Statistical methods for detecting differentially methylated loci and regions

Differential methylation region detection via an array-adaptive normalized kernel-weighted model

A differentially methylated region (DMR) is a genomic region that has significantly different methylation patterns between biological conditions. Identifying DMRs between different biological conditions is critical for developing disease biomarkers. Although methods for detecting DMRs in microarray data have been introduced, developing methods with high precision, recall, and accuracy in determining the true length of DMRs remains a challenge. In this study, we propose a normalized kernel-weighted model to account for similar methylation profiles using the relative probe distance from "nearby" CpG sites. We also extend this model by proposing an array-adaptive version in attempt to account for the differences in probe spacing between Illumina's Infinium 450K and EPIC bead array respectively. We also study the asymptotic results of our proposed statistic. We compare our approach with a popular DMR detection method via simulation studies under large and small treatment effect settings. We also discuss the susceptibility of our method in detecting the true length of the DMRs under these two settings. Lastly, we demonstrate the biological usefulness of our method when combined with pathway analysis methods on oral cancer data. We have created an R package called idDMR, downloadable from GitHub repository with link: https://github.com/DanielAlhassan/idDMR, that allows for the convenient implementation of our array-adaptive DMR method.

Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model

DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.

Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions

We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data.

Automated calibration for stability selection in penalised regression and graphical models

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.

An ecologist's guide for studying DNA methylation variation in wild vertebrates

The field of molecular biology is advancing fast with new powerful technologies, sequencing methods and analysis software being developed constantly. Commonly used tools originally developed for research on humans and model species are now regularly used in ecological and evolutionary research. There is also a growing interest in the causes and consequences of epigenetic variation in natural populations. Studying ecological epigenetics is currently challenging, especially for vertebrate systems, because of the required technical expertise, complications with analyses and interpretation, and limitations in acquiring sufficiently high sample sizes. Importantly, neglecting the limitations of the experimental setup, technology and analyses may affect the reliability and reproducibility, and the extent to which unbiased conclusions can be drawn from these studies. Here, we provide a practical guide for researchers aiming to study DNA methylation variation in wild vertebrates. We review the technical aspects of epigenetic research, concentrating on DNA methylation using bisulfite sequencing, discuss the limitations and possible pitfalls, and how to overcome them through rigid and reproducible data analysis. This review provides a solid foundation for the proper design of epigenetic studies, a clear roadmap on the best practices for correct data analysis and a realistic view on the limitations for studying ecological epigenetics in vertebrates. This review will help researchers studying the ecological and evolutionary implications of epigenetic variation in wild populations.

Analysis of DNA methylation at birth and in childhood reveals changes associated with season of birth and latitude

BACKGROUND

Seasonal variations in environmental exposures at birth or during gestation are associated with numerous adult traits and health outcomes later in life. Whether DNA methylation (DNAm) plays a role in the molecular mechanisms underlying the associations between birth season and lifelong phenotypes remains unclear.

METHODS

We carried out epigenome-wide meta-analyses within the Pregnancy And Childhood Epigenetic Consortium to identify associations of DNAm with birth season, both at differentially methylated probes (DMPs) and regions (DMRs). Associations were examined at two time points: at birth (21 cohorts, N = 9358) and in children aged 1-11 years (12 cohorts, N = 3610). We conducted meta-analyses to assess the impact of latitude on birth season-specific associations at both time points.

RESULTS

We identified associations between birth season and DNAm (False Discovery Rate-adjusted p values < 0.05) at two CpGs at birth (winter-born) and four in the childhood (summer-born) analyses when compared to children born in autumn. Furthermore, we identified twenty-six differentially methylated regions (DMR) at birth (winter-born: 8, spring-born: 15, summer-born: 3) and thirty-two in childhood (winter-born: 12, spring and summer: 10 each) meta-analyses with few overlapping DMRs between the birth seasons or the two time points. The DMRs were associated with genes of known functions in tumorigenesis, psychiatric/neurological disorders, inflammation, or immunity, amongst others. Latitude-stratified meta-analyses [higher (≥ 50°N), lower (< 50°N, northern hemisphere only)] revealed differences in associations between birth season and DNAm by birth latitude. DMR analysis implicated genes with previously reported links to schizophrenia (LAX1), skin disorders (PSORS1C, LTB4R), and airway inflammation including asthma (LTB4R), present only at birth in the higher latitudes (≥ 50°N).

CONCLUSIONS

In this large epigenome-wide meta-analysis study, we provide evidence for (i) associations between DNAm and season of birth that are unique for the seasons of the year (temporal effect) and (ii) latitude-dependent variations in the seasonal associations (spatial effect). DNAm could play a role in the molecular mechanisms underlying the effect of birth season on adult health outcomes.

Differentially methylated genomic regions of lettuce seeds relate to divergence across morphologically distinct horticultural types

Heritable cytosine methylation plays a role in shaping plant phenotypes; however, no information is available about DNA methylation in cultivated lettuce (Lactuca sativa), one of the most important leafy vegetables. Whole-genome bisulfite sequencing (WGBS) performed on seeds of 95 accessions from eight morphologically distinct horticultural types (Batavia, butterhead, iceberg, Latin, leaf, oilseed, romaine and stem) revealed a high level of methylation in lettuce genome with an average methylation of 90.6 % in the CG context, 72.9 % in the CHG context and 7.5 % in the CHH context. Although WGBS did not show substantial differences in overall methylation levels across eight horticultural types, 350 differentially methylated regions (DMR) were identified. Majority of the 41 pivotal DMR overlapped with genomic features predicted or confirmed to be involved in plant growth and development. These results provide the first insight into lettuce DNA methylation and indicate a potential role for heritable variation in cytosine methylation in lettuce morphology. The results reveal that differences in methylation profiles of morphologically distinct horticultural types are already detectable in seeds. Identified DMR can be a focus of the future functional studies.

Epigenome-Wide Associations of Placental DNA Methylation and Behavioral and Emotional Difficulties in Children at 3 Years of Age

The placenta is a key organ for fetal and brain development. Its epigenome can be regarded as a biochemical record of the prenatal environment and a potential mechanism of its association with the future health of the fetus. We investigated associations between placental DNA methylation levels and child behavioral and emotional difficulties, assessed at 3 years of age using the Strengths and Difficulties Questionnaire (SDQ) in 441 mother-child dyads from the EDEN cohort. Hypothesis-driven and exploratory analyses (on differentially methylated probes (EWAS) and regions (DMR)) were adjusted for confounders, technical factors, and cell composition estimates, corrected for multiple comparisons, and stratified by child sex. Hypothesis-driven analyses showed an association of cg26703534 (AHRR) with emotional symptoms, and exploratory analyses identified two probes, cg09126090 (intergenic region) and cg10305789 (PPP1R16B), as negatively associated with peer relationship problems, as well as 33 DMRs, mostly positively associated with at least one of the SDQ subscales. Among girls, most associations were seen with emotional difficulties, whereas in boys, DMRs were as much associated with emotional than behavioral difficulties. This study provides the first evidence of associations between placental DNA methylation and child behavioral and emotional difficulties. Our results suggest sex-specific associations and might provide new insights into the mechanisms of neurodevelopment.

Epigenetic Regulation of Inflammatory Mechanisms and a Psychological Symptom Cluster in Patients Receiving Chemotherapy

BACKGROUND

A psychological symptom cluster is the most common cluster identified in oncology patients. Although inflammatory mechanisms are hypothesized to underlie this cluster, epigenetic contributions are unknown.

OBJECTIVES

This study's purpose was to evaluate associations between the occurrence of a psychological symptom cluster and levels of DNA methylation for inflammatory genes in a heterogeneous sample of patients with cancer receiving chemotherapy.

METHODS

Prior to their second or third cycle of chemotherapy, 1,071 patients reported the occurrence of 38 symptoms using the Memorial Symptom Assessment Scale. A psychological cluster was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using Illumina Infinium 450K and EPIC microarrays. Expression-associated CpG (eCpG) loci in the promoter region of 114 inflammatory genes on the 450K and 112 genes on the EPIC microarray were evaluated for associations with the psychological cluster. Robust rank aggregation was used to identify differentially methylated genes across both samples. Significance was assessed using a false discovery rate of 0.05 under the Benjamini-Hochberg procedure.

RESULTS

Cluster of differentiation 40 ( CD40 ) was differentially methylated across both samples. All six promoter eCpGs for CD40 that were identified across both samples were hypomethylated in the psychological cluster group.

CONCLUSIONS

This study is the first to suggest associations between a psychological symptom cluster and differential DNA methylation of a gene involved in tissue inflammation and cell-mediated immunity. Our findings suggest that increased CD40 expression through hypomethylation of promoter eCpG loci is involved in the occurrence of a psychological symptom cluster in patients receiving chemotherapy. These findings suggest a direction for mechanistic studies.

Re-analysis of publicly available methylomes using signal detection yields new information

Cytosine methylation is an epigenetic mark that participates in regulation of gene expression and chromatin stability in plants. Advancements in whole genome sequencing technologies have enabled investigation of methylome dynamics under different conditions. However, the computational methods for analyzing bisulfite sequence data have not been unified. Contention remains in the correlation of differentially methylated positions with the investigated treatment and exclusion of noise, inherent to these stochastic datasets. The prevalent approaches apply Fisher's exact test, logistic, or beta regression, followed by an arbitrary cut-off for differences in methylation levels. A different strategy, the MethylIT pipeline, utilizes signal detection to determine cut-off based on a fitted generalized gamma probability distribution of methylation divergence. Re-analysis of publicly available BS-seq data from two epigenetic studies in Arabidopsis and applying MethylIT revealed additional, previously unreported results. Methylome repatterning in response to phosphate starvation was confirmed to be tissue-specific and included phosphate assimilation genes in addition to sulfate metabolism genes not implicated in the original study. During seed germination plants undergo major methylome reprogramming and use of MethylIT allowed us to identify stage-specific gene networks. We surmise from these comparative studies that robust methylome experiments must account for data stochasticity to achieve meaningful functional analyses.

LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model

BACKGROUND

DNA methylation plays an important role in studying the epigenetics of various biological processes including many diseases. Although differential methylation of individual cytosines can be informative, given that methylation of neighboring CpGs are typically correlated, analysis of differentially methylated regions is often of more interest.

RESULTS

We have developed a probabilistic method and software, LuxHMM, that uses hidden Markov model (HMM) to segment the genome into regions and a Bayesian regression model, which allows handling of multiple covariates, to infer differential methylation of regions. Moreover, our model includes experimental parameters that describe the underlying biochemistry in bisulfite sequencing and model inference is done using either variational inference for efficient genome-scale analysis or Hamiltonian Monte Carlo (HMC).

CONCLUSIONS

Analyses of real and simulated bisulfite sequencing data demonstrate the competitive performance of LuxHMM compared with other published differential methylation analysis methods.

Common DNA methylation changes in biliary tract cancers identify subtypes with different immune characteristics and clinical outcomes

BACKGROUND

DNA methylation-associated studies on biliary tract cancer (BTC), including cholangiocarcinoma (CCA) and gallbladder cancer (GBC), may improve the BTC classification scheme. We proposed to identify the shared methylation changes of BTCs and investigate their associations with genomic aberrations, immune characteristics, and survival outcomes.

METHODS

Multi-dimensional data concerning mutation, DNA methylation, immune-related features, and clinical data of 57 CCAs and 48 GBCs from Eastern Hepatobiliary Surgery Hospital (EHSH) and 36 CCAs in the TCGA-CHOL cohort were analyzed.

RESULTS

In our cohort including 24 intrahepatic CCAs (iCCAs), 20 perihilar CCAs (pCCAs), 13 distal CCAs (dCCAs), and 48 GBCs, 3369 common differentially methylated regions (DMRs) were identified by comparing tumor and non-tumor samples. A lower level of methylation changes of these common DMRs was associated with fewer copy number variations, fewer mutational burden, and remarkably longer overall survival (OS, hazard ratio [HR] = 0.07, 95% confidence interval [CI] 0.01-0.65, P = 0.017). Additionally, a 12-marker model was developed and validated for prognostication after curative surgery (HR = 0.21, 95% CI 0.10-0.43, P < 0.001), which exhibited undifferentiated prognostic effects in subgroups defined by anatomic location (iCCAs, d/pCCAs, GBCs), TNM stage, and tumor purity. Its prognostic utility remained significant in multivariable analysis (HR = 0.26, 95% CI 0.11-0.59, P = 0.001). Moreover, the BTCs with minimal methylation changes exhibited higher immune-related signatures, infiltration of CD8⁺ lymphocytes, and programmed death-ligand 1 (PD-L1) expression, indicating an inflamed tumor immune microenvironment (TIME) with PD-L1 expression elicited by immune attack, potentially suggesting better immunotherapy efficacy.

CONCLUSIONS

In BTCs, DNA methylation is a powerful tool for molecular classification, serving as a robust indicator of genomic aberrations, survival outcomes, and tumor immune microenvironment. Our integrative analysis provides insights into the prognostication after curative surgery and patient selection for immunotherapy.

A novel nonparametric computational strategy for identifying differential methylation regions

Background

DNA methylation has long been known as an epigenetic gene silencing mechanism. For a motivating example, the methylomes of cancer and non-cancer cells show a number of methylation differences, indicating that certain features characteristics of cancer cells may be related to methylation characteristics. Robust methods for detecting differentially methylated regions (DMRs) could help scientists narrow down genome regions and even find biologically important regions. Although some statistical methods were developed for detecting DMR, there is no default or strongest method. Fisher’s exact test is direct, but not suitable for data with multiple replications, while regression-based methods usually come with a large number of assumptions. More complicated methods have been proposed, but those methods are often difficult to interpret.

Results

In this paper, we propose a three-step nonparametric kernel smoothing method that is both flexible and straightforward to implement and interpret. The proposed method relies on local quadratic fitting to find the set of equilibrium points (points at which the first derivative is 0) and the corresponding set of confidence windows. Potential regions are further refined using biological criteria, and finally selected based on a Bonferroni adjusted t-test cutoff. Using a comparison of three senescent and three proliferating cell lines to illustrate our method, we were able to identify a total of 1077 DMRs on chromosome 21.

Conclusions

We proposed a completely nonparametric, statistically straightforward, and interpretable method for detecting differentially methylated regions. Compared with existing methods, the non-reliance on model assumptions and the straightforward nature of our method makes it one competitive alternative to the existing statistical methods for defining DMRs.

Differential Methylation Analysis for Bisulfite Sequencing (BS-Seq) Data

Bisulfite sequencing (BS-seq) technology has enabled the detection and measurement of DNA methylation at the single-nucleotide level. A fundamental question in functional epigenomics research is whether DNA methylation varies under different biological contexts. Thus, identifying differentially methylated loci/regions (DML/DMRs) is a key task in BS-seq data analysis. Here we describe detailed procedures to perform differential methylation analyses for BS-seq using the Bioconductor package DSS. The analysis scheme in this chapter will guide researchers through differential methylation analyses by providing step-by-step instructions for analytical tools.

Epigenetics of Aggression

Aggression is a complex behavioral trait modulated by both genetic and environmental influences on gene expression. By controlling gene expression in a reversible yet potentially lasting manner in response to environmental stimulation, epigenetic mechanisms represent prime candidates in explaining both individual differences in aggression and the development of elevated aggressive behaviors following life adversity. In this manuscript, we review the evidence for an epigenetic basis in the development and expression of aggression in both humans and related preclinical animal models. In particular, we discuss reports linking DNA methylation, histone post-translational modifications, as well as non-coding RNA, to the regulation of a variety of genes implicated in the neurobiology of aggression including neuropeptides, the serotoninergic and dopaminergic systems, and stress response related systems. While clinical reports do reveal interesting patterns of DNA methylation underlying individual differences and experience-induced aggressive behaviors, they do, in general, face the challenge of linking peripheral observations to central nervous system regulations. Preclinical studies, on the other hand, provide detailed mechanistic insights into the epigenetic reprogramming of gene expression following life adversities. Although the functional link to aggression remains unclear in most, these studies together do highlight the involvement of epigenetic events driven by DNA methylation, histone modifications, and non-coding RNA in the neuroadaptations underlying the development and expression of aggression.

ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw

BACKGROUND

Whole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid's parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO was made simple to set up, run and interpret, and its implementation ensures reproducibility by including both package management and containerization.

RESULTS

We evaluated ARPEGGIO in two ways. First, we tested EAGLE-RC's performance with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. Second, using the same initial dataset, we show agreement between ARPEGGIO's output and published results. Compared to other similar workflows, ARPEGGIO is the only one supporting polyploid data.

CONCLUSIONS

The goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation. ARPEGGIO is available at https://github.com/supermaxiste/ARPEGGIO .

Introduction to Single-Cell DNA Methylation Profiling Methods

DNA methylation is an epigenetic mechanism that is related to mammalian cellular differentiation, gene expression regulation, and disease. In several studies, DNA methylation has been identified as an effective marker to identify differences between cells. In this review, we introduce single-cell DNA-methylation profiling methods, including experimental strategies and approaches to computational data analysis. Furthermore, the blind spots of the basic analysis and recent alternatives are briefly described. In addition, we introduce well-known applications and discuss future development.

Combinatorial therapy in tumor microenvironment: Where do we stand?

The tumor microenvironment plays a pivotal role in tumor initiation and progression by creating a dynamic interaction with cancer cells. The tumor microenvironment consists of various cellular components, including endothelial cells, fibroblasts, pericytes, adipocytes, immune cells, cancer stem cells and vasculature, which provide a sustained environment for cancer cell proliferation. Currently, targeting tumor microenvironment is increasingly being explored as a novel approach to improve cancer therapeutics, as it influences the growth and expansion of malignant cells in various ways. Despite continuous advancements in targeted therapies for cancer treatment, drug resistance, toxicity and immune escape mechanisms are the basis of treatment failure and cancer escape. Targeting tumor microenvironment efficiently with approved drugs and combination therapy is the solution to this enduring challenge that involves combining more than one treatment modality such as chemotherapy, surgery, radiotherapy, immunotherapy and nanotherapy that can effectively and synergistically target the critical pathways associated with disease pathogenesis. This review shed light on the composition of the tumor microenvironment, interaction of different components within tumor microenvironment with tumor cells and associated hallmarks, the current status of combinatorial therapies being developed, and various growing advancements. Furthermore, computational tools can also be used to monitor the significance and outcome of therapies being developed. We addressed the perceived barriers and regulatory hurdles in developing a combinatorial regimen and evaluated the present status of these therapies in the clinic. The accumulating depth of knowledge about the tumor microenvironment in cancer may facilitate further development of effective treatment modalities. This review presents the tumor microenvironment as a sweeping landscape for developing novel cancer therapies.

Epigenetic Alterations of Maternal Tobacco Smoking during Pregnancy: A Narrative Review

In utero exposure to maternal tobacco smoking is the leading cause of birth complications in addition to being associated with later impairment in child’s development. Epigenetic alterations, such as DNA methylation (DNAm), miRNAs expression, and histone modifications, belong to possible underlying mechanisms linking maternal tobacco smoking during pregnancy and adverse birth outcomes and later child’s development. The aims of this review were to provide an update on (1) the main results of epidemiological studies on the impact of in utero exposure to maternal tobacco smoking on epigenetic mechanisms, and (2) the technical issues and methods used in such studies. In contrast with miRNA and histone modifications, DNAm has been the most extensively studied epigenetic mechanism with regard to in utero exposure to maternal tobacco smoking. Most studies relied on cord blood and children’s blood, but placenta is increasingly recognized as a powerful tool, especially for markers of pregnancy exposures. Some recent studies suggest reversibility in DNAm in certain genomic regions as well as memory of smoking exposure in DNAm in other regions, upon smoking cessation before or during pregnancy. Furthermore, reversibility could be more pronounced in miRNA expression compared to DNAm. Increasing evidence based on longitudinal data shows that maternal smoking-associated DNAm changes persist during childhood. In this review, we also discuss some issues related to cell heterogeneity as well as downstream statistical analyses used to relate maternal tobacco smoking during pregnancy and epigenetics. The epigenetic effects of maternal smoking during pregnancy have been among the most widely investigated in the epigenetic epidemiology field. However, there are still huge gaps to fill in, including on the impact on miRNA expression and histone modifications to get a better view of the whole epigenetic machinery. The consistency of maternal tobacco smoking effects across epigenetic marks and across tissues will also provide crucial information for future studies. Advancement in bioinformatic and biostatistics approaches is key to develop a comprehensive analysis of these biological systems.

Differential Methylation in the GSTT1 Regulatory Region in Sudden Unexplained Death and Sudden Unexpected Death in Epilepsy

Sudden cardiac death (SCD) is a diagnostic challenge in forensic medicine. In a relatively large proportion of the SCDs, the deaths remain unexplained after autopsy. This challenge is likely caused by unknown disease mechanisms. Changes in DNA methylation have been associated with several heart diseases, but the role of DNA methylation in SCD is unknown. In this study, we investigated DNA methylation in two SCD subtypes, sudden unexplained death (SUD) and sudden unexpected death in epilepsy (SUDEP). We assessed DNA methylation of more than 850,000 positions in cardiac tissue from nine SUD and 14 SUDEP cases using the Illumina Infinium MethylationEPIC BeadChip. In total, six differently methylated regions (DMRs) between the SUD and SUDEP cases were identified. The DMRs were located in proximity to or overlapping genes encoding proteins that are a part of the glutathione S-transferase (GST) superfamily. Whole genome sequencing (WGS) showed that the DNA methylation alterations were not caused by genetic changes, while whole transcriptome sequencing (WTS) showed that DNA methylation was associated with expression levels of the GSTT1 gene. In conclusion, our results indicate that cardiac DNA methylation is similar in SUD and SUDEP, but with regional differential methylation in proximity to GST genes.

Discovery and validation of methylation signatures in blood-based circulating tumor cell-free DNA in early detection of colorectal carcinoma: a case-control study

BACKGROUND

Early detection of colorectal carcinoma (CRC) would help to identify tumors when curative treatments are available and beneficial. However, current screening methods for CRC, e.g., colonoscopy, may affect patients' compliance due to the uncomfortable, invasive and time-consuming process. In recent decades, methylation profiles of blood-based circulating tumor DNA (ctDNA) have shown promising results in the early detection of multiple tumors. Here we conducted a study to investigate the performance of ctDNA methylation markers in early detection of CRC.

RESULTS

In total, 742 participants were enrolled in the study including CRC (n = 332), healthy control (n = 333), benign colorectal disease (n = 65) and advanced adenoma (n = 12). After age-matched and randomization, 298 participants (149 cancer and 149 healthy control) were included in training set and 141 (67 cancer and 74 healthy control) were in test set. In the training set, the specificity was 89.3% (83.2-93.7%) and the sensitivity was 88.6% (82.4-93.2%). In terms of different stages, the sensitivities were 79.4% (62.1-91.2%) in patients with stage I, 88.9% (77.3-95.8%) in patients with stage II, 91.4% (76.9-98.2%) in patients with stage III and 96.2% (80.3-99.9%) in patients with stage IV. Similar results were validated in the test set with the specificity of 91.9% (83.1-97.0%) and sensitivity of 83.6% (72.5-91.6%). Sensitivities for stage I-III were 87.0% (79.7-92.4%) in the training set and 82.5% (70.2-91.3%) in the test set, respectively. In the unmatched total population, the positive ratios were 7.8% (5.2-11.2%) in healthy control, 30.8% (19.9-43.5%) in benign colorectal disease and 58.3% (27.5-84.7%) in advanced adenoma, while the sensitivities of stage I-IV were similar with training and test sets. Compared with methylated SEPT9 model, the present model had higher sensitivity (87.0% [81.8-91.2%] versus 41.2% [34.6-48.1%], P < 0.001) under comparable specificity (90.1% [85.4-93.7%] versus 90.6% [86.0-94.1%]).

CONCLUSIONS

Together our findings showed that ctDNA methylation markers were promising in the early detection of CRC. Further validation of this model is warranted in prospective studies.

Childhood DNA methylation as a marker of early life rapid weight gain and subsequent overweight

BACKGROUND

High early postnatal weight gain has been associated with childhood adiposity; however, the mechanism remains unknown. DNA methylation is a hypothesised mechanism linking early life exposures and subsequent disease. However, epigenetic changes associated with high early weight gain have not previously been investigated. Our aim was to investigate the associations between early weight gain, peripheral blood DNA methylation, and subsequent overweight/obese. Data from the UK Avon Longitudinal study of Parents and Children (ALSPAC) cohort were used to estimate associations between early postnatal weight gain and epigenome-wide DNA CpG site methylation (Illumina 450 K Methylation Beadchip) in blood in childhood (n = 125) and late adolescence (n = 96). High weight gain in the first year (a change in weight z-scores > 0.67), both unconditional (rapid weight gain) and conditional on birthweight (rapid thrive), was related to individual CpG site methylation and across regions using the meffil pipeline, with and without adjustment for cell type proportions, and with 5% false discovery rate correction. Variation in methylation at high weight gain-associated CpG sites was then examined with regard to body composition measures in childhood and adolescence. Replication of the differentially methylated CpG sites was sought using whole-blood DNA samples from 104 children from the UK Southampton Women's Survey.

RESULTS

Rapid infant weight gain was associated with small (+ 1% change) increases in childhood methylation (age 7) for two distinct CpG sites (cg01379158 (NT5M) and cg11531579 (CHFR)). Childhood methylation at one of these CpGs (cg11531579) was also higher in those who experienced rapid weight gain and were subsequently overweight/obese in adolescence (age 17). Rapid weight gain was not associated with differential DNA methylation in adolescence. Childhood methylation at the cg11531579 site was also suggestively associated with rapid weight gain in the replication cohort.

CONCLUSIONS

This study identified associations between rapid weight gain in infancy and small increases in childhood methylation at two CpG sites, one of which was replicated and was also associated with subsequent overweight/obese. It will be important to determine whether loci are markers of early rapid weight gain across different, larger populations. The mechanistic relevance of these differentially methylated sites requires further investigation.

Cell-Free DNA-Methylation-Based Methods and Applications in Oncology

Liquid biopsy based on cell-free DNA (cfDNA) enables non-invasive dynamic assessment of disease status in patients with cancer, both in the early and advanced settings. The analysis of DNA-methylation (DNAm) from cfDNA samples holds great promise due to the intrinsic characteristics of DNAm being more prevalent, pervasive, and cell- and tumor-type specific than genomics, for which established cfDNA assays already exist. Herein, we report on recent advances on experimental strategies for the analysis of DNAm in cfDNA samples. We describe the main steps of DNAm-based analysis workflows, including pre-analytics of cfDNA samples, DNA treatment, assays for DNAm evaluation, and methods for data analysis. We report on protocols, biomolecular techniques, and computational strategies enabling DNAm evaluation in the context of cfDNA analysis, along with practical considerations on input sample requirements and costs. We provide an overview on existing studies exploiting cell-free DNAm biomarkers for the detection and monitoring of cancer in early and advanced settings, for the evaluation of drug resistance, and for the identification of the cell-of-origin of tumors. Finally, we report on DNAm-based tests approved for clinical use and summarize their performance in the context of liquid biopsy.

DNA methylation during human adipogenesis and the impact of fructose

BACKGROUND

Increased adipogenesis and altered adipocyte function contribute to the development of obesity and associated comorbidities. Fructose modified adipocyte metabolism compared to glucose, but the regulatory mechanisms and consequences for obesity are unknown. Genome-wide methylation and global transcriptomics in SGBS pre-adipocytes exposed to 0, 2.5, 5, and 10 mM fructose, added to a 5-mM glucose-containing medium, were analyzed at 0, 24, 48, 96, 192, and 384 h following the induction of adipogenesis.

RESULTS

Time-dependent changes in DNA methylation compared to baseline (0 h) occurred during the final maturation of adipocytes, between 192 and 384 h. Larger percentages (0.1% at 192 h, 3.2% at 384 h) of differentially methylated regions (DMRs) were found in adipocytes differentiated in the glucose-containing control media compared to adipocytes differentiated in fructose-supplemented media (0.0006% for 10 mM, 0.001% for 5 mM, and 0.005% for 2.5 mM at 384 h). A total of 1437 DMRs were identified in 5237 differentially expressed genes at 384 h post-induction in glucose-containing (5 mM) control media. The majority of them inversely correlated with the gene expression, but 666 regions were positively correlated to the gene expression.

CONCLUSIONS

Our studies demonstrate that DNA methylation regulates or marks the transformation of morphologically differentiating adipocytes (seen at 192 h), to the more mature and metabolically robust adipocytes (as seen at 384 h) in a genome-wide manner. Lower (2.5 mM) concentrations of fructose have the most robust effects on methylation compared to higher concentrations (5 and 10 mM), suggesting that fructose may be playing a signaling/regulatory role at lower concentrations of fructose and as a substrate at higher concentrations.

DAMEfinder: a method to detect differential allele-specific methylation

Background

DNA methylation is a highly studied epigenetic signature that is associated with regulation of gene expression, whereby genes with high levels of promoter methylation are generally repressed. Genomic imprinting occurs when one of the parental alleles is methylated, i.e., when there is inherited allele-specific methylation (ASM). A special case of imprinting occurs during X chromosome inactivation in females, where one of the two X chromosomes is silenced, to achieve dosage compensation between the sexes. Another more widespread form of ASM is sequence dependent (SD-ASM), where ASM is linked to a nearby heterozygous single nucleotide polymorphism (SNP).

Results

We developed a method to screen for genomic regions that exhibit loss or gain of ASM in samples from two conditions (treatments, diseases, etc.). The method relies on the availability of bisulfite sequencing data from multiple samples of the two conditions. We leverage other established computational methods to screen for these regions within a new R package called DAMEfinder. It calculates an ASM score for all CpG sites or pairs in the genome of each sample, and then quantifies the change in ASM between conditions. It then clusters nearby CpG sites with consistent change into regions. In the absence of SNP information, our method relies only on reads to quantify ASM. This novel ASM score compares favorably to current methods that also screen for ASM. Not only does it easily discern between imprinted and non-imprinted regions, but also females from males based on X chromosome inactivation. We also applied DAMEfinder to a colorectal cancer dataset and observed that colorectal cancer subtypes are distinguishable according to their ASM signature. We also re-discover known cases of loss of imprinting.

Conclusion

We have designed DAMEfinder to detect regions of differential ASM (DAMEs), which is a more refined definition of differential methylation, and can therefore help in breaking down the complexity of DNA methylation and its influence in development and disease.

A blind and independent benchmark study for detecting differeally methylated regions in plants

MOTIVATION

Bisulfite sequencing (BS-seq) is a state-of-the-art technique for investigating methylation of the DNA to gain insights into the epigenetic regulation. Several algorithms have been published for identification of differentially methylated regions (DMRs). However, the performances of the individual methods remain unclear and it is difficult to optimally select an algorithm in application settings.

RESULTS

We analyzed BS-seq data from four plants covering three taxonomic groups. We first characterized the data using multiple summary statistics describing methylation levels, coverage, noise, as well as frequencies, magnitudes and lengths of methylated regions. Then, simulated datasets with most similar characteristics to real experimental data were created. Seven different algorithms (metilene, methylKit, MOABS, DMRcate, Defiant, BSmooth, MethylSig) for DMR identification were applied and their performances were assessed. A blind and independent study design was chosen to reduce bias and to derive practical method selection guidelines. Overall, metilene had superior performance in most settings. Data attributes, such as coverage and spread of the DMR lengths, were found to be useful for selecting the best method for DMR detection. A decision tree to select the optimal approach based on these data attributes is provided. The presented procedure might serve as a general strategy for deriving algorithm selection rules tailored to demands in specific application settings.

AVAILABILITY AND IMPLEMENTATION

Scripts that were used for the analyses and that can be used for prediction of the optimal algorithm are provided at https://github.com/kreutz-lab/DMR-DecisionTree. Simulated and experimental data are available at https://doi.org/10.6084/m9.figshare.11619045.

CONTACT

ckreutz@imbi.uni-freiburg.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA methylation analysis in plants: review of computational tools and future perspectives

Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.

coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes

Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

Computational approaches for characterizing the tumor immune microenvironment

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

Differentially methylated regions in bipolar disorder and suicide

The addition of a methyl group to, typically, a cytosine-guanine dinucleotide (CpG) creates distinct DNA methylation patterns across the genome that can regulate gene expression. Aberrant DNA methylation of CpG sites has been associated with many psychiatric disorders including bipolar disorder (BD) and suicide. Using the SureSelect^XT system, Methyl-Seq, we investigated the DNA methylation status of CpG sites throughout the genome in 50 BD individuals (23 subjects who died by suicide and 27 subjects who died from other causes) and 31 nonpsychiatric controls. We identified differentially methylated regions (DMRs) from three analyses: (a) BD subjects compared to nonpsychiatric controls (BD-NC), (b) BD subjects who died by suicide compared to nonpsychiatric controls (BDS-NC), and (c) BDS subjects compared to BD subjects who died from other causes (BDS-BDNS). One DMR from the BDS-NC analysis, located in ARHGEF38, was significantly hypomethylated (23.4%) in BDS subjects. This finding remained significant after multiple testing (P_Bootstrapped = 9.0 × 10^-3 ), was validated using pyrosequencing, and was more significant in males. A secondary analysis utilized Ingenuity Pathway Analysis to identify enrichment in nominally significant DMRs. This identified an association with several pathways including axonal guidance signaling, calcium signaling, β-adrenergic signaling, and opioid signaling. Our comprehensive study provides further support that DNA methylation alterations influence the risk for BD and suicide. However, further investigation is required to confirm these associations and identify their functional consequences.

Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.

Umap and Bismap: quantifying genome and methylome mappability

Short-read sequencing enables assessment of genetic and biochemical traits of individual genomic regions, such as the location of genetic variation, protein binding and chemical modifications. Every region in a genome assembly has a property called 'mappability', which measures the extent to which it can be uniquely mapped by sequence reads. In regions of lower mappability, estimates of genomic and epigenomic characteristics from sequencing assays are less reliable. These regions have increased susceptibility to spurious mapping from reads from other regions of the genome with sequencing errors or unexpected genetic variation. Bisulfite sequencing approaches used to identify DNA methylation exacerbate these problems by introducing large numbers of reads that map to multiple regions. Both to correct assumptions of uniformity in downstream analysis and to identify regions where the analysis is less reliable, it is necessary to know the mappability of both ordinary and bisulfite-converted genomes. We introduce the Umap software for identifying uniquely mappable regions of any genome. Its Bismap extension identifies mappability of the bisulfite-converted genome. A Umap and Bismap track hub for human genome assemblies GRCh37/hg19 and GRCh38/hg38, and mouse assemblies GRCm37/mm9 and GRCm38/mm10 is available at https://bismap.hoffmanlab.org for use with genome browsers.

Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing

With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here, we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation. We find that the new method improves the specificity and sensitivity of lists of regions and accurately controls the false discovery rate.

A survey of the approaches for identifying differential methylation using bisulfite sequencing data

DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data; however, there is no clear consensus among existing approaches. A comprehensive survey of these approaches would be of great benefit to potential users and researchers to get a complete picture of the available resources. In this article, we present a detailed survey of 22 such approaches focusing on their underlying statistical models, primary features, key advantages and major limitations. Importantly, the intrinsic drawbacks of the approaches pointed out in this survey could potentially be addressed by future research.

Ranking genomic features using an information-theoretic measure of epigenetic discordance

Background

Establishment and maintenance of DNA methylation throughout the genome is an important epigenetic mechanism that regulates gene expression whose disruption has been implicated in human diseases like cancer. It is therefore crucial to know which genes, or other genomic features of interest, exhibit significant discordance in DNA methylation between two phenotypes. We have previously proposed an approach for ranking genes based on methylation discordance within their promoter regions, determined by centering a window of fixed size at their transcription start sites. However, we cannot use this method to identify statistically significant genomic features and handle features of variable length and with missing data.

Results

We present a new approach for computing the statistical significance of methylation discordance within genomic features of interest in single and multiple test/reference studies. We base the proposed method on a well-articulated hypothesis testing problem that produces p- and q-values for each genomic feature, which we then use to identify and rank features based on the statistical significance of their epigenetic dysregulation. We employ the information-theoretic concept of mutual information to derive a novel test statistic, which we can evaluate by computing Jensen-Shannon distances between the probability distributions of methylation in a test and a reference sample. We design the proposed methodology to simultaneously handle biological, statistical, and technical variability in the data, as well as variable feature lengths and missing data, thus enabling its wide-spread use on any list of genomic features. This is accomplished by estimating, from reference data, the null distribution of the test statistic as a function of feature length using generalized additive regression models. Differential assessment, using normal/cancer data from healthy fetal tissue and pediatric high-grade glioma patients, illustrates the potential of our approach to greatly facilitate the exploratory phases of clinically and biologically relevant methylation studies.

Conclusions

The proposed approach provides the first computational tool for statistically testing and ranking genomic features of interest based on observed DNA methylation discordance in comparative studies that accounts, in a rigorous manner, for biological, statistical, and technical variability in methylation data, as well as for variability in feature length and for missing data.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2777-6) contains supplementary material, which is available to authorized users.

An integrated package for bisulfite DNA methylation data analysis with Indel-sensitive mapping

Background

DNA methylation plays crucial roles in most eukaryotic organisms. Bisulfite sequencing (BS-Seq) is a sequencing approach that provides quantitative cytosine methylation levels in genome-wide scope and single-base resolution. However, genomic variations such as insertions and deletions (indels) affect methylation calling, and the alignment of reads near/across indels becomes inaccurate in the presence of polymorphisms. Hence, the simultaneous detection of DNA methylation and indels is important for exploring the mechanisms of functional regulation in organisms.

Results

These problems motivated us to develop the algorithm BatMeth2, which can align BS reads with high accuracy while allowing for variable-length indels with respect to the reference genome. The results from simulated and real bisulfite DNA methylation data demonstrated that our proposed method increases alignment accuracy. Additionally, BatMeth2 can calculate the methylation levels of individual loci, genomic regions or functional regions such as genes/transposable elements. Additional programs were also developed to provide methylation data annotation, visualization, and differentially methylated cytosine/region (DMC/DMR) detection. The whole package provides new tools and will benefit bisulfite data analysis.

Conclusion

BatMeth2 improves DNA methylation calling, particularly for regions close to indels. It is an autorun package and easy to use. In addition, a DNA methylation visualization program and a differential analysis program are provided in BatMeth2. We believe that BatMeth2 will facilitate the study of the mechanisms of DNA methylation in development and disease. BatMeth2 is an open source software program and is available on GitHub (https://github.com/GuoliangLi-HZAU/BatMeth2/).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2593-4) contains supplementary material, which is available to authorized users.

DRD4 methylation as a potential biomarker for physical aggression: An epigenome-wide, cross-tissue investigation

Epigenetic processes that regulate gene expression, such as DNA methylation (DNAm), have been linked to individual differences in physical aggression. Yet, it is currently unclear whether: (a) DNAm patterns in humans associate with physical aggression independently of other co-occurring psychiatric and behavioral symptoms; (b) whether these patterns are observable across multiple tissues; and (c) whether they may function as a causal versus noncausal biomarker of physical aggression. Here, we used a multisample, cross-tissue design to address these questions. First, we examined genome-wide DNAm patterns (buccal swabs; Illumina 450k) associated with engagement in physical fights in a sample of high-risk youth (n = 119; age = 16-24 years; 53% female). We identified one differentially methylated region in DRD4, which survived genome-wide correction, associated with physical aggression above and beyond co-occurring symptomatology (e.g., ADHD, substance use), and showed strong cross-tissue concordance with both blood and brain. Second, we found that DNAm sites within this region were also differentially methylated in an independent sample of young adults, between individuals with a history of chronic-high versus low physical aggression (peripheral T cells; ages 26-28). Finally, we ran a Mendelian randomization analysis using GWAS data from the EAGLE consortium to test for a causal association of DRD4 methylation with physical aggression. Only one genetic instrument was eligible for the analysis, and results provided no evidence for a causal association. Overall, our findings lend support for peripheral DRD4 methylation as a potential biomarker of physically aggressive behavior, with no evidence yet of a causal relationship.

Alzheimer's disease DNA methylome of pyramidal layers in frontal cortex: laser-assisted microdissection study

OBJECTIVE

To study DNA methylation patterns of cortical pyramidal layers susceptible to late-onset Alzheimer's disease (LOAD) neurodegeneration.

METHODS

Laser-assisted microdissection to select pyramidal layers' cells in frontal cortex of 32 human brains (18 LOAD) and Infinium DNA Methylation 450K analysis were performed to find differential methylated positions and regions, in addition to the corresponding gene set functional enrichment analyses.

RESULTS

Differential hypermethylation in several genomic regions and genes mainly in HOXA3, GSTP1, CXXC1-3 and BIN1. The functional enrichment analysis revealed genes significantly related to oxidative-stress and synapsis.

CONCLUSION

The present results indicate the differentially methylated genes related to neural projections, synapsis, oxidative stress and epigenetic regulator genes and represent the first epigenome of cortical pyramidal layers in LOAD.

Global, integrated analysis of methylomes and transcriptomes from laser capture microdissected bronchial and alveolar cells in human lung

Gene regulatory analysis of highly diverse human tissues in vivo is essentially constrained by the challenge of performing genome-wide, integrated epigenetic and transcriptomic analysis in small selected groups of specific cell types. Here we performed genome-wide bisulfite sequencing and RNA-seq from the same small groups of bronchial and alveolar cells isolated by laser capture microdissection from flash-frozen lung tissue of 12 donors and their peripheral blood T cells. Methylation and transcriptome patterns differed between alveolar and bronchial cells, while each of these epithelia showed more differences from mesodermally-derived T cells. Differentially methylated regions (DMRs) between alveolar and bronchial cells tended to locate at regulatory regions affecting promoters of 4,350 genes. A large number of pathways enriched for these DMRs including GTPase signal transduction, cell death, and skeletal muscle. Similar patterns of transcriptome differences were observed: 4,108 differentially expressed genes (DEGs) enriched in GTPase signal transduction, inflammation, cilium assembly, and others. Prioritizing using DMR-DEG regulatory network, we highlighted genes, e.g., ETS1, PPARG, and RXRG, at prominent alveolar vs. bronchial cell discriminant nodes. Our results show that multi-omic analysis of small, highly specific cells is feasible and yields unique physiologic loci distinguishing human lung cell types in situ.

Altered DNA methylation associated with a translocation linked to major mental illness

Recent work has highlighted a possible role for altered epigenetic modifications, including differential DNA methylation, in susceptibility to psychiatric illness. Here, we investigate blood-based DNA methylation in a large family where a balanced translocation between chromosomes 1 and 11 shows genome-wide significant linkage to psychiatric illness. Genome-wide DNA methylation was profiled in whole-blood-derived DNA from 41 individuals using the Infinium HumanMethylation450 BeadChip (Illumina Inc., San Diego, CA). We found significant differences in DNA methylation when translocation carriers (n = 17) were compared to related non-carriers (n = 24) at 13 loci. All but one of the 13 significant differentially methylated positions (DMPs) mapped to the regions surrounding the translocation breakpoints. Methylation levels of five DMPs were associated with genotype at SNPs in linkage disequilibrium with the translocation. Two of the five genes harbouring significant DMPs, DISC1 and DUSP10, have been previously shown to be differentially methylated in schizophrenia. Gene Ontology analysis revealed enrichment for terms relating to neuronal function and neurodevelopment among the genes harbouring the most significant DMPs. Differentially methylated region (DMR) analysis highlighted a number of genes from the MHC region, which has been implicated in psychiatric illness previously through genetic studies. We show that inheritance of a translocation linked to major mental illness is associated with differential DNA methylation at loci implicated in neuronal development/function and in psychiatric illness. As genomic rearrangements are over-represented in individuals with psychiatric illness, such analyses may be valuable more widely in the study of these conditions.

An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data

BACKGROUND

DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior. However, the performance of most currently available methods for methylation analysis is hampered by their inability to directly account for statistical dependencies between neighboring methylation sites, thus ignoring significant information available in WGBS reads.

RESULTS

We present a powerful information-theoretic approach for genome-wide modeling and analysis of WGBS data based on the 1D Ising model of statistical physics. This approach takes into account correlations in methylation by utilizing a joint probability model that encapsulates all information available in WGBS methylation reads and produces accurate results even when applied on single WGBS samples with low coverage. Using the Shannon entropy, our approach provides a rigorous quantification of methylation stochasticity in individual WGBS samples genome-wide. Furthermore, it utilizes the Jensen-Shannon distance to evaluate differences in methylation distributions between a test and a reference sample. Differential performance assessment using simulated and real human lung normal/cancer data demonstrate a clear superiority of our approach over DSS, a recently proposed method for WGBS data analysis. Critically, these results demonstrate that marginal methods become statistically invalid when correlations are present in the data.

CONCLUSIONS

This contribution demonstrates clear benefits and the necessity of modeling joint probability distributions of methylation using the 1D Ising model of statistical physics and of quantifying methylation stochasticity using concepts from information theory. By employing this methodology, substantial improvement of DNA methylation analysis can be achieved by effectively taking into account the massive amount of statistical information available in WGBS data, which is largely ignored by existing methods.

Identification of Differentially Methylated Sites with Weak Methylation Effects

Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments. Most statistical methods for DMC detection do not consider the dependency of methylation patterns across the genome, thus possibly inflating type I error. Furthermore, small sample sizes and weak methylation effects among different phenotype categories make it difficult for these statistical methods to accurately detect DMCs. To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs. To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit. Analyses of simulated data that replicated the effects of the herbicide glyphosate on DNA methylation in Arabidopsis thaliana show that WFMM results in higher sensitivity and specificity in detecting DMCs compared to methylKit, especially when the methylation differences among phenotype groups are small. Moreover, the performance of WFMM is robust with respect to small sample sizes, making it particularly attractive considering the current high costs of bisulfite sequencing. Analysis of empirical Arabidopsis thaliana data under varying glyphosate dosages, and the analysis of monozygotic (MZ) twins who have different pain sensitivities-both datasets have weak methylation effects of <1%-show that WFMM can identify more relevant DMCs related to the phenotype of interest than methylKit. Differentially methylated regions (DMRs) are genomic regions with different DNA methylation status across biological samples. DMRs and DMCs are essentially the same concepts, with the only difference being how methylation information across the genome is summarized. If methylation levels are determined by grouping neighboring cytosine sites, then they are DMRs; if methylation levels are calculated based on single cytosines, they are DMCs.

Use of methylation profiling to identify significant differentially methylated genes in bone marrow mesenchymal stromal cells from acute myeloid leukemia

The present study aimed to characterize the epigenetic architecture by studying the DNA methylation signature in bone marrow mesenchymal stem cells (BM‑MSCs) from patients with acute myeloid leukemia (AML). Microarray dataset GSE79695 was downloaded from the Gene Expression Omnibus database. Differentially methylated sites and differentially methylated CpG islands were identified in BM‑MSC samples from patients with AML compared with controls. MicroRNAs (miRs) encoding genes covering differentially methylated sites were found and the regulation network was constructed. Pathway enrichment analysis of hypermethylated genes and hypomethylated genes was performed, followed by protein‑protein interaction (PPI) network construction. Moreover, the identified differentially methylated genes were compared with the leukemia‑related marker/therapeutic genes from the literature. Overall, 228 hypermethylated CpG site probes covering 183 gene symbols and 523 hypomethylated CpG sites probes covering 362 gene symbols were identified in the BM‑MSCs from AML patients. Furthermore, 4 genes with CpG island hypermethylation were identified, including peptidase M20 domain containing 1 (PM20D1). The hsa‑miR‑596‑encoding gene MIR596 was found to be hypermethylated and the regulation network based on hsa‑miR‑596 and its targets (such as cytochrome P450 family 1 subfamily B member 1) was constructed. Hypermethylated and hypomethylated genes were enriched in different Kyoto Encyclopedia of Genes and Genomes pathways, including 'hsa05221: Acute myeloid leukemia' and 'hsa05220: Chronic myeloid leukemia', which the hypomethylated gene mitogen‑activated protein kinase 3 (MAPK3) was involved in. In addition, MAPK3, lysine demethylase 2B and RAP1A, member of RAS oncogene family were hubs in the PPI network of methylated genes. In conclusion, PM20D1 with hypermethylation of CpG islands may be associated with the energy expenditure of patients with AML. Furthermore, the aberrantly hypermethylated miR‑159‑encoding gene MIR159 may be a potential biomarker of AML.

Estimation of a significance threshold for epigenome-wide association studies

Epigenome-wide association studies (EWAS) are designed to characterise population-level epigenetic differences across the genome and link them to disease. Most commonly, they assess DNA-methylation status at cytosine-guanine dinucleotide (CpG) sites, using platforms such as the Illumina 450k array that profile a subset of CpGs genome wide. An important challenge in the context of EWAS is determining a significance threshold for declaring a CpG site as differentially methylated, taking multiple testing into account. We used a permutation method to estimate a significance threshold specifically for the 450k array and a simulation extrapolation approach to estimate a genome-wide threshold. These methods were applied to five different EWAS datasets derived from a variety of populations and tissue types. We obtained an estimate of α=2.4×10-7 for the 450k array, and a genome-wide estimate of α=3.6×10-8. We further demonstrate the importance of these results by showing that previously recommended sample sizes for EWAS should be adjusted upwards, requiring samples between ∼10% and ∼20% larger in order to maintain type-1 errors at the desired level.

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

Modern high-throughput biotechnologies such as microarray and next generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high-throughput experiment, only limited amount of data are observed for each individual feature, thus the classical 'large p, small n' problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical features, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the over-correction problem and propose several alternative solutions. Our strategy is rooted in the fact that in the Big Data era, large amount of historical data are available which should be taken advantage of. Our strategy presents a new framework to enhance the Bayesian hierarchical model. Through simulation and real data analysis, we demonstrated superior performance of the proposed strategy. Our new strategy also enables borrowing information across different platforms which could be extremely useful with emergence of new technologies and accumulation of data from different platforms in the Big Data era. Our method has been implemented in R package "adaptiveHM", which is freely available from https://github.com/benliemory/adaptiveHM.

Methods for discovering genomic loci exhibiting complex patterns of differential methylation

BACKGROUND

Cytosine methylation is widespread in most eukaryotic genomes and is known to play a substantial role in various regulatory pathways. Unmethylated cytosines may be converted to uracil through the addition of sodium bisulphite, allowing genome-wide quantification of cytosine methylation via high-throughput sequencing. The data thus acquired allows the discovery of methylation 'loci'; contiguous regions of methylation consistently methylated across biological replicates. The mapping of these loci allows for associations with other genomic factors to be identified, and for analyses of differential methylation to take place.

RESULTS

The segmentSeq R package is extended to identify methylation loci from high-throughput sequencing data from multiple experimental conditions. A statistical model is then developed that accounts for biological replication and variable rates of non-conversion of cytosines in each sample to compute posterior likelihoods of methylation at each locus within an empirical Bayesian framework. The same model is used as a basis for analysis of differential methylation between multiple experimental conditions with the baySeq R package. We demonstrate the capability of this method to analyse complex data sets in an analysis of data derived from multiple Dicer-like mutants in Arabidopsis. This reveals several novel behaviours at distinct sets of loci in response to loss of one or more of the Dicer-like proteins that indicate an antagonistic relationship between the Dicer-like proteins at at least some methylation loci. Finally, we show in simulation studies that this approach can be significantly more powerful in the detection of differential methylation than many existing methods in data derived from both mammalian and plant systems.

CONCLUSIONS

The methods developed here make it possible to analyse high-throughput sequencing of the methylome of any given organism under a diverse set of experimental conditions. The methods are able to identify methylation loci and evaluate the likelihood that a region is truly methylated under any given experimental condition, allowing for downstream analyses that characterise differences between methylated and non-methylated regions of the genome. Futhermore, diverse patterns of differential methylation may also be characterised from these data.

QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model

Background

As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task.

Results

We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes.

Conclusion

QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m¹A-Seq, Par-CLIP, RIP-Seq, etc.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1808-4) contains supplementary material, which is available to authorized users.

Genome-wide analysis of the nucleus accumbens identifies DNA methylation signals differentiating low/binge from heavy alcohol drinking

Alcohol-use disorders encompass a range of drinking levels and behaviors, including low, binge, and heavy drinking. In this regard, investigating the neural state of individuals who chronically self-administer lower doses of alcohol may provide insight into mechanisms that prevent the escalation of alcohol use. DNA methylation is one of the epigenetic mechanisms that stabilizes adaptations in gene expression and has been associated with alcohol use. Thus, we investigated DNA methylation, gene expression, and the predicted neural effects in the nucleus accumbens core (NAcc) of male rhesus macaques categorized as "low" or "binge" drinkers, compared to "alcohol-naïve" and "heavy" drinkers based on drinking patterns during a 12-month alcohol self-administration protocol. Using genome-wide CpG-rich region enrichment and bisulfite sequencing, the methylation levels of 2.6 million CpGs were compared between alcohol-naïve (AN), low/binge (L/BD), and heavy/very heavy (H/VHD) drinking subjects (n = 24). Through regional clustering analysis, we identified nine significant differential methylation regions (DMRs) that specifically distinguished ANs and L/BDs, and then compared those DMRs among H/VHDs. The DMRs mapped to genes encoding ion channels, receptors, cell adhesion molecules, and cAMP, NF-κβ and Wnt signaling pathway proteins. Two of the DMRs, linked to PDE10A and PKD2L2, were also differentially methylated in H/VHDs, suggesting an alcohol-dose independent effect. However, two other DMRs, linked to the CCBE1 and FZD5 genes, had L/BD methylation levels that significantly differed from both ANs and H/VHDs. The remaining five DMRs also differentiated L/BDs and ANs. However, H/VHDs methylation levels were not distinguishable from either of the two groups. Functional validation of two DMRs, linked to FZD5 and PDE10A, support their role in regulating gene expression and exon usage, respectively. In summary, the findings demonstrate that L/BD is associated with unique DNA methylation signatures in the primate NAcc, and that the methylation signatures identify synaptic genes that may play a role in preventing the escalation of alcohol use.

Small-Magnitude Effect Sizes in Epigenetic End Points are Important in Children's Environmental Health Studies: The Children's Environmental Health and Disease Prevention Research Center's Epigenetics Working Group

BACKGROUND

Characterization of the epigenome is a primary interest for children's environmental health researchers studying the environmental influences on human populations, particularly those studying the role of pregnancy and early-life exposures on later-in-life health outcomes.

OBJECTIVES

Our objective was to consider the state of the science in environmental epigenetics research and to focus on DNA methylation and the collective observations of many studies being conducted within the Children's Environmental Health and Disease Prevention Research Centers, as they relate to the Developmental Origins of Health and Disease (DOHaD) hypothesis.

METHODS

We address the current laboratory and statistical tools available for epigenetic analyses, discuss methods for validation and interpretation of findings, particularly when magnitudes of effect are small, question the functional relevance of findings, and discuss the future for environmental epigenetics research.

DISCUSSION

A common finding in environmental epigenetic studies is the small-magnitude epigenetic effect sizes that result from such exposures. Although it is reasonable and necessary that we question the relevance of such small effects, we present examples in which small effects persist and have been replicated across populations and across time. We encourage a critical discourse on the interpretation of such small changes and further research on their functional relevance for children's health.

CONCLUSION

The dynamic nature of the epigenome will require an emphasis on future longitudinal studies in which the epigenome is profiled over time, over changing environmental exposures, and over generations to better understand the multiple ways in which the epigenome may respond to environmental stimuli.

A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation

Whole-genome bisulphite sequencing (WGBS) can identify important methylation differences between diseased and healthy samples. However, results from...

DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS). We also show how this approach can be leveraged to identify disease-associated changes in DNA methylation, suggesting mechanisms through which these alterations might affect disease. From a data modeling perspective, ABBA has the distinctive feature of automatically adapting to different correlation structures in CpG methylation levels across the genome while taking into account the distance between CpG sites as a covariate. Our simulation study shows that ABBA has greater power to detect DMRs than existing methods, providing an accurate identification of DMRs in the large majority of simulated cases. To empirically demonstrate the method’s efficacy in generating biological hypotheses, we performed WGBS of primary macrophages derived from an experimental rat system of glomerulonephritis and used ABBA to identify >1000 disease-associated DMRs. Investigation of these DMRs revealed differential DNA methylation localized to a 600 bp region in the promoter of the Ifitm3 gene. This was confirmed by ChIP-seq and RNA-seq analyses, showing differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis), and a consistent change in Ifitm3 expression. Our ABBA analysis allowed us to propose a new role for Ifitm3 in the pathogenesis of glomerulonephritis via a mechanism involving promoter hypermethylation that is associated with Ifitm3 repression in the rat strain susceptible to glomerulonephritis.