DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients
2012; Springer Nature; Volume: 31; Issue: 6 Linguagem: Inglês
10.1038/emboj.2011.503
ISSN1460-2075
AutoresMichael Volkmar, Sarah Dedeurwaerder, Daniel A. Cunha, Matladi Ndlovu, Matthieu Defrance, Rachel Deplus, Emilie Calonne, Ute Volkmar, Mariana Igoillo‐Esteve, Najib Naamane, S Del Guerra, Matilde Masini, Marco Bugliani, Piero Marchetti, Miriam Cnop, Décio L. Eizirik, François Fuks,
Tópico(s)Diabetes and associated disorders
ResumoArticle31 January 2012Open Access DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients Michael Volkmar Michael Volkmar Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Sarah Dedeurwaerder Sarah Dedeurwaerder Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Daniel A Cunha Daniel A Cunha Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Matladi N Ndlovu Matladi N Ndlovu Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Matthieu Defrance Matthieu Defrance Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Rachel Deplus Rachel Deplus Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Emilie Calonne Emilie Calonne Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Ute Volkmar Ute Volkmar Department of Molecular Evolution, Institute for Cellular and Molecular Biology (IZMB), University of Bonn, Bonn, Germany Search for more papers by this author Mariana Igoillo-Esteve Mariana Igoillo-Esteve Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Najib Naamane Najib Naamane Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Silvia Del Guerra Silvia Del Guerra Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Matilde Masini Matilde Masini Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Marco Bugliani Marco Bugliani Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Piero Marchetti Piero Marchetti Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Miriam Cnop Miriam Cnop Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Division of Endocrinology, Erasmus Hospital, Brussels, Belgium Search for more papers by this author Decio L Eizirik Decio L Eizirik Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author François Fuks Corresponding Author François Fuks Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Michael Volkmar Michael Volkmar Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Sarah Dedeurwaerder Sarah Dedeurwaerder Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Daniel A Cunha Daniel A Cunha Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Matladi N Ndlovu Matladi N Ndlovu Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Matthieu Defrance Matthieu Defrance Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Rachel Deplus Rachel Deplus Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Emilie Calonne Emilie Calonne Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Ute Volkmar Ute Volkmar Department of Molecular Evolution, Institute for Cellular and Molecular Biology (IZMB), University of Bonn, Bonn, Germany Search for more papers by this author Mariana Igoillo-Esteve Mariana Igoillo-Esteve Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Najib Naamane Najib Naamane Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Silvia Del Guerra Silvia Del Guerra Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Matilde Masini Matilde Masini Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Marco Bugliani Marco Bugliani Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Piero Marchetti Piero Marchetti Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy Search for more papers by this author Miriam Cnop Miriam Cnop Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Division of Endocrinology, Erasmus Hospital, Brussels, Belgium Search for more papers by this author Decio L Eizirik Decio L Eizirik Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author François Fuks Corresponding Author François Fuks Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium Search for more papers by this author Author Information Michael Volkmar1, Sarah Dedeurwaerder1, Daniel A Cunha2, Matladi N Ndlovu1, Matthieu Defrance1, Rachel Deplus1, Emilie Calonne1, Ute Volkmar3, Mariana Igoillo-Esteve2, Najib Naamane2, Silvia Del Guerra4, Matilde Masini4, Marco Bugliani4, Piero Marchetti4, Miriam Cnop2,5, Decio L Eizirik2 and François Fuks 1 1Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium 2Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium 3Department of Molecular Evolution, Institute for Cellular and Molecular Biology (IZMB), University of Bonn, Bonn, Germany 4Metabolic Unit, Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy 5Division of Endocrinology, Erasmus Hospital, Brussels, Belgium *Corresponding author. Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles, 808 Route de Lennik, Brussels 1070, Belgium. Tel.: +32 2 555 62 45; Fax: +32 2 555 62 57; E-mail: [email protected] The EMBO Journal (2012)31:1405-1426https://doi.org/10.1038/emboj.2011.503 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info In addition to genetic predisposition, environmental and lifestyle factors contribute to the pathogenesis of type 2 diabetes (T2D). Epigenetic changes may provide the link for translating environmental exposures into pathological mechanisms. In this study, we performed the first comprehensive DNA methylation profiling in pancreatic islets from T2D and non-diabetic donors. We uncovered 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets. These methylation changes were not present in blood cells from T2D individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose. For a subgroup of the differentially methylated genes, concordant transcriptional changes were present. Functional annotation of the aberrantly methylated genes and RNAi experiments highlighted pathways implicated in β-cell survival and function; some are implicated in cellular dysfunction while others facilitate adaptation to stressors. Together, our findings offer new insights into the intricate mechanisms of T2D pathogenesis, underscore the important involvement of epigenetic dysregulation in diabetic islets and may advance our understanding of T2D aetiology. Introduction Type 2 diabetes (T2D) has developed into a major public health concern. While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009). Lifestyle and behavioural factors play an important role in determining T2D risk. For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs β-cell dysfunction in female rat offspring (Ng et al, 2010). In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004). The pancreatic islets of Langerhans are of central importance in the development of T2D. Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet β-cells to regulate glucose homeostasis. β-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b; Tabak et al, 2009) and results in T2D (UKPDSG, 1995). The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007; Scott et al, 2007; Sladek et al, 2007; Zeggini et al, 2007, 2008) has placed the insulin-producing β-cells at centre stage. These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development. Most T2D risk genes identified in these genome-wide association studies (GWAS) affect β-cell mass and/or function (Florez, 2008). While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010). Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence. DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications. These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008; Park et al, 2008). DNA methylation occurs as 5-methyl cytosine mostly in the context of CpG dinucleotides, so-called CpG sites. It is the best-studied epigenetic modification and governs transcriptional regulation and silencing (for review, see Suzuki and Bird, 2008). Unlike the relatively study genome, the methylome changes in a dynamic way during development, tissue differentiation and aging. Pathologically altered DNA methylation is well described in various cancers (reviewed in Jones and Baylin, 2007) and its role is starting to be revealed in several other diseases such as multiple sclerosis (Casaccia-Bonnefil et al, 2008), Alzheimer's disease (Mastroeni et al, 2009) and systemic lupus erythematosus (Javierre et al, 2010). About 75% of human gene promoters are associated with CpG islands (CGIs) (Jones and Baylin, 2007; Suzuki and Bird, 2008), which are clusters of 500 bp to 2 kb length with a comparatively high frequency of CpG dinucleotides. They usually harbour low levels of DNA methylation but can become hypermethylated; this CGI hypermethylation was demonstrated to abrogate transcription of tumour suppressor genes during tumourigenesis (Jones and Baylin, 2007). Lately, DNA methylation changes in CpG sites adjoining yet outside of CGIs, so-called CGI shores (Irizarry et al, 2009), are gaining increased attention. Intriguingly, CpG sites in these shore sequences, in addition to those within CGIs, are proposed to display differential DNA methylation between cancer and normal cells as well as between cells of different tissues (Irizarry et al, 2009). The goal of the present work was to clarify the hitherto poorly understood connection between DNA methylation and T2D pathogenesis and to determine whether identified epigenetic changes translate into functional effects that impinge on pancreatic β-cell function. For this, we have explored DNA methylation landscapes in islets isolated from T2D patients and non-diabetic individuals. Results Identification of the T2D-related differential DNA methylation profile We performed DNA methylation profiling to analyse the methylomes of freshly isolated islets from 16 human cadaveric donors of similar age, BMI and ethnicity (5 diabetic and 11 non-diabetic Caucasian donors; Table I). Using electron microscopy (EM), we examined the purity of the islet preparations (see Supplementary data). In diabetic islets, decreases in the percentage of β-cells have been reported (Sakuraba et al, 2002; Rahier et al, 2008). As shifts in the composition of islet cell types (especially β-cells that constitute about two thirds of the islet cell mass) with different epigenomes might overlay T2D-related changes in the cells’ DNA methylation patterns, we used EM to estimate the percentage of β-cells. The amount of β-cells in three randomly chosen control islet preparations was 66.3±0.9%. Diabetic islets (n=3, randomly chosen) contained only marginally less β-cells, accounting for 59.7±1.7% of total islet cell number (cf. Supplementary Table S1, Materials and methods, Supplementary data). Table 1. Pancreatic islet donor characteristics CTL T2D Number of samples 11 5 Age (years) 57.2±15.2 64.4±5.3 (P=0.33) BMI (kg/m2) 27.0±1.1 26.5±2.2 (P=0.8) Glucose-induced insulin secretion (ratio of secretion at 16.7 mM/3.3 mM glucose) 3.59±1.05 1.24±0.3 (P 0.7) are shown next to the branches. Note that the order of samples in the heatmap in Figure 1A follows the one established by supervised clustering. (C) Pie chart depicting the 276 CpG sites showing differential methylation between T2D and control samples (see also Supplementary Table S2). Note the high proportion of hypomethylation events as compared with hypermethylation events. (D) LINE-1 repetitive element DNA methylation for CTL and T2D samples by BPS (n=11 for CTL; n=5 for T2D). Download figure Download PowerPoint We then set out to evaluate the descriptive power of the CpG sites in the filtered data set to differentiate diabetic from non-diabetic specimens in sample-wise comparisons. We therefore extracted the methylation values for each sample and performed a supervised clustering (Figure 1B, cf. Materials and methods). As expected, the resulting dendrogram shows that samples group together in two clusters containing exclusively control (CTL, yellow bar) or diabetic (T2D, blue bar) samples, indicating that class identity (CTL, T2D) is the most important separation criterion (Figure 1B, left-most branch). To assess clustering confidence in an unbiased way and to overcome inherently subjective visual interpretation of the results depicted in the heatmap (Figure 1A), a bootstrapping analysis was carried out after dendrogram computation (cf. Materials and methods). The obtained bootstrap of 0.85 indicates significant statistical support for the bipartite distribution between diabetic and non-diabetic samples based on the analysis of the CpGs contained in the filtered data set. The occasional high bootstrap values adjoined to sample pairs illustrate similarities in the DNA methylation profiles of these samples. These data demonstrate that human pancreatic islets undergo DNA methylation alterations in T2D that are discernible by means of DNA methylation profiles. T2D-related aberrations encompass mostly promoter-specific DNA hypomethylation The above experiments enabled us to collect the first comprehensive DNA methylation data set for T2D human islets. We identified 276 CpG sites, affiliated to 254 gene promoters, showing differential methylation between normal and diseased samples (Figure 1C; Supplementary Table S2). Strikingly, 266 of these 276 CpGs (96%) showed decreased methylation levels, while only 10 were hypermethylated (Figure 1C). This unexpected finding contrasts with the well-known DNA methylation changes observed in cancers, where gene-specific hypomethylation and hypermethylation are more or less evenly distributed (Jones and Baylin, 2007). With respect to global DNA methylation, cancers generally display hypomethylation (Jones and Baylin, 2007; Tost, 2010), primarily in repetitive DNA. To test whether the observed T2D-related changes are gene specific or whether they reflect global hypomethylation in the genome of islet cells, we measured DNA methylation levels of the repetitive LINE-1 element in control and diabetic samples with bisulphite pyrosequencing (BPS). Analysing DNA methylation of LINE-1, which makes up ∼20% of human genome, provides an accurate estimate of global DNA methylation changes (Yang et al, 2004). Figure 1D shows that repetitive elements are not differentially methylated in T2D, as substantiated by the strong overlap between CTL and T2D samples, indicating the absence of global hypomethylation in T2D islets. As an additional quality control, we examined the set of 276 differentially methylated CpG sites for overlap with known single-nucleotide polymorphism (SNP) positions to be excluded from further data analysis. We found no overlap with the reported 180 potentially problematic CpG sites contained in the Humanmethylation27 array (Bell et al, 2011) and therefore continued our analyses with the full set of 276 CpGs. BS validation of T2D-related differential DNA methylation To corroborate the observed Infinium measurements (cf. Figure 1 and Supplementary Table S2), we applied BPS and in some cases conventional BS to randomly selected, differentially methylated CpG sites. In all 19 cases tested, differential DNA methylation at the respective CpG sites was confirmed by BPS (Figure 2; Supplementary Figure S4). Where implemented, BS also confirmed the DNA methylation profiling data (Figure 2A; Supplementary Figure S4A). Figure 2A depicts an exemplary analysed gene, ALDH3B1, for which the Infinium data were confirmed by BS and BPS. Additional validated genes are shown in Figure 2B (CASP10) and Figure 2C (PPP2R4 alias PP2A). We discovered two differentially methylated CpG sites inside a CGI in the IGF2/IGF2AS locus. The differential methylation of one of the CpGs in this region was tested and confirmed by BPS (Figure 2D). Further examples are shown in Supplementary Figure S4. A direct comparison of methylation percentages obtained by the Infinium Methylation assay and BPS (Figure 2E) yielded a highly positive correlation (Pearson's correlation R=0.927) confirming the validity of the data. BPS analysis of three negative controls constituting high (>90%), intermediate (∼40%) and low ( 2 kb from the nearest CGI (‘other CpGs’ in Figure 3A and B and Supplementary Table S3). Additionally, about one quarter of CpGs resides in CGI shores (1–2000 bp from a CGI border) while another quarter of CpG sites was located inside CGIs (Figure 3A; Supplementary Tables S2 and S3). A more detailed representation of the CpG distribution reveals the sites of differential methylation inside the CGI shores to be located preferentially close to the CGIs (bar chart in Figure 3B). This distribution of differentially methylated sites inside CGI shores is similar to regions showing differential methylation in cancer (c-DMRs; Irizarry et al, 2009), between tissues (t-DMRs; Irizarry et al, 2009) and during differentiation/cell reprogramming (r-DMRs; Doi et al, 2009). However, an overall comparison of differential methylation locations between these DMRs (Doi et al, 2009; Irizarry et al, 2009) and our data (Figure 3A and B) shows significant disparity, with a predominance of DNA methylation changes >2 kb away from CGIs (‘other CpGs’ in Figure 3A and B and Supplementary Table S3), thus distinguishing the localisation of the DNA methylation profile in T2D islets from those found in tumours, between tissues and in stem cells. Figure 3.Classification of differentially methylated CpG sites and regulatory element analysis of affected genes. (A) Classification of the CpG sites according to their location relative to CpG islands. Most of the differentially methylated CpG sites are affiliated to genes not possessing a CpG island or are >2 kb away from the nearest CpG island (termed ‘other CpGs’); only about one quarter of the affected CpGs is located inside a CGI (‘CGI’) and about one quarter is located in CGI shores (‘CGI shore’), that is, distance to the CGI is between 1 and 2000 bp. The observed distribution of differentially methylated CpGs is significantly different from that of the Infinium array (χ2 goodness-of-fit test P<2.2 × 10−16). (B) Representation as bar plot with spatial resolution of the CGI shore shows that differential methylation in CGI shores occurs predominantly close to CGI borders (0–0.5 and 0.5–1 kb). (C) Classification of the promoters affiliated to the CpG sites based on the promoter CpG content. The observed distribution of promoter classes among genes associated with differentially methylated CpGs is significantly distinct from that of the entirety of genes represented by CpG sites on the Infinium array (χ2 goodness-of-fit test P=7.1 × 10−14) (for numerical representation of classifications in A and C: cf. Supplementary Table S3). (D) Direct plotting of CpG ratio (cf. Materials and methods) shows T2D-associated differential methylation (blue bars) predominantly in low CpG density promoters. (E) Prediction of putative TF binding sites using the set of low CpG differentially methylated gene promoters (CpG ratio <0.5) with the Pscan transcription factor motif analysis software. Statistically significant overrepresented binding sites have been found for members of the GATA transcription factor family. Download figure Download PowerPoint We next analysed the occurrence of these differentially methylated CpG sites in relation to CpG density of the affiliated gene promoters. Saxonov et al (2006) discovered a bipartite distribution of gene promoters with minor overlap between both classes when categorising promoter sequences by means of their CpG content. They discovered that promoters are either relatively depleted of CpG sites (low CpG promoters, LCPs) or enriched in CpG sites (high CpG promoters, HCPs), preferentially around the transcription start site (TSS). Weber et al (2007) introduced a third class of promoters called intermediate CpG promoters (ICPs), to account for the overlap between the classes mentioned above. They also developed precise classification criteria for th
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