Genome-wide promoter methylation analysis in neuroblastoma identifies prognostic methylation biomarkers

ChIP-seq is a powerful method for obtaining genome-wide maps of protein-DNA interactions and epigenetic modifications. CHANCE (CHip-seq ANalytics and Confidence Estimation) is a standalone package for ChIP-seq quality control and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identifies biases, compares the user's data with ENCODE's large collection of published datasets, performs multi-sample normalization, checks against quantitative PCR-validated control regions, and produces informative graphical reports. CHANCE is available at https://github.com/songlab/chance.     

Genome-wide methylation profiling identifies novel methylated genes in neuroblastoma tumors

Neuroblastoma is a very heterogeneous tumor of childhood. The clinical spectra range from very aggressive metastatic disease to spontaneous regression, even without therapy. Aberrant DNA methylation pattern is a common feature of most cancers. For neuroblastoma, it has been demonstrated both for single genes as well as genome-wide, where a so-called methylator phenotype has been described. Here, we present a study using Illumina 450K methylation arrays on 60 neuroblastoma tumors. We show that aggressive tumors, characterized by International Neuroblastoma Risk Group (INRG) as stage M, are hypermethylated compared to low-grade tumors. On the contrary, INRG stage L tumors display more non-CpG methylation. The genes with the highest number of hypermethylated CpG sites in INRG M tumors are TERT, PCDHGA4, DLX5, and DLX6-AS1. Gene ontology analysis showed a representation of neuronal tumor relevant gene functions among the differentially methylated genes. For validation, we used a set of independent tumors previously analyzed with the Illumina 27K methylation arrays, which confirmed the differentially methylated sites. Top candidate genes with aberrant methylation were analyzed for altered gene expression through the R2 platform (http://r2.amc.nl), and for correlations between methylation and gene expression in a public dataset. Altered expression in nonsurvivors was found for the genes B3GALT4 and KIAA1949, CLIC5, DLX6-AS, TERT, and PIRT, and strongest correlations were found for TRIM36, KIAA0513, and PIRT. Our data indicate that methylation profiling can be used for patient stratification and informs on epigenetically deregulated genes with the potential of increasing our knowledge about the underlying mechanisms of tumor development.     

Genome-wide methylation profiling identifies hypermethylated biomarkers in high-grade cervical intraepithelial neoplasia

Epigenetic modifications, such as aberrant DNA promoter methylation, are frequently observed in cervical cancer. Identification of hypermethylated regions allowing discrimination between normal cervical epithelium and high-grade cervical intraepithelial neoplasia (CIN2/3), or worse, may improve current cervical cancer population-based screening programs. In this study, the DNA methylome of high-grade CIN lesions was studied using genome-wide DNA methylation screening to identify potential biomarkers for early diagnosis of cervical neoplasia. Methylated DNA Immunoprecipitation (MeDIP) combined with DNA microarray was used to compare DNA methylation profiles of epithelial cells derived from high-grade CIN lesions with normal cervical epithelium. Hypermethylated differentially methylated regions (DMRs) were identified. Validation of nine selected DMRs using BSP and MSP in cervical tissue revealed methylation in 63.2–94.7% high-grade CIN and in 59.3–100% cervical carcinomas. QMSP for the two most significant high-grade CIN-specific methylation markers was conducted exploring test performance in a large series of cervical scrapings. Frequency and relative level of methylation were significantly different between normal and cancer samples. Clinical validation of both markers in cervical scrapings from patients with an abnormal cervical smear confirmed that frequency and relative level of methylation were related with increasing severity of the underlying CIN lesion and that ROC analysis was discriminative. These markers represent the COL25A1 and KATNAL2 and their observed increased methylation upon progression could intimate the regulatory role in carcinogenesis. In conclusion, our newly identified hypermethylated DMRs represent specific DNA methylation patterns in high-grade CIN lesions and are candidate biomarkers for early detection.     

Genome-wide methylation profiling identifies hypermethylated biomarkers in high-grade cervical intraepithelial neoplasia

Epigenetic modifications, such as aberrant DNA promoter methylation, are frequently observed in cervical cancer. Identification of hypermethylated regions allowing discrimination between normal cervical epithelium and high-grade cervical intraepithelial neoplasia (CIN2/3), or worse, may improve current cervical cancer population-based screening programs. In this study, the DNA methylome of high-grade CIN lesions was studied using genome-wide DNA methylation screening to identify potential biomarkers for early diagnosis of cervical neoplasia. Methylated DNA Immunoprecipitation (MeDIP) combined with DNA microarray was used to compare DNA methylation profiles of epithelial cells derived from high-grade CIN lesions with normal cervical epithelium. Hypermethylated differentially methylated regions (DMRs) were identified. Validation of nine selected DMRs using BSP and MSP in cervical tissue revealed methylation in 63.2–94.7% high-grade CIN and in 59.3–100% cervical carcinomas. QMSP for the two most significant high-grade CIN-specific methylation markers was conducted exploring test performance in a large series of cervical scrapings. Frequency and relative level of methylation were significantly different between normal and cancer samples. Clinical validation of both markers in cervical scrapings from patients with an abnormal cervical smear confirmed that frequency and relative level of methylation were related with increasing severity of the underlying CIN lesion and that ROC analysis was discriminative. These markers represent the COL25A1 and KATNAL2 and their observed increased methylation upon progression could intimate the regulatory role in carcinogenesis. In conclusion, our newly identified hypermethylated DMRs represent specific DNA methylation patterns in high-grade CIN lesions and are candidate biomarkers for early detection.     

Integrative analysis of DNA methylation and gene expression through machine learning identifies stomach cancer diagnostic and prognostic biomarkers

DNA methylation is an early event in tumorigenesis. Here, by integrative analysis of DNA methylation and gene expression and utilizing machine learning approaches, we introduced potential diagnostic and prognostic methylation signatures for stomach cancer. Differentially-methylated positions (DMPs) and differentially-expressed genes (DEGs) were identified using The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) data. A total of 256 DMPs consisting of 140 and 116 hyper- and hypomethylated positions were identified between 443 tumour and 27 nontumour STAD samples. Gene expression analysis revealed a total of 2821 DEGs with 1247 upregulated and 1574 downregulated genes. By analysing the impact of cis and trans regulation of methylation on gene expression, a dominant negative correlation between methylation and expression was observed, while for trans regulation, in hypermethylated and hypomethylated genes, there was mainly a negative and positive correlation with gene expression, respectively. To find diagnostic biomarkers, we used 28 hypermethylated probes locating in the promoter of 27 downregulated genes. By implementing a feature selection approach, eight probes were selected and then used to build a support vector machine diagnostic model, which had an area under the curve of 0.99 and 0.97 in the training and validation (GSE30601 with 203 tumour and 94 nontumour samples) cohorts, respectively. Using 412 TCGA-STAD samples with both methylation and clinical data, we also identified four prognostic probes by implementing univariate and multivariate Cox regression analysis. In summary, our study introduced potential diagnostic and prognostic biomarkers for STAD, which demands further validation.     

Genome-wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood

Childhood obesity is a major public health issue. Here we investigated whether differential DNA methylation was associated with childhood obesity. We studied DNA methylation profiles in whole blood from 78 obese children (mean BMI Z-score: 2.6) and 71 age- and sex-matched controls (mean BMI Z-score: 0.1). DNA samples from obese and control groups were pooled and analyzed using the Infinium HumanMethylation450 BeadChip array. Comparison of the methylation profiles between obese and control subjects revealed 129 differentially methylated CpG (DMCpG) loci associated with 80 unique genes that had a greater than 10% difference in methylation (P-value < 0.05). The top pathways enriched among the DMCpGs included developmental processes, immune system regulation, regulation of cell signaling, and small GTPase-mediated signal transduction. The associations between the methylation of selected DMCpGs with childhood obesity were validated using sodium bisulfite pyrosequencing across loci within the FYN, PIWIL4, and TAOK3 genes in individual subjects. Three CpG loci within FYN were hypermethylated in obese individuals (all P < 0.01), while obesity was associated with lower methylation of CpG loci within PIWIL4 (P = 0.003) and TAOK3 (P = 0.001). After building logistic regression models, we determined that a 1% increase in methylation in TAOK3, multiplicatively decreased the odds of being obese by 0.91 (95% CI: 0.86 – 0.97), and an increase of 1% methylation in FYN CpG3, multiplicatively increased the odds of being obese by 1.03 (95% CI: 0.99 – 1.07). In conclusion, these findings provide evidence that childhood obesity is associated with specific DNA methylation changes in whole blood, which may have utility as biomarkers of obesity risk.     

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.     

Genome-wide DNA methylation analysis identifies a metabolic memory profile in patient-derived diabetic foot ulcer fibroblasts

Diabetic foot ulcers (DFUs) are a serious complication of diabetes. Previous exposure to hyperglycemic conditions accelerates a decline in cellular function through metabolic memory despite normalization of glycemic control. Persistent, hyperglycemia-induced epigenetic patterns are considered a central mechanism that activates metabolic memory; however, this has not been investigated in patient-derived fibroblasts from DFUs. We generated a cohort of patient-derived lines from DFU fibroblasts (DFUF), and site- and age-matched diabetic foot fibroblasts (DFF) and non-diabetic foot fibroblasts (NFF) to investigate global and genome-wide DNA methylation patterns using liquid chromatography/mass spectrometry and the Illumina Infinium HumanMethylation450K array. DFFs and DFUFs demonstrated significantly lower global DNA methylation compared to NFFs (p = 0.03). Hierarchical clustering of differentially methylated probes (DMPs, p = 0.05) showed that DFFs and DFUFs cluster together and separately from NFFs. Twenty-five percent of the same probes were identified as DMPs when individually comparing DFF and DFUF to NFF. Functional annotation identified enrichment of DMPs associated with genes critical to wound repair, including angiogenesis (p = 0.07) and extracellular matrix assembly (p = 0.035). Identification of sustained DNA methylation patterns in patient-derived fibroblasts after prolonged passage in normoglycemic conditions demonstrates persistent metabolic memory. These findings suggest that epigenetic-related metabolic memory may also underlie differences in wound healing phenotypes and can potentially identify therapeutic targets.     

Genome-wide DNA methylation analysis reveals phytoestrogen modification of promoter methylation patterns during embryonic stem cell differentiation

Background

Environmental challenges during development affect the fetal epigenome, but the period(s) vulnerable to epigenetic dysregulation is(are) not clear. By employing a soy phytoestrogen, genistein, that is known to alter the epigenetic states of the A^vy allele during embryogenesis, we have explored the sensitive period for epigenetic regulation. The post-implantation period, when de novo DNA methylation actively proceeds, is amenable to in vitro analysis using a mouse embryonic stem (ES) cell differentiation system.

Methods and Findings

Mouse ES cells were differentiated in the presence or absence of genistein, and DNA methylation patterns on day 10 were compared by microarray-based promoter methylation analysis coupled with a methylation-sensitive endonuclease (HpaII/McrBC)-dependent enrichment procedure. Moderate changes in methylation levels were observed in a subset of promoters following genistein treatment. Detailed investigation of the Ucp1 and Sytl1 promoters further revealed that genistein does not affect de novo methylation occurring between day 0 and day 4, but interferes with subsequent regulatory processes and leads to a decrease in methylation level for both promoters.

Conclusion

Genistein perturbed the methylation pattern of differentiated ES cells after de novo methylation. Our observations suggest that, for a subset of genes, regulation after de novo DNA methylation in the early embryo may be sensitive to genistein.     

Genome-wide DNA methylation analysis identifies a metabolic memory profile in patient-derived diabetic foot ulcer fibroblasts

Diabetic foot ulcers (DFUs) are a serious complication of diabetes. Previous exposure to hyperglycemic conditions accelerates a decline in cellular function through metabolic memory despite normalization of glycemic control. Persistent, hyperglycemia-induced epigenetic patterns are considered a central mechanism that activates metabolic memory; however, this has not been investigated in patient-derived fibroblasts from DFUs. We generated a cohort of patient-derived lines from DFU fibroblasts (DFUF), and site- and age-matched diabetic foot fibroblasts (DFF) and non-diabetic foot fibroblasts (NFF) to investigate global and genome-wide DNA methylation patterns using liquid chromatography/mass spectrometry and the Illumina Infinium HumanMethylation450K array. DFFs and DFUFs demonstrated significantly lower global DNA methylation compared to NFFs (p = 0.03). Hierarchical clustering of differentially methylated probes (DMPs, p = 0.05) showed that DFFs and DFUFs cluster together and separately from NFFs. Twenty-five percent of the same probes were identified as DMPs when individually comparing DFF and DFUF to NFF. Functional annotation identified enrichment of DMPs associated with genes critical to wound repair, including angiogenesis (p = 0.07) and extracellular matrix assembly (p = 0.035). Identification of sustained DNA methylation patterns in patient-derived fibroblasts after prolonged passage in normoglycemic conditions demonstrates persistent metabolic memory. These findings suggest that epigenetic-related metabolic memory may also underlie differences in wound healing phenotypes and can potentially identify therapeutic targets.     

Genome-wide DNA methylation analysis in precursor B-cells

DNA methylation is responsible for regulating gene expression and cellular differentiation and for maintaining genomic stability during normal human development. Furthermore, it plays a significant role in the regulation of hematopoiesis. In order to elucidate the influence of DNA methylation during B-cell development, genome-wide DNA methylation status of pro-B, pre-BI, pre-BII, and naïve-B-cells isolated from human umbilical cord blood was determined using the methylated CpG island recovery assay followed by next generation sequencing. On average, 182–200 million sequences were generated for each precursor B-cell subset in 10 biological replicates. An overall decrease in methylation was observed during the transition from pro-B to pre-BI, whereas no differential methylation was observed in the pre-BI to pre-BII transition or in the pre-BII to naïve B-cell transition. Most of the methylated regions were located within intergenic and intronic regions not present in a CpG island context. Putative novel enhancers were identified in these regions that were differentially methylated between pro-B and pre-BI cells. The genome-wide methylation profiles are publically available and may be used to gain a better understanding of the involvement of atypical DNA methylation in the pathogenesis of malignancies associated with precursor B-cells.     

Genome-wide DNA methylation analysis in precursor B-cells

DNA methylation is responsible for regulating gene expression and cellular differentiation and for maintaining genomic stability during normal human development. Furthermore, it plays a significant role in the regulation of hematopoiesis. In order to elucidate the influence of DNA methylation during B-cell development, genome-wide DNA methylation status of pro-B, pre-BI, pre-BII, and naïve-B-cells isolated from human umbilical cord blood was determined using the methylated CpG island recovery assay followed by next generation sequencing. On average, 182–200 million sequences were generated for each precursor B-cell subset in 10 biological replicates. An overall decrease in methylation was observed during the transition from pro-B to pre-BI, whereas no differential methylation was observed in the pre-BI to pre-BII transition or in the pre-BII to naïve B-cell transition. Most of the methylated regions were located within intergenic and intronic regions not present in a CpG island context. Putative novel enhancers were identified in these regions that were differentially methylated between pro-B and pre-BI cells. The genome-wide methylation profiles are publically available and may be used to gain a better understanding of the involvement of atypical DNA methylation in the pathogenesis of malignancies associated with precursor B-cells.     

Genome-wide analysis of DNA methylation patterns

Cytosine methylation is the most common covalent modification of DNA in eukaryotes. DNA methylation has an important role in many aspects of biology, including development and disease. Methylation can be detected using bisulfite conversion, methylation-sensitive restriction enzymes, methyl-binding proteins and anti-methylcytosine antibodies. Combining these techniques with DNA microarrays and high-throughput sequencing has made the mapping of DNA methylation feasible on a genome-wide scale. Here we discuss recent developments and future directions for identifying and mapping methylation, in an effort to help colleagues to identify the approaches that best serve their research interests.     

Quantitative analysis of RASSF1A promoter methylation in hepatocellular carcinoma and its prognostic implications

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide and is caused by the accumulation of genetic and epigenetic alterations in regulatory genes. In this study, we used methylight to detect the methylation status of the RASSF1A promoter in 87 paired HCC samples and analysed the relationship between methylation status and clinicopathological parameters, including prognosis after surgery. We found that the methylation level of the RASSF1A promoter in HCC tissues was significantly higher than that in the corresponding non-tumorous tissues (p < 0.0001). Furthermore, the methylation level of the RASSF1A gene promoter in HCC samples was higher in patients with a tumor size ?6 cm (p = 0.0149) and in patients younger than 50 years old (p = 0.0175). However, hypermethylation of the RASSF1A promoter in HCC tissues did not affect the overall survival of patients (p = 0.611). Thus, RASSF1A promoter hypermethylation may not be a useful biomarker for the prognosis of HCC.     

Genome‐wide promoter methylation analysis identifies epigenetic silencing of MAPK13 in primary cutaneous melanoma

The involvement of epigenetic alterations in the pathogenesis of melanoma is increasingly recognized. Here, we performed genome‐wide DNA methylation analysis of primary cutaneous melanoma and benign melanocytic nevus interrogating 14 495 genes using BeadChip technology. This genome‐wide view of promoter methylation in primary cutaneous melanoma revealed an array of recurrent DNA methylation alterations with potential diagnostic applications. Among 106 frequently hypermethylated genes, there were many novel methylation targets and tumor suppressor genes. Highly recurrent methylation of the HOXA9, MAPK13, CDH11, PLEKHG6, PPP1R3C, and CLDN11 genes was established. Promoter methylation of MAPK13, encoding p38δ, was present in 67% of primary and 85% of metastatic melanomas. Restoration of MAPK13 expression in melanoma cells exhibiting epigenetic silencing of this gene reduced proliferation, indicative of tumor suppressive functions. This study demonstrates that DNA methylation alterations are widespread in melanoma and suggests that epigenetic silencing of MAPK13 contributes to melanoma progression.     

Genome-wide high throughput analysis of DNA methylation in eukaryotes

Cytosine methylation is the quintessential epigenetic mark. Two well-established methods, bisulfite sequencing and methyl-DNA immunoprecipitation (MeDIP) lend themselves to the genome-wide analysis of DNA methylation by high throughput sequencing. Here we provide an overview and brief review of these methods. We summarize our experience with MeDIP followed by high throughput Illumina/Solexa sequencing, exemplified by the analysis of the methylated fraction of the Neurospora crassa genome ("methylome"). We provide detailed methods for DNA isolation, processing and the generation of in vitro libraries for Illumina/Solexa sequencing. We discuss potential problems in the generation of sequencing libraries. Finally, we provide an overview of software that is appropriate for the analysis of high throughput sequencing data generated by Illumina/Solexa-type sequencing by synthesis, with a special emphasis on approaches and applications that can generate more accurate depictions of sequence reads that fall in repeated regions of a chosen reference genome.