Dataset Information

Differential DNA methylation of MSI2 and its correlation with diabetic traits.

ABSTRACT: Differential DNA methylation with hyperglycemia is significantly associated with Type 2 Diabetes (T2D). Longtime extended exposure to high blood glucose levels can affect the epigenetic signatures in all organs. However, the relevance of the differential DNA methylation changes with hyperglycemia in blood with pancreatic islets remains unclear. We investigated differential DNA methylation in relation to glucose homeostasis based on the Oral Glucose Tolerance Test (OGTT) in a population-based cohort. We found a total of 382 differential methylation sites from blood DNA in hyperglycemia and type 2 diabetes subgroups using a longitudinal and cross-sectional approach. Among them, three CpG sites were overlapped; they were mapped to the MSI2 and CXXC4 genes. In a DNA methylation replication study done by pyrosequencing (n = 440), the CpG site of MSI2 were shown to have strong associations with the T2D group (p value = 2.20E-16). The differential methylation of MSI2 at chr17:55484635 was associated with diabetes-related traits, in particular with insulin sensitivity (QUICKI, p value = 2.20E-16) and resistance (HOMA-IR, p value = 1.177E-07). In human pancreatic islets, at the single-base resolution (using whole-genome bisulfite sequencing), the 292 CpG sites in the ±5kb at chr17:55484635 were found to be significantly hypo-methylated in donors with T2D (average decrease = 13.91%, 95% confidence interval (CI) = 4.18~ 17.06) as compared to controls, and methylation patterns differed by sex (-9.57%, CI = -16.76~ -6.89) and age (0.12%, CI = -11.17~ 3.77). Differential methylation of the MSI2 gene (chr17:55484635) in blood and islet cells is strongly related to hyperglycemia. Our findings suggest that epigenetic perturbation on the target site of MSI2 gene in circulating blood and pancreatic islets should represent or affect hyperglycemia.

SUBMITTER: Jeon JP

PROVIDER: S-EPMC5443489 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differential DNA methylation of MSI2 and its correlation with diabetic traits.

Jeon Jae-Pil JP Koh In-Uk IU Choi Nak-Hyun NH Kim Bong-Jo BJ Han Bok-Ghee BG Lee Suman S

PloS one 20170524 5

Differential DNA methylation with hyperglycemia is significantly associated with Type 2 Diabetes (T2D). Longtime extended exposure to high blood glucose levels can affect the epigenetic signatures in all organs. However, the relevance of the differential DNA methylation changes with hyperglycemia in blood with pancreatic islets remains unclear. We investigated differential DNA methylation in relation to glucose homeostasis based on the Oral Glucose Tolerance Test (OGTT) in a population-based coh ...[more]

PMID: 28542303

Similar Datasets

Project description:BackgroundEpigenetic gene silencing is one of the major causes of carcinogenesis. Its widespread occurrence in cancer genome could inactivate many cellular pathways including DNA repair, cell cycle control, apoptosis, cell adherence, and detoxification. The abnormal promoter methylation might be a potential molecular marker for cancer management.MethodsFor rapid identification of potential targets for aberrant methylation in gynecological cancers, methylation status of the CpG islands of 34 genes was determined using pooled DNA approach and methylation-specific PCR. Pooled DNA mixture from each cancer type (50 cervical cancers, 50 endometrial cancers and 50 ovarian cancers) was made to form three test samples. The corresponding normal DNA from the patients of each cancer type was also pooled to form the other three control samples. Methylated alleles detected in tumors, but not in normal controls, were indicative of aberrant methylation in tumors. Having identified potential markers, frequencies of methylation were further analyzed in individual samples. Markers identified are used to correlate with clinico-pathological data of tumors using chi2 or Fisher's exact test.ResultsAPC and p16 were hypermethylated across the three cancers. MINT31 and PTEN were hypermethylated in cervical and ovarian cancers. Specific methylation was found in cervical cancer (including CDH1, DAPK, MGMT and MINT2), endometrial cancer (CASP8, CDH13, hMLH1 and p73), and ovarian cancer (BRCA1, p14, p15, RIZ1 and TMS1). The frequencies of occurrence of hypermethylation in 4 candidate genes in individual samples of each cancer type (DAPK, MGMT, p16 and PTEN in 127 cervical cancers; APC, CDH13, hMLH1 and p16 in 60 endometrial cancers; and BRCA1, p14, p16 and PTEN in 49 ovarian cancers) were examined for further confirmation. Incidence varied among different genes and in different cancer types ranging from the lowest 8.2% (PTEN in ovarian cancer) to the highest 56.7% (DAPK in cervical cancer). Aberrant methylation for some genes (BRCA1, DAPK, hMLH1, MGMT, p14, p16, and PTEN) was also associated with clinico-pathological data.ConclusionThus, differential methylation profiles occur in the three types of gynecologic cancer. Detection of methylation for critical loci is potentially useful as epigenetic markers in tumor classification. More studies using a much larger sample size are needed to define the potential role of DNA methylation as marker for cancer management.

Project description:A subset of individuals with type 1 diabetes will develop diabetic kidney disease (DKD). DKD is heritable and large-scale genome-wide association studies have begun to identify genetic factors that influence DKD. Complementary to genetic factors, we know that a person's epigenetic profile is also altered with DKD. This study reports analysis of DNA methylation, a major epigenetic feature, evaluating methylome-wide loci for association with DKD. Unique features (n = 485,577; 482,421 CpG probes) were evaluated in blood-derived DNA from carefully phenotyped White European individuals diagnosed with type 1 diabetes with (cases) or without (controls) DKD (n = 677 samples). Explicitly, 150 cases were compared to 100 controls using the 450K array, with subsequent analysis using data previously generated for a further 96 cases and 96 controls on the 27K array, and de novo methylation data generated for replication in 139 cases and 96 controls. Following stringent quality control, raw data were quantile normalized and beta values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls; resultant P-values for array-based data were adjusted for multiple testing. Genes with significantly increased (hypermethylated) and/or decreased (hypomethylated) levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways. Twenty-two loci demonstrated statistically significant fold changes associated with DKD and additional support for these associated loci was sought using independent samples derived from patients recruited with similar inclusion criteria. Markers associated with CCNL1 and ZNF187 genes are supported as differentially regulated loci (P < 10-8), with evidence also presented for AFF3, which has been identified from a meta-analysis and subsequent replication of genome-wide association studies. Further supporting evidence for differential gene expression in CCNL1 and ZNF187 is presented from kidney biopsy and blood-derived RNA in people with and without kidney disease from NephroSeq. Evidence confirming that methylation sites influence the development of DKD may aid risk prediction tools and stimulate research to identify epigenomic therapies which might be clinically useful for this disease.

Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Project description:BackgroundOrganophosphates (OP) are widely used insecticides that acutely inhibit acetylcholinesterase enzyme activity. There is great interest in improving the understanding of molecular mechanisms related to chronic OP exposure induced toxicity. We aim to elucidate epigenetic changes associated with OP exposure, using untargeted analysis of genome-wide DNA methylation data.MethodsIn a population-based case control study of Parkinson's disease (PD), we assessed ambient OP exposure via residential and workplace proximity to commercial applications. We investigated associations between OP exposure and genome-wide DNA methylation (Illumina 450 k) in 580 blood samples (342 PD patients, 238 controls) and 259 saliva samples (128 patients, 131 controls). To identify differential methylation related to OP exposure, we controlled for age, sex, European ancestry, and PD status; in addition, we stratified by disease status.ResultsWe identified 70 genome-wide significant CpGs, including cg01600516 in ALOX12 (cor = 0.27, p = 1.73E-11) and two CpGs in HLA genes, cg01655658 (cor = -0.24, p = 2.80E-09) in HLA-L (pseudogene) and cg15680603 (cor = 0.20, p = 7.94E-07) in HLA-DPA1. Among the 70 CpGs located in 41 genes, 14 were also differentially methylated in saliva samples. The most overrepresented pathway was the nicotinic acetylcholine receptor signaling pathway (fold enrichment = 15.63, p = 1.01E-03, FDR = 1.64E-01). Expanding to a larger number of genes (CpG p < 5E-04, FDR < 2.25E-01; 1077 CpGs, 662 genes), the most enriched pathway shifted to the muscarinic acetylcholine receptor 1 and 3 signaling pathway (p-value = 5.36E-04, FDR = 4.73E-02). When we stratified by PD status, results were similar. Of the 70 significant CpGs, 63 were detected among both patients and controls and 7 were only associated with OP exposure among patients.ConclusionsThis study finds chronic low-level OP exposure is associated with differential DNA methylation in blood and saliva, both in elderly population controls and PD patients. Our study results suggest that long-term sub-acute OP exposure influences methylation in genes enriched for muscarinic and nicotinic acetylcholine receptor pathways.

Dataset Information

Differential DNA methylation of MSI2 and its correlation with diabetic traits.

Publications

Differential DNA methylation of MSI2 and its correlation with diabetic traits.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets