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Epigenetic changes may fill a critical gap in our understanding of kidney disease development, as they not only reflect metabolic changes but are also preserved and transmitted during cell division. We conducted a genome-wide cytosine methylation analysis of 399 human kidney samples, along with single-nuclear open chromatin analysis on over 60,000 cells from 14 subjects, including controls, and diabetes and hypertension attributed chronic kidney disease (CKD) patients. We identified and validated differentially methylated positions associated with disease states, and discovered that nearly 30% of these alterations were influenced by underlying genetic variations, including variants known to be associated with kidney disease in genome-wide association studies. We also identified regions showing both methylation and open chromatin changes. These changes in methylation and open chromatin significantly associated gene expression changes, most notably those playing role in metabolism and expressed in proximal tubules. Our study further demonstrated that methylation risk scores (MRS) can improve disease state annotation and prediction of kidney disease development. Collectively, our results suggest a causal relationship between epigenetic changes and kidney disease pathogenesis, thereby providing potential pathways for the development of novel risk stratification methods., (© 2024. The Author(s).)

Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation was associated with the respective trait. The majority of these findings showed directionally consistent associations with the respective clinical outcomes chronic kidney disease and moderately increased albuminuria. Associations of DNA methylation with kidney function, such as CpGs at JAZF1, PELI1 and CHD2 were validated in kidney tissue. Methylation at PHRF1, LDB2, CSRNP1 and IRF5 indicated causal effects on kidney function. Enrichment analyses revealed pathways related to hemostasis and blood cell migration for estimated glomerular filtration rate, and immune cell activation and response for urinary albumin-to-creatinineratio-associated CpGs., (© 2021. The Author(s).)

DNA N 6 -methyladenine (6mA) is a non-canonical DNA modification that is present at low levels in different eukaryotes 1-8 , but its prevalence and genomic function in higher plants are unclear. Using mass spectrometry, immunoprecipitation and validation with analysis of single-molecule real-time sequencing, we observed that about 0.2% of all adenines are 6mA methylated in the rice genome. 6mA occurs most frequently at GAGG motifs and is mapped to about 20% of genes and 14% of transposable elements. In promoters, 6mA marks silent genes, but in bodies correlates with gene activity. 6mA overlaps with 5-methylcytosine (5mC) at CG sites in gene bodies and is complementary to 5mC at CHH sites in transposable elements. We show that OsALKBH1 may be potentially involved in 6mA demethylation in rice. The results suggest that 6mA is complementary to 5mC as an epigenomic mark in rice and reinforce a distinct role for 6mA as a gene expression-associated epigenomic mark in eukaryotes.

Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis-subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal-like subtype into two different prognosis-subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes., (© 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)

CpG island methylator phenotype of breast cancer is associated with widespread aberrant methylation at specified CpG islands and distinct patient outcomes. However, the influence of copy number contributing to the prognosis of tumors with different CpG island methylator phenotypes is still unclear. We analyzed both genetic (copy number) and epigenetic alterations in 765 breast cancers from The Cancer Genome Atlas data portal and got a panel of 15 biomarkers for copy number and methylation status evaluation. The gene panel identified two groups corresponding to distinct copy number profiles. In status of mere-loss copy number, patients were faced with a greater risk if they presented a higher CpG islands methylation pattern in biomarker panels. But for samples presenting merely-gained copy number, higher methylation level of CpG islands was associated with improved viability. In all, the integration of copy number alteration and methylation information enhanced the classification power on prognosis. Moreover, we found the molecular subtypes of breast cancer presented different distributions in two CpG island methylation phenotypes. Generated by the same set of human methylation 450K data, additional copy number information could provide insights into survival prediction of cancers with less heterogeneity and might help to determine the biomarkers for diagnosis and treatment for breast cancer patients in a more personalized approach.

Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlas's identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The human disease methylation database (DiseaseMeth, http://bioinfo.hrbmu.edu.cn/diseasemeth/) is an interactive database that aims to present the most complete collection and annotation of aberrant DNA methylation in human diseases, especially various cancers. Recently, the high-throughput microarray and sequencing technologies have promoted the production of methylome data that contain comprehensive knowledge of human diseases. In this DiseaseMeth update, we have increased the number of samples from 3610 to 32 701, the number of diseases from 72 to 88 and the disease-gene associations from 216 201 to 679 602. DiseaseMeth version 2.0 provides an expanded comprehensive list of disease-gene associations based on manual curation from experimental studies and computational identification from high-throughput methylome data. Besides the data expansion, we also updated the search engine and visualization tools. In particular, we enhanced the differential analysis tools, which now enable online automated identification of DNA methylation abnormalities in human disease in a case-control or disease-disease manner. To facilitate further mining of the disease methylome, three new web tools were developed for cluster analysis, functional annotation and survival analysis. DiseaseMeth version 2.0 should be a useful resource platform for further understanding the molecular mechanisms of human diseases., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

DNA methylation is an epigenetic modification of cytosines that undergoes dynamic changes in a temporal, spatial and cell-type-specific manner. Recent advances in technology have permitted the profiling of high-throughput methylomes in large numbers of biological samples. Various computational tools have been developed to identify and analyze DNA methylation dynamics in a variety of critical biological processes. As DNA methylation is becoming increasingly viewed as a dynamic process, the mechanisms governing DNA methylation dynamics and its roles in the transcriptional regulatory network are of great interest. It has been reported that DNA methylation dynamics plays essential roles in multiple biological processes, including development and cancer. As a functional event, the dynamics of DNA methylation have become increasingly relevant to many researchers. Here, we review state-of-the-art advances at three levels (genome-wide identification, regulatory mechanism investigation and the functional annotation) in the field of DNA methylation dynamics, as well as the future perspective of DNA methylation dynamics., (© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Dementia has emerged as a major societal issue because of the worldwide aging population and the absence of any effective treatment. DNA methylation is an epigenetic mechanism that evidently plays a role in Alzheimer's disease (AD). Folate acts through one-carbon metabolism to support the methylation of multiple substrates including DNA. We aimed to test the hypothesis that folic acid supplementation alters DNA methylation profiles in AD models. Mouse Neuro-2a cells expressing human APP695 (N2a-APP cells) were incubated with folic acid (2.8-20 μmol/L). AD transgenic mice were fed either folate-deficient or control diets and gavaged daily with water or folic acid (600 μg/kg). Gene methylation profiles were determined by methylated DNA immunoprecipitation-DNA microarray (MeDIP-chip). Differentially methylated regions (DMRs) were determined by Quantitative Differentially Methylated Regions analysis, and differentially methylated genes (DMGs) carrying at least three DMRs were selected for pathway analysis. Folic acid up-regulated DNA methylation levels in N2a-APP cells and AD transgenic mouse brains. Functional network analysis of folic acid-induced DMGs in these AD models revealed subnetworks composed of 24 focus genes in the janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway and 12 focus genes in the long-term depression (LTD) signaling pathway. In conclusion, these results revealed a role for folic acid in the JAK-STAT and LTD signaling pathways which may be relevant to AD pathogenesis. This novel finding may stimulate reinvestigation of folic acid supplementation as a prophylactic or therapeutic treatment for AD.

As one of the most widely studied epigenetic modifications, DNA methylation has an important influence on human traits and cancers. Dynamic variations in DNA methylation have been reported in malignant neoplasm and aging; however, the mechanisms remain poorly understood. By constructing an age-associated and cancer-related weighted network (ACWN) based on the correlation of the methylation level and the protein-protein interaction, we found that DNA methylation changes associated with age were closely related to the occurrence of cancer. Additional analysis of 102 module genes mined from the ACWN revealed discrimination based on two main patterns. One pattern involved methylation levels that increased with aging and were higher in cancer patients compared with normal controls (HH pattern). The other pattern involved methylation levels that decreased with aging and were lower in cancer compared with normal (LL pattern). Upon incorporation with gene expression levels, 25 genes were filtered based on negative regulation by DNA methylation. These genes were regarded as potential cancer risk markers that were influenced by age in the process of carcinogenesis. Our results will facilitate further studies regarding the impact of the epigenetic effects of aging on diseases and will aid in the development of tailored cancer preventive strategies.

DNA methylation is a key epigenetic mark that is critical for gene regulation in multicellular eukaryotes. Although various human cell types may have the same genome, these cells have different methylomes. The systematic identification and characterization of methylation marks across cell types are crucial to understand the complex regulatory network for cell fate determination. In this study, we proposed an entropy-based framework termed SMART to integrate the whole genome bisulfite sequencing methylomes across 42 human tissues/cells and identified 757 887 genome segments. Nearly 75% of the segments showed uniform methylation across all cell types. From the remaining 25% of the segments, we identified cell type-specific hypo/hypermethylation marks that were specifically hypo/hypermethylated in a minority of cell types using a statistical approach and presented an atlas of the human methylation marks. Further analysis revealed that the cell type-specific hypomethylation marks were enriched through H3K27ac and transcription factor binding sites in cell type-specific manner. In particular, we observed that the cell type-specific hypomethylation marks are associated with the cell type-specific super-enhancers that drive the expression of cell identity genes. This framework provides a complementary, functional annotation of the human genome and helps to elucidate the critical features and functions of cell type-specific hypomethylation., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

DNA methylation patterns may serve as a key in determining cell phenotypes and functions. Adjacent CpG patterns may provide insight into methylation functional mechanisms. Some regions display different DNA methylation patterns between normal and cancer tissues, but the same average methylation level. Here, we developed a method (CellMethy) to infer a region in which all CpGs exhibit concordant methylation (CM) and to quantify the extent of CM in the region. Using simulation data, CellMethy showed high performance in discovering the concordant methylation patterns (AUC = 0.89). CellMethy was then applied to RRBS data including 11 normal tissues and 12 tumors. We found that the extent of CM exhibited wider differentials among tissues than did the average methylation levels from the CM regions, with 45% of CM regions occurring specifically in one tissue and mainly in tumors. Then, we identified CM regions through genome wide bisulfite sequencing (GWBS) data on breast cancer. Approximately 82% of CM regions revealed a significantly different extent of CM between cancer and normal tissues. CellMethy can accurately describe concordantly methylated regions, and the results suggest that CM might also serve as a stable marker of cell sub-populations.

Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.

DNA methylation is related closely to sequence contexts and chromatin modifications; however, their potential differences in different genomic regions across cell types remain largely unexplored. We used publicly available genome-scale DNA methylation and histone modification profiles to study their relationships among different genomic regions in human embryonic stem cells (H1), H1-derived neuronal progenitor cultured cells (NPC), and foetal fibroblasts (IMR90) using the Random forests classifier. Histone modifications achieved high accuracy in modelling DNA methylation patterns on a genome scale in the three cell types. The inclusion of sequence features helped improve accuracy only in non-promoter regions of IMR90. Furthermore, the top six feature combinations obtained by mean decrease Gini were important indicators of different DNA methylation patterns, suggesting that H3K4me2 and H3K4me3 are important indicators that are independent of genomic regions and cell types. H3K9me3 was IMR90-specific and exhibited a genomic region-specific correlation with DNA methylation. Variations of essential chromatin modification signals may effectively discriminate changes of DNA methylation between H1 and IMR90. Genes with different co-variations of epigenetic marks exhibited genomic region-specific biological relevance. This study provides an integrated strategy to identify systematically essential epigenetic and genetic elements of genomic region-specific and cell type-specific DNA methylation patterns.

Objective: To illustrate the role of methylation level of hMLH1 gene promoter in different stages of gastric carcinogenesis by methylation-specific PCR (MSP) detection of samples from paracancerous tissue and gastric cancer tissue., Methods: Methylation status of hMLH1 gene promoter of 40 patients undergoing radical stomach cancer operation in the Tumor Research Institute of China Medical University between January 2006 and August 2006 was detected by MSP. For each patient, 2 samples were chosen from the cancer site, paracancerous tissues of 1 cm, 3 cm, 5 cm away from the cancer site, separately. One sample was used in pathology examination, and the other in methylation detection., Results: Positive rates of hMLH1 gene promoter methylation in the paracancerous tissues of 1 cm, 3 cm, 5 cm away from the cancer site were 10%(4/40), 12.5%(5/40) and 2.5%(1/40) respectively, which were significantly lower than 32.5%(13/40) in cancer site(all P<0.05). Pathological examination showed precancerous lesions in 23 samples of paracancerous 1 cm and 3 cm tissues and normal tissues in 24 samples of paracancerous 5 cm tissues. Positive rates of hMLH1 gene promoter methylation in the cancer site, paracancerous tissue and normal gastric tissue were 32.5%(13/40), 8.7%(2/23) and 0(0/24) (P<0.01). For cancer tissue penetrated the gastric serosa, 8 out of 14 tissue samples were positive methylation (57.1%), which was significantly higher compared with 5 out of 26 tissue samples without penetration of gastric serosa(19.2%). Positive rate of hMLH1 gene promoter methylation in tissue samples with 7 or more of metastatic lymphatic node number was 61.5%(8/13), which was higher compared to that with less than 7(5/27, 18.5%) (P<0.05). No significant differences of positive rate of hMLH1 gene promoter methylation were found between different tumor gross types, tumor grow pattern, tumor differentiation degree, patient age and sex(all P>0.05)., Conclusion: Hypermethylation of hMLH1 gene promoter may be associated with the carcinogenesis stages and progression of human gastric cancer.

DNA methylation undergoes dynamic changes during mouse development and plays crucial roles in embryogenesis, cell-lineage determination and genomic imprinting. Bisulfite sequencing enables profiling of mouse developmental methylomes on an unprecedented scale; however, integrating and mining these data are challenges for experimental biologists. Therefore, we developed DevMouse, which focuses on the efficient storage of DNA methylomes in temporal order and quantitative analysis of methylation dynamics during mouse development. The latest release of DevMouse incorporates 32 normalized and temporally ordered methylomes across 15 developmental stages and related genome information. A flexible query engine is developed for acquisition of methylation profiles for genes, microRNAs, long non-coding RNAs and genomic intervals of interest across selected developmental stages. To facilitate in-depth mining of these profiles, DevMouse offers online analysis tools for the quantification of methylation variation, identification of differentially methylated genes, hierarchical clustering, gene function annotation and enrichment. Moreover, a configurable MethyBrowser is provided to view the base-resolution methylomes under a genomic context. In brief, DevMouse hosts comprehensive mouse developmental methylome data and provides online tools to explore the relationships of DNA methylation and development. Database URL: http://www.devmouse.org/

Polycomb group (PcG) proteins are epigenetic regulators that are essential for stem cell differentiation. Identifying PcG binding profiles is important for understanding the mechanisms of PcG-mediated repression in mammals. We used a mapping-convergence (M-C) algorithm using support vector machine (SVM) technology for genome-wide identification of PcG target genes in human embryonic stem cells. The method combined histone modifications and transcription factor binding motifs, eliminating the need for negative training samples as in traditional SVM. Good prediction accuracy comprising 3-fold cross-validation was obtained. In the analysis of 3133 PcG target genes identified by the model, PcG proteins were observed to suppress gene expression during differentiation. The results suggested that PcG and DNA methylation non-redundantly repress gene expression during differentiation. The genome-wide identification of PcG target genes will aid the further analysis of PcG mechanisms., (Copyright © 2012 Elsevier B.V. All rights reserved.)

High-throughput bisulfite sequencing is widely used to measure cytosine methylation at single-base resolution in eukaryotes. It permits systems-level analysis of genomic methylation patterns associated with gene expression and chromatin structure. However, methods for large-scale identification of methylation patterns from bisulfite sequencing are lacking. We developed a comprehensive tool, CpG&#95;MPs, for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG&#95;MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy. CpG&#95;MPs identified large amounts of genomic regions with different methylation patterns across five human bisulfite sequencing data during cellular differentiation. Different sequence features and significantly cell-specific methylation patterns were observed. These potentially functional regions form candidate regions for functional analysis of DNA methylation during cellular differentiation. CpG&#95;MPs is the first user-friendly tool for identifying methylation patterns of genomic regions from bisulfite sequencing data, permitting further investigation of the biological functions of genome-scale methylation patterns.

Chromatin components and DNA methylation play important roles in regulation of gene expression in mammalian genomes. However, the mechanism underlying how they regulate gene transcription, independently or synergistically, remains largely unknown. We constructed an epigenetic interaction network (EIN) of chromatin components, DNA methylation and gene expression by combining partial correlation coefficient with Pearson correlation coefficient. In EIN, we identified nine direct factors for gene expression. They constitute three interaction modules which synergistically affect gene expression. We introduced a new combination strategy to test how these direct factors in each module regulate gene expression synergistically. We found two inter-attracted patterns and one inter-repulsed patterns among the three modules. Furthermore, we identified 22 indirect factors for gene expression which have effect on gene expression via direct factors. DNA methylation, for example, could regulate gene expression through H3K4me3 and Pol II. Our approach has the potential to help in uncovering inherent relationships between epigenetic factors and gene transcription and guiding experiment.

DNA methylation is an important epigenetic modification for genomic regulation in higher organisms that plays a crucial role in the initiation and progression of diseases. The integration and mining of DNA methylation data by methylation-specific PCR and genome-wide profiling technology could greatly assist the discovery of novel candidate disease biomarkers. However, this is difficult without a comprehensive DNA methylation repository of human diseases. Therefore, we have developed DiseaseMeth, a human disease methylation database (http://bioinfo.hrbmu.edu.cn/diseasemeth). Its focus is the efficient storage and statistical analysis of DNA methylation data sets from various diseases. Experimental information from over 14,000 entries and 175 high-throughput data sets from a wide number of sources have been collected and incorporated into DiseaseMeth. The latest release incorporates the gene-centric methylation data of 72 human diseases from a variety of technologies and platforms. To facilitate data extraction, DiseaseMeth supports multiple search options such as gene ID and disease name. DiseaseMeth provides integrated gene methylation data based on cross-data set analysis for disease and normal samples. These can be used for in-depth identification of differentially methylated genes and the investigation of gene-disease relationship.

DNA methylation changes in repetitive elements (REs) are associated with the regulation of gene transcription, embryonic development, differentiation and carcinogenesis. However, genome-wide analysis of DNA methylation of human REs is lacking. Here, we performed genome-wide methylation analysis of REs in nine repeat types in human embryonic stem cells (H1) and fetal fibroblasts (IMR90), and found that the potential for changes in the DNA methylation of REs was different among the nine repeat types and within different genomic regions. DNA methylation changes in the nine repeat types were related to the GC content and CpG density of the sequence contexts. The differentially methylated REs and targeted genes of different repeat types were associated with gene silencing in the transition from H1 to IMR90 cells. Our results suggest that a quarter of REs are involved in the reprogramming of DNA methylation which may play important epigenetic roles during cellular differentiation., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI) network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions., Results: We constructed a weighted human PPI network (WHPN) using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN) was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching PubMed manually. We found that 31 of the optimized genes were targeted as drug response markers in DrugBank., Conclusions: Here we have shown that network theory combined with epigenetic characteristics provides a favorable platform from which to identify cancer-related genes. We prioritized 154 potential cancer-related genes with aberrant methylation that might contribute to the further understanding of cancers.

DNA methylation plays critical roles in transcriptional regulation and chromatin remodeling. Differentially methylated regions (DMRs) have important implications for development, aging and diseases. Therefore, genome-wide mapping of DMRs across various temporal and spatial methylomes is important in revealing the impact of epigenetic modifications on heritable phenotypic variation. We present a quantitative approach, quantitative differentially methylated regions (QDMRs), to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy. QDMR was applied to synthetic methylation patterns and methylation profiles detected by methylated DNA immunoprecipitation microarray (MeDIP-chip) in human tissues/cells. This approach can give a reasonable quantitative measure of methylation difference across multiple samples. Then DMR threshold was determined from methylation probability model. Using this threshold, QDMR identified 10,651 tissue DMRs which are related to the genes enriched for cell differentiation, including 4740 DMRs not identified by the method developed by Rakyan et al. QDMR can also measure the sample specificity of each DMR. Finally, the application to methylation profiles detected by reduced representation bisulphite sequencing (RRBS) in mouse showed the platform-free and species-free nature of QDMR. This approach provides an effective tool for the high-throughput identification of potential functional regions involved in epigenetic regulation., (© The Author(s) 2011. Published by Oxford University Press.)

The aberrant hypermethylation of CpG islands in promoter regions of genes plays an important role in the onset and progression of Breast cancer. Meanwhile, it is highly associated with human genomic features. Two feature selection algorithms: t-test and CfsSubsetEval were used to obtain efficient feature subsets. We discovered 14 significant feature subsets by CfsSubsetEval, which can distinguish hypermethylated genes from control genes. As a result, 393 unconfirmed hypermethylated genes in Breast cancer were prioritized. These genes were assigned the hypermethylated scores and were supported by literature and Gene Ontology enrichment. This paper suggests that the feature subsets could be served as discriminating genomic markers to infer novel hypermethylated genes in cancer potentially., (2009 Elsevier Ltd. All rights reserved.)

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

Epigenetic markers are potential biomarkers for diabetes and related complications. Using a prospective cohort from the Hong Kong Diabetes Register, we perform two independent epigenome-wide association studies to identify methylation markers associated with baseline estimated glomerular filtration rate (eGFR) and subsequent decline in kidney function (eGFR slope), respectively, in 1,271 type 2 diabetes subjects. Here we show 40 (30 previously unidentified) and eight (all previously unidentified) CpG sites individually reach epigenome-wide significance for baseline eGFR and eGFR slope, respectively. We also develop a multisite analysis method, which selects 64 and 37 CpG sites for baseline eGFR and eGFR slope, respectively. These models are validated in an independent cohort of Native Americans with type 2 diabetes. Our identified CpG sites are near genes enriched for functional roles in kidney diseases, and some show association with renal damage. This study highlights the potential of methylation markers in risk stratification of kidney disease among type 2 diabetes individuals. Epigenetic markers are potential biomarkers for diabetes and related complications. Here, the authors identify CpG sites associated with kidney function and its subsequent decline using both single-site and multisite analyses, which are shown to have functional significance in the kidney. [ABSTRACT FROM AUTHOR]

Gene expression is modulated by multiple mechanisms, including genetic and/or epigenetic regulation, and associated with the processes of cellular differentiation and morphogenesis. Single nucleotide polymorphisms (SNPs) and DNA methylation play important roles in regulating gene expression. In this study, we focused on revealing the relationship between SNPs, DNA methylation and gene expression in two human populations genome-wide through proposing four regulation patterns and developed maximum likelihood estimate models. Using simulated data with different correlation coefficients between any two traits, the power of our approach showed a favourable performance and relative stability. In all, 6733 SNP–CpG-gene pairs including 957 genes were obtained in Northern European ancestry (CEU) population. As the results showed, SNPs and DNA methylation had approximately the same effect on expression regulation of 49% genes, which was termed cooperative/antagonistic regulation pattern. Less than 30% of genes are controlled only by one of the factors (SNP/DNA methylation). The others showed SNPs that affect methylation have no consequent effects or crosstalk regulation on gene expression. Similar result was shown in Yourba (YRI) population. Specific genes were inferred using the different mechanisms of gene regulation involved in complex diseases by combining literature. This approach provides a method to comprehensively assess regulation patterns of gene expression in the whole genome. [ABSTRACT FROM PUBLISHER]