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Impact of Consuming Extra-Virgin Olive Oil or Nuts within a Mediterranean Diet on DNA Methylation in Peripheral White Blood Cells within the PREDIMED-Navarra Randomized Controlled Trial: A Role for Dietary Lipids
research-article
Author(s):
Ana Arpón
1
,
2 ,
Fermín I. Milagro
1
,
2
,
3 ,
Cristina Razquin
3
,
4
,
5 ,
Dolores Corella
3
,
6 ,
Ramón Estruch
3
,
7 ,
Montserrat Fitó
3
,
8 ,
Amelia Marti
1
,
3
,
5 ,
Miguel A. Martínez-González
3
,
4
,
5 ,
Emilio Ros
3
,
9 ,
Jordi Salas-Salvadó
3
,
10 ,
José-Ignacio Riezu-Boj
1
,
2
,
5 ,
J. Alfredo Martínez
1
,
2
,
3
,
5
,
11
,
*
Publication date (Electronic):
23 December 2017
Journal:
Nutrients
Publisher:
MDPI
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There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
DNA methylation could be reversible and mouldable by environmental factors, such as dietary exposures. The objective was to analyse whether an intervention with two Mediterranean diets, one rich in extra-virgin olive oil (MedDiet + EVOO) and the other one in nuts (MedDiet + nuts), was influencing the methylation status of peripheral white blood cells (PWBCs) genes. A subset of 36 representative individuals were selected within the PREvención con DIeta MEDiterránea (PREDIMED-Navarra) trial, with three intervention groups in high cardiovascular risk volunteers: MedDiet + EVOO, MedDiet + nuts, and a low-fat control group. Methylation was assessed at baseline and at five-year follow-up. Ingenuity pathway analysis showed routes with differentially methylated CpG sites (CpGs) related to intermediate metabolism, diabetes, inflammation, and signal transduction. Two CpGs were specifically selected: cg01081346– CPT1B/ CHKB-CPT1B and cg17071192– GNAS/GNASAS, being associated with intermediate metabolism. Furthermore, cg01081346 was associated with PUFAs intake, showing a role for specific fatty acids on epigenetic modulation. Specific components of MedDiet, particularly nuts and EVOO, were able to induce methylation changes in several PWBCs genes. These changes may have potential benefits in health; especially those changes in genes related to intermediate metabolism, diabetes, inflammation and signal transduction, which may contribute to explain the role of MedDiet and fat quality on health outcomes.
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Most cited references28
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Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation.
Nizar Touleimat, Jörg Tost (2012)
Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium(®) Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.
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DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients
Michael Volkmar, Sarah Dedeurwaerder, Daniel Cunha … (2012)
Introduction Type 2 diabetes (T2D) has developed into a major public health concern. While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009). Lifestyle and behavioural factors play an important role in determining T2D risk. For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs β-cell dysfunction in female rat offspring (Ng et al, 2010). In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004). The pancreatic islets of Langerhans are of central importance in the development of T2D. Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet β-cells to regulate glucose homeostasis. β-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b; Tabak et al, 2009) and results in T2D (UKPDSG, 1995). The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007; Scott et al, 2007; Sladek et al, 2007; Zeggini et al, 2007, 2008) has placed the insulin-producing β-cells at centre stage. These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development. Most T2D risk genes identified in these genome-wide association studies (GWAS) affect β-cell mass and/or function (Florez, 2008). While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010). Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence. DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications. These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008; Park et al, 2008). DNA methylation occurs as 5-methyl cytosine mostly in the context of CpG dinucleotides, so-called CpG sites. It is the best-studied epigenetic modification and governs transcriptional regulation and silencing (for review, see Suzuki and Bird, 2008). Unlike the relatively study genome, the methylome changes in a dynamic way during development, tissue differentiation and aging. Pathologically altered DNA methylation is well described in various cancers (reviewed in Jones and Baylin, 2007) and its role is starting to be revealed in several other diseases such as multiple sclerosis (Casaccia-Bonnefil et al, 2008), Alzheimer's disease (Mastroeni et al, 2009) and systemic lupus erythematosus (Javierre et al, 2010). About 75% of human gene promoters are associated with CpG islands (CGIs) (Jones and Baylin, 2007; Suzuki and Bird, 2008), which are clusters of 500 bp to 2 kb length with a comparatively high frequency of CpG dinucleotides. They usually harbour low levels of DNA methylation but can become hypermethylated; this CGI hypermethylation was demonstrated to abrogate transcription of tumour suppressor genes during tumourigenesis (Jones and Baylin, 2007). Lately, DNA methylation changes in CpG sites adjoining yet outside of CGIs, so-called CGI shores (Irizarry et al, 2009), are gaining increased attention. Intriguingly, CpG sites in these shore sequences, in addition to those within CGIs, are proposed to display differential DNA methylation between cancer and normal cells as well as between cells of different tissues (Irizarry et al, 2009). The goal of the present work was to clarify the hitherto poorly understood connection between DNA methylation and T2D pathogenesis and to determine whether identified epigenetic changes translate into functional effects that impinge on pancreatic β-cell function. For this, we have explored DNA methylation landscapes in islets isolated from T2D patients and non-diabetic individuals. Results Identification of the T2D-related differential DNA methylation profile We performed DNA methylation profiling to analyse the methylomes of freshly isolated islets from 16 human cadaveric donors of similar age, BMI and ethnicity (5 diabetic and 11 non-diabetic Caucasian donors; Table I). Using electron microscopy (EM), we examined the purity of the islet preparations (see Supplementary data). In diabetic islets, decreases in the percentage of β-cells have been reported (Sakuraba et al, 2002; Rahier et al, 2008). As shifts in the composition of islet cell types (especially β-cells that constitute about two thirds of the islet cell mass) with different epigenomes might overlay T2D-related changes in the cells' DNA methylation patterns, we used EM to estimate the percentage of β-cells. The amount of β-cells in three randomly chosen control islet preparations was 66.3±0.9%. Diabetic islets (n=3, randomly chosen) contained only marginally less β-cells, accounting for 59.7±1.7% of total islet cell number (cf. Supplementary Table S1, Materials and methods, Supplementary data). To perform DNA methylation profiling, we used the recently developed Infinium Methylation Assay (Illumina® Infinium® HumanMethylation27 BeadChip; Supplementary Figure S1; Bibikova et al, 2009). This assay interrogates the methylation status of 27 578 CpG sites corresponding to 14 475 consensus coding sequences and well-known cancer genes (Bibikova et al, 2009). Although the Infinium methylation assay is not a genome-wide DNA methylation technology, it is a useful screening tool that is sensitive, specific and highly reproducible (Bibikova et al, 2009) allowing for analysis of a defined set of CpG sites in a large number of samples. The analysed CpGs are primarily located in proximal promoter regions (and preferentially inside CGIs) and the array encompasses probes with an average coverage of 1.9 CpG sites per promoter. As an initial step, we 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 (Supplementary Figure S2). This outcome highlights two facts: first, diabetic DNA methylation profiles are more similar to each other than to the methylation profile of any control sample, indicating the possibility of a T2D-specific DNA methylation profile; second, the existence of a single branch containing the five diabetic samples shows that the DNA methylation changes are sufficiently pronounced (even in the unfiltered data sets) to distinguish diabetic from control samples. To further substantiate the correlation of variations of DNA methylation with T2D, we performed a principal component analysis on the data set (Materials and methods; Supplementary Figure S3) and compared the resulting principal components with the T2D state in a point-biserial correlation (Lowry, 1998–2011). We found that PC #1 and PC #3, together accounting for 37% of the variance in the data set, correlate well with T2D state (Supplementary Figure S3). This is indicative of distinct, T2D-specific changes in the epigenome of pancreatic islets. Following this initial data analysis, we identified T2D-related methylation changes by filtering the data sets for CpG sites showing significant differences in DNA methylation levels between control and T2D groups (cf. Materials and methods and Supplementary Table S2). The results of the filtering are shown as a heatmap (Figure 1A). The depicted methylation profiles discriminate between control samples (left side, indicated by yellow bar above heatmap) and T2D samples (right side, indicated by blue bar). It is already apparent from the above data that there are marked DNA methylation changes in T2D islets. The number of differentially methylated CpG loci in T2D islets is in the same range as reported for other non-malignant conditions analysed with the same technology platform (19 in T1D-related nephropathy; Bell et al, 2010); 84 and 360, respectively, in analyses of ageing in cells and tissue specimens (Bork et al, 2009; Rakyan et al, 2010). We then set out to evaluate the descriptive power of the CpG sites in the filtered data set to differentiate diabetic from non-diabetic specimens in sample-wise comparisons. We therefore extracted the methylation values for each sample and performed a supervised clustering (Figure 1B, cf. Materials and methods). As expected, the resulting dendrogram shows that samples group together in two clusters containing exclusively control (CTL, yellow bar) or diabetic (T2D, blue bar) samples, indicating that class identity (CTL, T2D) is the most important separation criterion (Figure 1B, left-most branch). To assess clustering confidence in an unbiased way and to overcome inherently subjective visual interpretation of the results depicted in the heatmap (Figure 1A), a bootstrapping analysis was carried out after dendrogram computation (cf. Materials and methods). The obtained bootstrap of 0.85 indicates significant statistical support for the bipartite distribution between diabetic and non-diabetic samples based on the analysis of the CpGs contained in the filtered data set. The occasional high bootstrap values adjoined to sample pairs illustrate similarities in the DNA methylation profiles of these samples. These data demonstrate that human pancreatic islets undergo DNA methylation alterations in T2D that are discernible by means of DNA methylation profiles. T2D-related aberrations encompass mostly promoter-specific DNA hypomethylation The above experiments enabled us to collect the first comprehensive DNA methylation data set for T2D human islets. We identified 276 CpG sites, affiliated to 254 gene promoters, showing differential methylation between normal and diseased samples (Figure 1C; Supplementary Table S2). Strikingly, 266 of these 276 CpGs (96%) showed decreased methylation levels, while only 10 were hypermethylated (Figure 1C). This unexpected finding contrasts with the well-known DNA methylation changes observed in cancers, where gene-specific hypomethylation and hypermethylation are more or less evenly distributed (Jones and Baylin, 2007). With respect to global DNA methylation, cancers generally display hypomethylation (Jones and Baylin, 2007; Tost, 2010), primarily in repetitive DNA. To test whether the observed T2D-related changes are gene specific or whether they reflect global hypomethylation in the genome of islet cells, we measured DNA methylation levels of the repetitive LINE-1 element in control and diabetic samples with bisulphite pyrosequencing (BPS). Analysing DNA methylation of LINE-1, which makes up ∼20% of human genome, provides an accurate estimate of global DNA methylation changes (Yang et al, 2004). Figure 1D shows that repetitive elements are not differentially methylated in T2D, as substantiated by the strong overlap between CTL and T2D samples, indicating the absence of global hypomethylation in T2D islets. As an additional quality control, we examined the set of 276 differentially methylated CpG sites for overlap with known single-nucleotide polymorphism (SNP) positions to be excluded from further data analysis. We found no overlap with the reported 180 potentially problematic CpG sites contained in the Humanmethylation27 array (Bell et al, 2011) and therefore continued our analyses with the full set of 276 CpGs. BS validation of T2D-related differential DNA methylation To corroborate the observed Infinium measurements (cf. Figure 1 and Supplementary Table S2), we applied BPS and in some cases conventional BS to randomly selected, differentially methylated CpG sites. In all 19 cases tested, differential DNA methylation at the respective CpG sites was confirmed by BPS (Figure 2; Supplementary Figure S4). Where implemented, BS also confirmed the DNA methylation profiling data (Figure 2A; Supplementary Figure S4A). Figure 2A depicts an exemplary analysed gene, ALDH3B1, for which the Infinium data were confirmed by BS and BPS. Additional validated genes are shown in Figure 2B (CASP10) and Figure 2C (PPP2R4 alias PP2A). We discovered two differentially methylated CpG sites inside a CGI in the IGF2/IGF2AS locus. The differential methylation of one of the CpGs in this region was tested and confirmed by BPS (Figure 2D). Further examples are shown in Supplementary Figure S4. A direct comparison of methylation percentages obtained by the Infinium Methylation assay and BPS (Figure 2E) yielded a highly positive correlation (Pearson's correlation R=0.927) confirming the validity of the data. BPS analysis of three negative controls constituting high (>90%), intermediate (∼40%) and low ( 2 kb from the nearest CGI (‘other CpGs' in Figure 3A and B and Supplementary Table S3). Additionally, about one quarter of CpGs resides in CGI shores (1–2000 bp from a CGI border) while another quarter of CpG sites was located inside CGIs (Figure 3A; Supplementary Tables S2 and S3). A more detailed representation of the CpG distribution reveals the sites of differential methylation inside the CGI shores to be located preferentially close to the CGIs (bar chart in Figure 3B). This distribution of differentially methylated sites inside CGI shores is similar to regions showing differential methylation in cancer (c-DMRs; Irizarry et al, 2009), between tissues (t-DMRs; Irizarry et al, 2009) and during differentiation/cell reprogramming (r-DMRs; Doi et al, 2009). However, an overall comparison of differential methylation locations between these DMRs (Doi et al, 2009; Irizarry et al, 2009) and our data (Figure 3A and B) shows significant disparity, with a predominance of DNA methylation changes >2 kb away from CGIs (‘other CpGs' in Figure 3A and B and Supplementary Table S3), thus distinguishing the localisation of the DNA methylation profile in T2D islets from those found in tumours, between tissues and in stem cells. We next analysed the occurrence of these differentially methylated CpG sites in relation to CpG density of the affiliated gene promoters. Saxonov et al (2006) discovered a bipartite distribution of gene promoters with minor overlap between both classes when categorising promoter sequences by means of their CpG content. They discovered that promoters are either relatively depleted of CpG sites (low CpG promoters, LCPs) or enriched in CpG sites (high CpG promoters, HCPs), preferentially around the transcription start site (TSS). Weber et al (2007) introduced a third class of promoters called intermediate CpG promoters (ICPs), to account for the overlap between the classes mentioned above. They also developed precise classification criteria for the three classes, which we utilised in an adapted form: in this study, we considered positions −700 to +500 relative to the TSS for the promoter classification (cf. Materials and methods), since both CpG density and differential DNA methylation are distributed symmetrically around the TSS (Saxonov et al, 2006). Figure 3C 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), while LCP class promoters have seldomly been investigated. Their role as sites of hypomethylation in T2D therefore remains to be explored. Looking directly at the CpG ratio of the promoter sequences (cf. Materials and methods), the Infinium array resembles the distribution found in a comprehensive set of human promoters (cf. Weber et al, 2007 and Figure 3D, red bars). The CpG ratio of the promoters displaying differential methylation in T2D islets (Figure 3D, blue bars) is clearly distinct from the probes represented on the Infinium array and also contrasts with the CpG distribution found in promoters throughout the genome. Following the distribution of the blue bars in Figure 3D, it becomes apparent that most T2D-linked differentially methylated promoters are relatively depleted in CpG sites (CpG ratio 40 gene variants of T2D susceptibility genes known to date cannot fully explain T2D predisposition. Our study points to the involvement of epigenetic alterations in T2D thus underscoring the previously established contribution of lifestyle habits to its development. Combining the advantages of genome-scanning techniques and epigenome analyses might pave the way to better comprehend the pathogenesis of T2D. It will be of great interest to examine SNPs in the differentially methylated genes in T2D described in this study since the interplay between SNPs and differential (allele-specific) DNA methylation has recently been described (Shoemaker et al, 2010). Linked to the topic of allele-specific DNA methylation, it is noteworthy that a number of the genes found to display differential methylation are also reported to be imprinted (Supplementary data). It could hence be speculated that at least a partial loss of imprinting occurs in T2D islets. In conclusion, we report the first comprehensive and detailed analysis of epigenetic changes in T2D, specifically an altered DNA methylation profile in the pancreatic islets of T2D patients with a major preponderance of hypomethylation in sequences outside CGIs. These aberrant methylation events affect over 250 genes, a subset of which is also differentially expressed. The dysregulation of these genes in T2D may notably be linked to β-cell functionality, cell death and adaptation to metabolic stress. Examination of two genes identified by methylation profiling, NIBAN and CHAC1, revealed their biological functions in distinct processes of the ER stress response. Furthermore, our data highlight genes belonging to biological processes whose involvement in T2D is not yet fully understood, such as inflammation and ion transporters/channels/sensors. Importantly, it can be envisaged that the uncovered DNA methylation changes might be, on one part, indicative of reactions of the islet cells to the diabetic condition and on another part, might be causal of T2D. A challenge in the future is to provide further evidence for the primary effects of methylation changes in the diabetic condition. Taken together, our DNA methylation study on human islets thus lays the ground to further unravel the biological complexity of T2D and outlines an unexpected level of epigenetic regulation in islets, which must be taken into account in future studies aiming to understand the pathogenesis of T2D. Materials and methods Isolation of pancreatic islets From September 2004 to November 2009, pancreatic islets of Langerhans were isolated from pancreata of 5 T2D and 11 non-diabetic male cadaveric donors in Pisa, Italy, with the approval of the local Ethical Committee, and as described previously (Del Guerra et al, 2005). Glucose-induced insulin secretion was measured as described. The diagnosis of T2D was based on the previously described clinical criteria (ADA, 1997; Genuth et al, 2003). Islet purity and β-cell content Reliable purity assessment for diabetic islets is challenging. In T2D, the degranulated β-cells contain less insulin and zinc (Ostenson et al, 2006) and the qualitative dithizone assessment (which targets zinc) therefore underestimates T2D islet purity. Hence, we used EM to analyse islet purity in some of the samples used in the methylation profiling after >2 days in culture (Supplementary data; n=3 for T2D and non-diabetic samples, respectively) as described (Welsh et al, 2005). Blood sampling After obtaining written informed consent, blood was sampled from 12 male T2D patients and 12 male age- and BMI-matched controls in K2EDTA tubes. Methylation profiling using the Infinium assay Genomic DNAs were isolated from pancreatic islets using the Wizard® SV Genomic DNA kit (Promega Corp.) and from 200 μl of blood using the QIAamp DNA Mini kit (Qiagen, Hilden, Germany). In all, 1 μg of genomic DNA was treated with sodium bisulphite using the EZ DNA Methylation™ kit (Zymo Research) according to 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 (Illumina, Inc., San Diego) according to manufacturer's protocol. Data acquisition was done with the Illumina BeadArrayReader and quality control was performed using the Methylation module (version 1.0.5) of the GenomeStudio™ software (version 1.0.2.20706). Data normalisation was omitted due to resulting in essentially unchanged data sets (background normalisation) or due to inapplicability (Loess, quantile, Bayes) to methylation datasets because of their heteroscedasticity (Cancer Genome Atlas Research Network, 2011). For quality control, a standard quality analysis was performed for each array assessing Bisulphite conversion efficiency, hybridisation efficiency and specificity, single base extension rate, target removal as well as staining for negative and non-polymorphic probes (GenomeStudio Controls Dashboard). Data handling, comparisons and so on were performed with the Methylation module of the GenomeStudio software package (Illumina), MS Excel, R 2.8.0-2.11.1, Openstat and MicroarrayAnalyse v1.0 (Graessler, 2008). CGI and promoter class annotation Annotations for the Infinium HumanMethylation27 provided by Illumina were augmented with respect to (i) the position of the analysed CpG relative to the nearest CGI (inside a CGI, in CGI shore or >2 kb 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 CGIs, the CGI map provided by Bock et al (2007) (combined epigenetic score >0.5; genome assembly hg18/NCBI36) was used as reference; the CpGs were classified into three categories according to Irizarry et al (2009). Designation of the CpGs is as follows: ‘inside CGI' if the CpG was inside a CGI, ‘CGI shore' if the CpG was located within a 2-kb region around a CGI and ‘other CpG' otherwise (distance to closest CGI >2 kb). Promoters of the gene loci affiliated to the analysed CpG sites were classified according to their CpG content. First, we extracted sequences ranging from positions −700 to +500 relative to the TSS from UCSC genome browser database, then calculated the CpG ratio and the GC content of these sequences in sliding windows of 500 with 5 bp offsets. For classification criteria, we followed the definition by Weber et al (2007). In short, promoters were defined as HCPs if at least one 500 bp window contained a CpG ratio >0.75 together with a GC content >0.55 whereas in LCPs no 500 bp window reached a CpG ratio of at least 0.48. All promoters not fitting in either of the above promoter classes were termed ICPs. Five 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 of 0.15 and P<0.01 (Mann–Whitney) were set as filtering criteria. In all, 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 et al, 2007) in which the phylogenetic analysis was conducted. The dendrogram was computed using the UPGMA method applying the ‘number of differences' model (Sneath and Sokal, 1973). 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, 1985). Bootstrap values of 0.7 or higher were considered significant and are shown next to the branches in Figure 1B. Conventional BS and BPS In all, 750 ng genomic DNA was subjected to bisulphite conversion using the Epitect® Bisulfite Kit (Qiagen) or the EZ DNA Methylation kit (Zymo Research) according to manufacturer's protocol. Elution of the converted DNA was generally performed with 26 μl elution buffer and 8 μl of the eluted DNA were used as template in subsequent PCRs. To ensure sufficient amount of product, amplifications were generally performed as nested PCRs. PCR and sequencing primers for BPS were deduced using the PyroMark® Assay Design 2.0 software (Qiagen). Primers for pre-amplification and conventional BS 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. PCR and sequencing primers are listed in Supplementary Table S7. The pre-amplification PCR was conducted with primers (see EF, ER primers in Supplementary Table S7) amplifying 400–720 bp 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 3 mM MgCl2, 1 mM of each dNTP, 12% (v/v) DMSO, 500 nM of each primer and optionally 500 mM Betaine in heated-lid thermocyclers under the following conditions: 95°C 3:00; 25 × (94°C 0:30; 51°C 0:40; 72°C 1:30); 72°C 5:00 and cooled afterwards to 10°C. For conventional BS, nested PCRs were conducted as described above using 35 PCR cycles and a decreased elongation time of 1 min (for PCR primers cf. Supplementary Table S7). 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 the HotStarTaq PCR kit (Qiagen) according to manufacturer's recommendations. Reactions were performed in heated-lid thermocyclers under the following conditions: 95°C 15:00; 45 × (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. Microarray gene expression studies In all, 100 ng total RNA was prepared and hybridised onto Affymetrix Human HG-U133A chips, according to the protocols described in the Affymetrix GeneChip® Expression Analysis Manual (Affymetrix, Santa Clara, CA). Chips were scanned in an Affymetrix GeneChip Scanner 3000 and their quality verified with the Microarray Analysis Suite 5.0 (Affymetrix) software and functions from the R/Bioconductor affy and affyPLM packages (Gautier et al, 2004; Bolstad et al, 2005) (R version 2.8.0; Bioconductor version 2.3). Raw gene expression data were normalised using the Robust Multiarray Average (RMA) method described by Irizarry et al (2003) implemented in the affy package. Gene expression ratios were calculated using the limma package by fitting a linear model on each gene (Smyth, 2005). For comparison of differentially methylated loci with expression profiles of non-diabetic islets (Bhandare et al, 2010), we utilised the extracted expression data (downloaded from ArrayExpress; accession number E-MTAB-191; http://www.ebi.ac.uk/microarray-as/ae/). INS-1E cell and human islet culture The rat insulin-producing INS-1E cell line (a kind gift from Professor C Wollheim, Centre Medical Universitaire, Geneva, Switzerland) was cultured in RPMI-1640 (with 2 mM GlutaMAX-I) containing 5% FBS (Asfari et al, 1992; Cnop et al, 2007a) and used at passages 59–73. Human islets were isolated from 11 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 28 mM glucose as previously described (Cunha et al, 2008; Igoillo-Esteve et al, 2010; Ladriere et al, 2010). The percentage of β-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 Immuno Research 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 0.5 mM in the presence of 1% BSA (Cunha et al, 2008). The chemical ER stressors THA (diluted in DMSO and used at a final concentration of 1 μM), CPA (diluted in DMSO and used at final concentration of 25 μM), TUN (diluted in PBS and used at a final concentration of 5 μg/ml) and BRE (diluted in ethanol and used at a final concentration of 0.1 μg/ml) were obtained from Sigma-Aldrich. The control condition contained similar dilutions of vehicle. Assessment of β-cell death Cell death was measured using the neutral red kit (Sigma, TOX4) following manufacturer's instructions. Briefly, cells were incubated with 17 mM neutral red for 3 h at 37°C, washed and the dye extracted for absorbance measurement in a spectrophotometer. Quantitative evaluation of INS-1E cell apoptosis was done by fluorescence microscopy following staining with the DNA-binding dyes propidium iodide (5 μg/ml) and Hoechst 33342 (5 μg/ml) (Cnop et al, 2007a). Caspase 3 activation was assessed by western blot, as previously described (Gurzov et al, 2009), using anti-cleaved caspase 3 antibody (1:1000; from Cell Signaling, Beverly, MA, USA). RNA interference Genes were knocked down using siRNA. The Niban siRNA was SMARTpool (L-080179-01 from Dharmacon, Chicago, IL, USA). Stealth RNAi (Invitrogen, Carlsbad, CA) was used for CHAC1 (RSS324745), MKNK2 (RSS312292), PER2 (RSS329646), BCL2 (RSS340652) and NR4A1 (RSS330510). The siRNAs for SFRS2IP (s161636) and GUCA2B (s133784) were Silencer Select from Ambion (Austin, TX). A negative control (siCTL) of 21 nucleotide duplex RNA with no known sequence homology was obtained from Qiagen. Lipid–RNA complexes were formed in Optimem1 with 1.5 μl Lipofectamine 2000 (Invitrogen) to 150 nM siRNA and added at a final concentration of 30 nM siRNA for transfection as described (Cunha et al, 2008). Transfected cells were cultured for 2 days and subsequently treated. The achieved knockdown was 74.8±10.4% for MKNK2 (P=0.03), 86.4±3.5% for GUCA2B (P=0.003), 34.2±12.9% for PER2 (P=0.098), 58.3±5.9% for SFRS2IP (P=0.04), 72.4±3.2% for CHAC1 (P=0.02), 72.5±3.6% for NR4A1 (P=0.001), 47.2±3.8% for Bcl-2 (P=0.01) and 50.7±4.8% for NIBAN (P=0.02), as measured at the mRNA level, except for Bcl-2 that was measured by western blotting. Real-time PCR Poly(A)+ RNA was isolated and reverse transcribed as previously described (Chen et al, 2001). The PCR was done in 3 mM MgCl2, 0.5 μM forward and reverse primers, 2 μl SYBR Green PCR master mix (Qiagen) and 2 μl 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 μl using the standard curve (Overbergh et al, 1999). The expression level of the gene of interest was corrected for the expression of the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (Gapdh, for INS-1E cells) or β-actin (for human islets). PCR primers are listed in Supplementary Table SXXX. The different treatments utilised in the study did not change expression of the housekeeping gene (Igoillo-Esteve et al, 2010; data not shown). Statistics Significance of group-wise differences in DNA methylation profiles was measured by Mann–Whitney rank-sum test, P<0.01 was considered significant. Taking into account inter-individual differences in methylation levels and following Illumina Inc. recommendations, a 15% group-wise difference of methylation levels was set as a cutoff additional to P Mann–Whitney<0.01. Correlation between methylation values by Infinium and BPS was computed using Pearson's correlation test. Differences between distributions (CpG localisation, promoter class) were calculated with χ2 goodness-of-fit test (R 2.11.1); P-values were estimated from the resulting χ2 value. Significance of gene expression differences was tested by Bayes moderated t-test and P-values were FDR adjusted using Benjamini-Hochberg method (R package limma; Smyth, 2005); adj. P<0.05 was considered significant. Differences in glucose-stimulated insulin secretion, methylation as analysed by BPS and gene expression as analysed by RT–qPCR were assessed by Student's t-test, P<0.05 was considered significant. Data are represented as mean±s.d. unless indicated otherwise. Accession codes DNA methylation data sets for pancreatic islets and whole blood have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo) under accession numbers GSE21232 and GSE34008, respectively. Supplementary Material Supplementary data Supplementary Table S2 Supplementary Table S5 Supplementary Table S6 Supplementary Table S7 Review Process File
Author and article information
Journal
Journal ID (nlm-ta): Nutrients
Journal ID (iso-abbrev): Nutrients
Journal ID (publisher-id): nutrients
Title:
Nutrients
Publisher:
MDPI
ISSN
(Electronic):
2072-6643
Publication date
(Electronic):
23
December
2017
Publication date Collection:
January
2018
Volume: 10
Issue: 1
Electronic Location Identifier: 15
Affiliations
[1
]Department of Nutrition, Food Sciences and Physiology, University of Navarra, 31008
Pamplona, Spain;
aarpon.1@
123456alumni.unav.es
(A.A.);
fmilagro@
123456unav.es
(F.I.M.);
amarti@
123456unav.es
(A.M.);
jiriezu@
123456unav.es
(J.-I.R.-B.)
[3
]Spanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition (CIBERobn),
Institute of Health Carlos III, 28029 Madrid, Spain;
crazquin@
123456unav.es
(C.R.);
dolores.corella@
123456uv.es
(D.C.);
restruch@
123456clinic.ub.es
(R.E.);
mfito@
123456imim.es
(M.F.);
mamartinez@
123456unav.es
(M.A.M.-G.);
EROS@
123456clinic.ub.es
(E.R.);
jordi.salas@
123456urv.cat
(J.S.-S.)
[4
]Department of Preventive Medicine and Public Health, University of Navarra, 31008
Pamplona, Spain
[6
]Department of Preventive Medicine and Public Health, University of Valencia, 46010
Valencia, Spain
[7
]Department of Internal Medicine, Institut d’Investigacions Biomediques August Pi i
Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
[9
]Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions
Biomediques August Pi i Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona,
08036 Barcelona, Spain.
Author notes
Author information
Ana Arpón
https://orcid.org/0000-0002-9508-0431
Fermín I. Milagro
https://orcid.org/0000-0002-3228-9916
Dolores Corella
https://orcid.org/0000-0002-2366-4104
Amelia Marti
https://orcid.org/0000-0001-9832-7981
Jordi Salas-Salvadó
https://orcid.org/0000-0003-2700-7459
José-Ignacio Riezu-Boj
https://orcid.org/0000-0002-1885-8457
J. Alfredo Martínez
https://orcid.org/0000-0001-5218-6941
Article
Publisher ID: nutrients-10-00015DOI: 10.3390/nu10010015
PMC ID: 5793243
PubMed ID: 29295516
SO-VID: 6a5524ad-6963-4dcd-b81a-c2aee76ddcfe
Copyright © © 2017 by the authors.
License:
Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).
History
Date
received
: 27
November
2017
Date
accepted
: 19
December
2017
Categories
Subject:
Article
ScienceOpen disciplines: Nutrition & Dietetics