Methylated DNA Immunoprecipitation (MeDIP) using a custom type 2 diabetes and related phenotypes array
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE20553 | GEO | 2010/12/01
SECONDARY ACCESSION(S): PRJNA125195
REPOSITORIES: GEO
Similar Datasets
Project description:Custom array designed to tile Linkage Disequilibrium Blocks of T2D GWAS SNPs, monogenic candidates for T2D and Obesity, and all plausible imprinted loci from human and mouse data. Case Control comparison of MeDIP for Type 2 Diabetes. MeDIP versus Input fraction.
2010-12-01 | E-GEOD-20553 | biostudies-arrayexpress
Project description:BackgroundDNA methylation contributes to the regulation of gene expression during development and cellular differentiation. The recently developed Methylated DNA ImmunoPrecipitation (MeDIP) assay allows a comprehensive analysis of this epigenetic mark at the genomic level in normal and disease-derived cells. However, estimating the efficiency of the MeDIP technique is difficult without previous knowledge of the methylation status of a given cell population. Attempts to circumvent this problem have involved the use of in vitro methylated DNA in parallel to the investigated samples. Taking advantage of this stratagem, we sought to improve the sensitivity of the approach and to assess potential biases resulting from DNA amplification and hybridization procedures using MeDIP samples.FindingsWe performed MeDIP assays using in vitro methylated DNA, with or without previous DNA amplification, and hybridization to a human promoter array. We observed that CpG content at gene promoters indeed correlates strongly with the MeDIP signal obtained using in vitro methylated DNA, even when lowering significantly the amount of starting material. In analyzing MeDIP products that were subjected to whole genome amplification (WGA), we also revealed a strong bias against CpG-rich promoters during this amplification procedure, which may potentially affect the significance of the resulting data.ConclusionWe illustrate the use of in vitro methylated DNA to assess the efficiency and accuracy of MeDIP procedures. We report that efficient and reproducible genome-wide data can be obtained via MeDIP experiments using relatively low amount of starting genomic DNA; and emphasize for the precaution that must be taken in data analysis when an additional DNA amplification step is required.
| S-EPMC2949662 | biostudies-literature
Project description:The methylated DNA immunoprecipitation method (MeDIP) is a genome-wide, high-resolution approach that detects DNA methylation with oligonucleotide tiling arrays or high throughput sequencing platforms. A simplified high-throughput MeDIP assay will enable translational research studies in clinics and populations, which will greatly enhance our understanding of the human methylome. We compared three commercial kits, MagMeDIP Kit TM (Diagenode), Methylated-DNA IP Kit (Zymo Research) and Methylamp™ Methylated DNA Capture Kit (Epigentek), in order to identify which one has better reliability and sensitivity for genomic DNA enrichment. Each kit was used to enrich two samples, one from fresh tissue and one from a cell line, with two different DNA amounts. The enrichment efficiency of each kit was evaluated by agarose gel band intensity after Nco I digestion and by reaction yield of methylated DNA. A successful enrichment is expected to have a 1:4 to 10:1 conversion ratio and a yield of 80% or higher. We also evaluated the hybridization efficiency to genome-wide methylation arrays in a separate cohort of tissue samples. We observed that the MagMeDIP kit had the highest yield for the two DNA amounts and for both the tissue and cell line samples, as well as for the positive control. In addition, the DNA was successfully enriched from a 1:4 to 10:1 ratio. Therefore, the MagMeDIP kit is a useful research tool that will enable clinical and public health genome-wide DNA methylation studies.
| S-EPMC3337832 | biostudies-other
Project description:The global prevalence of type 2 diabetes (T2D) is increasing, and it is contributing to the susceptibility to diabetes and its related epidemic in offspring. Although the impacts of paternal T2D on metabolism of offspring have been well established, the exact molecular and mechanistic basis that mediates these impacts remains largely unclear. Here we show that paternal T2D increases the susceptibility to diabetes in offspring through the gametic epigenetic alterations. Paternal T2D led to glucose intolerance and insulin resistance in offspring. Relative to controls, offspring of T2D fathers exhibited altered gene expression patterns in the pancreatic islets, with downregulation of several genes involved in glucose metabolism and insulin signaling pathway. Epigenomic profiling of offspring pancreatic islets revealed numerous changes in cytosine methylation depending on paternal T2D, including reproducible changes in methylation over several insulin signaling genes. Paternal T2D altered overall methylome patterns in sperm, with a large portion of differentially methylated genes overlapped with that of pancreatic islets in offspring. Our study revealed, for the first time, that T2D can be inherited transgenerationally through the mammalian germline by an epigenetic manner. Examination of the effect of paternal T2D on the DNA methylation in the pancreatic islets of offspring and in the sperm of father.
2013-01-02 | E-GEOD-43238 | biostudies-arrayexpress
Project description:Epigenetic changes have been implicated in pathogenesis of asthma. We sought to determine if IL13, a key cytokine in airway inflammation and remodeling, induced epigenetic DNA methylation changes in the airways in conjunction with its transcriptional gene regulation. For our studies, we used a well-characterized transgenic mouse model of allergic airway inflammation induced by IL13. In this model, IL13 is conditionally overexpressed in the mouse lung when treated with doxycycline. Upon IL13 induction, these mice showed inflammatory cell infiltration, pronounced emphysema, increased pulmonary compliance, lung volume enlargement, mucus metaplasia, and increased expression of matrix metalloproteinases and cathepsins in the lung. We performed MeDIP microarray to examine the changes in DNA promoter methylation during IL13-induced allergic airway inflammation.
2012-04-07 | GSE37079 | GEO
Project description:Genes involved in distinct diabetes types suggest shared disease mechanisms. We show that rare ONECUT1 coding variants cause monogenic recessive diabetes (neonatal or very early-onset, syndromic) in two unrelated patients, and monogenic dominant diabetes (early adult-onset) in heterozygous relatives of these and 13 additional unrelated cases. Patients heterozygous for rare ONECUT1 coding variants define a subgroup of T2D with early-onset diabetes and other features. In addition, common regulatory ONECUT1 variants are associated with multifactorial T2D. Directed differentiation of human pluripotent stem cells to the pancreatic lineage revealed that loss of ONECUT1 impairs pancreatic progenitor formation and a subsequent endocrine program. We uncovered that ONECUT1 activates the pro-endocrine genes NKX6.1 and NKX2.2 through binding to their cis-regulatory elements. Globally, ONECUT1-directed gene transcription occurs in association with major islet transcription factors, at clusters of pancreas- and endocrine-specific enhancers within open chromatin. ONECUT1 regulates a transcriptional and epigenetic machinery critical for proper endocrine pancreatic development, involved in a spectrum of diabetes, monogenic recessive and dominant, and multifactorial.
2021-11-03 | PXD018887 | Pride
Project description:The global prevalence of type 2 diabetes (T2D) is increasing, and it is contributing to the susceptibility to diabetes and its related epidemic in offspring. Although the impacts of paternal T2D on metabolism of offspring have been well established, the exact molecular and mechanistic basis that mediates these impacts remains largely unclear. Here we show that paternal T2D increases the susceptibility to diabetes in offspring through the gametic epigenetic alterations. Paternal T2D led to glucose intolerance and insulin resistance in offspring. Relative to controls, offspring of T2D fathers exhibited altered gene expression patterns in the pancreatic islets, with downregulation of several genes involved in glucose metabolism and insulin signaling pathway. Epigenomic profiling of offspring pancreatic islets revealed numerous changes in cytosine methylation depending on paternal T2D, including reproducible changes in methylation over several insulin signaling genes. Paternal T2D altered overall methylome patterns in sperm, with a large portion of differentially methylated genes overlapped with that of pancreatic islets in offspring. Our study revealed, for the first time, that T2D can be inherited transgenerationally through the mammalian germline by an epigenetic manner.
2013-01-02 | GSE43238 | GEO
Project description:BackgroundThe recently emerged technology of methylated RNA immunoprecipitation sequencing (MeRIP-seq) sheds light on the study of RNA epigenetics. This new bioinformatics question calls for effective and robust peaking calling algorithms to detect mRNA methylation sites from MeRIP-seq data.MethodsWe propose a Bayesian hierarchical model to detect methylation sites from MeRIP-seq data. Our modeling approach includes several important characteristics. First, it models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model. Second, it incorporates a hidden Markov model (HMM) to account for the spatial dependency of neighboring read enrichment. Third, our Bayesian inference allows the proposed model to borrow strength in parameter estimation, which greatly improves the model stability when dealing with MeRIP-seq data with a small number of replicates. We use Markov chain Monte Carlo (MCMC) algorithms to simultaneously infer the model parameters in a de novo fashion. The R Shiny demo is available at https://qiwei.shinyapps.io/BaySeqPeak and the R/C ++ code is available at https://github.com/liqiwei2000/BaySeqPeak.ResultsIn simulation studies, the proposed method outperformed the competing methods exomePeak and MeTPeak, especially when an excess of zeros were present in the data. In real MeRIP-seq data analysis, the proposed method identified methylation sites that were more consistent with biological knowledge, and had better spatial resolution compared to the other methods.ConclusionsIn this study, we develop a Bayesian hierarchical model to identify methylation peaks in MeRIP-seq data. The proposed method has a competitive edge over existing methods in terms of accuracy, robustness and spatial resolution.
| S-EPMC8026011 | biostudies-literature
Project description:We applied a massively parallel reporter assay (MPRA) in lymphoblastoid cells to functionally evaluate 49,256 allelic pairs, representing 30,893 genetic variants in high, local linkage disequilibrium for 744 independent cis-expression quantitative trait loci (eQTLs) assessed for colocalization across 114 traits.
2021-05-24 | GSE174534 | GEO