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DNA methylation provides a pivotal layer of epigenetic regulation in eukaryotes that has significant involvement for numerous biological processes in health and disease. The function of methylation of cytosine bases in DNA was originally proposed as a "silencing" epigenetic marker and focused on promoter regions of genes for decades. Improved technologies and accumulating studies have been extending our understanding of the roles of DNA methylation to various genomic contexts including gene bodies, repeat sequences and transcriptional start sites. The demand for comprehensively describing DNA methylation patterns spawns a diversity of DNA methylation profiling technologies that target its genomic distribution. These approaches have enabled the measurement of cytosine methylation from specific loci at restricted regions to single-base-pair resolution on a genome-scale level. In this review, we discuss the different DNA methylation analysis technologies primarily based on the initial treatments of DNA samples: bisulfite conversion, endonuclease digestion and affinity enrichment, involving methodology evolution, principles, applications, and their relative merits. This review may offer referable information for the selection of various platforms for genome-wide analysis of DNA methylation.
Promoter silencing by ectopic de novo methylation of tumor suppressor genes has been proposed as comparable or equivalent to inactivating mutations as a factor in carcinogenesis. However, this hypotheses had not previously been tested by high resolution, high-coverage whole-genome methylation profiling in primary carcinomas. We have determined the genomic methylation status of a series of primary mammary carcinomas and matched control tissues by examination of more than 2.7 billion CpG dinucleotides. Most of the tumors showed variable losses of DNA methylation from all sequence compartments, but increases in promoter methylation were infrequent, very small in extent, and were observed largely at CpG-poor promoters. De novo methylation at the promoters of proto-oncogenes and tumor suppressor genes occurred at approximately the same frequency. The findings indicate that tumor suppressor silencing by de novo methylation is much less common than currently believed. We put forward a hypothesis under which the demethylation commonly observed in carcinomas is a manifestation of a defensive system that kills incipient cancer cells.
In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.
Cytosine DNA methylation in vertebrates is widespread, but methylation in plants is found almost exclusively at transposable elements and repetitive DNA. Within regions of methylation, methylcytosines are typically found in CG, CNG, and asymmetric contexts. CG sites are maintained by a plant homolog of mammalian Dnmt1 acting on hemi-methylated DNA after replication. Methylation of CNG and asymmetric sites appears to be maintained at each cell cycle by other mechanisms. We report a new type of DNA methylation in Arabidopsis, dense CG methylation clusters found at scattered sites throughout the genome. These clusters lack non-CG methylation and are preferentially found in genes, although they are relatively deficient toward the 5' end. CG methylation clusters are present in lines derived from different accessions and in mutants that eliminate de novo methylation, indicating that CG methylation clusters are stably maintained at specific sites. Because 5-methylcytosine is mutagenic, the appearance of CG methylation clusters over evolutionary time predicts a genome-wide deficiency of CG dinucleotides and an excess of C(A/T)G trinucleotides within transcribed regions. This is exactly what we find, implying that CG methylation clusters have contributed profoundly to plant gene evolution. We suggest that CG methylation clusters silence cryptic promoters that arise sporadically within transcription units.
Like the nucleus, mitochondria contain their own DNA and recent reports provide accumulating evidence that also the mitochondrial DNA (mtDNA) is subjective to DNA methylation. This evidence includes the demonstration of mitochondria-localised DNA methyltransferases and demethylases, and the detection of mtDNA methylation as well as hydroxymethylation. Importantly, differential mtDNA methylation has been linked to aging and diseases, including cancer and diabetes. However, functionality of mtDNA methylation has not been demonstrated. Therefore, we targeted DNA methylating enzymes (modifying cytosine in the CpG or GpC context) to the mtDNA. Unexpectedly, mtDNA gene expression remained unchanged upon induction of CpG mtDNA methylation, whereas induction of C-methylation in the GpC context decreased mtDNA gene expression. Intriguingly, in the latter case, the three mtDNA promoters were differentially affected in each cell line, while cellular function seemed undisturbed. In conclusion, this is the first study which directly addresses the potential functionality of mtDNA methylation. Giving the important role of mitochondria in health and disease, unravelling the impact of mtDNA methylation adds to our understanding of the role of mitochondria in physiological and pathophysiological processes.
We present pycoMeth, a toolbox to store, manage and analyze DNA methylation calls from long-read sequencing data obtained using the Oxford Nanopore Technologies sequencing platform. Building on a novel, rapid-access, read-level and reference-anchored methylation storage format MetH5, we propose efficient algorithms for haplotype aware, multi-sample consensus segmentation and differential methylation testing. We show that MetH5 is more efficient than existing solutions for storing Oxford Nanopore Technologies methylation calls, and carry out benchmarking for pycoMeth segmentation and differential methylation testing, demonstrating increased performance and sensitivity compared to existing solutions designed for short-read methylation data.
Methylation of cytosine bases in DNA is a critical epigenetic mark in many eukaryotes and has also been implicated in the development and progression of normal and diseased cells. Therefore, profiling DNA methylation across the genome is vital to understanding the effects of epigenetic. In recent years the Illumina HumanMethylation450 (HM450K) and MethylationEPIC (EPIC) BeadChip have been widely used to profile DNA methylation in human samples. The methods to predict the methylation states of DNA regions based on microarray methylation datasets are critical to enable genome-wide analyses.
Gene body methylation at CG dinucleotides is a widely conserved feature of methylated genomes but remains poorly understood. The Arabidopsis thaliana strain Cvi has depleted gene body methylation relative to the reference strain Col. Here, we leverage this natural epigenetic difference to investigate gene body methylation stability.
Whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) are widely used for measuring DNA methylation levels on a genome-wide scale. Both methods have limitations: WGBS is expensive and prohibitive for most large-scale projects; RRBS only interrogates 6-12% of the CpGs in the human genome. Here, we introduce methylation-sensitive restriction enzyme bisulfite sequencing (MREBS) which has the reduced sequencing requirements of RRBS, but significantly expands the coverage of CpG sites in the genome. We built a multiple regression model that combines the two features of MREBS: the bisulfite conversion ratios of single cytosines (as in WGBS and RRBS) as well as the number of reads that cover each locus (as in MRE-seq). This combined approach allowed us to estimate differential methylation across 60% of the genome using read count data alone, and where counts were sufficiently high in both samples (about 1.5% of the genome), our estimates were significantly improved by the single CpG conversion information. We show that differential DNA methylation values based on MREBS data correlate well with those based on WGBS and RRBS. This newly developed technique combines the sequencing cost of RRBS and DNA methylation estimates on a portion of the genome similar to WGBS, making it ideal for large-scale projects of mammalian genomes.
Different DNA methylation patterns presented on different tissues or cell types are considered as one of the main reasons accounting for the tissue-specific gene expressions. In recent years, many methods have been proposed to identify differentially methylated regions (DMRs) based on the mixture of methylation signals from homologous chromosomes. To investigate the possible influence of homologous chromosomes on methylation analysis, this paper proposed a method (MHap) to construct methylation haplotypes for homologous chromosomes in CpG dense regions. Through comparing the methylation consistency between homologous chromosomes in different cell types, it can be found that majority of paired methylation haplotypes derived from homologous chromosomes are consistent, while a lower methylation consistency was observed in the breast cancer sample. It also can be observed that the hypomethylation consistency of differentiated cells is higher than that of the corresponding undifferentiated stem cells. Furthermore, based on the methylation haplotypes constructed on homologous chromosomes, a method (MHap_DMR) is developed to identify DMRs between differentiated cells and the corresponding undifferentiated stem cells, or between the breast cancer sample and the normal breast sample. Through comparing the methylation haplotype modes of DMRs in two cell types, the DNA methylation changing directions of homologous chromosomes in cell differentiation and cancerization can be revealed. The code is available at: https://github.com/xqpeng/MHap_DMR.
DNA methylation is a stable epigenetic mark that is frequently altered in tumors. DNA methylation features are attractive biomarkers for disease states given the stability of DNA methylation in living cells and in biologic specimens typically available for analysis. Widespread accumulation of methylation in regulatory elements in some cancers (specifically the CpG island methylator phenotype, CIMP) can play an important role in tumorigenesis. High resolution assessment of CIMP for the entire genome, however, remains cost prohibitive and requires quantities of DNA not available for many tissue samples of interest. Genome-wide scans of methylation have been undertaken for large numbers of tumors, and higher resolution analyses for a limited number of cancer specimens. Methods for analyzing such large datasets and integrating findings from different studies continue to evolve. An approach for comparison of findings from a genome-wide assessment of the methylated component of tumor DNA and more widely applied methylation scans was developed.
Different methodological approaches are available to assess DNA methylation biomarkers. In this study, we evaluated two sodium bisulfite conversion-dependent methods, namely pyrosequencing and methylation-specific qPCR (MS-qPCR), with the aim of measuring the closeness of agreement of methylation values between these two methods and its effect when setting a cut-off. Methylation of tumor suppressor gene p16/INK4A was evaluated in 80 lung cancer patients from which cytological lymph node samples were obtained. Cluster analyses were used to establish methylated and unmethylated groups for each method. Agreement and concordance between pyrosequencing and MS-qPCR was evaluated with Pearson's correlation, Bland-Altman, Cohen's kappa index and ROC curve analyses. Based on these analyses, cut-offs were derived for MS-qPCR. An acceptable correlation (Pearson's R2 = 0.738) was found between pyrosequencing (PYRmean) and MS-qPCR (NMP; normalized methylation percentage), providing similar clinical results when categorizing data as binary using cluster analysis. Compared to pyrosequencing, MS-qPCR tended to underestimate methylation for values between 0 and 15%, while for methylation >30% overestimation was observed. The estimated cut-off for MS-qPCR data based on cluster analysis, kappa-index agreement and ROC curve analysis were much lower than that derived from pyrosequencing. In conclusion, our results indicate that independently of the approach used for estimating the cut-off, the methylation percentage obtained through MS-qPCR is lower than that calculated for pyrosequencing. These differences in data and therefore in the cut-off should be examined when using methylation biomarkers in the clinical practice.
Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures up to 36k CpGs per species that are well conserved across many mammalian species. We designed a set of probes that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development of epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others.
DNA methylation is a heritable epigenetic mark that plays a key role in regulating gene expression. Mathematical modeling has been extensively applied to unravel the regulatory mechanisms of this process. In this study, we aimed to investigate DNA methylation by performing a high-depth analysis of particular loci, and by subsequent modeling of the experimental results. In particular, we performed an in-deep DNA methylation profiling of two genomic loci surrounding the transcription start site of the D-Aspartate Oxidase and the D-Serine Oxidase genes in different samples (n = 51). We found evidence of cell-to-cell differences in DNA methylation status. However, these cell differences were maintained between different individuals, which indeed showed very similar DNA methylation profiles. Therefore, we hypothesized that the observed pattern of DNA methylation was the result of a dynamic balance between DNA methylation and demethylation, and that this balance was identical between individuals. We hence developed a simple mathematical model to test this hypothesis. Our model reliably captured the characteristics of the experimental data, suggesting that DNA methylation and demethylation work together in determining the methylation state of a locus. Furthermore, our model suggested that the methylation status of neighboring cytosines plays an important role in this balance.
Promoter CpG island hypermethylation has become recognized as an important mechanism for inactivating tumor suppressor genes or tumor-related genes in human cancers of various tissues. Gene inactivation in association with promoter CpG island hypermethylation has been reported to be four times more frequent than genetic changes in human colorectal cancers. Hepatocellular carcinoma is also one of the human cancer types in which aberrant promoter CpG island hypermethylation is frequently found. However, the number of genes identified to date as hypermethylated for hepatocellular carcinoma (HCC) is fewer than that for colorectal cancer or gastric cancer, which can be attributed to fewer attempts to perform genome-wide methylation profiling for HCC. In the present study, we used bead-array technology and coupled methylation-specific PCR to identify new genes showing cancer-specific methylation in HCC. Twenty-four new genes have been identified as hypermethylated at their promoter CpG island loci in a cancer-specific manner. Of these, TNFRSF10C, HOXA9, NPY, and IRF5 were frequently hypermethylated in hepatocellular carcinoma tissue samples and their methylation was found to be closely associated with inactivation of gene expression. Further study will be required to elucidate the clinicopathological implications of these newly found DNA methylation markers in hepatocellular carcinoma.
DNA methylation is a well-known epigenetic modification that plays a crucial role in gene regulation, but genome-wide analysis of DNA methylation remains technically challenging and costly. DNA methylation-dependent restriction enzymes can be used to restrict CpG methylation analysis to methylated regions of the genome only, which significantly reduces the required sequencing depth and simplifies subsequent bioinformatics analysis. Unfortunately, this approach has been hampered by complete digestion of DNA in CpG methylation-dense regions, resulting in fragments that are too small for accurate mapping. Here, we show that the activity of DNA methylation-dependent enzyme, LpnPI, is blocked by a fragment size smaller than 32 bp. This unique property prevents complete digestion of methylation-dense DNA and allows accurate genome-wide analysis of CpG methylation at single-nucleotide resolution. Methylated DNA sequencing (MeD-seq) of LpnPI digested fragments revealed highly reproducible genome-wide CpG methylation profiles for >50% of all potentially methylated CpGs, at a sequencing depth less than one-tenth required for whole-genome bisulfite sequencing (WGBS). MeD-seq identified a high number of patient and tissue-specific differential methylated regions (DMRs) and revealed that patient-specific DMRs observed in both blood and buccal samples predict DNA methylation in other tissues and organs. We also observed highly variable DNA methylation at gene promoters on the inactive X Chromosome, indicating tissue-specific and interpatient-specific escape of X Chromosome inactivation. These findings highlight the potential of MeD-seq for high-throughput epigenetic profiling.
The computational prediction of methylation levels at single CpG resolution is promising to explore the methylation levels of CpGs uncovered by existing array techniques, especially for the 450 K beadchip array data with huge reserves. General prediction models concentrate on improving the overall prediction accuracy for the bulk of CpG loci while neglecting whether each locus is precisely predicted. This leads to the limited application of the prediction results, especially when performing downstream analysis with high precision requirements.
Besides differential methylation, DNA methylation variation has recently been proposed and demonstrated to be a potential contributing factor to cancer risk. Here we aim to examine whether differential variability in methylation is also an important feature of obesity, a typical non-malignant common complex disease. We analyzed genome-wide methylation profiles of over 470,000 CpGs in peripheral blood samples from 48 obese and 48 lean African-American youth aged 14-20 y old. A substantial number of differentially variable CpG sites (DVCs), using statistics based on variances, as well as a substantial number of differentially methylated CpG sites (DMCs), using statistics based on means, were identified. Similar to the findings in cancers, DVCs generally exhibited an outlier structure and were more variable in cases than in controls. By randomly splitting the current sample into a discovery and validation set, we observed that both the DVCs and DMCs identified from the first set could independently predict obesity status in the second set. Furthermore, both the genes harboring DMCs and the genes harboring DVCs showed significant enrichment of genes identified by genome-wide association studies on obesity and related diseases, such as hypertension, dyslipidemia, type 2 diabetes and certain types of cancers, supporting their roles in the etiology and pathogenesis of obesity. We generalized the recent finding on methylation variability in cancer research to obesity and demonstrated that differential variability is also an important feature of obesity-related methylation changes. Future studies on the epigenetics of obesity will benefit from both statistics based on means and statistics based on variances.
Cloned animals usually exhibited many defects in physical characteristics or aberrant epigenetic reprogramming, especially in some important organ development. Osteoponin (OPN) is an extracellular-matrix protein involved in heart and bone development and diseases. In this study, we investigated the correlation between OPN mRNA and its promoter methylation changes by the 5-aza-dc treatment in fibroblast cell and promoter assay. Aberrant methylation of porcine OPN was frequently found in different tissues of somatic nuclear transferred cloning pigs, and bisulfite sequence data suggested that the OPN promoter region -2615 to -2239 nucleotides (nt) may be a crucial regulation DNA element. In pig ear fibroblast cell culture study, the demethylation of OPN promoter was found in dose-dependent response of 5-aza-dc treatment and followed the OPN mRNA reexpression. In cloned pig study, discrepant expression pattern was identified in several cloned pig tissues, especially in brain, heart, and ear. Promoter assay data revealed that four methylated CpG sites presenting in the -2615 to -2239 nt region cause significant downregulation of OPN promoter activity. These data suggested that methylation in the OPN promoter plays a crucial role in the regulation of OPN expression that we found in cloned pigs genome.
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