WO2012097903A1 - Methylation patterns of type 2 diabetes patients - Google Patents

Abstract

The present invention provides new target gene regions for use in prediction, prognosis, diagnosis and therapy of T2D, and/or follow up of intervention trials, based on the differential methylation profile of said targets in samples from subjects with type 2 diabetes and healthy subjects.

Description

METHYLATION PATTERNS OF TYPE 2 DIABETES PATIENTS

FIELD OF THE INVENTION The present invention is situated in the medical diagnostics, therapeutics field, more particular in the field of early prediction of type-2 diabetes, and methods for treating said type-2 diabetes, based on the new diagnostic tools and targets identified herein.

BACKGROUND OF THE INVENTION

Type-2 diabetes (T2D) has developed into a major public health problem. While previously considered a problem primarily for Western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide. Lifestyle and behavioural factors play an important role in determining T2D risk. 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 beta-cells to regulate glucose homeostasis. Beta-cell failure marks the irreversible deterioration of glucose tolerance and results in T2D (Cnop M et al., 2007 Diabetes Care 30: 677- 682; Tabak AG et al., 2009 Lancet 373: 2215-2221 ; U.K. prospective diabetes study 16. (1995) Diabetes 44: 1249-1258). The unbiased genome-wide search for T2D risk genes has placed the insulin-producing beta-cells residing in the islets at centre stage. These approaches have also inadvertently highlighted the complexity inherent in biological mechanisms critical to T2D development. Most T2D risk genes identified in these genome-wide association studies (GWAS) affect beta-cell mass and/or function (Florez JC et al., 2008 Diabetologia 51 : 1 100-1 1 10). The majority of studies in the field have characterised diabetes aetiology on the basis of genetics, while little or no progress was made regarding the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences. 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, i.e. nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Park JH et al., 2008 J Clin Invest 1 18: 2316-2324; Ling C et al., 2008 Diabetologia 51 : 615-622). Park et al. reports on the hypermethylation of the pdxl gene promotor, while Ling et al. reports the hypermethylation of the PPAEGC1A gene promoter. In both cases, the hypermethylation of the promoter results in gene silencing in T2D patients.

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 MM and Bird A 2008 Nat Rev Genet 9: 465-476). Unlike the relatively sturdy 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 PA and Baylin SB 2007 Cell 128: 683-692). About 75% of human gene promoters are associated with CpG islands, which are clusters of 500bp to 2kb length with a comparatively high frequency of CpG dinucleotides. They usually harbour low levels of DNA methylation but can become hypermethylated; this CpG island hypermethylation was demonstrated to abrogate tumour suppressor gene transcription during tumourigenesis. Lately, DNA methylation changes in CpG sites adjoining yet outside of CpG islands, so-called CpG island shores (Irizarry RA et al., 2009 Nat Genet 41 : 178-186), are gaining increased attention. Intriguingly, CpG sites in these shore sequences, in addition to those within CpG islands, are proposed to display differential DNA methylation between cancer and normal cells as well as between cells of different tissues. The goal of the present invention is to clarify the hitherto poorly understood connection between the DNA methylation status in T2D patients, i.e. both hyper- and/or hypomethylation with respect to a healthy subject, and its relation to T2D pathogenesis. The invention aims at providing new prognostic and diagnostic tools for identifying T2D at a very early stage and provides new targets for treatment of T2D. The invention will also provide the tools to evaluate the impact of new intervention therapies aiming to prevent T2D via epigenetic modulation.

SUMMARY OF THE INVENTION

In the context of methods for predicting, diagnosing and/or treating Type 2 Diabetes (T2D), the present invention provides methods for identifying nucleic acid target gene regions that serve as prognostic, diagnostic and therapeutic targets. It also provides the tools to evaluate the impact of new intervention therapies aiming to prevent T2D via epigenetic modulation. A nucleic acid target gene region can also refer to an amplified product of a nucleic acid target gene region, including an amplified product of a treated nucleic acid target gene region, where the nucleotide sequence of such an amplified product reflects the methylation state of the nucleic acid target gene region. One skilled in the art would recognize that the size or length of the nucleic acid target gene region may vary depending on the limitation, or limitations, of the equipment used to perform the analysis. The nucleic acid target gene region may comprise intragenic nucleic acid, a gene of interest, more than one gene of interest, at least one gene of interest or a portion of a gene of interest. Correspondingly a sequential or non-sequential series of nucleic acid target gene regions may be analyzed and exploited to map an entire gene or genome. The nucleic acid targets of the present invention are specified below in Table 4 and the sequence of the identified CpG regions are defined by SEQ ID Nos 1-276. The present invention thus provides a method for the prognosis, diagnosis or prediction of Type 2 Diabetes (T2D) and/or for the follow up of intervention therapies comprising the steps of:

a) measuring or analyzing the methylation status of one or more of the CpG site(s) defined in Table 4 (defined by SEQ ID Nos 1-276) in a sample of the subject, and

b) comparing the methylation status of said one or more CpG site(s) obtained from step a) with the methylation status of said CpG site(s) in a control sample,

wherein a difference in methylation status as detected in step b) indicates the subject has or is at risk of developing T2D, optionally comprising the step of:

c) comparing the methylation status of said one or more CpG site(s) obtained from step a) with the methylation status of said CpG site(s) in a sample obtained after an intervention therapy aimed to prevent or treat T2D.

Preferably, said difference in methylation status is due to hypermethylation or hypomethylation. In a preferred embodiment, said sample is pancreatic islet tissue, a blood sample, adipose tissue, muscle, or any other biological sample that serve as surrogate material for the pancreatic islet tissue.

In a particular embodiment, the methylation status of up to, or more than: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 1 10, 1 15, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, or 275 of the CpG sites as defined in Table 4 or by SEQ ID Nos 1 to 276, or of all 276 CpG sites as defined in Table 4 is analyzed.

In a more preferred embodiment, the methylation status of the following CpG sites: 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and 275, all taken from Table 4 (preferably defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and 275 respectively) is analyzed.

The invention further provides for a method of treating T2D by targeting one or more genes having aberrant methylation in T2D in one or more CpG sites defined by SEQ ID Nos 1-276 taken from Table 4.

Preferably, said targeting implies changing the methylation status by using demethylating or methylating agents, by changing the expression level, or by changing the protein activity of the protein encoded by said one or more genes.

In a preferred embodiment, said methylating agents are methyl donors such as folic acid, methionine, choline or any other chemicals capable of elevating DNA methylation.

In a more preferred embodiment, one or more of the following CpG sites are targeted: 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and 275, taken from Table 4. Said CpG sites are preferably defined by SEQ ID NOs 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and 275 respectively.

In preferred embodiments of the method according to the invention, the methylation status is analysed by one or more techniques selected from the group consisting of nucleic acid amplification, polymerase chain reaction (PCR), methylation specific PCR (MCP), methylated- CpG island recovery assay (MIRA), combined bisulfite-restriction analysis (COBRA), bisulfite pyrosequenceing, single-strand conformation polymorphism (SSCP) analysis, restriction analysis, microarray analysis, or bead-chip technology.

In preferred embodiments of the methods according to the invention, the patient is in a high risk group for developing diabetes or suffering from any beta-cell related disorder such as: type 1 diabetes mellitus, type 2 diabetes mellitus, hyperinsulinemia, obesity, neuroendocrine tumors or occurrence of insulinoma.

The invention further provides a method for identifying an agent that modulates one or more of the genes having aberrant methylation in T2D in any one or more of the CpG site(s) defined by SEQ ID Nos 1-276, taken from Table 4, comprising the steps of:

a) contacting the candidate agent with said one or more genes, and

c) analysing the modulation of said one or more gene by the candidate agent

Preferably, said agent modulates the methylation status, the expression level or the activity of said one or more gene. More preferably, one or more of the CpG site(s) defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275 taken from Table 4, are targeted.

The invention further provides for a method for establishing a reference methylation status profile comprising the steps of: measuring the methylation status of one or more CpG site(s) as defined by SEQ ID Nos 1-276, taken from Table 4, having aberrant methylation in T2D, in a sample of subject.

Preferably, said subject is healthy, thereby producing a reference profile of a healthy subject, or wherein said subject is suffering from T2D, thereby producing a T2D reference profile.

More preferably, said reference profile concerns the methylation status profile of up to, or more than: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 1 10, 1 15, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, or 275 of the CpG site(s) defined by SEQ ID Nos 1 to 276, taken from Table 4.

Even more preferably, said reference profile concerns the methylation status profile of the CpG site(s) defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4. The invention further provides a microarray or chip comprising one or more T2D-specific CpG sites defined by SEQ ID Nos 1 to 276, taken from Table 4.

Preferably, said microarray or chip comprises up to, or more than: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 1 10, 1 15, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, or 275 of the CpG site(s) defined by SEQ ID Nos 1 to 276 taken from Table 4.

More preferably, said microarray comprises the CpG site(s) defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4.

The invention also provides for the use of the methylation status of one or more of the CpG site(s) defined by SEQ ID Nos 1- 276, taken from Table 4, in the prognosis, diagnosis or prediction of Type 2 Diabetes (T2D).

Preferably, the methylation status of one or more of the CpG sites defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4 is used.

Finally, the invention provides a method for identifying T2D specific SNPs in comprising the step of comparing the sequence of one or more of the differentially methylated genes corresponding to one or more CpG site(s) defined by SEQ ID Nos 1-276 as defined in Table 4, in a sample from a healthy versus a T2D subject, wherein a difference or polymorphism in said one or more gene regions between the healthy and T2D subject sample is identified as a T2D-specific SNP.

Preferably, the differentially methylated CpG site(s) are selected from the group consisting of those defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4.

DESCRIPTION OF THE DRAWINGS

Figure 1. Hierarchical profile-based clustering and evaluation of T2D islet DNA methylation. (A) Supervised clustering based on the differentially methylated CpG loci separates T2D from control samples. As an indication of statistical significance, bootstrap values (>0.7) are shown next to the branches. (B) Pie chart depicting the 276 CpG sites showing differential methylation between T2D and control samples. Note the high proportion of hypomethylation events as compared to hypermethylation events. (C) LINE1 repetitive element DNA methylation for CTL and T2D samples by BPS. Figure 2. Validation of aberrantly methylated loci in T2D islets. (A) Shown is an exemplary locus, ALDH3B1. Methylation data for the indicated CpGs obtained with the Infinium assay as well as by conventional bisulfite sequencing (BS), and by bisulfite pyrosequencing (BPS), are compared. Detected DNA methylation levels at other loci (B-D) are also in good agreement between the Infinium methylation assay (bar charts above the gene scale) and BPS used for validation (bar and line charts below gene scale) (see Figure 6 for further examples). CpG islands are indicated by a green line below gene scale. (E) Validation of the Infinium DNA methylation data by BPS. Methylation values obtained by Infinium assay and BPS show high correlation (Spearman correlation coefficient R=0.873).

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 >2kb away from the nearest CpG island (termed "other CpGs" in the legend); only 7% of the affected CpGs are located inside a CGI ("CGI") and about one quarter is located in CGI shores ("CGI shore"), i.e. distance to the CGI is between 1 and 2000bp. (B) Classification of the promoters affiliated to the CpG sites based on the promoter CpG content, (for numerical representation of classifications in A and B: cf. Table 2) (C) 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 over- represented binding sites have been found for members of the GATA transcription factor family.

Figure 4. Biological pathways associated with differentially methylated loci. (A) Ingenuity Pathway Analysis reveals canonical pathways significantly enriched in T2D pancreatic islets. Measure of significance is indicated by Benjamini-Hoch erg-corrected p value (abbreviated as 'B- H p value in the x axis label of the depicted chart). (B) Manual curation of the biological functions associated with the differentially methylated genes unravelled three broad categories of cellular responses that might be affected in T2D islets. Some of these genes are part of processes leading to beta-cell dysfunction and cell death while others seem to facilitate beta-cell survival and adaptation to the T2D environment. (C) Biological functions of a selection of the differentially methylated loci are highlighted that we hypothesise may play a critical role of the respective gene in the diabetic islets.

Figure 5. Sample dendrogram resulting from unsupervised hierarchical clustering. For unsupervised clustering, 24,421 CpGs were used that satisfy Infinium assay detection p value of p<0.05 in every sample. Type 2 diabetic samples (indicated by vertical blue line) cluster together as a self-contained group distinct from the control samples (vertical yellow lines). The dendrogram was derived using the cluster analysis routine in the Methylation module of the GenomeStudio™ software (lllumina, Inc.) applying Euclidean distance metrics. Figure 6. Validation of differential DNA methylation in T2D. Conventional bisulfite sequencing (A) and bisulfite pyrosequencing (A-M) confirm differential DNA methylation identified by Infinium assay. CpGs affiliated to ADCY7, GLP2R and RUNX3 (K-M) served as controls for high, intermediate and low DNA methylation, respectively. Their methylation levels are unaffected by T2D and thus can serve as an additional measure of the concordance between methylation values obtained by Infinium assay and BPS. Generally, the diagram in the upper part of each subfigure depicts the group-wise averaged DNA methylation percentage as assessed by the Infinium assay (yellow: non-diabetic controls, CTL; blue: T2D), while in the lower part, results of bisulfite pyrosequencing (BPS) for the respective CpG position are shown. BPS results are presented as a bar chart only, when the BPS reaction covered a single CpG site and as a line graph when 2 or more sites were covered. Error bars indicate standard deviation of the respective values. A drawn to scale representation of the gene is given for perspective. CpG positions are represented by "lollipop" markers, the gene body appears as a grey bar and the TSS is indicated by an angled arrow. The CpG site analysed by Infinium assay and validated by BPS is fenced with dotted red lines for easier tracing. Green bars below the scale indicate CpG islands. Note that high (>80%) and low (<20%) methylation levels are consistent between Infinium and BPS analyses.

Figure 7. GATA transcription factor binding site prediction and test of significance.

(A) Results of the transcription factor binding site search among 172 CpG-poor differentially methylated gene promoters (CpG ratio <0.5) using Pscan (only the 3 most significant predictions are shown). The most significant motifs detected are affiliated to GATA factor proteins. (B) Comparison of significance for TF motif enrichment from random datasets with significance of GATA motif enrichment from the differentially methylated dataset. As a negative control, Pscan was run on 100 random sets of 172 gene promoters with same properties as the differentially methylated promoter dataset (CpG ratio <0.5; genes represented on Infinium but excluding the differentially methylated ones). For each random set the p value of most significant motif predicted by Pscan was kept. The distribution of p values was then computed and plotted (histogram in lower panel in B). For comparison, the p values of GATA motifs (cf. subfigure A) from the differentially methylated dataset are shown (vertical lines in upper panel of B).

Figure 8. Physiological role of selected differentially methylated genes in beta-cell apoptosis.

(A) NIBAN mRNA expression in human islets treated for 24h with oleate or palmitate (left panel) and with synthetic ER stressors thapsigargin (THA), tunicamycin (TUN) or brefeldin (BRE). CTL denotes control. Results represent mean ± S.E. of 3 - 5 independent experiments. (B) Niban mRNA expression in rat INS-1 E cells transfected with negative siRNA (siCTL) or Niban siRNA (siNiban) and treated for 16h with palmitate or cyclopiazonic acid (CPA). Results represent mean ± S.E. of 3 - 4 independent experiments. (C) Percentage of apoptosis in siRNA-transfected INS- 1 E cells after 16h of palmitate or CPA treatment. Lower panel: Western blot quantification of cleaved caspase 3 as an additional apoptosis marker. The blot is representative of 4 independent experiments. (D) CHAC1 mRNA expression in human islets treated for 24 h with oleate or palmitate (left panel) and with synthetic ER stressors. Results represent mean ± S.E. of 3 - 5 independent experiments. (E) Chad mRNA expression in rat INS-1 E cells transfected with negative siRNA (siCTRL) or Chad siRNA (siChac). (F) Apoptosis in siRNA-transfected INS-1 E cells after 16h of palmitate or CPA treatment. Results represent mean ± S.E. of 4 - 10 independent experiments. Lower panel: Western blot quantification of cleaved caspase 3 as an additional apoptosis marker. The blot is representative of 4 independent experiments. ^* p<0.05 against untreated (CTL) cells, # p<0.05 by paired ratio t-test.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS As used herein, the singular forms "a", "an", and "the" include both singular and plural referents unless the context clearly dictates otherwise. By way of example, "an antibody" refers to one or more than one antibody; "an antigen" refers to one or more than one antigen.

The terms "comprising", "comprises" and "comprised of as used herein are synonymous with "including", "includes" or "containing", "contains", and are inclusive or open-ended and do not exclude additional, non-recited members, elements or method steps.

The term "and/or" as used in the present specification and in the claims implies that the phrases before and after this term are to be considered either as alternatives or in combination.

As used herein, the term "level" or "expression level" refers to the expression level data that can be used to compare the expression levels of different genes among various samples and/or subjects. The term "amount" or "concentration" of certain proteins refers respectively to the effective (i.e. total protein amount measured) or relative amount (i.e. total protein amount measured in relation to the sample size used) of the protein in a certain sample.

All documents cited in the present specification are hereby incorporated by reference in their entirety. In particular, the teachings of all documents herein specifically referred to are incorporated herein by reference.

The term "CpG islands" is a region of genome DNA which shows higher frequency of 5 -CG-3' (CpG) dinucleotides than other regions of genome DNA. Methylation of DNA at CpG dinucleotides, in particularly, the addition of a methyl group to position 5 of the cytosine ring at CpG dinucleotides, is one of the epigenetic modifications in mammalian cells. CpG islands often occur in the promoter regions of genes and play a pivotal role in the control of gene expression. In normal tissues CpG islands are usually unmethylated, but a subset of islands becomes differentially methylated (hyper- or hypomethylated) during the development of a disease.

Detection of methylation state of CpG islands can be done by any known assay currently used in scientific research. Some non-limiting examples are: Methylation-Specific PCR (MSP), which is based on a chemical reaction of sodium bisulfite with DNA, converting unmethylated cytosines of CpG dinucleotides to uracil (UpG), followed by traditional PCR. Methylated cytosines will not be converted by the sodium bisulfite, and specific nucleotide primers designed to overlap with the CpG site of interest will allow determining the methylation status as methylated or unmethylated, based on the amount of PCR product formed. Alternatively, the HELP assay can be used, which is based on the differential ability of restriction enzymes to recognize and cleave methylated and unmethylated CpG DNA sites. Furthermore, ChlP-on-chip assays, based on the ability of commercially prepared antibodies to bind to DNA methylation-associated proteins like MCP2, can be used to determine the methylation status. Also restriction landmark genomic scanning, also based upon differential recognition of methylated and unmethylated CpG sites by restriction enzymes can be used. Methylated DNA immunoprecipitation (MeDIP), analogous to chromatin immunoprecipitation, can be used to isolate methylated DNA fragments for input into DNA detection methods such as DNA microarrays (MeDIP-chip) or DNA sequencing (MeDIP-seq). The unmethylated DNA is not precipitated. Further, methylated-CpG island recovery assay (MIRA) can be used. These techniques require the presence of methylated cytosine residues within the recognition sequence that affect the cleavage activity of restriction endonucleases (e.g., Hpall, Hhal) (Singer et al. (1979)). Southern blot hybridization and polymerase chain reaction (PCR)-based techniques can be used with along with this approach. In another embodiment, a bisulfite dependent methylation assay is known as a combined bisulfite-restriction analysis (COBRA assay) whereas PCR products obtained from bisulfite- treated DNA can also be analyzed by using restriction enzymes that recognize sequences containing 5'CG, such as Taql (5TCGA) or BstUI (5'CGCG) such that methylated and unmethylated DNA can be distinguished.

In another embodiment, a methylation detection technique is based on the ability of the MBD domain of the MeCP2 protein to selectively bind to methylated DNA sequences. The bacterially expressed and purified His-tagged methyl-CpG-binding domain is immobilized to a solid matrix and used for preparative column chromatography to isolate highly methylated DNA sequences. Restriction endonuclease-digested genomic DNA is loaded onto the affinity column and methylated-CpG island-enriched fractions are eluted by a linear gradient of sodium chloride. PCR or Southern hybridization techniques are used to detect specific sequences in these fractions. In addition, one can make use of MALDI-TOF for DNA methylation analysis. Using a combination of four base specific cleavage reactions, each CpG of a target region can be analyzed individually and is represented by multiple indicative mass signals. Another exemplary method for detecting the methylation status of a gene makes use of a bead chip such as the Infinium® bead chip sold by lllumina Inc. San Diego (US). In selected embodiments, the methods for determining the methylation state of (one or more) target gene regions may include treating a target nucleic acid molecule with a reagent that modifies nucleotides of the target nucleic acid molecule as a function of the methylation state of the target nucleic acid molecule, amplifying treated target nucleic acid molecule, fragmenting amplified target nucleic acid molecule, and detecting one or more amplified target nucleic acid molecule fragments, and based upon the fragments, such as size and/or number thereof, identifying the methylation state of a target nucleic acid molecule, or a nucleotide locus in the nucleic acid molecule, or identifying the nucleic acid molecule or a nucleotide locus therein as methylated or unmethylated. Fragmentation can be performed, for example, by treating amplified products under base specific cleavage conditions. Detection of the fragments can be effected by measuring or detecting a mass of one or more amplified target nucleic acid molecule fragments, for example, by mass spectrometry such as MALDI-TOF mass spectrometry. Detection also can be affected, for example, by comparing the measured mass of one or more target nucleic acid molecule fragments to the measured mass of one or more reference nucleic acid, such as measured mass for fragments of untreated nucleic acid molecules. In an exemplary method, the reagent modifies unmethylated nucleotides, and following modification, the resulting modified target is specifically amplified. In some embodiments, the methods for determining the methylation state of (one or more) target gene regions may include treating a target nucleic acid molecule with a reagent that modifies a selected nucleotide as a function of the methylation state of the selected nucleotide to produce a different nucleotide. In particular embodiments, the reagent that modifies unmethylated cytosine to produce uracil is bisulfite. In certain embodiments, the methylated or unmethylated nucleic acid base is cytosine. In another embodiment, a non- bisulfite reagent modifies unmethylated cytosine to produce uracil.

As used herein, a "nucleic acid target gene region" is a nucleic acid molecule that is examined using the methods disclosed herein. For the purposes of the application, "nucleic acid target gene region", "target gene", "target region", "region" and "gene" may be used interchangeably. A nucleic acid target gene region includes genomic DNA or a fragment thereof, which may or may not be part of a gene, a segment of mitochondrial DNA of a gene or RNA of a gene and a segment of RNA of a gene. Examples of "targets" as defined herein are listed in Table 4A by means of their gene name or Gene ID number. A nucleic target gene region may be further defined by its chromosome position range as is e.g. done in Table 4B for each target sequence identified herewith. The chromosome position ranges provided herein were gathered from the human reference sequence (genome Build hg18/NCBI36, March 2006), which was produced by the International Human Genome Sequencing Consortium.

As used herein, a "nucleic acid target gene molecule" is a molecule comprising a nucleic acid sequence of the nucleic acid target gene region. The nucleic acid target gene molecule may contain less than 10%, less than 20%, less than 30%, less than 40%, less than 50%, greater than 50%, greater than 60%, greater than 70% greater than 80%, greater than 90% or up to 100% of the sequence of the nucleic acid target gene region. A "target peptide" refers to a peptide encoded by a nucleic acid target gene.

As used herein, the "methylation state" or "methylation status" of a nucleic acid target gene region refers to the presence or absence of one or more methylated nucleotide bases or the ratio of methylated cytosine to unmethylated cytosine for a methylation site in a nucleic acid target gene region as defined herein.

For example, a nucleic acid target gene region containing at least one methylated cytosine can be considered methylated (i.e. the methylation state of the nucleic acid target gene region is methylated). A nucleic acid target gene region that does not contain any methylated nucleotides can be considered unmethylated.

Similarly, the methylation state of a nucleotide locus in a nucleic acid target gene region refers to the presence or absence of a methylated nucleotide at a particular locus in the nucleic acid target gene region.

For example, the methylation state of a cytosine at the 10th nucleotide in a nucleic acid target gene region is methylated when the nucleotide present at the 10th nucleotide in the nucleic acid target gene region is 5-methylcytosine. Similarly, the methylation state of a cytosine at the 10th nucleotide in a nucleic acid target gene region is unmethylated when the nucleotide present at the 10th nucleotide in the nucleic acid target gene region is cytosine (and not 5-methylcytosine).

Correspondingly the ratio of methylated cytosine to unmethylated cytosine for a methylation site(s) or locus can provide a methylation state of a nucleic acid target gene region. In certain embodiments the methylation state or status may be expressed as a percentage of methylateable nucleotides (e.g., cytosine) in a nucleic acid (e.g., amplicon or gene region) that are methylated (e.g., about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 95% or about 100% methylated; greater than 80% methylated, between 20% to 80% methylated, or less than 20% methylated). A nucleic acid may be "hypermethylated," which refers to the nucleic acid having a greater number of methylateable nucleotides that are methylated relative to a control or reference. A nucleic acid may be "hypomethylated," which refers to the nucleic acid having a smaller number of methylateable nucleotides that are methylated relative to a control or reference. The methylation status or state is determined in a CpG island in certain embodiments. Examples of target CpG islands according to the present invention are listed in Table 4B and in SEQ ID Nos 1-276. As used herein, a "characteristic methylation state" refers to a unique, or specific data set comprising the methylation state of at least one of the methylation sites of one or more nucleic acid(s), nucleic acid target gene region(s), gene(s) or group of genes of a sample obtained from a subject. It can be the combined data of the methylation state of a panel of multiple target genes according to the present invention in said sample, as compared to a reference sample from e.g. a healthy subject.

As used herein, "methylation ratio" refers to the number of instances in which a molecule or locus is methylated relative to the number of instances the molecule or locus is unmethylated. Methylation ratio can be used to describe a population of individuals or a sample from a single individual.

For example, a nucleotide locus having a methylation ratio of 50% is methylated in 50% of instances and unmethylated in 50% of instances. Such a ratio can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a population of individuals. Thus, when methylation in a first population or pool of nucleic acid molecules is different from methylation in a second population or pool of nucleic acid molecules, the methylation ratio of the first population or pool will be different from the methylation ratio of the second population or pool. Such a ratio also can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a single individual. For example, such a ratio can be used to describe the degree to which a nucleic acid target gene region of a group of cells from a tissue sample are methylated or unmethylated at a nucleotide locus or methylation site. As used herein, a "methylated nucleotide" or a "methylated nucleotide base" refers to the presence of a methyl moiety on a nucleotide base, where the methyl moiety is not present in a recognized typical nucleotide base. Cytosine does not contain a methyl moiety on its pyrimidine ring, however 5-methylcytosine contains a methyl moiety at position 5 of its pyrimidine ring. In this respect, cytosine is not a methylated nucleotide and 5-methylcytosine is a methylated nucleotide. As used herein, a "methylation site" is a nucleotide within a nucleic acid, nucleic acid target gene region or gene that is susceptible to methylation either by natural occurring events in vivo or by an event instituted to chemically methylate the nucleotide in vitro. As used herein, a "methylated nucleic acid molecule" refers to a nucleic acid molecule that contains one or more methylated nucleotides that is/are methylated.

As used herein "CpG island" refers to a G:C-rich region of genomic DNA containing a greater number of CpG dinucleotides relative to total genomic DNA, as defined in the art. It should be noted that differential methylation of the target genes according to the invention is not limited to CpG islands only, but can be in so-called "shores" or can be lying completely outside a CpG island region.

As used herein, a first nucleotide that is "complementary" to a second nucleotide refers to a first nucleotide that base-pairs, under high stringency conditions to a second nucleotide. An example of complementarity is Watson-Crick base pairing in DNA (e.g., A to T and C to G) and RNA (e.g., A to U and C to G). Thus, for example, G base-pairs, under high stringency conditions, with higher affinity to C than G base-pairs to G, A or T, and, therefore, when C is the selected nucleotide, G is a nucleotide complementary to the selected nucleotide.

As used herein, the term "correlates" as between a specific diagnosis or a therapeutic outcome of a sample or of an individual and the changes in methylation state of a nucleic acid target gene region refers to an identifiable connection between a particular diagnosis or therapy of a sample or of an individual and its methylation state.

As used herein, a "subject" includes, but is not limited to, an animal, plant, bacterium, virus, parasite and any other organism or entity that has nucleic acid. Among animal subjects are mammals, including primates, such as humans. As used herein, "subject" may be used interchangeably with "patient" or "individual".

As used herein, a "methylation" or "methylation state" correlated with a disease, disease outcome or outcome of a treatment regimen refers to a specific methylation state of a nucleic acid target gene region or nucleotide locus that is present or absent more frequently in subjects with a known disease, disease outcome or outcome of a treatment regimen, relative to the methylation state of a nucleic acid target gene region or nucleotide locus than otherwise occur in a larger population of individuals (e.g., a population of all individuals).

As used herein, "sample" refers to a composition containing a material to be detected, and includes e.g. "biological samples", which refer to any material obtained from a living source, for example an animal such as a human or other mammal that can suffer from diabetes or related disorders. The biological sample can be in any form, including a solid material such as a tissue, cells, a cell pellet, a cell extract, or a biopsy, or it can be in the form of a biological fluid such as urine, whole blood, plasma, or serum, or any other fluid sample produced by the subject. In addition, the sample can be solid samples of tissues or organs, such as collected tissues, including pancreatic tissues, more specifically pancreatic island of Langerhans cells. Samples can include pathological samples such as a formalin- fixed sample embedded in paraffin. If desired, solid materials can be mixed with a fluid or purified or amplified or otherwise treated. Samples examined using the methods described herein can be treated in one or more purification steps in order to increase the purity of the desired cells or nucleic acid in the sample, Samples also can be examined using the methods described herein without any purification steps to increase the purity of desired cells or nucleic acid. In particular, herein, the samples include a mixture of matrix used for mass spectrometric analyses and a biopolymer, such as a nucleic acid. Preferably, said sample is a pancreatic island of Langerhans cell extract or biopsy, more preferably an extract or pool of pancreatic beta-cells, or is whole blood, plasma or serum of a subject.

The term "beta cell related disorder" described in the methods or uses or kits of the invention encompasses all disorders related to beta cells such as: type 1 diabetes mellitus, type 2 diabetes mellitus, hyperinsulinemia, obesity, neuroendocrine tumors or occurrence of insulinoma.

The present invention identifies 276 differentially methylated CpG sites that are affiliated to 254 genes performing the to our knowledge first comprehensive DNA methylation profiling of human T2D pancreatic islets. The present invention shows that the uncovered DNA methylation changes in diabetic islets may reflect epigenetic adaptations acquired over time by long-lived beta-cells that were exposed to the stressful environment of T2D for many years. Without wanting to be bound by any specific theory, hypomethylation has for example been observed in oxidative stress, ER stress and apoptotic pathways, that may result from chronic exposure to high concentrations of free fatty acids and glucose in T2D. It can be assumed that beta-cells utilise different pathways to adapt to different stresses. The present invention therefore suggest a new concept: demethylation of key CpGs may play a more important role in T2D-related inflammatory and signal transduction responses than in metabolic pathways. Moreover, the identification of methylation changes in T2D islets outlines an unexpected level of epigenetic regulation in beta- cell function, which is very important for the understanding of the pathogenesis of T2D. The present invention postulates that the prevalent hypomethylation in T2D islets is indicative of processes involved in adaptation to the diabetic environment as well as biological pathways associated to beta-cell dysfunction and apoptosis are also activated (Figure 4C).

In terms of genomic features, T2D-associated differential DNA methylation was mainly detected in low and intermediate CpG promoters (LCP, ICP), high CpG promoters (HCP) are underrepresented. Upon analysing LCP and a subset of ICP genes (CpG ratio <0.5; Saxonov S et al., 2006 Proc Natl Acad Sci U S A 103: 1412-1417) the inventors identified the GAT A family transcription factors that are predicted to regulate a significant subset of these genes as being prone to differential methylation in healthy versus T2D subjects.

The physiological roles of the differentially methylated loci in T2D can coarsely be described as genes responding to (external) stimuli and to stress. Of note, Saxonov et al. found that a disproportionately high percentage of genes affiliated to these biological functions possess promoters with a low CpG density (Saxonov S et al., 2006 Proc Natl Acad Sci U S A 103: 1412- 1417). This might indicate a general principle with regard to the promoter class of the differentially methylated gene loci: chronic diseases such as T2D and SLE (Javierre BM, et al., 2010 Genome Res 20: 170-179) (LCP genes overrepresented) on one hand are distinct from diseases associated with cellular overgrowth (e.g. cancer; prevalence of HCP, relatively few LCP genes; (Martin-Subero Jl et al., 2009 Blood 1 13: 2488-2497; Martin-Subero Jl et al., 2009 PLoS One 4: e6986; Richter J et al., 2009 BMC Cancer 9: 455) on the other hand.

As acknowledged by McCarthy and Zeggini, the >20 gene variants of T2D susceptibility genes known to date cannot fully explain T2D predisposition (McCarthy and Zeggini 2009 Curr Diab Rep 9: 164-171). Our study points to the involvement of epigenetic alterations in T2D thus underscoring the previously established contribution of habit and lifestyle to its development. Combining the advantages of genome-scanning techniques and epigenome analyses will pave the way to better comprehend the pathogenesis of T2D.

In addition, the invention provides new tools for identifying T2D linked small nucleotide polymorfisms (SNPs) in the epigenetically regulated genes or CpG's according to the invention (cf. Table 4), based on the known interplay between SNPs and differential (allele-specific) DNA methylation (ASM) as e.g. described in Shoemaker R et al., Genome Res 2010 Jul;20(7):883- 889. The invention thus provides a method for identifying T2D-specific SNPs, based on the identification of new differentially methylated gene regions in Table 4, using techniques known in the art.

The invention thus provides an altered DNA methylation profile in the pancreatic islets of T2D patients with a major preponderance of hypomethylation in sequences outside of CpG islands. These aberrant methylation events affect over 250 genes, whose dysregulation in T2D may notably be linked to beta-cell dysfunction, cell death and adaptation to metabolic stress. In particular, the present invention highlights genes belonging to biological processes whose involvement in T2D is not yet fully understood, such as inflammation and ion transporters/channels/sensors, thereby further unravelling the biological complexity of T2D and providing new diagnostic and therapeutic possibilities. A similar molecular signature is present in other tissues (e.g. blood, adipose tissue, muscle etc) of easier access than the islets of Langerhans, thus providing means for disease prediction. The invention is illustrated by the following non-limiting examples.

EXAMPLES

Materials and Methods

Isolation of pancreatic islets. From September 2004 to November 2009, pancreatic islets of Langerhans were isolated from pancreata of 5 type 2 diabetic and 1 1 non-diabetic male cadaveric donors (Table 1) as described previously (Del Guerra S et al., 2005 Diabetes 54: 727-735). Glucose-induced insulin secretion was measured as described. The diagnosis of type 2 diabetes was based on previously described clinical criteria (ADA (1997) Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 20: 1 183-1 197; Genuth S et al., 2003 Diabetes Care 26: 3160-3167).

Table 1 : Patient data of pancreatic islet donors. While age and BMI are comparable between both groups, in vitro islet insulin secretion upon stimulation with 16.7mM glucose is significantly lower in islets of T2D donors, as expected (p values in brackets indicate results of two-tailed t- tests). CTL denotes non-diabetic controls.

Methylation Profiling Using the Infinium® Assay. Genomic DNAs were isolated from the above isolated pancreatic islets using Wizard® SV Genomic DNA kit (Promega Corp.). '\ [ g of genomic DNA was treated with sodium bisulfite using the EZ DNA Methylation™ kit (Zymo Research) according to the manufacturer's procedure, respecting the recommended alterations in protocol for consecutive Infinium® methylation analysis. Methylation status of 27,578 distinct CpG sites was analysed using the Infinium® HumanMethylation27 BeadChip array (lllumina, Inc., San Diego) according to the manufacturer's protocol. Data acquisition was done with the lllumina BeadArrayReader and quality control, data handling, comparisons etc. were performed with the Methylation module of the GenomeStudio™ software package (lllumina), MS Excel2003, R 2.9.0 software, Openstat and MicroarrayAnalyse v1.0 (Graessler J (2008) MicroarrayAnalyse v1 .0.).

CpG island and promoter class annotation. Annotations for the Infinium HumanMethylation27 provided by lllumina were augmented with respect to (i) the position of the analysed CpG relative to the nearest CpG island (inside a CpG island, in CpG island shore or more than 2kb away from an island) and (ii) the promoter class of the gene affiliated to the evaluated CpG (high/intermediate/low CpG content promoter). For all annotations, the human genome build 36.1 (hg18, March 2006) provided the basis. For classification of the CpG position relative to CpG islands, the CpG island map provided by Bock et al. (combined epigenetic score >0.5; genome assembly hg18/NCBI36) was used as reference (Bock C et al., 2007 PLoS Comput Biol 3: e1 10); the CpGs were classified into 3 categories according to (Irizarry RA et al., 2009 Nat Genet 41 : 178-186). Designation of the CpGs is as follows: "inside CGI" if the CpG was inside a CpG island, "CGI shore" if the CpG was located within a 2kb region around a CpG island, and "other CpG" otherwise (distance to closest CpG island >2kb).

Promoters of the gene loci affiliated to the analysed CpG sites were classified according to their CpG content. First, sequences ranging from positions -700 to +500 relative to the transcription start site (TSS) from the UCSC genome browser database were extracted, then the CpG ratio and the GC content of these sequences in sliding windows of 500bp with 5bp offsets was calculated. For classification criteria the definition by Weber et al. (Weber M et al., 2007 Nat Genet 39: 457-466) was followed. In short, promoters were defined as high CpG promoters (HCP) if at least one 500bp window contained a CpG ratio >0.75 together with a GC content >0.55 whereas in low CpG promoters (LCP) no 500bp window reached a CpG ratio of at least 0.48. All promoters not fitting in either of the above promoter classes were termed intermediate CpG promoters (ICP). 5 differentially methylated gene promoters (and a total of 54 gene promoters on the Infinium array) could not be classified due to great distance to TSS or lack of annotation.

Hierarchical Clustering. For unsupervised hierarchical clustering, the data sets were filtered for probes/CpG sites with a detection p value <0.05. CpGs not fitting into this criterion in at least one sample were excluded from further analysis. The clustering was performed with the average beta values (equals methylation percentage ÷ 100) of 24,421 CpGs for each sample using the cluster analysis function in GenomeStudio™ applying Euclidean distance metrics.

For UPGMA (Unweighted Pair Group Method using Arithmetic averages) clustering, the data sets for computation were assembled as follows: A group-wise |Δβ|>0.15 and p<0.01 (Mann-Whitney) were set as filtering criteria. 276 probes fitted into these criteria. The methylation percentage of the CpG site corresponding to each probe was extracted for each sample. Then, the methylation values were categorised into 10 equal classes and imported into MEGA4 (Tamura K et al., 2007 Mol Biol Evol 24: 1596-1599) in which the phylogenetic analysis was conducted. The dendrogram was computed using the UPGMA method applying the 'number of differences' model (Sneath PHA et al., (1973) Numerical taxonomy : the principles and practice of numerical classification: W. H. Freeman, San Francisco). To determine the validity of the sample clustering based on the methylation data a bootstrap test (10,000 sampling steps) was used to calculate the percentage of replicate trees in which the associated samples clustered together (Felsenstein J 1985 Evolution 39: 783-791). Bootstrap values of 0.7 or higher were considered significant and are shown next to the branches in Figure 1 A.

Conventional Bisulfite Sequencing and Bisulfite Pyrosequencing. 750ng genomic DNA was subjected to bisulfite conversion using the Epitect® Bisulfite Kit (Qiagen) or the EZ DNA Methylation™ kit (Zymo Research) according to the manufacturer's protocol. Elution of the converted DNA was generally performed with 26μΙ elution buffer and 8μΙ of the eluate was used as template in subsequent PCRs. To ensure sufficient amount of product, amplifications were generally performed as nested PCRs. PCR and sequencing primers for bisulfite pyrosequencing were deduced using the PyroMark® Assay Design 2.0 software (Qiagen). Primers for pre-amplification and conventional bisulfite sequencing were designed manually or with the help of BiSearch primer design tool (http://bisearch.enzim.hu) and evaluated using the GeneRunner software (v3.05 Hastings Software, Inc.). Primers were obtained from Eurogentec S.A. or Sigma-Aldrich Corp. Biotinylated primers were ordered HPLC purified, all other primers desalted.

The pre-amplification PCR was conducted with primers (see EF, ER primers in Table 3) amplifying 400-720bp spanning the CpG of interest and additionally as many as possible neighbouring CpG sites. CpG sites in the annealing positions of the PCR primers were avoided where possible; otherwise primers were ordered with ambiguities at the respective positions. PCR was conducted with 3mM MgCI₂, 1 mM of each dNTP, 12% (v/v) DMSO, 500nM of each primer and optionally 500mM Betaine in heated-lid thermocyclers under the following conditions: 95°C 3:00; 25x[94°C 0:30; 51 °C 0:40; 72°C 1 :30]; 72°C 5:00 and cooled afterwards to 10°C.

For conventional bisulfite sequencing (BS), nested PCRs were conducted as described above using 35 PCR cycles and a decreased elongation time of 1 minute (for PCR primers cf. Table 3). PCR products were separated on a 1 % agarose gel and single bands were cut and eluted from the gel. Cloning of the nested PCR products was performed with TOPO TA Cloning® kit (Invitrogen Corp.) and the plasmids were sequenced by Genoscreen.

For pyrosequencing, a nested PCR was performed with primers designed by the PyroMark® Assay Design software (Qiagen) using HotStarTaq PCR kit (Qiagen) according to the manufacturer's recommendations. Reactions were performed in heated-lid thermocyclers under the following conditions: 95°C 15:00; 45x[94°C 0:30; 55°C 0:30; 72°C 0:30]; 72°C 10:00 and finally cooled to 8°C. Sample preparation and pyrosequencing reactions were performed with the Pyromark™ Q24 system (Qiagen). For validation of Infinium assay-derived DNA methylation by BPS, usually three to five randomly chosen samples from each group (CTL and T2D) were analysed and DNA methylation degrees were averaged. Example 1 : Identification of the T2D-related Differential DNA Methylation Profile.

A comprehensive DNA methylation profiling was performed to analyse the methylomes of freshly isolated islets from 16 human cadaveric donors (5 diabetic and 1 1 non-diabetic donors; cf. Table 1 ). For this purpose, the recently developed Infinium Methylation Assay (lllumina® Infinium® HumanMethylation27 BeadChip; Bibikova M et la., 2009 Epigenomics 1 : 177-200) was used to interrogate the methylation status of more than 27,000 CpGs corresponding to over 14,000 genes. As an initial step, it was evaluated whether DNA methylation changes in T2D were sufficient to distinguish the diabetic from the control samples when comparing complete methylation profiles. For this, an unsupervised hierarchical clustering was performed which placed the diabetic islet samples as one self-contained group distinct from the control samples in the resulting dendrogram (Figure 5). This outcome highlights two facts: firstly, diabetic DNA methylation profiles are more similar to each other than to the methylation profile of any control sample, hinting towards a T2D-specific DNA methylation profile; secondly, the existence of a single branch containing the five diabetic samples shows that DNA methylation changes are sufficiently pronounced (even in the unfiltered datasets) to distinguish diabetic from control samples. This is indicative of distinct, T2D-specific changes in the epigenome of pancreatic islets. Following this initial analysis, T2D-related methylation changes were identified by filtering the datasets for CpG sites showing significant differences in DNA methylation levels between control and T2D groups (cf. Material and Methods and Table 4). The results of the filtering show that there are pronounced DNA methylation changes in T2D islets.

We then set out to evaluate the descriptive power of the CpG sites in the filtered dataset to differentiate diabetic from non-diabetic specimens in sample-wise comparisons. We therefore extracted the methylation values for each sample (cf. Materials and Methods) and performed a supervised clustering (cf. Figure 1A). As expected, the resulting dendrogram shows that samples group together in two clusters containing exclusively control (CTL) or diabetic (T2D, grey bar) samples, indicating that class identity (CTL.T2D) is the most important separation criterion (Figure 1A, left-most branch). To assess clustering confidence, a bootstrapping analysis was carried out additionally after dendrogram computation. 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 CpG contained in the filtered dataset. 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 discernable by means of DNA methylation profiles. Example 2: T2D-related Aberrations Encompass Mostly Promoter-specific DMA Hypomethylation.

The above experiments enabled us to collect the first comprehensive DNA methylation dataset for T2D human islets. 276 CpG sites were identified, affiliated to 254 gene promoters, showing differential methylation between normal and diseased samples (Figure 1 B and Table 4). Strikingly, 266 of these 276 CpGs (96%) showed decreased methylation levels, while only 10 (4%) were hypermethylated (Figure 1 B). This unexpected finding contrasts with the well-known DNA methylation changes observed in cancers, where gene-specific hypo- and hypermethylation are more or less evenly distributed (Jones PA and Baylin SB 2007 Cell 128: 683-692). With respect to global DNA methylation, cancers generally display hypomethylation (Tost J 2010 Mol Biotechnol 44: 71 -81 ), primarily in repetitive DNA, while they generally show gene-specific hypermethylation.

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 LINE1 element in control and diabetic samples with bisulfite pyrosequencing. Analysing DNA methylation of LINE-1 , which makes up approximately 20% of human genome, has been shown to serve as an accurate estimate of global DNA methylation changes (Yang AS et al., 2004 Nucleic Acids Res 32: e38). Figure 1 C 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. This indicates that the detected hypomethylation of the target CpG areas of the present invention (cf. Table 4), are not due to global hypomethylation in T2D, but are due to gene-specific hypomethylation in T2D patients.

Example 3: Bisulfite Sequencing Validation of T2D-related Differential DNA Methylation To corroborate the observed Infinium measurements (cf. Figure 1 and Table 4), bisulfite pyrosequencing (BPS) and in some cases conventional bisulfite genomic sequencing (BS) was applied to randomly selected differentially methylated CpG sites. In all 17 cases tested, differential DNA methylation at the respective CpG sites was confirmed by BPS (Figure 2 and Figure 5). Where implemented, bisulfite genomic sequencing (BS) also confirmed the DNA methylation profiling data (Figure 2A and Figure 6A). 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 2C (PPP2R4 alias PP2A). Two differentially methylated CpG sites inside a CpG island in the IGF2/IGF2AS locus were discovered. The differential methylation of one of the CpGs in this region was tested and confirmed by BPS (Figure 2D). Further examples are shown in Figure 6. A direct comparison of methylation percentages obtained by the Infinium Methylation assay and BPS (Figure 2E) yielded a highly positive correlation (Spearman correlation R=0.873) confirming their validity. BPS analysis of three negative controls constituting high (>90%), intermediate (~40%) and low (<10%) levels of DNA methylation showed expectedly no differential DNA methylation between sample groups (Figure 6K-M). Of note, the BPS experiments designed for validation of the methylation profiles encompassed neighbouring CpGs to the Infinium-assayed site in the majority of cases (cf. Figure 2 and Figure 6); the adjacent CpG sites often displayed similar DNA methylation levels. Hence, the validation experiments indicate that individual CpGs from the Infinium Methylation array can be used as informative markers for the methylation status of the respective surrounding regions. Taken together, bisulfite pyrosequencing and genomic bisulfite sequencing results confirm the methylation values obtained from the Infinium-based assay, thus validating the T2D-specific DNA methylation profiles.

Example 4: Genomic Features Associated with Differential DNA Methylation in T2D.

Having shown that the observed differential DNA methylation in T2D is gene-specific rather than global (cf. Figure 1 C), it was further determined whether this aberrant DNA methylation is (a) located within or outside of CpG islands, (b) prevalent in distinct promoter classes (that are based on CpG density) or (c) correlated with specific regulatory elements.

Previous work that investigated the localisation of differential DNA methylation in cancer and in different tissues has suggested that substantial DNA methylation differences occur in CpG island (CGI) shores (Irizarry RA et al., 2009 Nat Genet 41 : 178-186). Bioinformatic tools were used to determine the distance of differentially methylated CpGs to the nearest CGI. Utilising CGI prediction (Bock C et al., 2007 PLoS Comput Biol 3: e1 10), it was shown that approximately 65% of the differentially methylated CpG sites is located >2kb from the nearest CGI ("other CpGs" in Figure 3A and Table 2). Additionally, about one quarter of CpGs resides in CGI shores (1 to 2000bp from a CGI border) while only a small percentage is located inside CGIs (Figure 3A and Table 2). Thus, these results seem to distinguish the localisation of the DNA methylation profile in T2D islets from those found in tumours.

Next, the occurrence of these differentially methylated CpG sites in relation to CpG density of the affiliated gene promoters was analysed. Saxonov et al. discovered a bipartite distribution of gene promoters with minor overlap between both classes when categorising promoter sequences by means of their CpG content (Saxonov S et al., 2006 Proc Natl Acad Sci U S A 103: 1412-1417). They discovered that promoters are either relatively depleted of CpG sites (low CpG promoters) or they are enriched in CpG sites (high CpG promoters), preferentially around the transcription start site (TSS). Weber et al. introduced a third class of promoters called intermediate CpG promoters (ICP) (Weber et al., 2007 Nat Genet 39: 457-466), to account for the overlap between classes mentioned above. They also developed precise classification criteria for the three classes which were utilised here in an adapted form: positions -700 to +500 relative to the TSS were used for the promoter classification (cf. Material and Methods), since CpG density as well as differential DNA methylation are distributed symmetrically around the TSS (Saxonov S et al., 2006 Proc Natl Acad Sci U S A 103: 1412-1417). Figure 4B shows that most of the differentially methylated CpG sites from T2D islets are located in LCP and ICP class promoters. ICP class promoters have been described as regions of dynamic DNA methylation changes (Weber et al., 2007 Nat Genet 39: 457-466), while LCP class promoters have seldomly been investigated at all. Their role as sites of hypomethylation in T2D therefore remains to be explored.

In terms of genomic features, it was evaluated whether the observed aberrant DNA methylation in T2D islets correlated with specific regulatory elements. The use of computational approaches to extract functional meaning from the annotated genome has gained importance in resent years. Roider et al. underscored the need to first separate gene promoters on the basis of their CpG content before analysing the presence of enriched transcription factor-binding motifs (Roider HG et al., 2009 Nucleic Acids Res 37: 6305-6315). These authors found that promoters depleted of CpG sites often contain tissue-specific transcription factor binding sites. Therefore, CpG-depleted promoters were first extracted by selecting all differentially methylated promoters with a CpG ratio lower than 0.5 as performed by Saxonov et al. the extracted set of 172 CpG-poor promoters was then used as a starting point to detect putative transcriptional regulatory signals using the Pscan (Zambelli F et al., 2009 Nucleic Acids Res 37: W247-252) software and the TRANSFAC transcription factor motifs database (Matys V et al., 2006 Nucleic Acids Res 34: D108-1 10). As shown in Figure 3C and Figure 7A, significant enrichment of GATA transcription factor family binding sites was identified, namely GATA1 and GATA2 binding sites as well as a common binding sites assigned to all GATA proteins (p values for enrichment ranged from 2.38*10^"15 to 3.28x10^"13). This analysis was then repeated with a set of all differentially methylated LCP and ICP promoters (cf. Table 4) to enlarge the above mentioned set of 172 CpG ratio <0.5 promoters. This analysis yielded similar results (data not shown). To increase precision and to assess validity of our predictions, a negative control was performed by repeating the detection routine described above with 100 sets of 172 promoters randomly sampled from CpG-depleted genes (CpG ratio <0.5) that are not differentially methylated but present on the Infinium array. Importantly, the highest significance of enrichment (best p value) encountered in the searches with these random sets never exceeded the best p value obtained with the set of differentially methylated genes (Figure 7B). Combined, these data reveal a statistically significant enrichment of GATA transcription factor binding motifs in our set of promoters (CpG ratio <0.5) differentially methylated in T2D islets. Example 5: Biological Pathways Associated with the Differential DNA Methylation in Diabetic Islets

To determine the biological relevance of the differential DNA methylation in pancreatic islets, the affected genes were analysed with regard to their reported functions and the biological pathways they are part of. The obtained data was compared with the list of known T2D risk genes to find out whether these loci are also targets of epigenetic dysregulation. Of the approximately 20 known T2D risk genes, GRB10 was found to be differentially methylated in diabetic islets; other established T2D susceptibility loci revealed no significant differential DNA methylation in the analyses. Instead, aberrantly methylated genes with similar or identical biological functions to these known T2D risk genes were found (Table 4). For example, KCNQ1 and KCNJ11 (SNP variants of which are associated with higher T2D risk; (Scott LJ et al., 2007 Science 316: 1341- 1345)) were not significantly altered in their methylation levels but three other potassium channel genes, KCNE2, KCNJ1 and KCNK16, were changed in their promoter methylation state (Table 4). Other examples of T2D susceptibility loci for which genes with related or identical functions were identified are SLC30A8 and CDKAL1. In the datasets of the present invention, SLC30A8 was not differentially methylated but two other zinc transporter genes SLC39A5 and ZIM2 were hypomethylated. For the T2D risk gene CDKAL1, its methylation state was found to be unchanged in T2D islets, while its target gene CDK5R1 exhibited pronounced hypomethylation (Figure 6). In summary, although the promoter methylation of established T2D risk loci remained unchanged in the present profiling approach (with GRB10 as an exception), other genes with the same biological function (i.e. potassium, zinc transporters) or genes in the same regulatory networks (i.e. CDK5 pathway) as established T2D risk genes displayed aberrant DNA methylation. Interestingly GRB10, the only established T2D susceptibility gene from GWAS that was characterised as differentially methylated in this study (cf. Table 4), is also known to be imprinted (Morison IM et al., 2001 Nucleic Acids Res 29: 275-276). Furthermore, IGF2AS which is located inside the imprinted IGF2 locus, was found to display profound hypomethylation (cf. Figure 2D). This led to the more rigorously investigation of the number of imprinted genes that display altered DNA methylation in the present T2D samples. The conducted search resulted in the identification of GABRB3, ZIM2 and PER2 besides IGF2AS and GRB10. This finding suggests a partial loss of imprinting in the islets during pathogenesis of T2D.

The analyses described above found only few common T2D-candidate genes among the differentially methylated genes uncovered in this study. This could imply that T2D pathogenesis in islets is partially mediated by unexpected and thus previously unappreciated genes. To decipher their roles in the context of T2D islets, as a first step an Ingenuity Pathway Analysis (IPA) was performed to determine which canonical pathways were over-represented in our set of genes (Figure 4A). Inflammation-related processes were highly enriched, in particular the acute phase response and IL-8 signalling. Other pathways, such as apoptosis and death receptor signalling, emphasise the role of beta-cell loss in T2D. Enrichment for pathways involved in metabolism and internal and external cell structure (e.g. actin cytoskeleton and integrin signalling) may be indicative of altered islet function and architecture. Secondly, we performed an extensive manual curation according to a previously described beta- cell-targeted annotation (Kutlu B et al., 2003 Diabetes 52: 2701-2719; Ortis F et al., 2010 Diabetes 59: 358-374). In partial agreement with the IPA analysis, these genes were found to fall into three broad categories: (1) genes related to beta-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 4B). In the first category there were hypomethylated genes related to DNA damage and oxidative stress (e.g. GSTP1, ALDH3B1), endoplasmic reticulum (ER) stress responses (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD) (Figure 4B, left part). The second category, which comprises adaptation-related genes, contains few metabolism-associated genes (e.g. HK1, FBP2; Figure 3C, right part) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g. EGF, FGF1, IGF2AS), or transcription factors involved in important regulatory networks (for instance FOXA2IHNF3B, PAX4 and SOX6) (Figure 4B, right part).

Some genes of interest from the highlighted categories are depicted below, providing more functional background and a possible explanation of how these genes are connected to T2D pathogenesis. The identified differentially methylated genes can be classified according to their function as follows:

1. Dysfunction / cell death:

DNA damage / oxidative stress

ALDH3B1 encodes an aldehyde dehydrogenase that can protect cells from lipid peroxidation-induced cytotoxicity (Marchitti SA et al., 2007 Biochem Biophys Res Commun 356: 792-798).

GSTP1 plays an important role in detoxification by catalysing the conjugation of many hydrophobic and electrophilic compounds with reduced glutathione.

Endoplasmic Reticulum (ER) stress

NIBAN is induced during ER stress and may counteract the suppression of protein translation that occurs under this condition (Sun GD et al., 2007 Biochem Biophys Res Commun 360: 181-187).

CHAC1 is also induced by ER stress and may trigger apoptosis (Mungrue IN et al., 2009 J Immunol 182: 466-476).

Apoptosis

NR4A1 is involved in ER stress-induced apoptosis and can interact with the anti-apoptotic protein BCL2 (Contreras JL et al., 2003 Transpl Int 16: 537-542; Liang B et al., 2007 Exp Cell Res 313: 2833-2844).

MADD encodes a MAP-kinase activating death domain-containing protein with anti- apoptotic function. It may also play a role in glucose homeostasis (Dupuis J et al., 2010 Nat Genet 42: 105-1 16).

CASP10 is involved in advanced-glycation-endproduct-induced apoptosis (Lecomte M et al., 2004 Biochim Biophys Acta 1689: 202-21 1 ; Obrenovich ME et al., 2005 Sci Aging Knowledge Environ 2005: pe3) and can activate NF-kappa-B (Wang H et al., 2007 Biochim Biophys Acta 1770: 1528-1537). The CASP10 gene is absent from the mouse and rat genome; both species are frequently used as T2D model organisms (Reed JC et al., 2003 Genome Res 13: 1376-1388).

2. Adaptation:

Signal transduction

MAPK1 is an important regulator of beta-cell function (Lawrence M et al., 2008 Acta Physiol (Oxf) 192: 1 1-17), e.g. contributing directly to short- vs. long-term insulin response and regulation of pro-apoptotic CHOP10 (Lawrence MC, et al., 2007 Proc Natl Acad Sci U S A 104: 1 1518-1 1525). MAPK1 constitutes the centre of a regulatory network activated by elevated free fatty acid levels (Sengupta U et al., 2009 PLoS One 4: e8100) common in T2D patients. MAPK ERK signalling leads to dephosphorylation of cascade proteins by PP2A PPP2R4 (Guo J et al., 2010 Mol Cell Biochem: (Epub; DOI: 10.1007)) pointing towards an interaction between the identified processes, in this case signal transduction (adaptation category) and ER stress (dysfunction/cell death category) (cf. Figure 5B).

CDK5R1 acts as an activator of CDK5 (Ubeda M et al., 2004 Endocrinology 145: 3023- 3031) whose expression is regulated by glucose and which inhibits insulin secretion (Wei FY et al., 2005 Nat Med 1 1 : 1 104-1 108). Hyperglycemia-caused overactivation of CDK5 may contribute to beta-cell glucotoxicity (Ubeda M et al., 2006 J Biol Chem 281 : 28858-28864).

Hormones, growth factors

The growth factor EGF has been shown to increase beta-cell mass in human islets in vitro and in vivo (Suarez-Pinzon WL et al., 2005 Diabetes 54: 2596-2601 ) and protect against oxidative stress (Maeda H et al., 2004 Transplant Proc 36: 1 163-1 165). FGF1 stimulates beta-cell differentiation (Oberg-Welsh C and Welsh M 1996 Pancreas 12: 334-339) while its experimental attenuation has been shown to induce diabetes (Hart AW et al., 2000 Nature 408: 864-868).

The hypomethylated CpG detected in the insulin-like growth factor 2 (IGF2) locus, which is situated in a large open chromatin domain specific for pancreatic islets (Mutskov V, and Felsenfeld G 2009 Proc Natl Acad Sci U S A 106: 17419-17424), is located inside a CpG island between exons 3 and 4, which translates to a position inside IGF2AS according to the Infinium array probe annotation (Figure 3D). Interestingly, this hypomethylated CpG island is also present in the mouse homolog juxtaposed to mouse Igf2 DMR1. We speculate that this differential DNA methylation may influence the regulation of one or several isoforms of IGF2. Moreover, as IGF2AS has been described as a paternally imprinted antisense transcript of IGF2, there is a possibility that the differential DNA methylation could lead to altered IGF2AS expression thereby dysregulating IGF2.

Transcription factors

FOXA2 (HNF3B) is part of a transcription factor network regulating beta-cell differentiation (Lee CS et al., 2002 Diabetes 51 : 2546-2551 ; Sund NJ et al., 2001 Genes Dev 15: 1706-1715).

PAX4 promotes beta-cell proliferation and is anti-apoptotic in human islets (Brun T et al.,

2004 J Cell Biol 167: 1 123-1 135), while SOX6 inhibits proliferation (Iguchi H et al., 2007 J Biol Chem 282: 19052-19061); both transcription factors can affect beta-cell function (Iguchi H et al.,

2005 J Biol Chem 280: 37669-37680)

SIRT6 is a H3K9 and H3K56 histone deacetylase (HDAC) that is induced upon nutrient deprivation (Kanfi Y et al., 2008 FEBS Lett 582: 543-548). It is implicated in attenuating of NF- kappa-B signalling (Kawahara TL et al., 2009 Cell 136: 62-74) (cf. CAS P 10) as well as telomere chromatin regulation (Michishita E et al., 2008 Nature 452: 492-496) and might, via both mechanisms, influence beta-cell lifespan.

Metabolism

Among the few differentially methylated metabolism-related genes were HK1 and FBP2, both of which show elevated expression in diabetic islets and negatively affect glucose-induced insulin secretion (Kebede M et al., 2008 Diabetes 57: 1887-1895; Malmgren S et al., 2009 J Biol Chem 284: 32395-32404).

CD01 catalyses the first step in the major cysteine catabolism pathway (Stipanuk MH et al., 2006 J Nutr 136: 1652S-1659S). Its hypermethylation could lead to an elevated intracellular cysteine concentration which inhibits insulin secretion (Kaneko Y et al., 2006 Diabetes 55: 1391- 1397) but might promote glutathione synthesis (Williamson JM et al., 1982 Proc Natl Acad Sci U S A 79: 6246-6249) (cf. GSTP1 ) thus protecting cells from oxidative stress. However, inhibition of CD01 pathway-facilitated taurine synthesis may counterbalance that effect (Anuradha et al., 1999 Can J Physiol Pharmacol 77: 749-754). One interesting example is CASP10: significant hypomethylation was found in its promoter (cf. Figure 2B) and since caspase 10 is inducible by advanced glycation endproducts (Lecomte M et al., 2004 Biochim Biophys Acta 1689: 202-21 1 ; Obrenovich ME and Monnier VM 2005 Sci Aging Knowledge Environ 2005: pe3), this hypomethylation may be indicative of gene activation caused by elevated blood glucose levels that result in heightened non-enzymatic glycosylation events. Furthermore, at least two CASP10 isoforms have been demonstrated to be able to activate N F-KB (Wang H et al., 2007 Biochim Biophys Acta 1770: 1528-1537). This could explain the number of NF-κΒ targets in the present dataset (cf. Table 4) and the emergence of a N F-KB- centred network from the analysis of said dataset with the Ingenuity Pathway Analysis software (data not shown). Another compelling example with potentially far-reaching implications for the islets in T2D is the observed differential methylation inside a CGI of the IGF2 locus (cf. Figure 1 C). The localisation might bear importance as it corresponds to a locus control region (DMR1 , differentially methylated region 1 ) of the mouse homolog m/g 2 (Constancia M et al., 2000 Nat Genet 26: 203-206). It remains presently unclear whether the hypomethylation indicates partial imprinting failure (which would probably lead to a gene-dosage-like effect, i.e. affect all IGF2 isoforms) or whether DNA hypomethylation in this region will consequentially cause changes in active IGF2 isoform composition.

Collectively, the above analyses looking at biological pathways and affected genes that are associated with differential DNA methylation in diabetic islets provides novel insights into the molecular and cell-biological pathogenesis of T2D.

Table 2. Classification of differentially methylated CpG positions according to their position relative to CpG islands (CGI).

changed hypomethylated hypermethylated total on Infinium

CpG class

methylation CpGs CpGs array

CGI 20 19 1 1 1581

CGI shore

1-2000bp

75 68 7 8320

away from

CGI

other CpG

>2kb away 181 179 2 7677 from CGI

sum 276 266 10 27578

promoter number of hypomethylated hypermethylated total on Infinium class genes promoters promoters array

LCP 38 38 0 1 188 ICP 141 137 4 4038

HCP 70 64 6 9196 not

5 5 0 54

categorised

sum 254 266 10 14477

CGI shores were considered 1 -2000bp from CGIs; CpGs located further away from a CGI were designated Other CpGs'. The genes to which the differentially methylated CpGs are affiliated were classified according to their promoter CpG content class (cf. Material and Methods). For graphical representations see Figure 8.

Table 3. PCR primers used for pre-amplification, conventional bisulfite sequencing and bisulfite pyrosequencing. For PCR conditions see Material and Methods.

Code Description SEQ ID

No.

EF forward primer for pre-amplification

ER reverse primer for pre-amplification

IF forward primer for conventional BS sequencing

IR reverse primer for conventional BS sequencing

F forward primer for BPS PCR

R reverse primer for BPS PCR

S sequencing primer (BPS)

primer is biotinylated to facilitate PCR product purification

Bio prior to BPS primer name sequence (5' - 3')

ADCY7_EF TTGTTGGTTATGGAGTAGTAGGTTTAGAG 277

ADCY7_ER ATCATACCACTATACTCCAACCTAAATAAC 278

ADCY7_F1 GAGGGTGTTGGTAGATAGATG 279

ADCY7_R1 Bio [Btn]ACCCATACTAACTAATAAACTAATACTTC 280

ADCY7_S1 GTTAAGGGGAGTTATTTTTT 281

ADCY7_S2 TTTAGGTTTTGGGGTT 282

ADCY7_newS1 GGAGGGTTTTGATTAAGA 283

ALDH3B1_EF TTTAGGTTGTTTTTTAGTTYGGAGTTTAG 284

ALDH3B1_ER ACCTCRCTACCACTCCCCAAATC 285 Code Description SEQ ID

No.

ALDH3B1JF GGAGTTTAGTTTTGTTGTGTGGTTTTAG 286

ALDH3B1JR CTACCACTCCCCAAATCTACCTCTC 287

ALDH3B1_F1 TTGGTAGAGTTTTAGGTAGAGTATTT 288

ALDH3B1_R1 -Bio [Btn]AACCAAAACACAATAACCTCTAAATACA 289

ALDH3B1_S1 GGTTAGAGTTTAGTTTAGTAATTT 290

ALDH3B1_F2 GTGGTTTGGGAGGAGATG 291

ALDH3B1_R2-Bio [Btn]ACTACCCTTCCTCCTAACTATC 292

ALDH3B1_S2 TTTGGGAGGAGATGT 293

CASP10_EF GGTTAAGGAGGGTGGATTATAAGTTTAGG 294

CASP10_ER TCTACCTTTTTCCCTCCCCTTTTCC 295

CASP10_F1 GGTTGAGGTAGGAGAATTATTTGAATAT 296

CASP10_F2 AAGGAGGGTGGATTATAAGTTTAG 297

CASP10_R1 Bio [Btn]ATTAACCCTTTCTTATATCCACATACAAAT 298

CASP10_S1 ATAGGAGGTAGAGGTT 299

CASP10_S2 GTTTTTATTAAAAATATAAAAAATT 300

CASP10_2newS ATTAAAAATATAAAAAATTAGT 301

CDK5R1_EF GAGTGGGAATTTAGAGGTTATATTTGTG 302

CDK5R1_ER CCCAAACACRTACTACCTTTATTCCC 303

CDK5R1_F1 AGTGG G AATTTAG AG GTTATATTTGT 304

CDK5R1_R1 Bio [Btn]ACCTCCCTTCCCTCATCATAAATAAATAAC 305

CDK5R1_S1 GGGTTTAGGTTTGGT 306

CDK5R1_S2 TTGGTTTGGATTTTTGAG 307

CD01_EF AGTTTTGGATTTATTTTTATTTAGTTTGG 308

CD01_ER CAAATTCAAATCTATAAAATTCATCCTCC 309

CD01_F1 GGATTTATTTTTATTTAGTTTGGGGTAT 310

CD01_F2 ATTTTTTGGTTTAGGAGTGGAATTTA 31 1

CDO1_R1 Bi0 [Btn]AATCTCTCCCCCACTTTAAC 312

CD01_S1 ATTTTTATTTAGTTTGGGGTATAT 313

CD01_S2 GGAGTGGAATTTATTTTTAATTT 314

CD01_S3 GTTAGTTTTAGTAGTTATTTTTT 315

CD01_S4 TGGAGAGGGGAGAGG 316

FCN2_EF TGAAGTAAAGATTAGAAGAGATGGAGTTGG 317

FCN2_ER TACTTATCCCCACCTCACACCCTATCTC 318

FCN2_F1 GGAAATTTTTAGTTTTTGAAAGTTGGTAGT 319 Code Description SEQ ID

No.

FCN2_R1 Bio [Btn]AAAAAACTCCCTCTCTAAAATACCACATCC 320

FCN2_S1 GGTTTTTAGGTTTTGTATTAGG 321

GABRB3_EF GGAAAAAAAATGAGTTAATATAGGAAAGTAG 322

GABRB3_ER ACAACTACTCCTTAAACTACACCTCTTACC 323

GABRB3_F1 TGTGTATTGGTATATTAGGGTTTTTGTA 324

GABRB3_R1 Bio [BtnJCCAACCAAACTTAAAAACTAAATTATCAT 325

GABRB3_S1 ATTAGGGTTTTTGTATTAGTG 326

GABRB3_S2 GGAAGTAAGGATTTTTGTTTTATA 327

GCK, for conventional pyrosequencing:

GCK_EF652 GGTTTTAGGGGTTTGTTTTTGAGTTA 328

GCK_ER1324 TCACAATTTCCTCCTTTTCATTAAA 329

GCK_IF660 GGGTTTGTTTTTGAGTTAYGTTAAGTTG 330

GCKJR1321 CAATTTCCTCCTTTTCATTATTCTCC 331

GCK, for bisulfite pyrosequencing:

GCK_EF TGAGATATTGTTTTAGGATTTGAATAGGTGG 332

GCK_ER CCTAAATCCCTAAAAAAATATAATTCTCC 333

GCK_F1 GTTGTTTTTAGGTTATAGAAGGGAGAGG 334

GCK_R1 Bio ATCACAATTTCCTCCTTTTCATTATTC 335

GCK_S1 ATAGTTTAATATAATTAGGAGAGA 336

GCK_F2 GTTATTATG GTG ATG GG G ATG GAG 337

GCK_R2Bio CTCTCACATCCTAACCTACTTC 338

GCK_S2 GGTTGGAGTAGGAAATG 339

GLP2R_EF GAAAGTATAGTTGATTTAGGGAAGGTTTG 340

GLP2R_ER CTTACTTACTTAATAAAAACCAACAAAACC 341

GLP2R_F1 Bio [Btn]AGGGTAGAGAAGGAATTTTGAAGATTT 342

GLP2R_R1 ACCTCCTCTTACATTCCTCTTAATC 343

GLP2R_S1 ACACAAATCCTCTCCA 344

GP1 BB_737EF GTTTTGTTTTGGTGATAGGAGAATAA 345

GP1 BBJ 349ER CCAACAACAAAAACAATAAACTCAAC 346

GP1 BB_IF TTGGTGATAGGAGAATAATGTTGGTG 347

GP1 BBJR CAAAAACAATAAACTCAACRCCC 348

GP1 BB_F1 GGTATTAGGGGTTGGATGGA 349

GP1 BB_R1 -Bio [Btn]CCAACAAAAACAATAAACTCAAC 350

GP1 BB_S1 GTTTGGGTATTTAGAGATG 351 Code Description SEQ ID

No.

IGF2AS_EF AATTTTGTTTTTYGTTTTTTGGGGTT 352

IGF2AS_ER CACAAATCCCCTTAAAACCACTACC 353

IGF2AS_F1 GGGTGTAAGGAAGAAATTTAAGG 354

IGF2AS_F2 GGGTGTAAGGAAGAAATTTAAGG 355

IGF2AS_R1 Bio [Btn]ACCAAACCCAAAACTAACCTACCC 356

IGF2AS_S1 AAGGAAGAAATTTAAGGG 357

IGF2AS_S2 GGGAGGTTAGTAGGTTTTTT 358

LINE1 -EF ATTTTATATTTGGTTTAGAGGG 359

LINE1 -ER ATCAAAAATCAAAAACCCACTT 360

LINE-IF TTTTATATTTG GTTTAG AG GG 361

LINE-IR-Bio [Btn]TCAAAAATCAAAAACCCACTT 362

LRRC15_EF GTTTGGGGTAGTTTAGGTTTTGTTGAGG 363

LRRC15_ER CAAAACACATTTTCCTCTTTCCTTCTTTC 364

LRRC15_F1 TGTGGGTTGTAATGTAGAGG 365

LRRC15_R1 Bio [Btn]ATACTAAAATCTTTACCATTACTACTACTC 366

LRRC15_S1 AGGGGTATAGGGAAG 367

MADD_EF GATGGTTGAGGTTTGGTGGTTGT 368

MADD_ER AATCCAACACTTCCTCCTCCACC 369

MADD_F1 GGATGGGTTTGGGGGTTA 370

MADD_F2 AATTTTTGGATTGGGGGTGG 371

MADD_R1 Bio [Btn]CTATTCAAACAATACCAAACCTATCAATAA 372

MADD_R2Bio [Btn]ATTTATAACTCTCCCTCCTCTAAAC 373

MADD_S1 ATTTTGTGGTATGGTTATAAAT 374

MADD_S2 ATTGGGGGTGGGGAT 375

NIBAN_EF TTTAAGTATTATTTGGGAGTTTGTTAGAAAT 376

NIBAN_ER CCCTCTACCCAAATAAATCTTATTCC 377

NIBAN_F1 GGAAAGAGAAAAAAAAATGGAAAGATAGG 378

NIBAN_F2 GTTTTTTGGGGAGTTTTTTTTAGGTT 379

NIBAN_R1 Bio [Btn]CCCTCTACCCAAATAAATCTTATTC 380

NIBAN_R2Bio [Btn]ATCCACTTCCTCCAAAATATTTCTTA 381

NIBAN_S1 TTTGAAATTTTTTAAGATTTGATGA 382

NIBAN_S2 GTTTTAGTTTAGTTGGTGAGTT 383 Code Description SEQ ID

No.

NR4A1_EF TTTGGGTATATAGTAGTTTTGGTGGGTAG 384

NR4A1_ER ACCCAACTCCCATTCCTCAATATCC 385

NR4A1_F1 GGTTGTTAATAGGGGTTTTATGAGTGTT 386

NR4A1_R1 Bio [Btn]CTTTAAACCCTCTTAACTAAACCTAACTT 387

NR4A1_S1 ATGAGTGTTAGAGTTGT 388

NR4A1_S2 GTTTGTTAGGTTTGGG 389 distNR4A1_F1 GGTTTAGTTAAGAGGGTTTAAAGTGG 390 distNR4A1_R1 Bio [Btn]ATCCCAAAATTAATTAAAAACTCTTCCTA 391 distNR4A1_S1 GGTTTGGAGGTAGTATTATA 392 distNR4A1_altS1 ATAGGTTGGTTGGGT 393 distNR4A1_S2 GGTTTTTTTTATTTTTAGAGGT 394

PP2A_EF GATGGTTTTGGAGGATGATTTAGAGg 395

PP2A_ER TCAATTAACACCCCCACCCTTAAAC 396

PP2A_F1 AGGGTGTTGTAATATTTAAAAGAGTT 397

PP2A_R1 Bio [Btn]CTAACTATATCTACCCCTCAATCCT 398

PP2A_S1 GTTTGAGTTTGGATGTATTTT 399

PP2A_S2 GTAATATATGTTTGGATTAGGGA 400

PP2Adist_F1 GGAGATTATGGGGTTGTTATTTGT 401

PP2Adist_R1 Bio [Btn]AACTCACTCTCCCAATTATTCT 402

PP2Adist_S1 AGGTTTTTTTAGTTTGATGT 403

PP2Adist_S2 G G AGTGTG ATTAATTG GAT 404

PP2Adist_S3 AGTGGTTTTGGTTTAAG 405

RUNX3_EF GAGGAGGTTTTAGTGTTATAGTTTAGGGTT 406

RUNX3_ER AACCCTTAACTTTACAACCACTACTATTTTTCT 407

RUNX3_newF GAGTTTTTTAGGGATTTTAAGTAGTTTGG 408

RUNX3_R1 Bio [Btn]ACCCTTAACTTTACAACCACTACTATTT 409

RUNX3_S1 TGGAGGTTTGTGGTTTTTTGA 410

RUNX3_S2 GTTTTAG G ATTTTGTAG GT 41 1

SIRT6_EF2 GGAGGTGGGAATAAATATATTTGGAG 412

SIRT6_ER AACAAATACCTATAATCTCATCTACTCTACTCA 413

SIRT6_F1 GAG GTG AAG ATG GTTTTATTTTATAAGG 414

SIRT6_F2 GAGGTGGGAATAAATATATTTGGAGA 415

SIRT6_R1 Bio [Btn]ACAAAAAAACTCCATCTCAAAAATATAAT 416

SIRT6_R2Bio [Btn]ACTCCCCAACCTAACATTTAA 417

SIRT6_S1 ATTTTGATTTATGTATTTAATGAG 418 Code Description SEQ ID

No.

SIRT6_S2 GTGGGAATAAATATATTTGGAGAA 419

TPM3_EF TGAAGTTTTGGGATAGTTTTTAAGGATAGG 420

TPM3_ER CCTAATATAATCTTTCCAACTCCCTCCACC 421

TPM3_F1 GGGAGAATTGTTAAGGATTATGAGT 422

TPM3_R1 Bio [Btn]CAATTTACATCCAAAAAATCCTAATTACC 423

TPM3_S1 ATTAGTATTTAATATAGTTTGGGG 424

TPM3_S2 ATATTAGGAATTAAAAGTTAAAAT 425

Table 4: 276 CpGs showing significant differential DNA methylation between non-diabetic (CTL) and T2D pancreatic islet DNA. Filtering criteria were group-wise methylation difference of >15% and a Mann-Whitney test p value <0.01 between CTL and T2D samples. The table lists probe I D on the Infinium Human Methylation27 array, the corresponding gene symbol and the averaged methylation of the CTL and T2D groups (columns B, C) expressed as average 'beta' values representing methylation percentage ÷ 100. Column D displays the Mann-Whitney DiffScore that can be converted to a p value by the formula given below; negative values indicate hypomethylation while positive numbers indicate hypermethylation. The difference of DNA methylation ('delta beta') is given in the next column (E) followed by annotation (as provided by lllumina Inc.) of the position of the respective CpG site (columns F to AE). Additional annotation (cf. Material and Methods) of CpG position relative to CpG islands and promoter class of affiliated gene locus is given in columns AF to AN.

\DiffScore \

n = 10 io- ¹ (equation to derive p value from DiffScore)

Table 4A

Marker/

SEQ ID TargetID CT.AVG T2D.AVG T2D. DiffScore T2D.Delta SYMBOL No.

1 cg07359545 0,50844 0,20006 -33,393 -0,30839 GP1 BB

2 cg24147596 0,73354 0,44351 -30,382 -0,29003 ARL14

3 cg00668685 0,61704 0,33155 -33,393 -0,28549 SLC39A5

4 cg24315815 0,37857 0,10068 -22,601 -0,27789 PLSCR4

5 cg07730301 0,34266 0,07697 -27,372 -0,26569 ALDH3B1

6 cg20792294 0,3798 0,1 1663 -27,372 -0,26317 IGF2AS

7 eg 17568996 0,46368 0,2054 -27,372 -0,25828 NFAM1

8 eg 16745604 0,57836 0,32286 -33,393 -0,2555 CASP10

9 cg16179125 0,3028 0,04927 -27,372 -0,25353 CTSZ

10 cg1461 1 1 12 0,35456 0,10252 -22,601 -0,25204 LCN6

1 1 cg24512973 0,53361 0,28407 -30,382 -0,24954 MUC1

12 cg05413282 0,37317 0,12758 -24,942 -0,24559 PRSS3 cg03380645 0,70899 0,46709 -30,382 -0,2419 SFTPB cg15916061 0,41 106 0,16919 -33,393 -0,24187 SLC17A4 cg04106785 0,55006 0,30863 -30,382 -0,24144 CDK5R1 eg 17267907 0,6813 0,44244 -20,605 -0,23887 DEFA1 cg25587233 0,6334 0,39737 -30,382 -0,23603 PPP2R4 cg06690548 0,73288 0,49708 -24,942 -0,23581 SLC7A1 1 cg02992647 0,3779 0,14309 -24,942 -0,23482 DDX52 eg 10929387 0,57792 0,34389 -30,382 -0,23404 ITIH4 cg27635271 0,42305 0,19026 -20,605 -0,23279 SH3PX3 cg10737521 0,41657 0,1853 -27,372 -0,23127 KIAA0676 cg25182523 0,54546 0,31443 -33,393 -0,23102 NIBAN cg04413148 0,64878 0,41816 -20,605 -0,23062 CTRL eg 11682508 0,55279 0,3238 -22,601 -0,22899 FLJ30313 cg26062856 0,56203 0,33509 -22,601 -0,22694 ATP1 OA cg22224704 0,32503 0,09831 -20,605 -0,22672 GSTP1 cg05261299 0,68178 0,45677 -33,393 -0,22501 NR4A1 cg00447208 0,68948 0,46771 -33,393 -0,22177 TMC8 cg05342835 0,39494 0,17406 -30,382 -0,22089 SYNC1 cg05221264 0,72585 0,50503 -27,372 -0,22081 ELA2A cg031 12869 0,58591 0,36775 -22,601 -0,21815 FBXW12 cg18575221 0,55587 0,33778 -33,393 -0,21809 C14orf162 cg20777437 0,39448 0,17644 -20,605 -0,21805 CDCP2 cg06352750 0,33574 0,1 1775 -22,601 -0,21799 SDPR cg02092466 0,45725 0,23987 -33,393 -0,21738 NTSR2 eg 15952725 0,54778 0,33047 -22,601 -0,2173 ELA2A cg00845900 0,76385 0,5468 -30,382 -0,21705 CPA4 cg1 1884243 0,68865 0,47451 -30,382 -0,21414 FCN2 eg 16787352 0,317 0,10354 -27,372 -0,21346 ANKRD9 cg22324153 0,64482 0,43174 -27,372 -0,21309 SLC35D2 cg13718960 0,55576 0,34328 -30,382 -0,21248 RNASE1 cg07125166 0,65444 0,44212 -22,601 -0,21232 FBXW12 cg14238120 0,45349 0,24173 -24,942 -0,21 176 ELA3A cg00895324 0,66636 0,4557 -33,393 -0,21066 PCP4 cg20585500 0,69486 0,48427 -27,372 -0,21059 GPHA2 cg00931491 0,3316 0,12308 -22,601 -0,20852 SULT1A2 cg23732182 0,47595 0,268 -22,601 -0,20795 C21 orf84 eg 17890764 0,38584 0,17822 -27,372 -0,20762 ITIH4 cg12322132 0,27389 0,06684 -22,601 -0,20704 IGF2AS eg 12473775 0,4092 0,20221 -24,942 -0,20698 RHOD cg17100200 0,82506 0,61903 -30,382 -0,20602 GUCA2B cg26417554 0,51972 0,31379 -33,393 -0,20593 RPUSD3 cg24818418 0,66515 0,45992 -24,942 -0,20522 EGF cg26728422 0,48429 0,27918 -27,372 -0,2051 1 C16orf28 cg24691461 0,344 0,13898 -24,942 -0,20503 C20orf160 cg06003187 0,68053 0,47565 -33,393 -0,20488 GUCA2A eg 14975238 0,76419 0,5601 -33,393 -0,20409 BHLHB8 cg1 1504740 0,57628 0,37394 -33,393 -0,20234 GPR152 cg20870362 0,69413 0,49189 -33,393 -0,20224 CCIN cg10347418 0,57328 0,37135 -24,942 -0,20193 ZNF436 cg10779183 0,53323 0,33219 -20,605 -0,20104 ELA3A eg 18674980 0,52072 0,31974 -33,393 -0,20098 CA3 cg15937958 0,66304 0,4621 1 -30,382 -0,20093 UNQ473 cg09936839 0,5613 0,3604 -27,372 -0,20089 SIRT6 cg01617750 0,56394 0,36308 -33,393 -0,20086 CMTM8 cg05958352 0,53619 0,33591 -27,372 -0,20028 RNASE1 cg05722906 0,38694 0,18718 -33,393 -0,19976 CYP4F12 cg19391527 0,51673 0,31825 -22,601 -0,19847 PNOC cg05985767 0,55194 0,3535 -22,601 -0,19844 ANPEP cg03533058 0,72106 0,52315 -24,942 -0,19791 NR4A1 cg25994725 0,51807 0,3202 -22,601 -0,19787 C6orf81 cg14209518 0,62442 0,42683 -33,393 -0,19759 NNMT cg22213042 0,5146 0,31729 -24,942 -0,19731 CPA2 cg0761 1925 0,59727 0,4012 -30,382 -0,19607 KCNE2 cg20226764 0,54159 0,34596 -27,372 -0,19563 MLN cg10516359 0,6921 0,49654 -27,372 -0,19556 SLC35C1 cg16003913 0,44194 0,24672 -30,382 -0,19522 MPG eg 1 1566244 0,3693 0,17421 -22,601 -0,19509 GSTP1 cg20356482 0,68667 0,49202 -22,601 -0,19465 FBP2 cg27465569 0,62656 0,43268 -22,601 -0,19388 PUS7L cg1 1 161417 0,55474 0,36125 -20,605 -0,19349 SPACA3 cg09467501 0,28048 0,08724 -22,601 -0,19324 PYY cg25406518 0,60539 0,41235 -22,601 -0,19305 DAK cg25021 182 0,67542 0,48257 -24,942 -0,19285 ELA2B eg 19826026 0,59605 0,40325 -20,605 -0,1928 ARHGDIB cg04749372 0,63931 0,44678 -33,393 -0,19253 INMT cg06812844 0,62316 0,43069 -24,942 -0,19246 TRPM2 cg08300860 0,83084 0,63852 -30,382 -0,19232 LDB3 cg19101893 0,52598 0,3341 1 -27,372 -0,19186 CCDC23 cg24101578 0,59174 0,40051 -22,601 -0,19123 CDH22 eg 15940569 0,43065 0,23981 -33,393 -0,19084 GABRB3 cg04577715 0,70734 0,5166 -30,382 -0,19074 SPINK1 cg24901042 0,28693 0,09625 -30,382 -0,19068 TMPRSS2 cg12379145 0,70335 0,51285 -24,942 -0,19051 PRDM2 eg 13424229 0,64759 0,45728 -24,942 -0,19031 CP A3 eg 16863382 0,70664 0,51714 -24,942 -0,1895 CTRB1 cg17941312 0,78193 0,59244 -30,382 -0,18949 PSF1 eg 19058765 0,56107 0,3722 -24,942 -0,18887 APOH cg18669381 0,28368 0,095 -24,942 -0,18868 ARHGEF19 cg10287137 0,55443 0,36597 -30,382 -0,18846 P2RY2 cg0671 1560 0,65068 0,46235 -22,601 -0,18833 MAMDC4 eg 12456510 0,63271 0,4445 -27,372 -0,18821 TFF2 cg1 1310496 0,70759 0,51992 -22,601 -0,18767 PNLIPRP2 eg 12205230 0,41466 0,22753 -33,393 -0,18713 TPM3 cg05659947 0,38449 0,19785 -30,382 -0,18664 C4BPB cg21330703 0,29139 0,10525 -30,382 -0,18614 DRD2 cg03668539 0,63595 0,45134 -27,372 -0,18461 PEX1 1 G cg13192155 0,64721 0,46264 -27,372 -0,18457 ERAS cg143961 17 0,44127 0,25705 -22,601 -0,18422 MYR8 cg27069753 0,57448 0,39056 -30,382 -0,18392 ELA3B cg27442349 0,70032 0,5165 -33,393 -0,18382 NFKBIB eg 16245261 0,54923 0,36543 -33,393 -0,1838 PRKCDBP eg 15322932 0,25714 0,07434 -22,601 -0,1828 ALDH3B1 cg21846903 0,33219 0,15069 -22,601 -0,1815 VTN cg16173067 0,47713 0,29612 -24,942 -0,18102 SDCBP2 cg05473677 0,31299 0,13203 -22,601 -0,18096 OSTalpha cg25839766 0,66351 0,48283 -33,393 -0,18068 PSG1 cg05546044 0,68086 0,50022 -27,372 -0,18065 MAPK1 eg 17964955 0,63541 0,45504 -33,393 -0,18037 RBP3 cg12061236 0,53357 0,35351 -20,605 -0,18007 AKAP12 cg15241708 0,61441 0,43482 -22,601 -0,1796 CNOT6 123 eg 17795586 0,68094 0,50162 -22,601 -0,17932 P2RY13

124 cg13316191 0,21331 0,035 -33,393 -0,17831 CDCA7L

125 cg23032612 0,55341 0,37514 -33,393 -0,17827 POSTN

126 cg26934034 0,721 13 0,54333 -20,605 -0,1778 SDSL

127 cg14430151 0,73371 0,5561 -22,601 -0,17761 FLJ35725

128 cg15201291 0,62099 0,4436 -22,601 -0,17739 CYP2C8

129 cg09837943 0,62908 0,45203 -33,393 -0,17705 ZIM2

130 cg14992108 0,6733 0,49628 -30,382 -0,17702 SNTB1

131 cg04958389 0,64846 0,47169 -24,942 -0,17676 PRSS2

132 cg26750002 0,49005 0,31332 -24,942 -0,17673 RASIP1

133 cg23106559 0,59618 0,41964 -30,382 -0,17654 METTL1

134 cg04929865 0,58822 0,412 -27,372 -0,17621 BGN

135 cg21030400 0,55299 0,37687 -20,605 -0,17612 M N 2

136 cg26164184 0,70699 0,5309 -30,382 -0,1761 FCN2

137 eg 14659547 0,55295 0,377 -20,605 -0,17595 RETNLB

138 cg1 1081833 0,36515 0,18923 -20,605 -0,17592 LGALS2

139 cg24821554 0,71684 0,5412 -33,393 -0,17564 GUCY1 B2

140 eg 10883352 0,65332 0,47807 -27,372 -0,17525

141 eg 14930674 0,70229 0,52707 -24,942 -0,17521 SLC12A1

142 cg25787984 0,67256 0,49737 -22,601 -0,17518 DKK3

143 cg22215192 0,52312 0,34802 -27,372 -0,1751 AMY2A

144 cg04584523 0,79824 0,62326 -27,372 -0,17498 ELA3B

145 cg26848126 0,48696 0,312 -33,393 -0,17496 CYSLTR1

146 cg00078867 0,72867 0,55399 -33,393 -0,17468 WAS

147 eg 10925082 0,59551 0,42107 -22,601 -0,17444 ARHGDIB

148 cg23815000 0,63681 0,46243 -22,601 -0,17438 LCN1

149 cg21307628 0,49794 0,32367 -20,605 -0,17427 URB

150 cg24939733 0,54862 0,37458 -22,601 -0,17405 FLJ38159

151 cg05186188 0,50671 0,33272 -20,605 -0,17398 AQP8

152 cg21880328 0,35241 0,17878 -24,942 -0,17363 CTTNBP2

153 eg 15746445 0,56673 0,39314 -30,382 -0,17359 TRPM8

154 cg19103609 0,65512 0,48159 -22,601 -0,17353 PKN1

155 eg 14324200 0,2402 0,06693 -20,605 -0,17327 SAMD1 1

156 eg 15662251 0,46529 0,29211 -20,605 -0,17318 PAQR7

157 cg16334519 0,62956 0,4565 -27,372 -0,17306 ARL14

158 cg25463779 0,66996 0,4969 -33,393 -0,17306 FAM101A

159 eg 16272420 0,53203 0,3592 -22,601 -0,17283 PNLIPRP2

160 cg10321 196 0,48322 0,31048 -20,605 -0,17274 SLC25A5

161 cg15746187 0,5458 0,37322 -20,605 -0,17258 FBX044

162 cg04802221 0,67159 0,49927 -33,393 -0,17232 LOC283849

163 eg 12940073 0,66151 0,48992 -24,942 -0,1716 ABCC12

164 eg 14672994 0,25101 0,07968 -24,942 -0,17132 FLJ20920

165 cg14401837 0,57374 0,40259 -20,605 -0,171 15 GPR154

166 cg121 13819 0,38805 0,2172 -30,382 -0,17086 THRAP2

167 cg21686987 0,68513 0,51457 -27,372 -0,17056 CTRB1

168 cg23082877 0,5774 0,40687 -33,393 -0,17054 RASIP1

169 cg04049033 0,51 186 0,34257 -24,942 -0,1693 RILP

170 cg03770548 0,6871 0,51814 -22,601 -0,16895 CDH24

171 cg00403724 0,44007 0,271 19 -27,372 -0,16888 TNMD

172 eg 15479752 0,73716 0,56894 -33,393 -0,16822 FFAR2

173 cg16796951 0,71 167 0,54357 -22,601 -0,1681 C20orf152

174 cg05441 133 0,64997 0,48194 -20,605 -0,16804 GDF2

175 cg22340747 0,54182 0,37387 -22,601 -0,16795 GATM

176 cg03245641 0,531 17 0,36345 -27,372 -0,16772 GPHA2

177 cg08816023 0,69959 0,53192 -24,942 -0,16767 FGF1 178 cg05828624 0,63651 0,46887 -24,942 -0,16764 REG1A

1 9 cg16101800 0,65212 0,4846 -27,372 -0,16751 PCK1

180 cg12958813 0,6332 0,46582 -22,601 -0,16738 ATP6V1 G3

181 cg04086834 0,50839 0,34123 -33,393 -0,16716 SMAP1

182 cg16545105 0,77694 0,61005 -27,372 -0,16689 CRHBP

183 cg15154229 0,47631 0,30967 -24,942 -0,16664 CPA1

184 cg25809905 0,75705 0,5906 -33,393 -0,16645 ITGA2B

185 cg05379350 0,48283 0,31683 -24,942 -0,166 GIT1

186 cg02131995 0,5007 0,33481 -24,942 -0,16588 HAMP

187 cg01515887 0,67205 0,50666 -30,382 -0,16539 SAA2

188 cg12932195 0,69631 0,53125 -20,605 -0,16506 ALG12

189 cg21660392 0,74294 0,57795 -22,601 -0,16499 ABCA8

190 cg23320649 0,32424 0,15933 -22,601 -0,16491 C3orf18

191 cg1 1812202 0,58843 0,42406 -24,942 -0,16437 PNLIP

192 cg23276695 0,49954 0,33541 -22,601 -0,16414 CNR1

193 cg26066361 0,5455 0,38154 -30,382 -0,16395 CLEC7A

194 cg01 197831 0,74458 0,58071 -27,372 -0,16387 FBP2

195 cg14123992 0,46171 0,29784 -27,372 -0,16387 APOE

196 cg21049762 0,71941 0,55553 -27,372 -0,16387 TCIRG1

197 cg07572435 0,73412 0,57048 -27,372 -0,16365 LY6D

198 cg03156547 0,50299 0,33944 -22,601 -0,16355 CDH24

199 cg27160701 0,61567 0,45215 -27,372 -0,16352 SBEM

200 cg02763671 0,59935 0,436 -24,942 -0,16335 RANBP1

201 cg16792160 0,5345 0,37194 -24,942 -0,16255 ASAH2

202 cg22686523 0,69283 0,53036 -20,605 -0,16246 FLJ25006

203 cg0551771 1 0,58187 0,41958 -22,601 -0,16229 ST6GALNAC6

204 cg00259755 0,20497 0,04299 -27,372 -0,16198 PWWP2

205 cg14481339 0,66172 0,50038 -22,601 -0,16134 KCNJ1

206 eg 16752583 0,36106 0,2002 -22,601 -0,16087 TRPV6

207 cg2001 1 134 0,38458 0,22426 -22,601 -0,16032 DDO

208 cg09676390 0,37676 0,21652 -22,601 -0,16024 ADARB1

209 cg02497428 0,69676 0,53671 -20,605 -0,16005 IGSF6

210 cg22586527 0,5303 0,37027 -27,372 -0,16004 SOX6

21 1 cg24607535 0,75289 0,59303 -22,601 -0,15986 CDH26

212 cg05744229 0,52241 0,36268 -22,601 -0,15973 MYH7

213 eg 19856444 0,56152 0,40199 -27,372 -0,15953 SLC39A12

214 cg055351 13 0,54181 0,38254 -22,601 -0,15926 CHST4

215 cg01593385 0,47541 0,31639 -30,382 -0,15902 FGG

216 cg15013019 0,40922 0,25043 -30,382 -0,15879 LYL1

217 cg20392764 0,74737 0,58867 -33,393 -0,1587 ASCL2

218 eg 13379236 0,50373 0,34528 -22,601 -0,15844 EGF

219 cg05663262 0,55965 0,40135 -20,605 -0,1583 HYI

220 cg23756272 0,81269 0,65444 -24,942 -0,15825 BCL2

221 cg07908874 0,58667 0,42849 -20,605 -0,15818 TUBGCP2

222 eg 15770654 0,77312 0,61529 -27,372 -0,15783 IIP45

223 cg25915982 0,47213 0,31467 -27,372 -0,15746 GRB10

224 cg27655855 0,63662 0,47915 -20,605 -0,15746 CST9L

225 cg06186808 0,67558 0,51826 -20,605 -0,15732 LOC161247

226 cg04151683 0,48781 0,33057 -20,605 -0,15723 LRRN6C

227 cg02552945 0,46844 0,31 151 -22,601 -0,15693 C5

228 cg09069593 0,61571 0,45921 -24,942 -0,1565 GPR1 14

229 cg07959477 0,56274 0,40634 -22,601 -0,15639 CLEC1A

230 cg07294734 0,3251 0,1688 -20,605 -0,1563 ATP5D

231 eg 13587552 0,55762 0,40135 -22,601 -0,15628 SCNN1 D

232 cg22598744 0,61064 0,4544 -22,601 -0,15623 MLANA 233 eg 18034859 0,84475 0,6887 -33,393 -0,15605 MYLK2

234 cg08886154 0,60656 0,45066 -27,372 -0,1559 PAX4

235 eg 17260954 0,67254 0,51666 -20,605 -0,15588 ATP1 OA

236 cg24363955 0,67324 0,51767 -24,942 -0,15556 FLJ14054

237 cg01260219 0,74052 0,5852 -24,942 -0,15532 ADAMTS8

238 cg02882813 0,61333 0,45804 -33,393 -0,1553 CST5

239 cg02347487 0,70923 0,55405 -27,372 -0,15519 NALP14

240 eg 17886959 0,31953 0,16445 -24,942 -0,15508 MT2A

241 cg06434428 0,73339 0,57837 -27,372 -0,15502 HAPLN1

242 eg 12288726 0,51054 0,35584 -24,942 -0,1547 ARF5

243 cg02121427 0,44954 0,29497 -33,393 -0,15456 LRRC15

244 cg1 1750883 0,59256 0,43806 -22,601 -0,1545 C1 orf42

245 cg10978355 0,22295 0,06865 -33,393 -0,15431 CKMT2

246 cg12619162 0,8215 0,66737 -24,942 -0,15414 FXYD4

247 cg22194129 0,42427 0,27023 -22,601 -0,15404 CLEC4C

248 cg26624914 0,45809 0,30406 -20,605 -0,15403 AQP3

249 cg08996986 0,77349 0,61971 -22,601 -0,15377 EPS8L1

250 cg22415472 0,68373 0,53014 -24,942 -0,15359 SLC5A7

251 cg26203861 0,42393 0,2708 -33,393 -0,15313 TMEM58

252 cg05275605 0,64618 0,49312 -27,372 -0,15306 C21 orf123

253 cg25107791 0,48191 0,32904 -20,605 -0,15287 CLPS

254 eg 10003443 0,50097 0,34831 -33,393 -0,15266 FOXA2

255 cg031 16740 0,6439 0,49125 -30,382 -0,15265 TSPAN4

256 cg04578090 0,67607 0,52378 -22,601 -0,1523 PROCA1

257 cg09440243 0,64619 0,49389 -22,601 -0,1523 PTPRD

258 eg 1 1479877 0,32623 0,17416 -20,605 -0,15207 B3GALT5

259 eg 12658552 0,631 17 0,47961 -22,601 -0,15156 SFRS2IP

260 cg2671 1820 0,33734 0,18621 -22,601 -0,151 13 MYF6

261 cg06323290 0,70643 0,55533 -20,605 -0,151 1 HK1

262 eg 17327492 0,57214 0,4215 -20,605 -0,15064 FSHR

263 cg26065841 0,73882 0,58819 -30,382 -0,15064 CHAC1

264 cg13944141 0,46884 0,31871 -30,382 -0,15013 PRSS2

265 eg 19782598 0,69015 0,54007 -30,382 -0,15008 FLJ30046

266 cg21518947 0,69864 0,54862 -27,372 -0,15002 CABP4

267 cg16872071 0,39948 0,54964 20,605 0,15016 RALGDS

268 cg24815853 0,33013 0,48094 22,601 0,15081 KLK12

269 cg15780361 0,38725 0,55509 27,372 0,16785 ALS2CR1 1

270 cg07644368 0,30195 0,47843 33,393 0,17648 CD01

271 eg 10805676 0,68341 0,86024 20,605 0,17683 MRPL28

272 eg 14948436 0,68126 0,86352 20,605 0,18226 KCNK16

273 eg 14204735 0,33629 0,52427 20,605 0,18799 CYB561

274 cg26365553 0,31438 0,50655 20,605 0,19217 MADD

275 cg05664072 0,2089 0,4055 30,382 0,1966 PER2

276 cg04660410 0,38579 0,59548 27,372 0,20969 VILL Table 4B

9 136912707 GenelD:2220 NM_004108.2 P35; FCNL; EBP-37; ficolin-2;

14 102045158 GenelD:122416 NM_152326.2 MGC21990;

9 98186585 GenelD:1 1046 NM_007001 .1 hfrc; HFRC1 ; SQV7L;

UGTrel8; MGC1 17215;

14 20341 153 GenelD:6035 NM_198232.1 RIB1 ; RNS1 ; MGC12408;

3 48388617 GenelD:285231 NM_207102.1 Fbw12; FBX035;

MGC120385; MGC120386; MGC120387;

1 22200613 GenelD:10136 NM_005747.3 ELA3;

21 40160387 GenelD:5121 NM_006198.2 PEP-19;

1 1 64459768 GenelD:170589 NM_130769.2 GPA2; ZSIG51 ;

16 28515789 GenelD:6799 NM_001054.2 STP2; HAST4; P-PST;

ST1A2; TSPST2;

21 43722518 GenelD:1 14038 NM_153752.1 MGC129789;

3 52839856 GenelD:3700 NM_002218.3 H4P; IHRP; PK120; ITIHL1 ;

DKFZp686G21 125;

1 1 2122232 GenelD:51214 NT_009237.17 PEG8;

1 1 66581226 GenelD:29984 NM_014578.2 Rho; ARHD; RHOM;

RHOHP1 ;

1 42390900 GenelD:2981 NM_007102.1 UGN; GCAP-II;

3 9861232 GenelD:285367 NM_173659.2 FLJ34707; MGC29784;

4 1 1 1054039 GenelD:1950 NM_001963.2 URG;

16 1369016 GenelD:65259 NM_023076.2 FLJ23360;

20 30062162 GenelD:140706 NM_080625.2 dJ310O13.5;

1 42402385 GenelD:2980 NM_033553.2 GUCA2; STARA; GUANYLIN;

7 97679527 GenelD:168620 NM_177455.2 MIST1 ;

1 1 66976619 GenelD:390212 NM_206997.1 PGR5;

9 36159175 GenelD:881 NM_005893.1

1 23566513 GenelD:80818 NM_030634.1 ZNF; KIAA1710;

1 22201054 GenelD:10136 NM_005747.3 ELA3;

8 86537833 GenelD:761 NM_005181 .2 CAIN;

19 47638973 GenelD:284340 NM_198477.1 VCC1 ;

19 4132854 GenelD:51548 NM_016539.1 SIR2L6;

3 32254265 GenelD:152189 NM_178868.3 CKLFSF8;

14 20340764 GenelD:6035 NM_198232.1 RIB1 ; RNS1 ; MGC12408;

19 15644791 GenelD:66002 NM_023944.1 F22329_1 ;

8 28229651 GenelD:5368 NM_006228.2 PPNOC;

15 88150515 GenelD:290 NM_001 150.1 CD13; LAP1 ; PEPN; gp150;

12 50723838 GenelD:3164 NM_002135.3 HMR; N10; TR3; NP10;

GFRP1 ; NAK-1 ; NGFIB; NUR77; MGC9485;

6 3581 1413 GenelD:221481 NM_145028.3 FLJ25390;

1 1 1 13671846 GenelD:4837 NM_006169.2

7 129693863 GenelD:1358 NM_001869.1

21 34658253 GenelD:9992 NM_172201 .1 LQT5; LQT6; MIRP1 ;

MGC138292;

6 33879909 GenelD:4295 NM_002418.1

1 1 45784339 GenelD:55343 NM_018389.3 FUCT1 ; FLJ1 1320;

FLJ14841 ;

16 67072 GenelD:4350 NM 00101505 AAG; MDG; APNG; Midi ;

2.1 anpg; PIG1 1 ; PIG16;

CRA36.1 ;

1 1 67108362 GenelD:2950 NT_033903.7 PI; DFN7; GST3; FAEES3;

9 96395913 GenelD:8789 NM_003837.2

12 42440120 GenelD:83448 NM_031292.2 DKFZP434G1415;

DKFZp781 B2386;

157 3 161878388 GenelD:801 17 NM_025047.1 ARF7; FLJ22595;

158 12 123339189 GenelD:144347 NM_181709.2 FLJ44614;

159 10 1 18371034 GenelD:5408 NM_005396.3 PLRP2;

160 X 1 18485919 GenelD:292 NM_001 152.1 T2; T3; 2F1 ; ANT2;

161 1 1 1635710 GenelD:9361 1 NM 00101476 FBG3; FBX30; Fbx44;

5.1 Fbxo6a; MGC14140;

DKFZp781 J0852;

162 16 65781456 GenelD:283849 NM_178516.2 MGC88052;

163 16 46738165 GenelD:94160 NM_033226.1 MRP9; MGC27071 ;

164 17 45858056 GenelD:80221 NM_025149.3

165 7 34664018 GenelD:387129 NM_207173.1 GPRA; VRR1 ; NPSR1 ;

PGR14;

166 12 1 15200537 GenelD:23389 NM_015335.2 MED13L; FLJ21627;

KIAA1025; TRAP240L; PROSIT240;

DKFZp781 D01 12;

167 16 73810474 GenelD:1504 NM_001906.3 CTRB; FLJ42412;

MGC88037;

168 19 53935239 GenelD:54922 NM_017805.2 RAIN; FLJ20401 ;

169 17 150041 1 GenelD:83547 NM_031430.1 PP10141 ; FLJ31 193;

170 14 22594880 GenelD:64403 NM_144985.2 CDH1 1 L; FLJ25193;

171 X 99725945 GenelD:64102 NM_022144.1 TEM; CHM1 L; BRICD4;

tendin; myodulin; CHM1 - LIKE;

172 19 40632702 GenelD:2867 NM_005306.1 FFA2R; GPR43;

173 20 34018487 GenelD:140894 NM_080834.1 dJ954P9.1 ;

174 10 48036897 GenelD:2658 NM_016204.1 BMP9; BMP-9;

175 15 43470502 GenelD:2628 NM_001482.1 AGAT;

176 1 1 64459991 GenelD:170589 NM_130769.2 GPA2; ZSIG51 ;

177 5 142045723 GenelD:2246 NM_033136.1 AFGF; ECGF; FGFA;

ECGFA; ECGFB; HBGF1 ; GLIO703; ECGF-beta; FGF- alpha;

178 2 79201207 GenelD:5967 NM_002909.3 P19; PSP; PTP; REG; ICRF;

PSPS; PSPS1 ; MGC12447;

179 20 55569975 GenelD:5105 NM_002591 .2 PEPCK1 ; PEPCKC;

MGC22652;

180 1 196776292 GenelD:127124 NM_133262.2 Vma10; ATP6G3;

MGC1 19810; MGC1 19813;

181 6 71433785 GenelD:60682 NM_021940.2 SMAP-1 ; FLJ13159;

182 5 76284505 GenelD:1393 NM_001882.3 CRFBP; CRF-BP;

183 7 129807247 GenelD:1357 NM_001868.1 CPA;

184 17 39823254 GenelD:3674 NM_000419.2 GTA; CD41 ; GP2B; HPA3;

CD41 B; GPIIb;

185 17 24941283 GenelD:28964 NM_014030.2

186 19 40464757 GenelD:57817 NM_021 175.2 HEPC; HFE2B; LEAP1 ;

LEAP-1 ;

187 1 1 18226210 GenelD:6289 NM_030754.2

188 22 48699347 GenelD:79087 NM_024105.3 ECM39; hALG12; MGC3136;

PP14673; MGC1 1 1358;

189 17 64462140 GenelD:10351 NM_007168.2 KIAA0822;

190 3 50579617 GenelD:51 161 NM_016210.2 G20;

191 10 1 18295399 GenelD:5406 NM_000936.2 PL;

192 6 88912435 GenelD:1268 NM_016083.3 CB1 ; CNR; CB-R; CB1A;

CANN6; CB1 K5;

193 12 10173888 GenelD:64581 NM_022570.3 BGR; DECTIN1 ; CLECSF12;

194 9 96395762 GenelD:8789 NM_003837.2 195 19 50099708 GenelD:348 NM_000041 .2 AD2; MGC1571 ; apoprotein;

196 1 1 67562032 GenelD:10312 NM_006019.2 a3; Stv1 ; Vph1 ; Atp6i;

OC1 16; OPTB1 ; TIRC7; ATP6N1 C; ATP6V0A3; OC- 1 16kDa;

197 8 143865015 GenelD:8581 NM_003695.2 E48;

198 14 22594843 GenelD:64403 NM_144985.2 CDH1 1 L; FLJ25193;

199 12 53534691 GenelD:1 18430 NM_058173.1

200 22 18483541 GenelD:5902 NM_002882.2 MGC88701 ;

201 10 51678599 GenelD:56624 XM_936277.1

202 17 23965053 GenelD:124923 NM_144610.1

203 9 129702272 GenelD:30815 NM_013443.3 SIAT7F; ST6GALNACVI;

RP1 1 -203J24.3;

204 10 134059753 GenelD:170394 NM_138499.2 pp8607; FLJ46823;

bA432J24.1 ; RP1 1 -273H7.1 ;

205 1 1 128242529 GenelD:3758 NM_000220.2 ROMK; ROMK1 ; KIR1 .1 ;

206 7 142293853 GenelD:55503 NM_018646.2 CAT1 ; CATL; ZFAB; ECAC2;

ABP/ZF; LP6728;

HSA277909;

207 6 1 108431 15 GenelD:8528 NM_003649.2 DASOX; DDO-1 ; DDO-2;

FLJ45203;

208 21 45317773 GenelD:104 NM_001 1 12.2 RED1 ; ADAR2; ADAR2d;

ADAR2g; DRABA2;

DRADA2; ADAR2a-L1 ; ADAR2a-L2; ADAR2a-L3;

209 16 21572639 GenelD:10261 NM_005849.1 DORA;

210 1 1 16455987 GenelD:55553 NM_017508.1 HSSOX6;

21 1 20 57966890 GenelD:60437 NM_177980.1 VR20;

212 14 22974518 GenelD:4625 NM_000257.1 CMH1 ; MPD1 ; CMD1 S;

MYHCB; MGC138376; MGC138378;

213 10 18281 122 GenelD:221074 NM_152725.1 FLJ30499; MGC43205;

MGC51099; bA570F3.1 ;

214 16 701 17080 GenelD:10164 NM_005769.1 LSST;

215 4 155753599 GenelD:2266 NM_000509.4

216 19 13074451 GenelD:4066 NM_005583.3

217 1 1 2249693 GenelD:430 NM_005170.2 ASH2; HASH2; MASH2;

218 4 1 1 1053257 GenelD:1950 NM_001963.2 URG;

219 1 43702866 GenelD:81888 NM_031207.2 HT036; MGC20767; RP1 1 - 506B15.5;

220 18 59055398 GenelD:596 NT_025028.13 Bcl-2;

221 10 134972996 GenelD:10844 NM_006659.1 GCP2; SPBC97; Spc97p;

222 1 12001408 GenelD:60672 NM 00102537 FLJ12438; FLJ38609;

4.1

223 7 50816909 GenelD:2887 NT_033968.5 RSS; IRBP; MEG1 ; GRB-IR;

KIAA0207;

224 20 23497455 GenelD:128821 NM_080610.1 DA218C14.1 ;

225 14 23670741 GenelD:161247 NM_203402.1 MGC46490;

226 9 28708459 GenelD:158038 NM_152570.1 LERN3; LING02; FLJ31810;

227 9 122851435 GenelD:727 NM_001735.2 CPAMD4;

228 16 56133786 GenelD:221 188 NM_153837.1 PGR27;

229 12 10143146 GenelD:51267 NM_01651 1 .2 CLEC1 ; MGC34328;

230 19 1 193323 GenelD:513 NM_001687.4

231 1 1206304 GenelD:6339 NM_002978.2 ENaCd; SCNED; dNaCh;

ENaCdelta;

232 9 5880866 GenelD:2315 NM_00551 1 .1 MART1 ; MART-1 ;

233 20 29870349 GenelD:85366 NM_0331 18.2 KMLC; MLCK; skMLCK; 234 7 127043912 GenelD:5078 NM_006193.1

235 15 23577440 GenelD:57194 NT_026446.13 ATPVA; ATPVC; ATP10C;

KIAA0566;

236 5 32824224 GenelD:79614 NM_024563.2

237 1 1 129804543 GenelD:1 1095 NM_007037.3 METH2; ADAM-TS8;

238 20 23808078 GenelD:1473 NM_001900.4 MGC71922;

239 1 1 7015679 GenelD:338323 NM_176822.2 NOD5; GC-LRR;

240 16 55199525 GenelD:4502 NM_005953.2 MT2;

241 5 83052386 GenelD:1404 NM_001884.2 CRTL1 ;

242 7 127015093 GenelD:381 NM_001662.2

243 3 195572481 GenelD:131578 NM_130830.2 LIB;

244 1 150753574 GenelD:54544 NM_019060.1 NICE-1 ;

245 5 80565096 GenelD:1 160 NM_001825.1 SMTCK;

246 10 43187161 GenelD:53828 NM_173160.2 CHIF;

247 12 7792928 GenelD:170482 NMJ 30441 .2 DLEC; HECL; BDCA2;

CD303; CLECSF7;

CLECSF1 1 ; PRO34150; MGC125791 ; MGC125792; MGC125793;

248 9 33438382 GenelD:360 NM_004925.3 GIL;

249 19 60275375 GenelD:54869 NM_017729.2 DRC3; EPS8R1 ; MGC4642;

PP10566; FLJ20258;

MGC23164;

250 2 107968793 GenelD:60482 NM_021815.2 CHT; CHT1 ; hCHT;

251 1 200125284 GenelD:149345 NM_198149.1 C1 orf40; MGC129813;

252 21 4566931 1 GenelD:378832 NM_199175.1 PRED80;

253 6 35872936 GenelD:1208 NM_001832.2

254 20 22515170 GenelD:3170 NM_021784.3 HNF3B; TCF3B; MGC19807;

255 1 1 831335 GenelD:7106 NM_003271 .4 NAG2; NAG-2; TM4SF7;

TSPAN-4; TETRASPAN;

256 17 24063650 GenelD:14701 1 NM_152465.1 MGC39650;

257 9 9009757 GenelD:5789 NM_130391 .1 HPTP; PTPD; HPTPD;

MGC1 19750; MGC1 19751 ; MGC1 19752; MGC1 19753; HPTP-DELTA; R-PTP- DELTA;

258 21 39951370 GenelD:10317 NM_033170.1 B3T5; GLCT5; B3GalTx;

B3GalT-V; beta3Gal-T5;

259 12 44609923 GenelD:9169 NM_004719.1 SIP1 ; CASP1 1 ; SRRP129;

260 12 79625820 GenelD:4618 NM_002469.1 MRF4; HERCULIN;

261 10 70699885 GenelD:3098 NM_033496.1 HKI; HXK1 ; HK1 -ta; HK1 -tb;

262 2 49235663 GenelD:2492 NM_181446.1 LGR1 ; ODG1 ; FSHRO;

263 15 39032145 GenelD:79094 NM_0241 1 1 .2 MGC4504;

264 7 142178732 GenelD:5645 NM_002770.2 TRY2; TRY8; TRYP2;

MGC1 1 1 183; MGC120174;

265 13 77213286 GenelD:122060 NM_144595.2

266 1 1 66979224 GenelD:57010 NM_145200.2

267 9 134986976 GenelD:5900 NM_006266.2 RGF; RalGEF;

268 19 56230228 GenelD:43849 NM_019598.2 KLK-L5; MGC42603;

269 2 202192343 GenelD:151254 NM_152525.3 FLJ25351 ; FLJ40332;

270 5 1 15180684 GenelD:1036 NM_001801 .2

271 16 361991 GenelD:10573 NM_006428.3 p15; MAAT1 ; MGC8499;

272 6 39398137 GenelD:83795 NM_0321 15.2 TALK1 ; TALK-1 ;

273 17 58878036 GenelD:1534 NM_001915.3 FRRS2;

274 1 1 47247189 GenelD:8567 NM_130470.1 DENN; IG20; RAB3GEP;

KIAA0358; 275 2 238863033 GenelD:8864 NM_003894.3 FASPS; KIAA0347;

276 3 3801 1038 GenelD:50853 NM_015873.2

Table 4C

Marker/SEQ CpGlsland CpGlsland Class GCContent CpGRatio MaxGC MaxCpG ID No. Content Ratio

1 CGI true HCP 0,691 0,767 0,67 0,767

2 other_CpG false LCP 0,386 0,203 0,414 0,31

3 other_CpG false ICP 0,565 0,147 0,606 0,198

4 shore true HCP 0,61 0,824 0,668 0,885

5 other_CpG false ICP 0,587 0,28 0,672 0,379

6 CGI true No nd nd nd nd coord.

7 other_CpG false ICP 0,604 0,339 0,658 0,473

8 other_CpG false ICP 0,429 0,346 0,488 0,498

9 CGI true No nd nd nd nd coord.

10 other_CpG false ICP 0,624 0,172 0,66 0,309

1 1 other_CpG true ICP 0,609 0,284 0,646 0,406

12 other_CpG false ICP 0,504 0,137 0,556 0,22

13 other_CpG false ICP 0,588 0,077 0,646 0,144

14 other_CpG false LCP 0,398 0,044 0,432 0,093

15 other_CpG true HCP 0,807 1 ,033 0,802 1 ,055

16 other_CpG false ICP 0,474 0,209 0,51 0,282

17 shore false HCP 0,683 0,894 0,722 0,878

18 other_CpG true ICP 0,451 0,382 0,518 0,448

19 shore false HCP 0,478 0,733 0,554 0,857

20 other_CpG false ICP 0,537 0,291 0,578 0,391

21 shore false ICP 0,583 0,366 0,588 0,578

22 shore true HCP 0,71 1 0,939 0,802 1 ,031

23 shore false HCP 0,59 0,729 0,726 0,896

24 other_CpG false ICP 0,575 0,214 0,616 0,325

25 shore true ICP 0,663 0,62 0,72 0,707

26 shore true HCP 0,605 0,746 0,688 0,982

27 shore true HCP 0,62 0,815 0,554 1 ,044

28 other_CpG false ICP 0,606 0,228 0,628 0,329

29 other_CpG false ICP 0,609 0,181 0,648 0,24

30 other_CpG false ICP 0,559 0,261 0,58 0,368

31 other_CpG false ICP 0,489 0,253 0,54 0,312

32 other_CpG false ICP 0,513 0,241 0,552 0,289

33 other_CpG false HCP 0,586 0,62 0,62 0,779

34 other_CpG false ICP 0,496 0,326 0,552 0,482

35 other_CpG false ICP 0,46 0,335 0,572 0,396

36 shore false HCP 0,568 0,713 0,718 0,839

37 other_CpG false ICP 0,489 0,253 0,54 0,312

38 other_CpG false LCP 0,429 0,146 0,462 0,238

39 other_CpG true ICP 0,578 0,269 0,622 0,4

40 CGI true HCP 0,752 0,975 0,828 1 ,05

41 shore true HCP 0,585 0,801 0,682 0,775

42 other_CpG false ICP 0,525 0,254 0,558 0,369

43 other_CpG false ICP 0,513 0,241 0,552 0,289

44 other_CpG false ICP 0,525 0,182 0,578 0,31 1

45 other_CpG false ICP 0,482 0,381 0,51 0,57 other_CpG false ICP 0,55 0,209 0,588 0,337 other_CpG false ICP 0,555 0,156 0,594 0,325 other_CpG false ICP 0,643 0,291 0,662 0,444 other_CpG false ICP 0,537 0,291 0,578 0,391

CGI true No nd nd nd nd coord.

shore true HCP 0,656 0,62 0,708 0,755 other_CpG false ICP 0,595 0,189 0,646 0,258 shore true HCP 0,565 0,658 0,648 0,751 other_CpG false LCP 0,429 0,183 0,472 0,3 shore true HCP 0,689 0,837 0,748 0,849 other_CpG false ICP 0,543 0,17 0,592 0,255 other_CpG false ICP 0,588 0,261 0,638 0,357 shore true ICP 0,673 0,523 0,712 0,729 other_CpG false ICP 0,567 0,353 0,67 0,418 other_CpG false ICP 0,533 0,259 0,558 0,488 other_CpG false HCP 0,639 0,668 0,674 0,765 other_CpG false ICP 0,525 0,182 0,578 0,31 1 shore true ICP 0,461 0,565 0,584 0,651 other_CpG false ICP 0,501 0,239 0,528 0,367 shore true HCP 0,61 0,705 0,648 0,776 shore true HCP 0,729 0,819 0,672 0,762 other_CpG false ICP 0,525 0,254 0,558 0,369 other_CpG false ICP 0,592 0,152 0,616 0,18 other_CpG false ICP 0,56 0,304 0,612 0,466 other_CpG true ICP 0,565 0,385 0,646 0,555 other_CpG false ICP 0,606 0,228 0,628 0,329 other_CpG false ICP 0,555 0,21 0,61 0,272 other_CpG false ICP 0,48 0,275 0,534 0,407 other_CpG false LCP 0,409 0,12 0,43 0,162 other_CpG false ICP 0,467 0,2 0,552 0,328 other_CpG false ICP 0,545 0,325 0,618 0,414 shore true ICP 0,65 0,518 0,706 0,66 shore false ICP 0,605 0,282 0,644 0,37

CGI true No nd nd nd nd coord.

other_CpG true ICP 0,5 0,293 0,596 0,405 shore false HCP 0,58 0,696 0,588 0,751 other_CpG false ICP 0,529 0,1 19 0,586 0,214

CGI true HCP 0,625 0,644 0,708 0,75 shore false ICP 0,522 0,257 0,568 0,399 other_CpG false ICP 0,475 0,223 0,53 0,282 other_CpG false ICP 0,435 0,372 0,508 0,54 other_CpG false ICP 0,583 0,129 0,598 0,171 other_CpG false ICP 0,563 0,342 0,612 0,453 other_CpG false ICP 0,606 0,245 0,648 0,393 shore false HCP 0,586 0,756 0,74 0,752

CGI true HCP 0,645 0,558 0,752 0,836 shore false HCP 0,608 0,764 0,768 0,895 other_CpG false LCP 0,4 0,104 0,476 0,225

CGI true HCP 0,714 0,662 0,756 0,75 other_CpG false LCP 0,355 0,189 0,384 0,316 other_CpG false LCP 0,416 0,25 0,448 0,497 other_CpG false ICP 0,58 0,228 0,67 0,359 98 other_CpG true ICP 0,465 0,478 0,514 0,646

99 other_CpG false ICP 0,418 0,405 0,482 0,595

100 other_CpG false ICP 0,58 0,179 0,648 0,263

101 CGI true HCP 0,677 0,737 0,726 0,752

102 shore false ICP 0,641 0,292 0,682 0,399

103 other_CpG false ICP 0,553 0,283 0,59 0,423

104 other_CpG false ICP 0,543 0,271 0,628 0,298

105 other_CpG false ICP 0,487 0,084 0,51 0,182

106 other_CpG false ICP 0,422 0,131 0,484 0,154

107 shore true HCP 0,712 0,776 0,732 0,763

108 shore true HCP 0,554 0,794 0,61 0,755

109 CGI true ICP 0,495 0,34 0,564 0,364

1 10 other_CpG false LCP 0,403 0,287 0,456 0,418

1 1 1 other_CpG false ICP 0,525 0,217 0,568 0,342

1 12 shore false HCP 0,6 0,753 0,632 0,831

1 13 shore true HCP 0,636 0,66 0,712 0,759

1 14 other_CpG false ICP 0,587 0,28 0,672 0,379

1 15 other_CpG false ICP 0,493 0,22 0,528 0,339

1 16 other_CpG false ICP 0,502 0,175 0,584 0,24

1 17 other_CpG true ICP 0,617 0,41 1 0,678 0,493

1 18 other_CpG false ICP 0,545 0,1 13 0,57 0,148

1 19 shore true HCP 0,675 1 ,036 0,778 1 ,072

120 shore false ICP 0,51 1 0,179 0,62 0,234

121 CGI true HCP 0,657 0,752 0,626 0,765

122 shore true HCP 0,69 0,947 0,606 0,761

123 other_CpG false LCP 0,321 0,065 0,346 0,149

124 shore true HCP 0,642 0,752 0,688 0,802

125 other_CpG false LCP 0,326 0,252 0,388 0,416

126 other_CpG false ICP 0,57 0,213 0,628 0,337

127 other_CpG true LCP 0,41 0,321 0,47 0,529

128 other_CpG false LCP 0,351 0,082 0,438 0,13

129 shore true No nd nd nd nd coord.

130 shore false HCP 0,56 0,557 0,688 0,753

131 other_CpG false ICP 0,499 0,141 0,534 0,201

132 shore true ICP 0,6 0,361 0,658 0,456

133 shore true HCP 0,637 0,704 0,666 0,767

134 other_CpG false ICP 0,646 0,431 0,706 0,609

135 shore true HCP 0,727 0,782 0,788 0,89

136 other_CpG false ICP 0,578 0,269 0,622 0,4

137 other_CpG false ICP 0,455 0,177 0,504 0,249

138 other_CpG false ICP 0,53 0,293 0,608 0,483

139 other_CpG false ICP 0,5 0,254 0,568 0,332

140 other_CpG true ICP 0,539 0,399 0,712 0,502

141 other_CpG false LCP 0,352 0,243 0,44 0,372

142 shore false HCP 0,656 0,635 0,72 0,763

143 other_CpG false LCP 0,383 0,252 0,434 0,326

144 other_CpG false ICP 0,525 0,217 0,568 0,342

145 other_CpG false LCP 0,419 0,152 0,442 0,294

146 other_CpG true ICP 0,573 0,257 0,602 0,377

147 other_CpG false ICP 0,435 0,372 0,508 0,54

148 other_CpG false ICP 0,591 0,23 0,662 0,294

149 other_CpG false LCP 0,435 0,228 0,45 0,384

150 other_CpG false ICP 0,459 0,239 0,546 0,366 151 other_CpG true ICP 0,545 0,36 0,586 0,43

152 shore false HCP 0,65 0,966 0,758 0,961

153 other_CpG false LCP 0,341 0,258 0,394 0,343

154 shore true HCP 0,689 0,856 0,556 0,813

155 CGI true HCP 0,723 0,84 0,76 0,959

156 other_CpG false ICP 0,488 0,1 12 0,574 0,16

157 other_CpG false LCP 0,386 0,203 0,414 0,31

158 other_CpG false ICP 0,474 0,433 0,538 0,609

159 other_CpG false ICP 0,543 0,271 0,628 0,298

160 shore false HCP 0,595 0,705 0,614 0,751

161 other_CpG false HCP 0,703 0,662 0,676 0,755

162 other_CpG false ICP 0,57 0,186 0,616 0,253

163 other_CpG false ICP 0,505 0,15 0,53 0,216

164 CGI true HCP 0,67 0,602 0,694 0,751

165 other_CpG false ICP 0,484 0,229 0,59 0,308

166 other_CpG true HCP 0,777 0,968 0,742 1 ,022

167 other_CpG false ICP 0,58 0,228 0,67 0,359

168 shore false ICP 0,6 0,361 0,658 0,456

169 shore false ICP 0,608 0,487 0,69 0,657

170 other_CpG false ICP 0,594 0,181 0,63 0,213

171 other_CpG false LCP 0,376 0,095 0,424 0,168

172 other_CpG true ICP 0,572 0,448 0,632 0,559

173 other_CpG false ICP 0,482 0,074 0,522 0,12

174 other_CpG true ICP 0,572 0,387 0,636 0,525

175 other_CpG false LCP 0,399 0,21 0,44 0,343

176 other_CpG false ICP 0,55 0,209 0,588 0,337

177 other_CpG false ICP 0,537 0,254 0,598 0,346

178 other_CpG false ICP 0,43 0,109 0,488 0,229

179 other_CpG false ICP 0,499 0,322 0,522 0,492

180 other_CpG false LCP 0,366 0,176 0,42 0,305

181 shore false HCP 0,617 0,863 0,554 0,863

182 shore false ICP 0,467 0,306 0,562 0,448

183 shore false ICP 0,614 0,243 0,646 0,359

184 other_CpG false ICP 0,53 0,12 0,574 0,21 1

185 shore false HCP 0,739 0,789 0,834 0,88

186 other_CpG false ICP 0,575 0,322 0,63 0,478

187 other_CpG false ICP 0,514 0,164 0,558 0,249

188 shore true HCP 0,734 0,749 0,716 0,765

189 other_CpG false LCP 0,316 0,133 0,392 0,266

190 shore false ICP 0,626 0,586 0,72 0,719

191 other_CpG false LCP 0,371 0,291 0,43 0,512

192 other_CpG false ICP 0,481 0,249 0,514 0,308

193 other_CpG false LCP 0,369 0,245 0,438 0,395

194 other_CpG true ICP 0,5 0,293 0,596 0,405

195 other_CpG false ICP 0,594 0,35 0,664 0,475

196 shore true ICP 0,676 0,452 0,724 0,661

197 other_CpG false ICP 0,639 0,302 0,69 0,429

198 other_CpG false ICP 0,594 0,181 0,63 0,213

199 other_CpG false LCP 0,409 0,18 0,476 0,243

200 shore false HCP 0,731 0,912 0,684 0,821

201 other_CpG false LCP 0,42 0,189 0,436 0,286

202 other_CpG false ICP 0,505 0,108 0,566 0,184

203 shore true HCP 0,673 0,723 0,756 0,776 204 CGI true HCP 0,791 0,969 0,83 0,801

205 other_CpG false ICP 0,4 0,249 0,506 0,412

206 other_CpG false ICP 0,568 0,207 0,638 0,24

207 other_CpG false ICP 0,504 0,184 0,558 0,25

208 shore true HCP 0,806 1 ,026 0,774 0,915

209 other_CpG false LCP 0,381 0,233 0,448 0,567

210 other_CpG false LCP 0,319 0,133 0,334 0,218

21 1 other_CpG false ICP 0,44 0,172 0,552 0,283

212 other_CpG false ICP 0,56 0,14 0,616 0,219

213 other_CpG false LCP 0,387 0,135 0,404 0,187

214 other_CpG false ICP 0,461 0,266 0,528 0,343

215 other_CpG false ICP 0,44 0,138 0,506 0,318

216 CGI true ICP 0,666 0,406 0,748 0,644

217 shore true HCP 0,708 0,829 0,688 0,93

218 other_CpG false LCP 0,429 0,183 0,472 0,3

219 other_CpG false LCP 0,391 0,22 0,438 0,28

220 other_CpG true No nd nd nd nd coord.

221 CGI true HCP 0,714 0,856 0,686 0,799

222 shore true HCP 0,61 0,814 0,552 0,848

223 shore true No nd nd nd nd coord.

224 other_CpG false ICP 0,51 0,181 0,576 0,264

225 shore false ICP 0,58 0,208 0,658 0,335

226 other_CpG false LCP 0,375 0,1 15 0,43 0,238

227 other_CpG false LCP 0,373 0,168 0,45 0,317

228 other_CpG false ICP 0,552 0,165 0,612 0,267

229 other_CpG false ICP 0,426 0,239 0,52 0,371

230 CGI true HCP 0,656 0,82 0,578 0,759

231 other_CpG false ICP 0,641 0,276 0,688 0,34

232 other_CpG false ICP 0,417 0,249 0,484 0,5

233 other_CpG false ICP 0,602 0,175 0,632 0,356

234 other_CpG false ICP 0,544 0,158 0,608 0,274

235 other_CpG true No nd nd nd nd coord.

236 other_CpG false LCP 0,356 0,16 0,374 0,199

237 shore false HCP 0,545 0,452 0,6 0,766

238 other_CpG false ICP 0,551 0,188 0,61 0,225

239 other_CpG false LCP 0,409 0,13 0,462 0,195

240 shore true HCP 0,577 0,709 0,566 0,757

241 shore false ICP 0,453 0,292 0,556 0,377

242 CGI true HCP 0,689 0,802 0,674 0,757

243 other_CpG false ICP 0,527 0,324 0,64 0,444

244 shore false ICP 0,505 0,173 0,54 0,255

245 other_CpG true ICP 0,557 0,419 0,642 0,613

246 other_CpG false ICP 0,546 0,268 0,604 0,414

247 other_CpG false ICP 0,507 0,261 0,55 0,369

248 shore false ICP 0,639 0,522 0,722 0,692

249 other_CpG false ICP 0,565 0,181 0,596 0,247

250 shore false HCP 0,621 0,669 0,632 0,756

251 other_CpG true ICP 0,693 0,475 0,752 0,665

252 other_CpG true ICP 0,645 0,393 0,666 0,613

253 other_CpG false ICP 0,516 0,19 0,596 0,303

254 shore true HCP 0,578 0,609 0,66 0,856 255 other_CpG false HCP 0,738 0,889 0,702 0,797

256 shore true HCP 0,653 0,745 0,706 0,895

257 other_CpG false LCP 0,301 0,33 0,326 0,578

258 other_CpG false ICP 0,499 0,187 0,538 0,268

259 other_CpG false LCP 0,332 0,151 0,384 0,193

260 shore true ICP 0,488 0,337 0,574 0,494

261 other_CpG false ICP 0,458 0,176 0,506 0,238

262 other_CpG false LCP 0,441 0,172 0,47 0,226

263 shore true HCP 0,658 0,708 0,686 0,757

264 other_CpG false ICP 0,499 0,141 0,534 0,201

265 other_CpG false LCP 0,382 0,229 0,422 0,431

266 other_CpG false ICP 0,622 0,26 0,656 0,408

267 shore false HCP 0,697 0,771 0,748 0,76

268 other_CpG false ICP 0,549 0,188 0,596 0,351

269 shore true ICP 0,505 0,557 0,644 0,696

270 shore true HCP 0,548 0,844 0,636 0,755

271 shore true HCP 0,624 0,61 0,738 0,755

272 other_CpG false ICP 0,551 0,186 0,638 0,266

273 CGI true HCP 0,744 0,796 0,744 0,81

274 shore false HCP 0,595 0,594 0,658 0,758

275 shore true HCP 0,697 0,975 0,686 0,762

276 shore true ICP 0,59 0,527 0,67 0,704

The methylation pattern of any one of the markers above is important on the diagnostic method according to the present invention. More particularly, it was shown that using the methylation pattern of a panel of the following genes is sufficient for obtaining good prognostic results: markers 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and 275. Said CpG sites are defined by SEQ ID NOs 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and 275 respectively Example 6: Functional analysis of differentially methylated genes reveals their involvement in specific biological processes in islets and beta-cells

This experiment sought to provide experimental proof that at least some of the differentially methylated genes could indeed affect beta-cell function and viability. For this, the biological role of two of the differentially methylated genes with previously unknown function in beta-cells, namely NIBAN and CHAC1 , was further studied.

ER stress response can initiate apoptosis and has been implicated in beta-cell demise in diabetes. To test the role of NIBAN and CHAC1 in ER stress, human islets were treated with the physiological ER stressors oleate and palmitate or the synthetic ER stressors thapsigargin (THA), tunicamycin (TUN) or brefeldin (BRE). Expression of NIBAN was induced about two-fold by the saturated fatty acid palmitate (Figures 8A, line 3) but not with oleate (Figure 8A, line 2), which is a less potent inducer of ER stress. A similar induction of expression was observed for CHAC1 when islets were treated with palmitate (Figure 8D, column 3) but not with oleate (Figure 8D, column 2). The synthetic ER stressors thapsigargin (THA), tunicamycin (TUN) and brefeldin (BRE) also increased NIBAN and CHAC1 mRNA expression in human islets (Figure 8A and 7D, right panels). Thapsigargin, which causes ER calcium depletion by blocking the sarcoendoplasmic reticulum Ca2+ ATPase (SERCA), tunicamycin, which blocks glycosylation of nascent proteins (Hickman Set al., 1977 J Biol Chem 252: 4402-4408), and especially brefeldin, which inhibits ER-to-Golgi transport, induced NIBAN and CHAC1 gene expression. The magnitude of ER stress induced by these three chemicals and palmitate, as measured by ATF3, CHOP and BiP mRNA expression, was closely correlated with the NIBAN and CHAC1 induction.

Next, the functional role of NIBAN and CHAC1 expression on the outcome of ER stress in beta- cells was determined. For this purpose, the rat beta-cell line INS-1 E was exposed to palmitate or cyclopiazonic acid (CPA), a specific inhibitor of the SERCA pump. ER stress induced by these agents increased Niban mRNA expression (columns 1 , 3, 5 in Figure 8B). As shown in Figure 8B, expression of Niban is efficiently diminished by a specific siRNA (siNiban; compare columns 1 and 2, 3 and 4, 5 and 6). RNAi-mediated knockdown of Niban increased apoptosis induced by palmitate (Figure 8C; columns 3, 4) as well as CPA (columns 5, 6 in Figure 8C). This is mirrored by an elevated caspase 3 activation (Figure 8C, lower panel). Taken together, the increase of Niban expression during ER stress and augmented apoptosis after its knockdown indicates an anti-apoptotic role for Niban during the beta-cell ER stress response. Chad mRNA expression was also induced by palmitate and CPA in INS-1 E cells (Figure 8E). Chad knockdown by siRNA (siChac) significantly reduced its expression upon incubation with palmitate (Figure 8E, columns 3, 4) or CPA (columns 5, 6 in Figure 8E). Next it was analysed whether the knockdown of Chad affected ER stress-induced apoptosis stimulated by palmitate and CPA (Figure 8F). We found that Chad knockdown protected beta-cells from apoptosis (cf. columns 3, 4 and 5, 6 in Figure 8F), which was confirmed by a lessened activation of caspase 3 (Figure 8F, lower panel). The induction of Chad expression by ER stressors and the fact that its knockdown protected beta-cells from ER stress-induced apoptosis suggest that Chad is involved in apoptosis triggered after the different branches of ER stress response fail to re-establish ER homeostasis in beta-cells.

These experiments investigated two novel genes with opposite effects on ER stress-induced apoptosis in human islets, CHAC1 and NIBAN. Expression of both genes is strongly induced by ER stress, elicited by different agents. While NIBAN protects beta-cells against apoptosis, the activation of CHAC1 indicates failure of the processes restoring beta-cell functionality and heralds ER stress-induced apoptosis.

These experiments provide the proof of concept that the genes identified by the inventors to be differentially methylated in type-ll diabetes mellitus patients versus healthy subjects, are indeed functionally implicated in beta-cell physiology. Methods:

INS-1 E Cell and Human Islet Culture. The rat insulin-producing INS-1 E cell line (a kind gift from Prof. C. Wollheim, Centre Medical Universitaire, Geneva, Switzerland) was cultured in RPMI 1640 (with 2mM GlutaMAX-l) containing 5% FBS and used at passages 59-73. Human islets were isolated from 1 1 organ donors (age 69±6 years; body mass index 26±1 kg/m2) in Pisa, Italy, as described above. The islets were cultured in Ham's F-10 medium containing 6.1 or 28mM glucose as previously described (Cunha DA, et al., 2008, J Cell Sci 121 : 2308-2318). The percentage of beta-cells, assessed in dispersed islet preparations following staining with mouse monoclonal anti-insulin antibody (1 : 1000, Sigma) and donkey anti-mouse IgG Rhodamine (1 :200, Jackson ImmunoResearch Europe, Soham, Cambridgeshire, UK), was 53±3%. Palmitate and oleate (Sigma-Aldrich, Schnelldorf, Germany) were dissolved in 90% ethanol, and used at a final concentration of 0.5mM in the presence of 1 % BSA. The chemical ER stressors thapsigargin (diluted in DMSO and used at a final concentration of 1 μΜ), cyclopiazonic acid (CPA, diluted in DMSO and used at final concentration of 25μΜ), tunicamycin (diluted in PBS and used at a final concentration of 5μg ml) and brefeldin (diluted in ethanol and used at a final concentration of O. ^g/ml) were obtained from Sigma-Aldrich. The control condition contained similar dilutions of vehicle.

Assessment of Beta-Cell Apoptosis. Quantitative evaluation of INS-1 E cell apoptosis was done by fluorescence microscopy following staining with the DNA-binding dyes propidium iodide (5[ g/m\) and Hoechst 33342 (5[ g/m\). Caspase 3 activation was assessed by Western blot, as previously described (Gurzov et al., 2009, Cell Death Differ 16: 1539-1550), using anti-cleaved caspase 3 antibody (1 : 1000; from Cell Signaling, Beverly, MA, USA).

RNA Interference. NIBAN and CHAC1 were knocked down using small interfering RNA (siRNA). The Niban siRNA was SMARTpool (L-080179-01 from Dharmacon, Chicago, IL, USA) and CHAC1 was Stealth RNAiTM (RSS324745 from Invitrogen, Carlsbad, CA, USA). A negative control of 21 nucleotide duplex RNA with no known sequence homology was obtained from Qiagen (Hilden, Germany). Lipid-RNA complexes were formed in Optimeml with 1 .5μΙ Lipofectamine 2000 (Invitrogen) to 150nM siRNA and added at a final concentration of 30nM siRNA for transfection as described. Transfected cells were cultured for 2 days and subsequently treated.

Real-time PCR. Poly(A)+ RNA was isolated and reverse transcribed as previously described (Chen MC, et al., 2001 , Diabetologia 44: 325-332.). The PCR was done in 3mM MgCI2, 0.5μΜ forward and reverse primers, 2μΙ SYBR Green PCR master mix (Qiagen, Hilden, Germany) and 2μΙ cDNA. Standards for each gene were prepared using appropriate primers in a conventional PCR. The samples were assayed on a LightCycler instrument (Roche Diagnostics, Mannheim, Germany) and their concentration was calculated as copies per μΙ using the standard curve. The expression level of the gene of interest was corrected for the expression of the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (Gapdh, for INS-1 E cells) or beta-actin (for human islets). The different treatments utilised in the study did not change expression of the housekeeping gene (data not shown).

Claims

1. A method for the prognosis, diagnosis or prediction of Type 2 Diabetes (T2D) and/or follow up of T2D intervention therapies comprising the steps of:

a) measuring the methylation status of one or more of the CpG site(s) defined in Table 4 in a sample of the subject, defined by any one of SEQ ID Nos 1-276, and

b) comparing the methylation status of said one or more CpG site(s) obtained from step a) with the methylation status of said CpG site(s) in a control sample,

wherein a difference in methylation status as detected in step b) indicates the subject has or is at risk of developing T2D, optionally comprising the step of:

c) comparing the methylation status of said one or more CpG site(s) obtained from step a) with the methylation status of said CpG site(s) in a sample obtained after an intervention therapy aimed to prevent or treat T2D. 2. The method of claim 1 , wherein the difference in methylation status is due to hypermethylation or hypomethylation.

3. The method according to claim 1 or 2, wherein the sample is pancreatic islet tissue, a blood, or serum sample, adipose tissue, muscle, or any other biological sample that serve as surrogate material for the pancreatic islet tissue.

4. The method according to any one of claims 1 to 3, wherein the methylation status of up to or more than: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 1 10, 1 15, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275 or of all 276 CpG sites as defined by SEQ ID Nos 1 to 276 in Table 4, is measured.

5. The method according to any one of claims 1 to 4, wherein the methylation status of the CpG sites defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4, is measured.

6. A method of treating T2D by targeting one or more genes having aberrant methylation in T2D in any one or more of the CpG sites defined by SEQ ID Nos 1 to 276, taken from Table 4.

7. The method according to claim 6, wherein said one or more CpG sites are defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4. The method according to claim 6 or 7, wherein said targeting implies changing the methylation status by using demethylating or methylating agents, by changing the expression level, or by changing the protein activity of the protein encoded by said one or more genes.

The method according to claim 8, wherein said methylating agents are methyl donors such as folic acid, methionine, choline or any other chemicals capable of elevating DNA methylation.

The method according to claim 1 , wherein the methylation status is analysed by one or more techniques selected from the group consisting of nucleic acid amplification, polymerase chain reaction (PCR), methylation specific PCR (MCP), methylated-CpG island recovery assay (MIRA), combined bisulfite-restriction analysis (COBRA), bisulfite pyrosequenceing, single-strand conformation polymorphism (SSCP) analysis, restriction analysis, microarray analysis, or bead-chip technology.

The method according to anyone of claims 1 to 10, wherein the patient is in a high risk group for developing diabetes or suffering from any beta-cell related disorder such as: type 1 diabetes mellitus, type 2 diabetes mellitus, hyperinsulinemia, obesity, neuroendocrine tumors or occurrence of insulinoma.

A method for identifying an agent that modulates one or more of the genes having aberrant methylation in T2D in any one or more of the CpG sites defined by SEQ ID Nos

1 to 276, taken from Table 4, comprising the steps of:

a) contacting the candidate agent with said one or more genes, and

c) analysing the modulation of said one or more gene by the candidate agent.

The method according to claim 12, wherein said CpG sites are defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4.

14. The method according to claim 12 or 13, wherein said agent modulates the methylation status, the expression level or the activity of said one or more gene. 15. A method for establishing a reference methylation status profile comprising the steps of: measuring the methylation status of one or more genes corresponding to CpG sites defined by SEQ ID Nos 1-276 as defined in Table 4 having aberrant methylation in T2D, in a sample of subject. 16. The method according to claim 15, wherein said subject is healthy, thereby producing a reference profile of a healthy subject, or wherein said subject is suffering from T2D, thereby producing a T2D reference profile.

The method according to claim 15 or 16, wherein said reference profile concerns the methylation status profile of up to, or more than: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 1 10, 1 15, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, or 275 of the CpG sites defined by SEQ ID Nos 1 to 276 taken from Table 4.

The method according to any one of claims 15 to 17, wherein said reference profile concerns the methylation status profile of the CpG sites defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4.

19. A microarray or chip comprising one or more T2D-specific CpG regions as defined by SEQ ID Nos 1 to 276 taken from Table 4.

The microarray or chip according to claim 19, comprising up to, or more than: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 1 10, 1 15, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275 or all of the CpG sites defined by SEQ ID Nos 1 to 276 taken from Table 4.

The microarray or chip according to claim 19 or 20, comprising the CpG sites defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4.

Use of the methylation status of one or more of the CpG sites defined by SEQ ID Nos 1- 276 taken from Table 4, in the prognosis, diagnosis or prediction of Type 2 Diabetes (T2D).

The use according to claim 22, wherein said one or more of the CpG sites defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4.

24. A method for identifying T2D specific SNPs in comprising the step of comparing the sequence of one or more of the differentially methylated genes corresponding to CpG sites defined by SEQ ID Nos 1-276 taken from Table 4, in a sample from a healthy versus a T2D subject, wherein a difference or polymorphism in said one or more gene regions between the healthy and T2D subject sample is identified as a T2D-specific SNP. 25. The method according to claim 24, wherein said differentially methylated gene regions are selected from the group consisting of those corresponding to CpG sites defined by SEQ ID Nos 1 , 3, 5, 6, 8, 15, 17, 23, 26, 28, 50, 65, 71 , 92, 1 14, 129, 142, 178, 210, 223, 235, 263, 270, and/or 275, taken from Table 4.