Analysis Of DNA Methylation Data Research Articles

Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability

DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.

Analysis of human endogenous retrovirus in Alzheimer’s disease and Frontotemporal dementia affected tissues

AbstractBackgroundHuman Endogenous Retroviruses (HERVs), normally dormant within the eukaryotic genome, can be awaken by environmental factors that may induce neurodegeneration via brain inflammation and epigenetic dysregulation. We investigated different HERVs transcripts using blood and post‐mortem brain samples from genetic and sporadic Alzheimer’s disease (gAD and sAD) and Frontotemporal Dementia (sFTD and gFTD) patients, using quantitative PCR (RT‐qPCR) and microarray techniques. Only HERV‐K env transcript was upregulated in sporadic AD in blood. Genome‐wide DNA methylation data analysis revealed hypermethylation in several genes involved in HERVs machinery, including APOBEC3, CDK6, E2F5, PRODH and SLC4A8 in AD and FTD when compared to healthy controls in post‐mortem brain tissue. In summary, our preliminary findings suggest that HERVs may be implicated in AD and FTD neuropathogenesis.MethodRNA samples were obtained from sAD (n = 63), gAD (n = 10), sFTD (n = 46), gFTD (n = 15) and healthy controls (n = 39). Post‐mortem tissue (prefrontal cortex) from sAD (n = 10), gAD (n = 10), sFTD (n = 10), gFTD (n = 15) and healthy controls (n = 17). Quantification of different HERVs were measured by qRT‐PCR. Transcript levels within each of the samples were quantitated and normalized using the reference genes actin and GAPDH. 64 subjects were studied with Infinium MethylationEPIC array. Samples comprised healthy controls (n = 5), sAD (n = 5), gAD (n = 5), gFTD (n = 15) and sFTD (n = 10).ResultHERV‐K env transcript was increased in blood from sAD compared to healthy controls (p = 0.0044). No other HERV transcript was found to be significantly upregulated in sporadic and genetic AD and FTD. Microarray analysis from AD and FTD patients revealed hypermethylation in APOBEC3, E2F5, in all groups and CDK6, PRODH and SLC4A8 in FTD when compared to healthy controls in post‐mortem brain tissue.ConclusionIn this study we found that HERV‐K env transcript was upregulated in sAD from blood, which could be useful as a potential clinical biomarker. Future studies targeting the env protein should be performed to confirm the results found in this preliminary study. Hypermethylation of different genes involved in HERVs machinery suggest a different pathomechanism between AD and FTD that might help to differentiate them. Future functional studies for APOBEC3, CDK6, E2F5, PRODH and SLC4A8 could reveal the underlying biological mechanism leading to HERV activation.

Mesonephric-like adenocarcinoma harbours characteristic copy number variations and a distinct DNA methylation signature closely related to mesonephric adenocarcinoma of the cervix.

Mesonephric-like adenocarcinoma (MLA) of the female genital tract is an uncommon histotype that can arise in both the endometrium and the ovary. The exact cell of origin and histogenesis currently remain unknown. Here, we investigated whole genome DNA methylation patterns and copy number variations (CNVs) in a series of MLAs in the context of a large cohort of various gynaecological carcinoma types. CNV analysis of 19 MLAs uncovered gains of chromosomes 1q (18/19, 95%), 10 (15/19, 79%), 12 (14/19, 74%), and 2 (10/19, 53%), as well as loss of chromosome 1p (7/19, 37%). Gains of chromosomes 1q, 10, and 12 were also identified in the majority of mesonephric adenocarcinomas of the uterine cervix (MAs) as well as subsets of endometrioid carcinomas (ECs) and low-grade serous carcinomas of the ovary (LGSCs) but only in a minority of serous carcinomas of the uterine corpus (USCs), clear cell carcinomas (CCCs), and tubo-ovarian high-grade serous carcinomas (HGSCs). While losses of chromosome 1p together with gains of chromosome 1q were also identified in both MA and LGSC, gains of chromosome 2 were almost exclusively identified in MLA and MA. Unsupervised hierarchical clustering and t-SNE analysis of DNA methylation data (Illumina EPIC array) identified a co-clustering for MLAs and MAs, which was distinct from clusters of ECs, USCs, CCCs, LGSCs, and HGSCs. Group-wise comparisons confirmed a close epigenetic relationship between MLA and MA. These findings, in conjunction with the established histological and immunophenotypical overlap, suggest bona fide mesonephric differentiation, and support a more precise terminology of mesonephric-type adenocarcinoma instead of MLA in these tumours. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Epigenetic modification related to cognitive changes during a cognitive training intervention in depression

BackgroundDNA methylation as a biomarker is well suited to investigate dynamic processes, such as symptom improvement. For this study we focus on epigenomic state or trait markers as early signatures of cognitive improvement in individuals receiving a cognitive intervention. We performed a first epigenome-wide association study (EWAS) on patients with cognitive dysfunction in depression comparing those with vs without cognitive dysfunction and those cognitively improving vs non-improving following a cognitive intervention. MethodData from a randomized controlled trial (RCT) were used for this analysis, where cognitive function of 112 patients randomly assigned to a personalized cognitive intervention was compared to standard cognitive treatment. Cognition was measured for this study using the four cognitive tasks from the THINC-it battery. We compared individuals with cognitive impairment with individuals without cognitive impairment at baseline and after a cognitive intervention of 8 weeks. Blood for DNA methylation analysis (Illumina Infinium MethylationEPIC 850 k BeadChip) was collected at baseline and 8 weeks into the treatment. For the baseline analysis, after quality control, the final sample comprised 90 individuals, and analyses at week 8 were performed on 84 individuals. Data cleaning, quality control, and differential methylation analysis of DNA methylation data was performed using the RnBeads package (R). Analyses were corrected for gender, age, depression score (MADRS), reported years of education, height and weight, as well as surrogate variables estimated by the pipeline used. The within-individual paired longitudinal analysis was performed using Welch's t-test. ResultsAnalyses at baseline and at week 8 did not show any genome-wide significant CpGs (p < 5 × 10−8) comparing patients with and without cognitive impairment. The most significant result in the baseline analysis comparing the groups with and without cognitive impairment at baseline is located in an open Sea region with predominantly regulatory qualities (cg10962945; 6.61 × 10−7). The most significant CpG at 8 weeks was also located in open sea, though in exon 13 of the NTRK2-gene, linked to the BDNF pathway (cg13620631, 5.56 × 10−7). Finally, a within-individual paired longitudinal analysis with only patients that show improved cognitive function over time was performed, showing 65 CpGs that overlapped between the 1% most significant of this analysis and the 1% most significant CpGs from the cross-sectional analysis at 8 weeks. ConclusionOur result suggest that DNA methylation can be suitable to capture early signs of treatment response of a cognitive intervention in depression. In our layered approach we could capture dynamics that can help differentiate between biological trait and state markers of cognitive function in depression. Despite not being genome-wide significant, the CpG locations returned by our analysis comparing patients with and without cognitive impairment, are in line with prior knowledge on pathways and genes relevant for depression treatment and cognition.

Perturbed epigenetic transcriptional regulation in AML with IDH mutations causes increased susceptibility to NK cells

Isocitrate dehydrogenase (IDH) mutations are found in 20% of acute myeloid leukemia (AML) patients. However, only 30–40% of the patients respond to IDH inhibitors (IDHi). We aimed to identify a molecular vulnerability to tailor novel therapies for AML patients with IDH mutations. We characterized the transcriptional and epigenetic landscape with the IDH2i AG-221, using an IDH2 mutated AML cell line model and AML patient cohorts, and discovered a perturbed transcriptional regulatory network involving myeloid transcription factors that were partly restored after AG-221 treatment. In addition, hypermethylation of the HLA cluster caused a down-regulation of HLA class I genes, triggering an enhanced natural killer (NK) cell activation and an increased susceptibility to NK cell-mediated responses. Finally, analyses of DNA methylation data from IDHi-treated patients showed that non-responders still harbored hypermethylation in HLA class I genes. In conclusion, this study provides new insights suggesting that IDH mutated AML is particularly sensitive to NK cell-based personalized immunotherapy.

Primary papillary epithelial tumor of the sella and posterior pituitary tumor show similar (epi)genetic features and constitute a single neuro-oncological entity.

"Primary papillary epithelial tumor of the sella (PPETS)" is a recently described rare tumor entity of the central nervous system (CNS) with stereotypic location in the sella. Comprehensive molecular investigations and epigenetic profiles of PPETS have not been performed to date. We report a comprehensive clinical, histopathologic, and molecular assessment of 5 PPETS cases in comparison with a cohort composed of 7 choroid plexus papilloma (CPP), 7 central neurocytoma (CN), 15 posterior pituitary tumor (PPT) including 4 pituicytoma, 6 granular cell tumors of the sellar region (GCT), and 5 spindle cell oncocytoma. All PPETS had good outcomes. Immunohistochemically, PPETS tumors showed positive staining with TTF1, EMA, AE1/AE3, MAP2, and Vimentin, but were negatively stained with Syn, GFAP, CgA, and S100, and sporadically stained with Ki-67. In unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding analyses of DNA-methylation data, PPETS and PPT tumors formed a distinct cluster irrespective of their histologic types. However, PPETS tumors did not cluster together with CPP and CN samples. Similar findings were obtained when our samples were projected into the reference cohort of the brain tumor classifier. Substantial fractions of the PPETS and PPT tumors shared broadly similar chromosomal copy number alterations. No mutations were detected using targeted next-generation sequencing. Though more cases are needed to further elucidate the molecular pathogenesis of these tumors, our findings indicate that PPETS and PPT tumors may constitute a single neurooncological entity.

DNA Methylation Data Analysis Using Msuite.

DNA methylation is a widespread epigenetic modification responsible for many biological regulation pathways. The development of various powerful biochemical assays, including conventional bisulfite treatment-based and emerging bisulfite-free techniques, has promised high-resolution DNA methylome profiling and significantly propelled the DNA methylation research field. However, the analysis of large-scale data generated from such assays is still complex and challenging. In this paper, we present a step-by-step protocol for using Msuite for whole-spectrum DNA methylation data analysis, from quality control, read alignment, to methylation call and data visualization. The Msuite package and a testing dataset are freely available at https://github.com/hellosunking/Msuite.

DNA-Methylation Analysis as a Tool for Thymoma Classification

Thymomas are malignant thymic epithelial tumors that are difficult to diagnose due to their rarity and complex diagnostic criteria. They represent a morphologically heterogeneous class of tumors mainly defined by "organo-typical" architectural features and cellular composition. The diagnosis of thymoma is burdened with a high level of inter-observer variability and the problem that some type-specific morphological alterations are more on the continuum than clear-cut. Methylation pattern-based classification may help to increase diagnostic precision, particularly in borderline cases. We applied array-based DNA methylation analysis to a set of 113 thymomas with stringent histological annotation. Unsupervised clustering and t-SNE analysis of DNA methylation data clearly segregated thymoma samples mainly according to the current WHO classification into A, AB, B1, B2, B2/B3, B3, and micronodular thymoma with lymphoid stroma. However, methylation analyses separated the histological subgroups AB and B2 into two methylation classes: mono-/bi-phasic AB-thymomas and conventional/"B1-like" B2-thymomas. Copy number variation analysis demonstrated methylation class-specific patterns of chromosomal alterations. Our study demonstrates that the current WHO classification is generally well reflected at the methylation level but suggests that B2- and AB-thymomas are (epi)genetically heterogeneous. Methylation-based classifications could help to refine diagnostic criteria for thymoma classification, improve reproducibility, and may affect treatment decisions.

Integrative analysis reveals novel associations between DNA methylation and the serum metabolome of adolescents with type 2 diabetes: A cross-sectional study.

ObjectiveRates of type 2 diabetes (T2D) among adolescents are on the rise. Epigenetic changes could be associated with the metabolic alterations in adolescents with T2D.MethodsWe performed a cross sectional integrated analysis of DNA methylation data from peripheral blood mononuclear cells with serum metabolomic data from First Nation adolescents with T2D and controls participating in the Improving Renal Complications in Adolescents with type 2 diabetes through Research (iCARE) cohort study, to explore the molecular changes in adolescents with T2D.ResultsOur analysis showed that 43 serum metabolites and 36 differentially methylated regions (DMR) were associated with T2D. Several DMRs were located near the transcriptional start site of genes with established roles in metabolic disease and associated with altered serum metabolites (e.g. glucose, leucine, and gamma-glutamylisoleucine). These included the free fatty acid receptor-1 (FFAR1), upstream transcription factor-2 (USF2), and tumor necrosis factor-related protein-9 (C1QTNF9), among others.ConclusionsWe identified DMRs and metabolites that merit further investigation to determine their significance in controlling gene expression and metabolism which could define T2D risk in adolescents.

Efficient Targeted DNA Methylation with dCas9-Coupled DNMT3A-DNMT3L Methyltransferase.

Epigenome editing is a powerful approach for the establishment of a chromatin environment with desired properties at a selected genomic locus, which is used to influence the transcription of target genes and to study properties and functions of gene regulatory elements. Targeted DNA methylation is one of the most often used types of epigenome editing, which typically aims for gene silencing by methylation of gene promoters. Here, we describe the design principles of EpiEditors for targeted DNA methylation and provide step-by-step guidelines for the realization of this approach. We focus on the dCas9 protein as the state-of-the-art DNA targeting module fused to 10×SunTag as the most frequently used system for editing enhancement. Further, we discuss different flavors of DNA methyltransferase modules used for this purpose including the most specific variants developed recently. Finally, we explain the principles of gRNA selection, outline the setup of the cell culture experiments, and briefly introduce the available options for the downstream DNA methylation data analysis.

HGG-45. Characterization of spinal diffuse midline gliomas, H3 K28M-mutant

Diffuse midline gliomas (DMGs) are malignant gliomas that arise in the midline structures of the central nervous system. Due to their aggressive and diffuse growth and a two-year survival rate of less than 10%, DMGs are assigned to CNS WHO grade 4. Depending on the localization, median age of patients is about 11‒20 years. Genetically, most tumors are defined by a K28M-mutation in one of the highly homologous genes encoding histone protein H3. Since DMGs most frequently occur in pons and thalamus, comparatively little is known about spinal DMGs. Therefore, we histologically, molecularly, and clinically characterized spinal DMGs and analyzed, in which aspects they differ from DMGs of other localizations. Our cohort currently consists of 25 spinal DMGs and 40 pontine/thalamic reference cases. Histological, immunohistochemical and molecular analyses (DNA methylation, DNA panel sequencing) were done from FFPE tissue. Spinal DMGs were histologically very heterogeneous, both regarding different areas of single tumors as well as in comparison to other spinal and reference cases. First cluster analyses of DNA methylation data indicated a separation into three main clusters enriched for pontine, thalamic or spinal cases. The cluster enriched for spinal cases contained many tumors from elderly patients. Overall, mean age of patients with spinal DMGs was 28 years. Patients were significantly older than those with pontine DMGs. 19/20 spinal DMGs were H3-3A K28M-mutant, while one tumor had an H3-2B mutation. 4/19 (21%) spinal DMGs had mutations in FGFR1, and 6/10 (60%) in NF1. Three tumors had KRAS or BRAF mutations. In summary, first analyses suggest slight histological differences of spinal DMGs compared to DMGs of other localizations. Preliminary cluster analyses of DNA methylation data showed an enrichment of clusters for different localizations. About one third of spinal DMGs had mutations in a gene associated with the MAPK-signaling pathway.

A novel workflow for the qualitative analysis of DNA methylation data

DNA methylation is an epigenetic modification that plays a pivotal role in major biological mechanisms, such as gene regulation, genomic imprinting, and genome stability. Different combinations of methylated cytosines for a given DNA locus generate different epialleles and alterations of these latter have been associated with several pathological conditions. Existing computational methods and statistical tests relevant to DNA methylation analysis are mostly based on the comparison of average CpG sites methylation levels and they often neglect non-CG methylation. Here, we present EpiStatProfiler, an R package that allows the analysis of CpG and non-CpG based epialleles starting from bisulfite sequencing data through a collection of dedicated extraction functions and statistical tests. EpiStatProfiler is provided with a set of useful auxiliary features, such as customizable genomic ranges, strand-specific epialleles analysis, locus annotation and gene set enrichment analysis. We showcase the package functionalities on two public datasets by identifying putative relevant loci in mice harboring the Huntington’s disease-causing Htt gene mutation and in Ctcf +/− mice compared to their wild-type counterparts. To our knowledge, EpiStatProfiler is the first package providing functionalities dedicated to the analysis of epialleles composition derived from any kind of bisulfite sequencing experiment.

The Prognostic Value and Function of HOXB5 in Acute Myeloid Leukemia.

BackgroundCurrently, cytogenetic and genetic markers are the most important for risk stratification and treatment of patients with acute myeloid leukemia (AML). Despite the identification of many prognostic factors, relatively few have made their way into clinical practice. Therefore, the identification of new AML biomarkers is useful in the prognosis and monitoring of AML and contributes to a better understanding of the molecular basis of the disease. Homeobox (HOX) genes are transcription factors that lead to cell differentiation blockade and malignant self-renewal. However, the roles of HOX genes in AML are still not fully understood and need further exploration, which may provide new strategies for the prognosis and monitoring of AML.MethodsWe analyzed the RNA sequencing and clinical data from The Cancer Genome Atlas (TCGA), VIZOME, GSE13159, and GSE9476 cohorts. Analyses were performed with GraphPad 7, the R language, and several online databases. We applied quantitative polymerase chain reaction, Western Blotting, CCK8 cell proliferation assays, and flow cytometry to verify the conclusions of the bioinformatics analysis.ResultsWe identified HOXB5 as the only gene among the HOX family that was not only elevated in AML but also a significant prognostic marker in AML patients. HOXB5 was highly expressed in AML patients with NPM1, FLT3, or DNMT3A mutations and was expressed at the highest level in patients with NPM1-FLT3-DNMT3A triple-mutant AML. Gene Ontology analysis and gene set enrichment analysis revealed that HOXB5 showed a negative correlation with the myeloid cell differentiation signature and that the tumor necrosis factor/nuclear factor κB signaling pathway was involved in the molecular mechanism. Moreover, we performed in silico protein–protein interaction analysis and 450K TCGA DNA methylation data analysis and found that HOXB5 interacted with two HOX genes (HOXA7 and HOXB4) that were commonly regulated by DNA methylation levels.ConclusionHOXB5 is associated with the malignant development of AML and may be a treatment target and biomarker for AML prognosis prediction.

Filtering High-Dimensional Methylation Marks With Extremely Small Sample Size: An Application to Gastric Cancer Data.

DNA methylations in critical regions are highly involved in cancer pathogenesis and drug response. However, to identify causal methylations out of a large number of potential polymorphic DNA methylation sites is challenging. This high-dimensional data brings two obstacles: first, many established statistical models are not scalable to so many features; second, multiple-test and overfitting become serious. To this end, a method to quickly filter candidate sites to narrow down targets for downstream analyses is urgently needed. BACkPAy is a pre-screening Bayesian approach to detect biological meaningful patterns of potential differential methylation levels with small sample size. BACkPAy prioritizes potentially important biomarkers by the Bayesian false discovery rate (FDR) approach. It filters non-informative sites (i.e., non-differential) with flat methylation pattern levels across experimental conditions. In this work, we applied BACkPAy to a genome-wide methylation dataset with three tissue types and each type contains three gastric cancer samples. We also applied LIMMA (Linear Models for Microarray and RNA-Seq Data) to compare its results with what we achieved by BACkPAy. Then, Cox proportional hazards regression models were utilized to visualize prognostics significant markers with The Cancer Genome Atlas (TCGA) data for survival analysis. Using BACkPAy, we identified eight biological meaningful patterns/groups of differential probes from the DNA methylation dataset. Using TCGA data, we also identified five prognostic genes (i.e., predictive to the progression of gastric cancer) that contain some differential methylation probes, whereas no significant results was identified using the Benjamin-Hochberg FDR in LIMMA. We showed the importance of using BACkPAy for the analysis of DNA methylation data with extremely small sample size in gastric cancer. We revealed that RDH13, CLDN11, TMTC1, UCHL1, and FOXP2 can serve as predictive biomarkers for gastric cancer treatment and the promoter methylation level of these five genes in serum could have prognostic and diagnostic functions in gastric cancer patients.

Analysis of Methylation Dynamics Reveals a Tissue-Specific, Age-Dependent Decline in 5-Methylcytosine Within the Genome of the Vertebrate Aging Model Nothobranchius furzeri.

Erosion of the epigenetic DNA methylation landscape is a widely recognized hallmark of aging. Emerging advances in high throughput sequencing techniques, in particular DNA methylation data analysis, have resulted in the establishment of precise human and murine age prediction tools. In vertebrates, methylation of cytosine at the C5 position of CpG dinucleotides is executed by DNA methyltransferases (DNMTs) whereas the process of enzymatic demethylation is highly dependent on the activity of the ten-eleven translocation methylcytosine dioxygenase (TET) family of enzymes. Here, we report the identification of the key players constituting the DNA methylation machinery in the short-lived teleost aging model Nothobranchius furzeri. We present a comprehensive spatio-temporal expression profile of the methylation-associated enzymes from embryogenesis into late adulthood, thereby covering the complete killifish life cycle. Data mining of the N. furzeri genome produced five dnmt gene family orthologues corresponding to the mammalian DNMTs (DNMT1, 2, 3A, and 3B). Comparable to other teleost species, N. furzeri harbors multiple genomic copies of the de novo DNA methylation subfamily. A related search for the DNMT1 recruitment factor UHRF1 and TET family members resulted in the identification of N. furzeri uhrf1, tet1, tet2, and tet3. Phylogenetic analysis revealed high cross-species similarity on the amino acid level of all individual dnmts, tets, and uhrf1, emphasizing a high degree of functional conservation. During early killifish development all analyzed dnmts and tets showed a similar expression profile characterized by a strong increase in transcript levels after fertilization, peaking either at embryonic day 6 or at the black eye stage of embryonic development. In adult N. furzeri, DNA methylation regulating enzymes showed a ubiquitous tissue distribution. Specifically, we observed an age-dependent downregulation of dnmts, and to some extent uhrf1, which correlated with a significant decrease in global DNA methylation levels in the aging killifish liver and muscle. The age-dependent DNA methylation profile and spatio-temporal expression characteristics of its enzymatic machinery reported here may serve as an essential platform for the identification of an epigenetic aging clock in the new vertebrate model system N. furzeri.

HGG-26. SINGLE-CELL RNA-SEQ OF PEDIATRIC HIGH-GRADE GLIOMAS IDENTIFIES COMMON ONCOGENIC PROCESSES AMONG DISTINCT TUMOR HISTOLOGIES

BackgroundPediatric high-grade glioma (PHGG) is a deadly childhood brain tumor that responds poorly to treatment. PHGG comprises two major subtypes: cortical tumors with wild-type H3K27 and diffuse midline gliomas (DMG) that occur in the midline and have characteristic H3K27M mutations. Cortical PHGG is heterogeneous with multiple molecular subtypes. In order to identify underlying commonalities in cortical PHGG that might lead to better treatment modalities, we performed molecular profiling, including single-cell RNA-Seq (scRNA-Seq), on PHGG samples from Children’s Hospital Colorado. MethodsNineteen cortical PHGG tumor samples, one DMG and one normal margin sample obtained at biopsy were disaggregated to isolate viable cells. Fifteen were glioblastomas (GBM), including five with epithelioid and/or giant cell features and five radiation-induced glioblastomas (RIG). There were also four non-GBM PHGG. We performed scRNA-Seq using 10X Genomics v.3 library preparation to enable capture of infiltrating immune cells. We also performed bulk RNA-Seq and DNA methylation profiling. ResultsAfter eliminating patient-specific and cell-cycle effects, RIG, epithelioid GBM, and other GBM each formed identifiable subgroups in bulk RNA-Seq and scRNA-Seq datasets. In the scRNA-Seq data, clusters with cells from multiple tumor samples included a PDGFRA-positive population expressing oligodendrocyte progenitor markers, astrocytic, mesenchymal and stemlike populations, macrophage/monocyte immune cells, and a smaller T-cell population. Analyses of DNA methylation data showed PDGFRA and CDK4 amplification and CDKN2A deletion are common alterations among PHGG. Inferred copy number variation analysis of the single-cell data confirmed that individual tumors include populations that both include and lack the molecular alterations identified in the methylation data. RNA velocity studies to define tumor cells of origin and further analyses of the immune cell populations are underway. ConclusionsSingle-cell analysis of PHGG confirms a large degree of tumor heterogeneity but also shows that PHGG have stemlike, mesenchymal and immune cell populations with common characteristics.

The Interaction Analysis of SNP Variants and DNA Methylation Identifies Novel Methylated Pathogenesis Genes in Congenital Heart Diseases.

Congenital heart defect (CHD) is a rare and complicated disease with a high mortality rate. Its etiology remains unclear and includes many aspects. DNA methylation has been indicated to be involved in heart development in the early stage of life, and aberrant methylation level was related to CHDs. This study provides the first evidence of the cross talk of SNP variants and DNA methylation in clarifying CHD underlying genomic cause. We gathered whole exome sequencing (WES) data for Group 1 consisting of patients with PA (n = 78), TOF (n = 20), TAPVC (n = 78), and PDA (n = 40), and 100 healthy children as control group. Rare non-synonymous mutations and novel genes were found and highlighted. Meanwhile, we carried out the second analysis of DNA methylation data from patients with PA (n = 3), TAPVC (n = 3), TOF (n = 3), and PDA (n = 2), and five healthy controls using 850 K array in Group 2. DNA methylation was linked to WES data, and we explored an obvious overlap of hyper/hypomethylated genes. Next, we identified some candidate genes by Fisher’s exact test and Burden analysis; then, those methylated genes were figured out by the criteria of the mutation located in the CpG islands of the genome, differential methylation sites (DMS), and DNA methylation quantitative trait loci (meQTLs) in the database, respectively. Also, the interaction of differentially methylated candidate genes with known CHD pathogenetic genes was depicted in a molecular network. Taken together, our findings show that nine novel genes (ANGPTL4, VEGFA, PAX3, MUC4, HLA-DRB1, TJP2, BCR, PKD1, and HK2) in methylation level are critical to CHD and reveal a new insight into the molecular pathogenesis of CHD.

Estimands in epigenome-wide association studies

BackgroundIn DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.ResultsWe present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.ConclusionsThe usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

DNA methylation changes induced by BDE-209 are related to DNA damage response and germ cell development in GC-2spd

Decabrominated diphenyl ether (BDE-209) is generally utilized in multiple polymer materials as common brominated flame retardant. BDE-209 has been listed as persistent organic pollutants (POPs), which was considered to be reproductive toxin in the environment. But it still remains unclear about the effects of BDE-209 on DNA methylation and the induced-male reproductive toxicity. Due to the extensive epigenetic regulation in germ line development, we hypothesize that BDE-209 exposure impacts the statue of DNA methylation in spermatocytes in vitro. Therefore, the mouse GC-2spd (GC-2) cells were used for the genome wide DNA methylation analysis after treated with 32 μg/mL BDE-209 for 24 hr. The results showed that BDE-209 caused genomic methylation changes with 32,083 differentially methylated CpGs in GC-2 cells, including 16,164 (50.38%) hypermethylated and 15,919 (49.62%) hypomethylated sites. With integrated analysis of DNA methylation data and functional enrichment, we found that BDE-209 might affect the functional transcription in cell growth and sperm development by differential gene methylation. qRT-PCR validation demonstrated the involvement of p53-dependent DNA damage response in the GC-2 cells after BDE-209 exposure. In general, our findings indicated that BDE-209-induced genome wide methylation changes could be interrelated with reproductive dysfunction. This study might provide new insights into the mechanisms of male reproductive toxicity under the environmental exposure to BDE-209.

Integrative Analysis of DNA Methylation Data and Transcriptome Data Identified a DNA Methylation-Dysregulated Four-LncRNA Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma.

Increasing evidence has demonstrated the crosstalk between DNA epigenetic alterations and aberrant expression of long non-coding RNAs (lncRNAs) during carcinogenesis. However, epigenetically dysregulated lncRNAs and their functional and clinical roles in Head and Neck Squamous Cell Carcinoma (HNSCC) are still not explored. In this study, we performed an integrative analysis of DNA methylation data and transcriptome data and identified a DNA methylation-dysregulated four-lncRNA signature (DNAMeFourLncSig) from 596 DNA methylation-dysregulated lncRNAs using a machine-learning-based feature selection method, which classified the patients of the discovery cohort into two risk groups with significantly different survival including overall survival, disease-specific survival, and progression-free survival. Then the DNAMeFourLncSig was implemented to another two HNSCC patient cohorts and showed similar prognostic values in both. Results from multivariable Cox regression analysis revealed that the DNAMeFourLncSig might be an independent prognostic factor. Furthermore, the DNAMeFourLncSig was substantially correlated with the complete response rate of chemotherapy and may predict chemotherapy response. Functional in silico analysis found that DNAMeFourLncSig-related mRNAs were mainly enriched in cell differentiation, tissue development and immune-related pathways. Overall, our study will improve our understanding of underlying transcriptional and epigenetic mechanisms in HNSCC carcinogenesis and provided a new potential biomarker for the prognosis of patients with HNSCC.