011;7:e1002300.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Yang BT, Dayeh TA, Kirkpatrick CL, Taneera J, Kumar R, Groop L, et al. Insulin promoter DNA methylation correlates negatively with insulin gene expression and positively with HbA(1c) levels in human pancreatic islets. Diabetologia. 2011;54:360–7.View ArticlePubMedGoogle Scholar
  10. Yang BT, Dayeh TA, Volkov PA, Kirkpatrick CL, Malmgren S, Jing X, et al. Increased DNA methylation and decreased expression of PDX-1 in pancreatic islets from patients with type 2 diabetes. Mol Endocrinol. 2012;26:1203–12.View ArticlePubMedGoogle Scholar
  11. Bell CG, Teschendorff AE, Rakyan VK, Maxwell AP, Beck S, Savage DA. Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus. BMC Med Genet. 2010;3:33.Google Scholar
  12. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98:288–95.View ArticlePubMedGoogle Scholar
  13. Rönn T, Volkov P, Davegardh C, Dayeh T, Hall E, Olsson AH, et al. A six months exercise intervention influences the genome-wide DNA methylation pattern in human adipose tissue. PLoS Genet. 2013;9:e1003572.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–9.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Ohuchi H. Molecular aspects of eye evolution and development: from the origin of retinal cells to the future of regenerative medicine. Acta Med Okayama. 2013;67:203–12.PubMedGoogle Scholar
  16. Nguyen DV, Shaw LC, Grant MB. Inflammation in the pathogenesis of microvascular complications in diabetes. Front Endocrinol. 2012;3:170.View ArticleGoogle Scholar
  17. Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93:137–88.View ArticlePubMedGoogle Scholar
  18. Reis JS, Veloso CA, Mattos RT, Purish S, Nogueira-Machado JA. Oxidative stress: a review on metabolic signaling in type 1 diabetes. Arquivos Brasileiros Endocrinol Metabol. 2008;52:1096–105.Google Scholar
  19. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41:W77–83.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Klein R, Klein BE, Moss SE. Epidemiology of proliferative diabetic retinopathy. Diabetes Care. 1992;15:1875–91.View ArticlePubMedGoogle Scholar
  21. Freeman WM, Bixler GV, Brucklacher RM, Lin CM, Patel KM, VanGuilder HD, et al. A multistep validation process of biomarkers for preclinical drug development. Pharmacogenomics J. 2010;10:385–95.View ArticlePubMedGoogle Scholar
  22. Blum A, Socea D, Ben-Shushan RS, Keinan-Boker L, Naftali M, Segol G, et al. A decrease in VEGF and inflammatory markers is associated with diabetic proliferative retinopathy. Eur Cytokine Netw. 2012;23:158–62.PubMedGoogle Scholar
  23. Joergensen C, Hovind P, Schmedes A, Parving HH, Rossing P. Vitamin D levels, microvascular complications, and mortality in type 1 diabetes. Diabetes Care. 2011;34:1081–5.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Muni RH, Kohly RP, Lee EQ, Manson JE, Semba RD, Schaumberg DA. Prospective study of inflammatory biomarkers and risk of diabetic retinopathy in the diabetes control and complications trial. JAMA Ophthalmol. 2013;131:514–21.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Agardh E. Rapid, bloody, and blinding diabetic retinopathy. Acta Ophthalmol (Copenh). 2010;88:274–8.View ArticleGoogle Scholar
  26. Gustavsson C, Agardh E, Bengtsson B, Agardh CD. TNF-alpha is an independent serum marker for proliferative retinopathy in type 1 diabetic patients. J Diabetes Complicat. 2008;22:309–16.View ArticlePubMedGoogle Scholar
  27. Rathcke CN, Vestergaard H. YKL-40 – an emerging biomarker in cardiovascular disease and diabetes. Cardiovasc Diabetol. 2009;8:61.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Sakamoto F, Katakami N, Kaneto H, Yasuda T, Takahara M, Miyashita K, et al. Association of serum YKL-40 levels with urinary albumin excretion rate in young Japanese patients with type 1 diabetes mellitus. Endocr J. 2013;60:73–9.View ArticlePubMedGoogle Scholar
  29. Michelsen AE, Rathcke CN, Skjelland M, Holm S, Ranheim T, Krohg-Sorensen K, et al. Increased YKL-40 expression in patients with carotid atherosclerosis. Atherosclerosis. 2010;211:589–95.View ArticlePubMedGoogle Scholar
  30. Hu C, Zhang R, Yu W, Wang J, Wang C, Pang C, et al. CPVL/CHN2 genetic variant is associated with diabetic retinopathy in Chinese type 2 diabetic patients. Diabetes. 2011;60:3085–9.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Sheu WH, Kuo JZ, Lee IT, Hung YJ, Lee WJ, Tsai HY, et al. Genome-wide association study in a Chinese population with diabetic retinopathy. Hum Mol Genet. 2013;22:3165–73.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Cho GJ, Ryu S, Kim YH, Kim YS, Cheon EW, Park JM, et al. Upregulation of glucose-dependent insulinotropic polypeptide and its receptor in the retina of streptozotocin-induced diabetic rats. Curr Eye Res. 2002;25:381–8.View ArticlePubMedGoogle Scholar
  33. Allione A, Marcon F, Fiorito G, Guarrera S, Siniscalchi E, Zijno A, et al. Novel epigenetic changes unveiled by monozygotic twins discordant for smoking habits. PLoS One. 2015;10:e0128265.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Monick MM, Beach SR, Plume J, Sears R, Gerrard M, Brody GH, et al. Coordinated changes in AHRR methylation in lymphoblasts and pulmonary macrophages from smokers. Am J Med Genet B Neuropsychiatr Genet. 2012;159B:141–51.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Richmond RC, Simpkin AJ, Woodward G, Gaunt TR, Lyttleton O, McArdle WL, et al. Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Hum Mol Genet. 2015;24:2201–17.View ArticlePubMedGoogle Scholar
  36. Cho H, Sobrin L. Genetics of diabetic retinopathy. Current Diabetes Reports. 2014;14:515.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Awata T, Yamashita H, Kurihara S, Morita-Ohkubo T, Miyashita Y, Katayama S, et al. A genome-wide association study for diabetic retinopathy in a Japanese population: potential association with a long intergenic non-coding RNA. PLoS One. 2014;9:e111715.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Petrovic D. Candidate genes for proliferative diabetic retinopathy. Biomed Res Int. 2013;2013:540416.PubMedPubMed CentralGoogle Scholar
  39. Dayeh TA, Olsson AH, Volkov P, Almgren P, Rönn T, Ling C. Identification of CpG-SNPs associated with type 2 diabetes and differential DNA methylation in human pancreatic islets. Diabetologia. 2013;56:1036–46.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Olsson AH, Volkov P, Bacos K, Dayeh T, Hall E, Nilsson EA, et al. Genome-wide associations between genetic and epigenetic variation influence mRNA expression and insulin secretion in human pancreatic islets. PLoS Genet. 2014;10:e1004735.View ArticlePubMedPubMed CentralGoogle Scholar

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© Agardh et al. 2015

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