Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA326103 | ENA |
REPOSITORIES: ENA
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Project description:We analyzed levels of 5-methyl cytosine nnnn CCCGGG target sites by sequential restriction digest by SmaI and XmaI enzymes, ligating Illumina adaptors to the restriction fragments and reading methylation-specific signatures at the ends of restriction fragments by paired ends Illumina high throughput sequencing. Digital restriction enzyme analysis of methylation (DREAM) was performed to determine the methylation profile of SW48 colon cancer cell line genomic DNA. Genomic DNA spiked in with unmethylated, partially methylated and fully methylated standards was sequentially cut at CCCGGG sites with the methylation-sensitive enzyme SmaI (blunt ends) and its methylation-tolerant neoschizomer XmaI (5'CCGG overhangs), creating different end sequences that represented methylation status of the CCCGGG sites. These end sequences were analyzed by Illumina high throughput sequencing. Methylation status at individual CCCGGG sites across the genome was determined by counting the methylated reads with the CCGGG signature and unmethylated reads with the GGG signature at the beginnings of the sequencing reads after alignment to the human genome.
2015-09-08 | E-GEOD-66296 | biostudies-arrayexpress
Project description:Digital Restriction Enzyme Analysis of Methylation (DREAM) of HL60 genomic DNA
| PRJNA312610 | ENA
Project description:Digital Restriction Enzyme Analysis of Methylation (DREAM) of SW48 colon cancer cell line genomic DNA.
| PRJNA276450 | ENA
Project description:Digital Restriction Enzyme Analysis of Methylation (DREAM) of normal breast epithelial cells and MCF7 genomic DNA
| PRJNA301889 | ENA
Project description:We describe a simple method, Digital Restriction Enzyme Analysis of Methylation (DREAM), based on sequential DNA digestion with a pair of methylation-blocked and methylation-tolerant neoschizomeric restriction enzymes SmaI/XmaI followed by end repair and ultra-deep sequencing. DREAM provides information on 160,000 unique CpG sites of which 39,000 are in CpG islands, and 33,000 are at transcription start sites (-1 kb to +1 kb) of 13,139 RefSeq genes. We compared DNA methylation values in white blood cells from 4 healthy individuals and found them to be remarkably uniform. Interindividual differences >30% were observed only at 227 of 28,331 (0.8%) of autosomal CCCGGG sites covered by 100+ sequencing reads. Similarly, differences at only 59 sites were observed between the cord and adult blood. Conserved methylation patterns in healthy blood cells contrasted with extensive changes affecting 18-40% of CpG sites in leukemia. The method is cost effective, quantitative (r2=0.93 when compared to bisulfite pyrosequencing), reproducible (r2=0.997), and can detect differences >25% with false positive rate <0.001. Accurate analysis of changes in DNA methylation will be useful in quantifying epigenetic effects of environment and nutrition, correlating developmental epigenetic variation with phenotypes, understanding epigenetics of cancer and chronic diseases, measuring the effects of drugs on DNA methylation or deriving new biological insights into mammalian genomes. Digital restriction enzyme analysis of methylation (DREAM) was performed to determine the DNA methylation profiles of healthy white blood cells from cord blood and adult blood, acute myeloid leukemia bone marrow, and two leukemia cell lines (HEL and K562). In this approach, genomic DNA is sequentially cut at CCCGGG sites with the methylation-sensitive enzyme SmaI (blunt ends) and its methylation-tolerant neoschizomer XmaI (5'CCGG overhangs), creating different end sequences that represent methylation status of the CCCGGG sites. These end sequences are analyzed by next-generation sequencing, and thereafter the methylation status at individual CCCGGG sites across the genome can be determined.
2012-11-08 | E-GEOD-39787 | biostudies-arrayexpress
Project description:Acute myeloid leukemia (AML) causes the most leukemia-related deaths in the United States. Although genetic changes are assessed in the clinic for risk stratification, current classifications are imperfect and epigenetic determinants of AML curability remain poorly understood. To address this gap in knowledge we performed genome-wide DNA methylation analysis using the next-generation sequencing-based Digital Restriction Enzyme Analysis of Methylation (DREAM) assay on 96 clinical AML samples, and 35 normal peripheral blood controls. We identified patterns of aberrant DNA hypermethylation in distinct subsets of cases, and these patterns were associated with unique clinical, and genetic features. Validation an extension of these findings was performed using 194 samples from The Cancer Genome Atlas (TCGA).
2017-01-19 | GSE92254 | GEO
Project description:We report that full length TET1 (TET1-FL) overexpression fails to induce global DNA demethylation in HEK293T cells. The preferential binding of TET1-FL to hypomethylated CpG islands (CGIs) through its CXXC domain leads to its inhibited 5-hydroxymethylcytosine (5hmC) production as methylation level increases. TET1-FL-induced 5hmC accumulates at CGI edges, while TET1 knockdown induces methylation spreading from methylated edges into hypomethylated CGIs. However, TET1 can regulate gene transcription independent of its dioxygenase catalytic function. Thus, our results identify TET1 as a maintenance DNA demethylase that does not purposely decrease methylation levels, but specifically maintains the DNA hypomethylation state of CGIs in adult cells. Digital restriction enzyme analysis of methylation (DREAM) was performed to determine the DNA methylation profiles of HEK293T cells overexpressing mTET1-CD, TET1-CD, mTET1-FL, or TET1-FL. In this approach, genomic DNA is sequentially cut at CCCGGG sites with the methylation sensitive enzyme SmaI (blunt ends) and its methylation-tolerant neoschizomer XmaI (5M-bM-^@M-^YCCGG overhangs), creating different end sequences that represent methylation status of the CCCGGG sites. These end sequences are analyzed by next generation sequencing, and thereafter the methylation status at individual CCCGGG sites across the genome can be determined.
2014-06-04 | E-GEOD-44038 | biostudies-arrayexpress
Project description:We describe a simple method, Digital Restriction Enzyme Analysis of Methylation (DREAM), based on sequential DNA digestion with a pair of methylation-blocked and methylation-tolerant neoschizomeric restriction enzymes SmaI/XmaI followed by end repair and ultra-deep sequencing. DREAM provides information on 160,000 unique CpG sites of which 39,000 are in CpG islands, and 33,000 are at transcription start sites (-1 kb to +1 kb) of 13,139 RefSeq genes. We compared DNA methylation values in white blood cells from 4 healthy individuals and found them to be remarkably uniform. Interindividual differences >30% were observed only at 227 of 28,331 (0.8%) of autosomal CCCGGG sites covered by 100+ sequencing reads. Similarly, differences at only 59 sites were observed between the cord and adult blood. Conserved methylation patterns in healthy blood cells contrasted with extensive changes affecting 18-40% of CpG sites in leukemia. The method is cost effective, quantitative (r2=0.93 when compared to bisulfite pyrosequencing), reproducible (r2=0.997), and can detect differences >25% with false positive rate <0.001. Accurate analysis of changes in DNA methylation will be useful in quantifying epigenetic effects of environment and nutrition, correlating developmental epigenetic variation with phenotypes, understanding epigenetics of cancer and chronic diseases, measuring the effects of drugs on DNA methylation or deriving new biological insights into mammalian genomes.
2012-11-08 | GSE39787 | GEO
Project description:Purpose: to characterize epigenetic changes following Twist1 mediated Epithelial-Mesenchymal Transition in human Methods: we characterized the epigenetic and transcriptome landscapes using whole genome transcriptome analysis by RNA-seq, DNA methylation by digital restriction enzyme analysis of methylation (DREAM) and histone modifications by CHIP-seq of H3K4me3 and H3K27me3 in immortalized human mammary epithelial cells relative to cells induced to undergo EMT by Twist1. Results: EMT is accompanied by focal hypermethylation and widespread global DNA hypomethylation, predominantly within transcriptionally repressed gene bodies. At the chromatin level, the number of gene promoters marked by H3K4me3 increases by more than one fifth; H3K27me3 undergoes dynamic genomic redistribution characterized by loss at half of gene promoters and overall reduction of peak size by almost one-half. This is paralleled by increased phosphorylation of EZH2 at serine 21. Among genes with highly altered mRNA expression, 23.1% switch between H3K4me3 and H3K27me3 marks, and those point to the master EMT targets and regulators CDH1, PDGFRA and ESRP1. Strikingly, Twist1 increases the number of bivalent genes by more than two fold. Inhibition of the H3K27 methyltransferases EZH2 and EZH1, which form part of the PRC2 complex, results in blocking EMT and stemness properties. Conclusion: Our findings demonstrate that the EMT program requires epigenetic remodeling by the Polycomb/Trithorax complexes leading to increased cellular plasticity which suggests that its inhibition will prevent EMT, and the associated breast cancer metastasis. DREAM profiles of human mammary epithelial cells before (HMLE_parental) and after Twist1 transfection (HMLE_Twist) were generated in monolayer (HMLE_Twist2D) and sphere culture by deep sequencing using Illumina GAIIx or Illumina hiseq2000. Furthermore, DREAM profile was also obtained in parental human mammary epithelial cells transfected with GFP
2013-12-10 | E-GEOD-53025 | biostudies-arrayexpress
Project description:DNA methylation alterations have similar patterns in normal aging tissue and in cancer. In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean. To identify age-dependent DNA methylation sites, we generated DNA methylation sequencing data for 29 purified normal adjacent human breast epithelia (age range 33-82 years old) using Digital Restriction Enzyme Analysis of Methylation (DREAM). Next, we validated the age-related sites in publicly available DNA methylation (450K array) of 97 normal adjacent TCGA samples. We found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39 /427) were outliers for DNA methylation from 6 publicly available DNA methylation datasets (GSE88883, GSE74214, GSE101961, GSE69914(normal), GSE69914(normal-adjacent), TCGA (Firehose Legacy)). We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) then in normal samples (15/228, 5.2%). Additionally, we found significant differences between predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer.
2021-05-26 | GSE160233 | GEO