Differential methylation of genes in individuals exposed to maternal diabetes in utero

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Abstract

Individuals exposed to maternal diabetes in utero are more likely to develop metabolic and cardiovascular diseases later in life. This may be partially attributable to epigenetic regulation of gene expression. We performed an epigenome-wide association study to examine whether differential DNA methylation, a major source of epigenetic regulation, can be observed in offspring of mothers with type 2 diabetes during the pregnancy (OMD) compared with offspring of mothers with no diabetes during the pregnancy (OMND). DNA methylation was measured in peripheral blood using the Illumina HumanMethylation450K BeadChip. A total of 423,311 CpG sites were analysed in 388 Pima Indian individuals, mean age at examination was 13.0 years, 187 of whom were OMD and 201 were OMND. Differences in methylation between OMD and OMND were assessed. Forty-eight differentially methylated CpG sites (with an empirical false discovery rate ≤0.05), mapping to 29 genes and ten intergenic regions, were identified. The gene with the strongest evidence was LHX3, in which six CpG sites were hypermethylated in OMD compared with OMND ( p ≤ 1.1 × 10 −5 ). Similarly, a CpG near PRDM16 was hypermethylated in OMD (1.1% higher, p = 5.6 × 10 −7 ), where hypermethylation also predicted future diabetes risk (HR 2.12 per SD methylation increase, p = 9.7 × 10 −5 ). Hypermethylation near AK3 and hypomethylation at PCDHGA4 and STC1 were associated with exposure to diabetes in utero ( AK3 : 2.5% higher, p = 7.8 × 10 −6 ; PCDHGA4 : 2.8% lower, p = 3.0 × 10 −5 ; STC1 : 2.9% lower, p =1.6 × 10 −5 ) and decreased insulin secretory function among offspring with normal glucose tolerance ( AK3 : 0.088 SD lower per SD of methylation increase, p = 0.02; PCDHGA4 : 0.08 lower SD per SD of methylation decrease, p = 0.03; STC1 : 0.072 SD lower per SD of methylation decrease, p = 0.05). Seventeen CpG sites were also associated with BMI ( p ≤ 0.05). Pathway analysis of the genes with at least one differentially methylated CpG ( p < 0.005) showed enrichment for three relevant biological pathways. Intrauterine exposure to diabetes can affect methylation at multiple genomic sites. Methylation status at some of these sites can impair insulin secretion, increase body weight and increase risk of type 2 diabetes.

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Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships.

D Dabelea, R L Hanson, R Lindsay … (2000)

Intrauterine exposure to diabetes is associated with an excess of diabetes and obesity in the offspring, but the effects of intrauterine exposure are confounded by genetic factors. To determine the role of the intrauterine diabetic environment per se, the prevalence of diabetes and the mean BMI were compared in siblings born before and after their mother was recognized as having diabetes. Nuclear families in which at least one sibling was born before and one after the mother was diagnosed with type 2 diabetes were selected. Consequently, the siblings born before and after differed in their exposure to diabetes in utero. A total of 58 siblings from 19 families in which at least one sibling had diabetes were examined at similar ages (within 3 years). The risk of diabetes was significantly higher in siblings born after the mother developed diabetes than in those born before the mother's diagnosis of diabetes (odds ratio 3.7, P = 0.02). In 52 families, among 183 siblings without diabetes, the mean BMI was 2.6 kg/m2 higher in offspring of diabetic than in offspring of nondiabetic pregnancies (P = 0.003). In contrast, there were no significant differences in risk of diabetes or BMI between offspring born before and after the father was diagnosed with diabetes. Intrauterine exposure to diabetes per se conveys a high risk for the development of diabetes and obesity in offspring in excess of risk attributable to genetic factors alone.

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Genome-Wide DNA Methylation Analysis of Human Pancreatic Islets from Type 2 Diabetic and Non-Diabetic Donors Identifies Candidate Genes That Influence Insulin Secretion

Tasnim Dayeh, Petr Volkov, Sofia Salö … (2014)

Introduction Type 2 diabetes (T2D) is a complex multifactorial disorder characterized by chronic hyperglycemia due to impaired insulin secretion from pancreatic β-cells, elevated glucagon secretion from pancreatic α-cells and insulin resistance in target tissues. As a result of aging populations and an increasing prevalence of obesity and physical inactivity, the number of patients with T2D has dramatically increased worldwide [1]. Family studies together with genome-wide association studies (GWAS) have shown that the genetic background also influences the risk of T2D [2], [3]. The majority of T2D single nucleotide polymorphisms (SNPs) identified by GWAS are associated with impaired insulin secretion rather than insulin action, pointing to pancreatic islet defects as key mechanisms in the pathogenesis of T2D [3]–[5]. However, the identified SNPs only explain a small proportion of the estimated heritability of T2D, suggesting that additional genetic factors remain to be identified [3]. Genetic variants can interact with environmental factors and thereby modulate the risk for T2D through gene-environment interactions [6]. The interaction between genes and environment may also happen through direct chemical modifications of the genome by so called epigenetic modifications, including DNA methylation and histone modifications [7]. These are known to influence the chromatin structure and DNA accessibility and can thereby regulate gene expression [8], [9]. Epigenetic alterations may subsequently influence phenotype transmission and the development of different diseases, including T2D [7], [10]. Our group has recently found increased DNA methylation in parallel with decreased expression of PPARGC1A, PDX-1 and INS in human pancreatic islets from patients with T2D by using a candidate gene approach [11]–[13]. Another group has analyzed DNA methylation of ∼0.1% of the CpG sites in the human genome in pancreatic islets from five T2D and 11 non-diabetic donors [14]. Animal studies further support the hypothesis that epigenetic modifications in pancreatic islets may lead to altered gene expression, impaired insulin secretion and subsequently diabetes [15]–[17]. Although these studies point towards a key role for epigenetic modifications in the growing incidence of T2D, comprehensive human epigenetic studies, covering most genes and regions in the genome in pancreatic islets from diabetic and non-diabetic donors, are still lacking. Human studies further need to link T2D associated epigenetic modifications with islet gene expression and eventually impaired insulin and/or glucagon secretion. Moreover, the human methylome has previously not been described in human pancreatic islets. In the present study, we analyzed the genome-wide DNA methylation pattern in pancreatic islets from patients with T2D and non-diabetic donors using the Infinium HumanMethylation450 BeadChip, which covers ∼480,000 CpG sites in 21,231 (99%) RefSeq genes. The degree of DNA methylation was further related to the transcriptome in the same set of islets. A number of genes that exhibited both differential DNA methylation and gene expression in human T2D islets were then selected for functional follow up studies; insulin and glucagon secretion were analyzed in clonal β- and α-cells, respectively where selected candidate genes had been either overexpressed or silenced. Also, reporter gene constructs were used to study the direct effect of DNA methylation on the transcriptional activity. Together, our study provides the first detailed map of the human methylome in pancreatic islets and it provides new target genes with altered DNA methylation and expression in human T2D islets that contribute to perturbed insulin and glucagon secretion. Results The methylome in human pancreatic islets To describe the methylome in pancreatic islets and unravel the epigenetic basis of T2D, DNA methylation of a total of 485,577 sites were analyzed in human pancreatic islets from 15 T2D and 34 non-diabetic donors by using the Infinium HumanMethylation450 BeadChip. The characteristics of the islet donors included in the genome-wide analysis of DNA methylation are described in Table 1. T2D donors had higher HbA1c levels, nominally higher BMI and lower glucose-stimulated insulin secretion compared with non-diabetic donors (Table 1). There were no differences in islet purity (P = 0.97; Figure S1A) or β-cell content (P = 0.43; Figure S1B) between T2D and non-diabetic islets. 10.1371/journal.pgen.1004160.t001 Table 1 Characteristics of human pancreatic islet donors included in the genome-wide analysis of DNA methylation in pancreatic islets. Non-diabetics (n = 34) T2D (n = 15) P-value Gender (Males/Females) (22/12) (10/5) HbA1c (%) 5.4±0.4 6.9±1.0 1.2×10−11 Age (years) 56.0±9.0 59.5±10.7 0.2 BMI (kg/m2) 25.9±2.3 28.3±4.7 0.06 Glucose-stimulated insulin secretion (ng/islet·h) at 16.7 mM glucose 1.24±1.40 0.74±1.23 0.04 Mann-Whitney U test was used for statistical analysis and data are presented as mean ± SD. A stringent quality control procedure was then performed and 2,546 (0.5%) sites were excluded for having a mean detection P-value>0.01 and as a result 483,031 sites generated reliable DNA methylation data and were used for further analysis. These 483,031 sites included 479,927 CpG sites, 3,039 non-CpG sites and 65 SNPs related to 21,231 RefSeq genes. Additional quality control steps were performed and all samples showed high bisulfite conversion efficiency (materials and methods). The probes included on the Infinium HumanMethylation450 BeadChip have been annotated based on their relation to the nearest gene and the probes may belong to any of the following genomic elements: TSS1500, TSS200, 5′UTR, 1st exon, gene body, 3′UTR or intergenic regions (Figure 1A). To describe the overall methylome in human pancreatic islets and to test whether there are global differences in DNA methylation in T2D islets, we calculated the average degree of DNA methylation of different genomic elements in T2D and non-diabetic islets. While genomic regions close to the transcription start site showed relatively low degrees of methylation (27.5±1.5% for TSS1500, 12.3±0.06% for TSS200, 23.0±1.2% for 5′UTR and 14.0±0.08% for 1st exon in non-diabetic islets), there was a higher degree of methylation in regions further away from the transcription start site (60.2±1.9% for gene body, 71.3±1.8% for 3′UTR and 57.6±2.0% for intergenic regions) (Figure 1B). The probes on the Infinium HumanMethylation450 BeadChip have also been annotated based on their genomic location relative to CpG islands as shown in Figure 1A, where CpG island shores cover regions 0–2 kb from CpG islands and shelves cover regions 2–4 kb from CpG islands. North and south are used to determine whether the CpG site is upstream or downstream from a CpG island and open sea are isolated CpG sites in the genome. We found that CpG islands are hypomethylated (14.9±0.07%), shelves and open sea are hypermethylated (72.9±1.8% for N shelf, 73.5±1.8% for S shelf and 69.2±1.9% for open sea), while shores show an intermediate degree of methylation (42.5±2.1% for N shore and 41.1±2.0% for S shore) in human islets (Figure 1C). The average degree of DNA methylation for any of the genomic regions did not differ in T2D versus non-diabetic islets (Figure 1B–C). 10.1371/journal.pgen.1004160.g001 Figure 1 The human methylome in pancreatic islets from 15 T2D and 34 non-diabetic donors. (A) All analyzed DNA methylation sites on the Infinium HumanMethylation450 BeadChip are mapped to gene regions based on their functional genome distribution and CpG island regions based on CpG content and neighbourhood context [68]. TSS: proximal promoter, defined as 200 bp or 1500 bp upstream of the transcription start site. UTR: untranslated region. CpG island: 200 bp (or more) stretch of DNA with a C+G content of >50% and an observed CpG/expected CpG in excess of 0.6. Shore: the flanking region of CpG islands, 0–2000 bp. Shelf: regions flanking island shores, i.e., covering 2000–4000 bp distant from the CpG island [68]. Global DNA methylation in human pancreatic islets of T2D and non-diabetic donors is shown for (B) each gene region and (C) CpG island regions. Global DNA methylation is calculated as average DNA methylation based on all CpG sites in each annotated region on the chip. (D) The absolute difference in DNA methylation of 3,116 individual sites, including 2,988 sites with decreased and 128 sites with increased DNA methylation in T2D compared with non-diabetic human islets with q 5% in T2D versus non-diabetic islets. Biological features of the genes that exhibit differential methylation in T2D islets We next performed a KEGG pathway analysis to identify biological pathways with enrichment of genes that exhibit differential DNA methylation in T2D versus non-diabetic islets. A total of 853 genes, represented by CpG sites with differential DNA methylation ≥5% in T2D islets (Table S1), were analyzed using WebGestalt. Relevant enriched KEGG pathways in T2D islets include pathways in cancer, axon guidance, MAPK signaling pathway, focal adhesion, ECM-receptor interaction and regulation of actin cytoskeleton (Table 2). We further performed a separate KEGG pathway analysis only including genes that exhibit increased DNA methylation in T2D islets and we then found an enrichment of genes in the complement and coagulation cascades; C4A and C4B (observed number of genes = 2, expected number of genes = 0.13 and P adjusted = 0.0175). We also tested if any of the genes in Table 2 exhibit differential expression in human β- compared with α-cell fractions using published data by Dorrell et al [26]. However, among the genes included in Table 2, there were no significant differences in expression in β- versus α-cells. 10.1371/journal.pgen.1004160.t002 Table 2 KEGG pathways with enrichment of genes that exhibit differential DNA methylation in pancreatic islets of 34 non-diabetic compared with 15 T2D donors. Pathway (total number of genes in pathway) Observed number of genes Expected number of genes Ratio observed/expected RawP-value AdjustedP-value Observed genes Pathways in cancer (326) 38 13.14 2.89 4.5×10−9 5.1×10−7 EP300, MAX, TRAF2, RXRA, EGF, CDKN1A, TGFB2, PDGFB, ETS1, TGFBR2, ABL1, CREBBP, ITGA3, AXIN2, COL4A1, WNT16, ARNT2, PTCH2, LAMA4, RARA, LAMC1, ZBTB16, RUNX1T1, PIK3R1, NTRK1, FGF12, RASSF5, VEGFA, FAS, SMAD3, LEF1, LAMC2, E2F2, BMP4, RUNX1, CCNA1, FGFR2, JAK1 Axon guidance (129) 20 5.20 3.85 2.3×10−7 2.6×10−5 RHOD, EPHA4, PLXNA2, NTN3, PLXNA1, PLXNB2, SEMA6A, SEMA5A, ABLIM2, SRGAP3, ABL1, ABLIM1, SEMA4A , SEMA4D, PAK2, SEMA5B , UNC5B, SEMA3A, EPHA8, FES MAPK signaling pathway (269) 27 10.84 2.49 1.3×10−5 0.0015 FGFR4, MAX, TRAF2, NTRK1, MAP4K4, EGF, FGF12, CACNA1H , PRKX, TGFB2, PDGFB, CACNA2D3, CACNA2D4, TGFBR2, FLNB, FAS, MAPKAPK2, GNA12, CACNA1C, FGFR2, RASGRF2, PAK2, IL1R2, MAP3K5, PLA2G6, PLA2G4E, MAP3K1 Focal adhesion (199) 21 8.02 2.62 5.7×10−5 0.0064 LAMA4, PXN, LAMC1, PIK3R1, VAV2, EGF, VEGFA, PDGFB, ITGB5, DIAPH1, PPP1CC, FLNB, CAPN2, ZYX, SPP1, ITGA3, PAK2, VWF, COL4A1, ITGB4 , LAMC2 ECM-receptor interaction (84) 12 3.38 3.55 0.0001 0.011 LAMA4, SPP1, SV2B, LAMC1, GP5, AGRN , ITGA3, VWF, COL4A1, ITGB4 , ITGB5, LAMC2 Regulation of actin cytoskeleton (211) 20 8.50 2.35 0.0004 0.045 FGFR4, PXN, SSH1, PIK3R1, VAV2, EGF, CHRM1, FGF12, GIT1, PDGFB, ITGB5, DIAPH1, PPP1CC, BAIAP2, GNA12, ITGA3, FGFR2, INSRR, PAK2, ITGB4 Hypermethylated genes in T2D islets are in bold, P-values have been adjusted for multiple testing using the Benjamini-Hochberg method. Previous GWAS have identified SNPs associated with T2D and/or obesity [3]. These SNPs have been linked to candidate genes, representing genes closest to respective risk SNPs. However, the SNPs identified in GWAS only explain a small proportion of the estimated heritability for T2D, proposing that there are additional genetic factors left to be discovered. These may include genetic factors interacting with epigenetics [27]. We therefore tested if any of 40 T2D candidate genes and 53 obesity genes identified by GWAS were differentially methylated in the human T2D islets [3]. The Infinium HumanMethylation450 BeadChip covers 1,525 CpG sites representing 39 of the T2D candidate genes and 1,473 CpG sites representing all 53 obesity genes. However, one should keep in mind that for a number of these SNPs it still remains unknown if the closest gene is the gene involved in T2D or obesity and if the identified SNP is the functional SNP. Therefore, to cover most regions harboring a genetic variant associated with T2D or obesity, we also investigated the level of DNA methylation for all CpG sites in a region 10 kb up- and downstream of intergenic SNPs associated with T2D (n = 28) and obesity (n = 41) (www.genome.gov/gwastudies. Accessed: March 18, 2013). We identified 44 methylation sites, representing 17 T2D candidate genes and one intergenic SNP that were differentially methylated in T2D versus non-diabetic islets with a FDR less than 5% (q 5% in T2D versus non-diabetic islets, which correspond to a fold change ranging from 7 to 28%. Only three sites in three different obesity genes were differentially methylated in T2D islets and one of these sites had an absolute difference in methylation >5% (Table 3). 10.1371/journal.pgen.1004160.t003 Table 3 Candidate genes and intergenic SNPs for T2D and obesity that exhibit differential DNA methylation in human pancreatic islets of 34 non-diabetic compared with 15 T2D donors. Gene symbol Probe ID Non-diabetic DNA methylation (%) T2D DNA methylation (%) Delta DNA methylation (%) P-value q-value Chr. Position Gene region GWAS trait ADAMTS9 cg25859972 41.19±5.74 34.53±6.83 −6.66 1.2×10−3 0.047 3 64645555 Body T2D ADCY5 cg27182923 61.63±6.82 56.96±5.94 −4.67 5.3×10−4 0.037 3 124612077 Body T2D ADCY5 cg01246398 58.41±6.73 52.33±6.35 −6.08 5.6×10−4 0.037 3 124648562 Body T2D ADCY5 cg25976932 61.85±7.04 55.45±7.33 −6.40 6.6×10−4 0.037 3 124621648 Body T2D ADCY5 cg02953559 71.50±6.41 64.82±6.40 −6.68 8.6×10−4 0.041 3 124647654 Body T2D ADCY5 cg15464481 62.27±9.16 53.27±8.45 −9.01 3.5×10−4 0.037 3 124634652 Body T2D BCL11A cg03606511 69.40±5.07 64.87±5.81 −4.52 1.4×10−3 0.049 2 60589034 Body T2D CDKAL1 cg12556823 85.75±2.64 85.30±3.64 −0.45 7.3×10−4 0.039 6 21131464 Body T2D DUSP8 cg14488974 71.10±3.93 67.35±3.70 −3.75 4.1×10−5 0.016 11 1536002 Body T2D FTO cg26982104 29.73±5.52 23.13±4.72 −6.60 3.6×10−4 0.037 16 52336836 Body T2D and obesity FTO cg01485549 64.84±9.62 56.14±10.53 −8.70 5.2×10−4 0.037 16 52346986 Body T2D and obesity HHEX cg20180364 61.64±4.26 58.49±4.91 −3.15 1.4×10−3 0.049 10 94438512 TSS1500 T2D HHEX cg09721427 30.62±4.09 27.26±3.75 −3.36 7.2×10−4 0.039 10 94438682 TSS1500 T2D HHEX cg16508068 55.98±5.16 51.97±4.77 −4.01 8.5×10−4 0.041 10 94441716 Body T2D HHEX cg26979504 44.33±4.65 38.62±5.44 −5.71 2.6×10−4 0.033 10 94441498 Body T2D HMGA2 cg03257822 10.29±1.49 9.09±0.92 −1.20 5.1×10−4 0.037 12 64503074 TSS1500;Body T2D HNF1B cg21250756 28.57±5.12 20.71±3.83 −7.86 5.2×10−6 0.004 17 33151413 Body T2D IGF2BP2 cg21531679 66.37±3.80 64.27±4.44 −2.10 1.4×10−3 0.049 3 186937720 Body T2D IRS1 cg04751089 40.02±6.28 33.09±4.40 −6.93 5.5×10−5 0.016 2 227367880 3′UTR T2D IRS1 cg05263838 41.54±8.39 34.27±4.43 −7.27 1.1×10−3 0.047 2 227367650 3′UTR T2D JAZF1 cg23597162 80.20±3.06 77.41±4.33 −2.79 6.1×10−5 0.016 7 28068866 Body T2D JAZF1 cg14491535 72.77±4.13 68.59±3.00 −4.18 5.0×10−4 0.037 7 28161615 Body T2D JAZF1 cg11526778 57.89±5.31 52.87±5.74 −5.02 6.0×10−4 0.037 7 28082308 Body T2D KCNQ1 cg08895013 72.11±3.47 68.49±3.95 −3.62 1.2×10−4 0.022 11 2424908 Body T2D KCNQ1 cg20533553 51.83±4.37 47.76±4.90 −4.08 6.3×10−4 0.037 11 2421398 TSS1500 T2D KCNQ1 cg26896748 87.66±4.31 83.55±6.85 −4.11 5.5×10−4 0.037 11 2817640 Body T2D KCNQ1 cg17305275 81.71±2.92 77.27±4.61 −4.44 1.1×10−3 0.047 11 2703251 Body T2D KCNQ1 cg03371125 53.56±5.40 48.66±5.26 −4.89 6.2×10−4 0.037 11 2421421 TSS1500 T2D KCNQ1 cg03600094 69.15±6.30 63.30±5.93 −5.85 1.5×10−4 0.023 11 2438051 TSS1500;Body T2D KCNQ1 cg01693193 72.51±5.35 66.36±5.69 −6.15 4.6×10−5 0.016 11 2661729 Body T2D KCNQ1 cg03170016 65.77±8.65 56.41±8.93 −9.36 2.6×10−4 0.033 11 2438718 TSS1500;Body T2D PPARG cg04908300 37.30±3.34 34.33±3.41 −2.98 1.3×10−4 0.022 3 12305532 5′UTR;1stExon T2D PPARG cg10499651 64.69±7.28 59.92±7.95 −4.77 1.4×10−3 0.049 3 12440415 Body T2D RBMS1 cg01757209 4.43±0.61 4.18±0.63 −0.26 1.3×10−3 0.047 2 160972619 Body T2D TCF7L2 cg26380291 64.60±4.36 60.94±4.43 −3.67 9.8×10−4 0.045 10 114777833 Body T2D TCF7L2 cg07591090 78.39±5.70 72.44±5.97 −5.95 1.1×10−3 0.047 10 114867606 Body T2D TCF7L2 cg06403317 72.53±6.12 65.26±7.39 −7.26 1.5×10−3 0.049 10 114867642 Body T2D TCF7L2 cg03384318 78.28±6.67 70.43±7.19 −7.85 4.4×10−4 0.037 10 114867679 Body T2D TCF7L2 cg03510732 56.92±8.96 47.90±8.59 −9.02 1.3×10−3 0.047 10 114861921 Body T2D THADA cg22076676 41.57±3.33 38.39±3.06 −3.18 1.2×10−3 0.047 2 43357439 Body T2D THADA cg24168549 80.90±4.90 75.99±4.62 −4.91 1.3×10−4 0.022 2 43507634 Body T2D THADA cg19211851 45.34±6.67 39.76±6.96 −5.58 7.9×10−4 0.040 2 43408970 Body T2D THADA cg01649611 70.96±4.93 63.16±5.91 −7.81 5.4×10−8 0.003 2 43374570 Body T2D rs5015480 cg20134984 77.88±3.57 74.62±5.58 −3.26 6.0×10−4 0.037 10 94446626 intergenic T2D FAIM2 cg04920032 19.84±3.59 15.68±3.01 −4.16 1.4×10−5 0.010 12 48549253 3′UTR Obesity NPC1 cg21243631 61.24±4.09 56.37±4.38 −4.87 1.0×10−6 0.001 18 19408258 Body Obesity VEGFA cg01298514 80.65±3.69 74.63±4.99 −5.72 3.7×10−5 0.018 6 43844785 TSS1500 Obesity Probes shown in bold are CpG sites with an absolute difference in DNA methylation >5% between non-diabetic and T2D human islets. q-values are based on a FDR analysis. Finally, based on literature search, genes with known functions in pancreatic islets and/or β-cells [28]–[40] (Figure 2E), the exocytotic process [41]–[44] (Figure 2F) and apoptosis [45] (Figure 2G) were found among the genes that showed differential DNA methylation in T2D islets. Differential mRNA expression and differential methylation in T2D islets DNA methylation of certain genomic regions may silence gene transcription [8], [12]. We therefore used microarray mRNA expression data to examine if any of the 853 genes that exhibit differential DNA methylation in T2D islets also exhibit differential mRNA expression in islets from the same donors. We found that 102 of the 853 differentially methylated genes were also differentially expressed in T2D compared with non-diabetic islets (Table S4). While 77 (∼75%) of the differentially expressed genes had an inverse relationship with DNA methylation, e.g. decreased DNA methylation was associated with increased gene expression in T2D islets, 26 (∼25%) had a positive relationship with DNA methylation, e.g. decreased DNA methylation was associated with decreased expression (Figure 3A and Table S4). Figure 3B–C describes the genomic distribution of the differentially methylated CpG sites that are located in/near genes that also exhibit differential expression in T2D islets. Interestingly, there was an overrepresentation of CpG sites in the 5′UTR only when differential DNA methylation and gene expression show an inverse relationship (Figure 3B). In addition, CpG sites in the open sea and northern shore were overrepresented while sites in the CpG islands were underrepresented when DNA methylation and gene expression show an inverse relationship (Figure 3C). These data suggest that differential DNA methylation in certain genomic regions may contribute to an inverse regulation of gene expression. In addition, we found an overrepresentation of differentially methylated CpG sites in the gene body regardless of whether methylation and gene expression show a positive or inverse relationship (Figure 3B), this is known as the DNA methylation paradox which still remains unexplained [46]. 10.1371/journal.pgen.1004160.g003 Figure 3 Relation between DNA methylation and gene expression in human pancreatic islets. (A) The number of genes that exhibit both differential DNA methylation and gene expression in pancreatic islets from 15 T2D versus 34 non-diabetic donors. One gene (SLC44A4) was counted twice in the figure because it showed increased expression in T2D islets and was associated with probes that showed both increased and decreased DNA methylation. Distribution of differentially methylated CpG sites located in/near genes that also exhibit differential expression in T2D islets with an inverse relationship or a positive relationship in relation to their (B) functional genome distribution and in relation to (C) CpG island regions. Decreased DNA methylation and increased mRNA expression of (D) CDKN1A and (E) PDE7B in pancreatic islets of T2D versus non-diabetic donors. (F) A diagram of the two luciferase reporter plasmids used to test the effect of DNA methylation on CDKN1A and PDE7B promoter activity is shown. The two plasmids contain 1500 bp of either the CDKN1A or the PDE7B promoter regions inserted into a pCpGL-basic vector. Methylated (grey and black bars) or mock-methylated (white bars) promoter constructs were transfected into clonal β-cells for 48 hours prior to luciferase assay. The data were normalized with co-transfected renilla luciferase control vector and are the average of three separate experiments with five replicates each. In each experiment, cells were transfected with an empty pCpGL-basic vector as a background control. * P 4000) and they were included for further analysis based on GenomeStudio quality control steps where control probes for staining, hybridization, extension and specificity were examined. The intensity of both sample dependent and sample independent built in controls was checked for the red and green channels using GenomeStudio. We next exported the DNA methylation data from GenomeStudio and used Bioconductor [82] and the lumi package [83] for further analyses. Individual probes were filtered based on their mean detection P-value and those with a P-value>0.01 were excluded from further analysis. As a result, DNA methylation data for 483,031 (99.5%) probes, including 479,927 CpG sites and 3,039 non-CpG sites were used for further analysis. Because M-values are more statistically valid [84], β-values were converted to M-values using the following equation: M = log2 β-value/(1−β-value). M-values were then used for further statistical analysis [84]. In order to correct for background fluorescence the median M-value of the built in negative controls was subtracted from M-values. Next a quantile normalization was performed as described [85]. The Universal Methylated Human DNA standard D5011 (Zymo Research), which is human DNA that has been enzymatically methylated in the CpG sites by M.SssI methyltransferase, was used as a positive control in every batch and it showed high levels of methylation (87.7±0.6%). The low intensity of Y chromosome loci in female samples was used as an additional control. Moreover, one human pancreatic islet sample was run in two different batches on two different days and used as a technical replicate. As the β-value is easier to interpret biologically, M-values were reconverted to β-values when describing the results and creating the figures. Pathway analysis The enrichment of KEGG pathways among genes that exhibit differential DNA methylation in T2D compared with non-diabetic islets was tested using WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt, March 2012). Microarray mRNA expression analysis mRNA expression of the human pancreatic islets was analyzed using the GeneChip Human Gene 1.0 ST array from Affymetrix (Santa Clara, CA, USA) as previously described [63]. Luciferase assays 1500 bp of the human CDKN1A or PDE7B promoters (sequences are given in Table S12) were inserted into a CpG-free firefly luciferase reporter vector (pCpGL-basic) kindly provided by Dr Klug and Dr Rehli [86]. Amplification of CDKN1A and PDE7B DNA sequences and insertion into the pCpGL-basic vector was done by GenScript (Piscataway, NJ, USA). The constructs were either mock-methylated or methylated using two different DNA methyltransferases; SssI and HhaI (New England Biolabs, Frankfurt am Main, Germany). While SssI methylates all cytosine residues within the double stranded dinucleotide recognition sequence CG, HhaI only methylates the internal cytosine residue in GCGC sequence. INS-1 832/13 β-cells were co-transfected with 100 ng pCpGL-vector either with or without respective insert together with 2 ng of pRL renilla luciferase control reporter vector (pRL-CMV vector, Promega, Madison, USA) as a control for transfection efficiency and luciferase activity was measured as previously described [12]. Overexpression of Cdkn1a, Pde7b and Sept9 in clonal β- and α-cells INS-1 832/13 β-cells were cultured as previously described [87] and αTC1-6 cells were cultured according to ATCC's instructions (ATCC, Manassas, VA). pcDNA3.1 expression vectors with rat cDNA for either Cdkn1a, Pde7b or Sept9, or the empty vector, were transfected into β- or α-cells with Lipofectamine LTX and Plus Reagent (Life Technologies, Paisley, UK), according to the manufacturer's instructions (sequences for Cdkn1a, Pde7b or Sept9 are given in Table S13). Overexpression was verified with real-time PCR using an ABI 7900 system (Applied Biosystems, Foster City, CA, USA) and a SYBR Green assay for Cdkn1a (fwd-primer: ATGTCCGACCTGTTCCACAC, rev-primer: CAGACGTAGTTGCCCTCCAG) or TaqMan assays (Life Technologies) for Pde7b (Rn00590117_m1) and Sept9 (Rn00582942_m1). Cyclophilin B (Rn03302274_m1 and Mm00478295_m1) was used as an endogenous control. Expression levels were calculated with the ΔΔCt method. Overexpression was also verified by Western Blot analysis and cells transfected with HA-tagged cDNAs for Cdkn1a, Pde7b and Sept9 were lysed in RIPA buffer (50 mM Tris pH 7.6, 150 mM NaCl, 0.1% SDS, 0.5% sodium deoxycholate, 1% Triton×100 and 1× protease inhibitor cocktail (P8340, Sigma-Aldrich, USA) and boiled with 6× sample buffer (60 mM Tris pH 6.8, 10% glycerol, 2% SDS, 10% β-mercaptoethanol and bromophenol blue). Samples were separated on gradient Mini-PROTEAN® TGX gels (Bio-Rad, Hercules, CA, USA) and transferred onto Hybond-LFP PVDF membranes (GE Healthcare, Piscataway, NJ, USA). Protein expression was detected with primary antibodies against HA tag (ab9110, Abcam Cambridge, UK) and β-actin (A5441, Sigma-Aldrich) and secondary DyLight 680/800 conjugated anti-mouse and anti-rabbit antibodies (35518 and 35571, Thermo Scientific, Rockford, USA) and blots were scanned in an ODYSSEY (Licor, Lincoln, NE, USA). Insulin secretion and content 48 hours post transfection of INS-1 832/13 β-cells, insulin secretion with indicated secretagogues was determined during 1 hour static incubations as previously described [87]. Insulin content of cells was determined after acid ethanol extraction of the hormone. Insulin secretion was normalized to total insulin content. Glucagon secretion αTC1-6 cells were transfected as described above. 48 hours post transfection clonal α-cells were pre-incubated in HEPES balanced salt solution (HBSS, 114 mM NaCl; 4.7 mM KCl; 1.2 mM KH2PO4; 1.16 mM MgSO4; 20 mM HEPES; 2.5 mM CaCl2; 25.5 mM NaHCO3; 0.2% BSA, pH 7.2) supplemented with 5.5 mM glucose. Secretion was then stimulated in 1 hour static incubation with HBSS supplemented with 1 or 16.7 mM glucose. Secreted glucagon was measured with a glucagon ELISA (Mercodia, Uppsala, Sweden) and normalized to total protein as determined by a BCA assay (Thermo Scientific). Proliferation assay INS-1 832/13 β-cells were transfected as described above. 72 hours post transfection the β-cells were washed with PBS and stained with 0.1% crystal violet in 0.15 M NaCl. Cells were then washed with water and allowed to dry. Methanol was added to wells and absorbance measured at 600 nm in an Infinite M200 plate reader (Tecan, Männerdorf, Switzerland). RNA interference INS-1 832/13 β-cells were transfected with Lipofectamine RNAiMAX (LifeTechnologies) according to the manufacturer's instructions with siRNA targeting Exoc3l (LifeTechnologies, ID: s146127) or negative control siRNA (5′-GAGACCCUAUCCGUGAUUAUU-3′). Following 24 hours incubation, cells were transferred onto Petri dishes and cultured another 24 hours. Exoc3l knock-down was verified with real-time PCR using an ABI 7900 system and assays for Exoc3l (Rn01432027_m1) and endogenous controls (Cyclophilin B, Rn03302274_m1 and Hprt, Rn01527840_m1) (Life Technologies). Electrophysiological measurements of exocytosis using the patch-clamp technique Electrophysiological measurements of exocytosis were performed on INS-1 832/13 β-cells as described [88]. Analysis of DNA methylation of selected genomic regions Pyrosequencing was used to technically validate the Infinium HumanMethylation450 BeadChip DNA methylation data. EpiTect Bisulfite Kit (Qiagen) was used for bisulfite conversion of human islet DNA. Primers were designed using the PyroMark Assay design Software 2.0 (Qiagen). Sequences are included in Table S14. Bisulfite converted DNA was amplified with the PyroMark PCR kit. Pyrosequencing was performed with PyroMark ID 96 and PyroMark Gold Q96 reagents (Qiagen) according to the manufacturer's instructions. Data were analyzed with the PyroMark Q96 2.5.7 software program. Sequenom's MassARRAY EpiTYPER protocol (Sequenom, San Diego, CA, USA) was used to measure DNA methylation of PDX1 in its distal promoter and enhancer regions according to our previous study [12]. Statistical analysis A principle component analysis was performed to examine batch effects and other possible sources of variation on the DNA methylation data. To identify differences in DNA methylation and mRNA expression between T2D and non-diabetic islets a linear regression model was used including batch, gender, BMI, age, islet purity and days of culture as covariates and DNA methylation or mRNA expression as quantitative variables. A false discovery rate (FDR) analysis was used to correct for multiple testing [18], [89], [90]. Chi2 tests were used to compare the expected number of probes on the Infinium HumanMethylation450 BeadChip with observed number of differentially methylated probes in T2D islets. Supporting Information Figure S1 Islet purity and β-cell content in human pancreatic islets. There were no significant differences in (A) purity or (B) β-cell content in pancreatic islets of non-diabetic compared with T2D human donors. The β-cell content was analyzed in 4.11±0.46 islets/donor. Mann-Whitney U test was used for statistical analysis and data are presented as mean ± SEM. (TIF) Click here for additional data file. Figure S2 Distribution of non-CpG sites analyzed with the Infinium HumanMethylation450 BeadChip based on their (A) functional genome distribution and (B) relation to CpG island regions. (TIF) Click here for additional data file. Figure S3 Impact of Cdkn1a, Pde7b and Sept9 on clonal β- and α-cells. Clonal INS-1 832/13 β-cells and αTC1-6 cells were used to study the impact of Cdkn1a, Pde7b and Sept9 on insulin and glucagon secretion, respectively. (A) Glucose-stimulated insulin secretion represented as the ratio of secretion at 16.7 over that at 2.8 mM glucose (fold change) in clonal β-cells overexpressing either Cdkn1a, Pde7b or Sept9 (black bars) compared with cells transfected with an empty pcDNA3.1 vector (white bar) (n = 5). * P≤0.05. (B) Insulin secretion in response to 2.8 mM glucose (white bars) or 2.8 mM glucose+35 mM KCl (black bars) in clonal β-cells overexpressing either Cdkn1a, Pde7b or Sept9 compared with cells transfected with an empty pcDNA3.1 vector (n = 4). (C) Fold-change of insulin secretion at 2.8 mM glucose+35 mM KCl over that at 2.8 mM glucose in clonal β-cells overexpressing either Cdkn1a, Pde7b or Sept9 (black bars) compared with control cells transfected with an empty pcDNA3.1 vector (white bar) (n = 4). * P≤0.05. (D) Overexpression of Cdkn1a, Pde7b and Sept9 with pcDNA3.1 expression vectors in clonal α-cells (αTC1-6) resulted in elevated mRNA levels (black bars) compared with cells transfected with an empty pcDNA3.1 vector (white bars) (n = 4), * P≤0.05. Overexpression at the protein level was determined by western blot with an anti HA-tag antibody. (TIF) Click here for additional data file. Figure S4 Technical validation of Infinium HumanMethylation450 BeadChip data. A human islet sample was bisulfite converted and analyzed on the Infinium array at two different occasions. The correlation for DNA methylation of 100 000 CpG sites from the two arrays was then calculated and presented in panel A. Seven CpG sites including cg21091547 (CDKN1A), cg27306443 (PDE7B), cg19654743 (SEPT9), cg04751089 (IRS1), cg20995304 (HDAC7), cg01649611 (THADA) and cg15572489 (PTPRN2) were selected for technical validation of the Infinium HumanMethylation450 BeadChip data using pyrosequencing. Correlations between DNA methylation data analyzed with the two different methods were all significant and are shown in panel B. Correlations were calculated using Spearman's test. (TIF) Click here for additional data file. Figure S5 Confirmation of Infinium HumanMethylation450 BeadChip data. DNA methylation data of 264 CpG sites analyzed in human pancreatic islets from both our study and in the study by Volkmar et al correlate positively in (A) non-diabetic and (B) T2D donors. Increased DNA methylation of CpG sites in the INS and PDX1 genes in T2D versus non-diabetic islets (* P<0.05) is shown in panel C and D, respectively. The degree of DNA methylation of CpG sites in previously known imprinted genes in pancreatic islets of non-diabetic donors is shown in panel E. (TIF) Click here for additional data file. Table S1 CpG sites with differential DNA methylation (q<0.05 and difference in DNA methylation ≥5%) in pancreatic islets from 34 non-diabetic versus 15 T2D human donors. (XLSX) Click here for additional data file. Table S2 DMRs with differential DNA methylation (q<0.05 and difference in methylation ≥5%) in pancreatic islets from 34 non-diabetic versus 15 T2D human donors. (DOCX) Click here for additional data file. Table S3 Non-CpG DNA methylation in human pancreatic islets. (DOCX) Click here for additional data file. Table S4 CpG sites with differential DNA methylation (q<0.05 and difference in methylation ≥5%) concurrent with a difference in mRNA expression (P≤0.05) of the nearest gene in pancreatic islets from 34 non-diabetic versus 15 T2D human donors. (XLSX) Click here for additional data file. Table S5 CpG sites that exhibit differential DNA methylation in pancreatic islets from T2D compared with non-diabetic islets in the study by Volkmar et al as well as in the present study with P<0.05. DNA methylation data from the 34 non-diabetic and 15 T2D human donors analyzed in the present study is presented in this table. (DOCX) Click here for additional data file. Table S6 Characteristics of 87 non-diabetic human donors of pancreatic islets used to examine the impact of HbA1c, age and BMI on DNA methylation of the 1,649 CpG that exhibit differential DNA methylation in pancreatic islets from 34 non-diabetic versus 15 T2D human donors. (DOCX) Click here for additional data file. Table S7 CpG sites that exhibit differential DNA methylation (q<0.05 and difference in methylation ≥5%) in pancreatic islets from 34 non-diabetic versus 15 T2D human donors in parallel with an association between HbA1c levels and differential DNA methylation (P<0.05) in pancreatic islets from 87 non-diabetic donors. (DOCX) Click here for additional data file. Table S8 CpG sites that exhibit differential DNA methylation (q<0.05 and difference in methylation ≥5%) in pancreatic islets from 34 non-diabetic versus 15 T2D human donors in parallel with an association between age and differential DNA methylation (P<0.05) in pancreatic islets from 87 non-diabetic donors. (DOCX) Click here for additional data file. Table S9 CpG sites that exhibit differential DNA methylation (q<0.05 and difference in methylation ≥5%) in pancreatic islets from 34 non-diabetic versus 15 T2D human donors in parallel with an association between BMI and differential DNA methylation (P<0.05) in pancreatic islets from 87 non-diabetic donors. (DOCX) Click here for additional data file. Table S10 Associations between DNA methylation and gene expression in pancreatic islets from 87 non-diabetic donors of CpG sites also showing differential DNA methylation (q<0.05 and difference in methylation ≥5%) concurrent with a difference in mRNA expression (P≤0.05) of the nearest gene in pancreatic islets from 34 non-diabetic versus 15 T2D human donors. (DOCX) Click here for additional data file. Table S11 DNA methylation in whole islets (n = 4) compared to FACS sorted β-cells (n = 3) of the 1649 CpG sites that showed differential methylation in pancreatic islets from 34 non-diabetic versus 15 T2D human donors. (XLSX) Click here for additional data file. Table S12 Sequences of CDKN1A and PDE7B inserted into the CpG-free firefly luciferase reporter vector (pCpGL-basic) and used for luciferase experiments. (DOCX) Click here for additional data file. Table S13 Sequences of Cdkn1a, Pde7b and Sept9 inserted into pcDNA3.1 expression vectors and used for the overexpression experiments. (DOCX) Click here for additional data file. Table S14 DNA sequences for pyrosequencing forward, reverse and sequencing primers. (DOCX) Click here for additional data file.

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EHMT1 controls brown adipose cell fate and thermogenesis through the PRDM16 complex

Haruya Ohno, Kosaku Shinoda, Kana Ohyama … (2013)

Brown adipose tissue (BAT) dissipates chemical energy in the form of heat as a defense against hypothermia and obesity. Current evidence indicates that brown adipocytes arise from Myf5 + dermotomal precursors through the action of PRDM16 (PR domain containing protein16) transcriptional complex 1,2 . However, the enzymatic component of the molecular switch that determines lineage specification of brown adipocytes remains unknown. Here we show that EHMT1 (euchromatic histone-lysine N-methyltransferase 1) is an essential BAT-enriched lysine methyltransferase in the PRDM16 transcriptional complex and controls brown adipose cell fate. Loss of EHMT1 in brown adipocytes causes a severe loss of brown fat characteristics and induces muscle differentiation in vivo through demethylation of histone 3 Lys 9 (H3K9me2 and 3) of the muscle-selective gene promoters. Conversely, EHMT1 expression positively regulates the BAT-selective thermogenic program by stabilizing the PRDM16 protein. Notably, adipose-specific deletion of EHMT1 leads to a marked reduction of BAT-mediated adaptive thermogenesis, obesity, and systemic insulin resistance. These data indicate that EHMT1 is an essential enzymatic switch that controls brown adipose cell fate and energy homeostasis.