Genome-wide DNA methylation profiling using Infinium® assay

Type-2 diabetes epigenetic biomarkers: present status and future directions for global and Indigenous health

Type-2 diabetes is a systemic condition with rising global prevalence, disproportionately affecting Indigenous communities worldwide. Recent advances in epigenomics methods, particularly in DNA methylation detection, have enabled the discovery of associations between epigenetic changes and Type-2 diabetes. In this review, we summarise DNA methylation profiling methods, and discuss how these technologies can facilitate the discovery of epigenomic biomarkers for Type-2 diabetes. We critically evaluate previous DNA methylation biomarker studies, particularly those using microarray platforms, and advocate for a shift towards sequencing-based approaches to improve genome-wide coverage. Furthermore, we emphasise the need for biomarker studies that include genetically diverse populations, especially Indigenous communities who are significantly impacted by Type-2 diabetes. We discuss research approaches and ethical considerations that can better facilitate Type-2 diabetes biomarker development to ensure that future genomics-based precision medicine efforts deliver equitable health outcomes. We propose that by addressing these gaps, future research can better capture the genetic and environmental complexities of Type-2 diabetes among populations at disproportionate levels of risk, ultimately leading to more effective diagnostic and therapeutic strategies.

Plasma cell-free DNA methylation profile before afatinib treatment is associated with progression-free and overall survival of patients with epidermal growth factor receptor gene mutation-positive non-small cell lung cancer

BACKGROUND

The present study aimed to clarify the clinical significance of the cell-free DNA (cfDNA) methylation profile of patients with non-small cell lung cancer (NSCLC) showing the epidermal growth factor receptor (EGFR) gene mutation.

METHODS

In 103 patients, genome-wide DNA methylation analysis using Infinium Methylation EPIC array was performed using samples of pre-tyrosine kinase inhibitor afatinib-treatment plasma cfDNA (n = 101) and post-afatinib cfDNA (n = 84).

RESULTS

Principal component analysis indicated that the cfDNA methylation profile was altered after afatinib treatment. Hierarchical clustering using the pre-afatinib cfDNA methylation profile revealed that cases with a fatal outcome were accumulated in specific clusters. Moreover, Kaplan-Meier analysis showed that the pre-afatinib cfDNA methylation profile was significantly associated with both progression-free survival (PFS) and overall survival (OS), whereas the post-afatinib profile was not. The genes for which pre-afatinib cfDNA methylation levels were associated with PFS were accumulated in the cadherin, Wnt, and EGFR signaling pathways. Activation of EGFR-related signaling due to DNA methylation alterations might overturn the effect of afatinib. Pre-afatinib levels of CEP170 and CHCHD6 cfDNA methylation were associated with both PFS and OS. Both pre- and post-afatinib cfDNA methylation levels of SLC9A3R2 and INTS1 were associated with bone metastasis. Using the cfDNA methylation levels at two CpG sites, cg12721600 and cg05905155, patients showing an overall response were predicted with a sensitivity of 96% or more.

CONCLUSIONS

The non-invasively measurable cfDNA methylation profile may reflect the corresponding profile in cancer cells, and that pre-treatment measurement may provide clinically useful information on EGFR mutation-positive NSCLC.

How epigenetics impacts stroke risk and outcomes through DNA methylation: A systematic review

The impact of DNA methylation (DNAm) on epigenetics has gained prominence in recent years due to its potential influence on ischemic stroke (IS) and treatment outcomes. DNAm is reversible and a better understanding of its role in IS could help identify novel therapeutic targets. The aim of this systematic review was to compile the available data on DNAm in the risk and prognosis of IS and to explore its therapeutic potential. The review process followed the PRISMA criteria. We searched the Pubmed and Cochrane databases to identify studies that used hypothesis free methodological approaches. Of the 459 studies identified, 34 met the inclusion criteria. The studies were categorized as follows: risk of IS; outcomes; and DNAm age. Most studies used genotyping array technology rather than whole-genome sequencing. DNAm testing was mainly based on blood samples. Most studies involved European cohorts. Most of the studies were performed at a single-center with recruitment at the time of stroke. In a few studies, health status was determined longitudinally. This systematic review shows that IS patients are biologically older than expected and present characteristic DNAm patterns related to stroke risk and outcomes. These patterns could be used to develop new treatments with epidrugs.

DNA hypomethylation promotes UHRF1-and SUV39H1/H2-dependent crosstalk between H3K18ub and H3K9me3 to reinforce heterochromatin states

Mono-ubiquitination of lysine 18 on histone H3 (H3K18ub), catalyzed by UHRF1, is a DNMT1 docking site that facilitates replication-coupled DNA methylation maintenance. Its functions beyond this are unknown. Here, we genomically map simultaneous increases in UHRF1-dependent H3K18ub and SUV39H1/H2-dependent H3K9me3 following DNMT1 inhibition. Mechanistically, transient accumulation of hemi-methylated DNA at CpG islands facilitates UHRF1 recruitment and E3 ligase activity toward H3K18. Notably, H3K18ub enhances SUV39H1/H2 methyltransferase activity and, in colon cancer cells, nucleates new H3K9me3 domains at CpG island promoters of DNA methylation-silenced tumor suppressor genes (TSGs). Disrupting UHRF1 enzyme activity prevents H3K9me3 accumulation while promoting PRC2-dependent H3K27me3 as a tertiary layer of gene repression in these regions. By contrast, disrupting H3K18ub-dependent SUV39H1/H2 activity enhances the transcriptional activating and antiproliferative effects of DNMT1 inhibition. Collectively, these findings reveal roles for UHRF1 and H3K18ub in regulating a hierarchy of repressive histone methylation signaling and rationalize a combination strategy for epigenetic cancer therapy.

UHRF1 ubiquitin ligase activity supports the maintenance of low-density CpG methylation

The RING E3 ubiquitin ligase UHRF1 is an established cofactor for DNA methylation inheritance. The model posits that nucleosomal engagement through histone and DNA interactions directs UHRF1 ubiquitin ligase activity toward lysines on histone H3 tails, creating binding sites for DNMT1 through ubiquitin interacting motifs (UIM1 and UIM2). However, the extent to which DNMT1 relies on ubiquitin signaling through UHRF1 in support of DNA methylation maintenance remains unclear. Here, with integrative epigenomic and biochemical analyses, we reveal that DNA methylation maintenance at low-density cytosine-guanine dinucleotides (CpGs) is particularly vulnerable to disruption of UHRF1 ubiquitin ligase activity and DNMT1 ubiquitin reading activity through UIM1. Hypomethylation of low-density CpGs in this manner induces formation of partially methylated domains (PMDs), a methylation signature observed across human cancers. In contrast, UIM2 disruption completely abolishes the DNA methylation maintenance function of DNMT1 in a CpG density-independent manner. In the context of DNA methylation recovery following acute DNMT1 depletion, we further reveal a 'bookmarking' function for UHRF1 ubiquitin ligase activity in support of DNA re-methylation. Collectively, these studies show that DNMT1-dependent DNA methylation inheritance is a ubiquitin-regulated process that is partially reliant on UHRF1 and suggest a disrupted UHRF1-DNMT1 ubiquitin signaling axis contributes to PMD formation in cancers.

Role of epigenetic mechanisms in the pathogenesis of chronic respiratory diseases and response to inhaled exposures: From basic concepts to clinical applications

Epigenetic modifications are chemical groups in our DNA (and chromatin) that determine which genes are active and which are shut off. Importantly, they integrate environmental signals to direct cellular function. Upon chronic environmental exposures, the epigenetic signature of lung cells gets altered, triggering aberrant gene expression programs that can lead to the development of chronic lung diseases. In addition to driving disease, epigenetic marks can serve as attractive lung disease biomarkers, due to early onset, disease specificity, and stability, warranting the need for more epigenetic research in the lung field. Despite substantial progress in mapping epigenetic alterations (mostly DNA methylation) in chronic lung diseases, the molecular mechanisms leading to their establishment are largely unknown. This review is meant as a guide for clinicians and lung researchers interested in epigenetic regulation with a focus on DNA methylation. It provides a short introduction to the main epigenetic mechanisms (DNA methylation, histone modifications and non-coding RNA) and the machinery responsible for their establishment and removal. It presents examples of epigenetic dysregulation across a spectrum of chronic lung diseases and discusses the current state of epigenetic therapies. Finally, it introduces the concept of epigenetic editing, an exciting novel approach to dissecting the functional role of epigenetic modifications. The promise of this emerging technology for the functional study of epigenetic mechanisms in cells and its potential future use in the clinic is further discussed.

Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts

Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone modifications, chromatin conformation, and non-coding RNA. The changes in gene regulation and expression can manifest in the form of various diseases and disorders such as cancer and congenital deformities. Over the last few decades, high-throughput experimental approaches have been used to identify and understand epigenetic changes, but these laboratory experimental approaches and biochemical processes are time-consuming and expensive. To overcome these challenges, machine learning and artificial intelligence (AI) approaches have been extensively used for mapping epigenetic modifications to their phenotypic manifestations. In this paper we provide a narrative review of published research on AI models trained on epigenomic data to address a variety of problems such as prediction of disease markers, gene expression, enhancer-promoter interaction, and chromatin states. The purpose of this review is twofold as it is addressed to both AI experts and epigeneticists. For AI researchers, we provided a taxonomy of epigenetics research problems that can benefit from an AI-based approach. For epigeneticists, given each of the above problems we provide a list of candidate AI solutions in the literature. We have also identified several gaps in the literature, research challenges, and recommendations to address these challenges.

Reduced Levels of miR-145-3p Drive Cell Cycle Progression in Advanced High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is the most lethal form of gynecologic cancer, with limited treatment options and a poor prognosis. Epigenetic factors, such as microRNAs (miRNAs) and DNA methylation, play pivotal roles in cancer progression, yet their specific contributions to HGSOC remain insufficiently understood. In this study, we performed comprehensive high-throughput analyses to identify dysregulated miRNAs in HGSOC and investigate their epigenetic regulation. Analysis of tissue samples from advanced-stage HGSOC patients revealed 20 differentially expressed miRNAs, 11 of which were corroborated via RT-qPCR in patient samples and cancer cell lines. Among these, miR-145-3p was consistently downregulated post-neoadjuvant therapy and was able to distinguish tumoural from control tissues. Further investigation confirmed that DNA methylation controls MIR145 expression. Functional assays showed that overexpression of miR-145-3p significantly reduced cell migration and induced G0/G1 cell cycle arrest by modulating the cyclin D1-CDK4/6 pathway. These findings suggest that miR-145-3p downregulation enhances cell proliferation and motility in HGSOC, implicating its restoration as a potential therapeutic target focused on G1/S phase regulation in the treatment of HGSOC.

BS-clock, advancing epigenetic age prediction with high-resolution DNA methylation bisulfite sequencing data

MOTIVATION

DNA methylation patterns provide precise and accurate estimates of biological age due to their robustness and predictable changes associated with aging processes. Although several methylation aging clocks have been developed in recent years, they are primarily designed for DNA methylation array data, which has limited CpG coverage and detection sensitivity compared to bisulfite sequencing data.

RESULTS

Here, we present BS-clock, a novel DNA methylation clock for human aging based on bisulfite sequencing data. Using BS-seq data from 529 samples retrieved from four tissues, our BS-clock achieves higher correlations with chronological age in multiple tissue types compared to existing array-based clocks. Our study revealed age-dependent aging rates across different age stages and disease conditions, and overall low cross-tissue prediction capability by applying the model trained on one tissue type to others. In summary, BS-clock overcomes limitations of array-based techniques, offering genome-wide CpG site coverage and more robust and accurate aging quantification. This research paves the way for advanced epigenetic studies of aging and holds promise for developing targeted interventions to promote healthy aging.

AVAILABILITY AND IMPLEMENTATION

All analysis codes for reproducing the results of the study are publicly available at https://github.com/hucongcong97/BS-clock.

Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns

This study investigates the ability to predict DNA methylation patterns in cervical cancer cells using decision-tree-based ensemble approaches and neural network-based models. The research findings suggest that a model based on random forest achieves a significant prediction accuracy of 91.35%. This projection was derived from comprehensive experimentation and a meticulous performance evaluation of the random forest model, employing a range of measures including Accuracy, Sensitivity, Specificity, Matthews Correlation Coefficient, F1-score, Recall, and Precision. The results indicate that the random forest model exhibits superior performance compared to other tree-based models such as the Simple Decision Tree and XGBoost, as well as neural network-based models including Convolutional Neural Networks, Feed Forward Networks, and Wavelet Neural Networks. The findings indicate that using random forest-based techniques has great potential for future study and might be highly valuable in clinical applications, especially in improving diagnostic and treatment strategies based on epigenetic profiles.

Applicability of epigenetic age models to next-generation methylation arrays

BACKGROUND

Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.

METHODS

We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.

RESULTS

The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.

CONCLUSION

Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.

Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma

Impaired telomere length (TL) maintenance in ovarian tissue may play a pivotal role in the onset of epithelial ovarian cancer (OvC). TL in either target or surrogate tissue (blood) is currently being investigated for use as a predictor in anti-OvC therapy or as a biomarker of the disease progression, respectively. There is currently an urgent need for an appropriate approach to chemotherapy response prediction. We performed a monochrome multiplex qPCR measurement of TL in peripheral blood leukocytes (PBL) and tumor tissues of 209 OvC patients. The methylation status and gene expression of the shelterin complex and telomerase catalytic subunit (hTERT) were determined within tumor tissues by High-Throughput DNA methylation profiling and RNA sequencing (RNA-Seq) analysis, respectively. The patients sensitive to cancer treatment (n = 46) had shorter telomeres in PBL compared to treatment-resistant patients (n = 93; P = 0.037). In the patients with a different therapy response, transcriptomic analysis showed alterations in the peroxisome proliferator-activated receptor (PPAR) signaling pathway (q = 0.001). Moreover, tumor TL shorter than the median corresponded to better overall survival (OS) (P = 0.006). TPP1 gene expression was positively associated with TL in tumor tissue (P = 0.026). TL measured in PBL could serve as a marker of platinum therapy response in OvC patients. Additionally, TL determined in tumor tissue provides information on OvC patients' OS.

mLiftOver: harmonizing data across Infinium DNA methylation platforms

MOTIVATION

Infinium DNA methylation BeadChips are widely used for genome-wide DNA methylation profiling at the population scale. Recent updates to probe content and naming conventions in the EPIC version 2 (EPICv2) arrays have complicated integrating new data with previous Infinium array platforms, such as the MethylationEPIC (EPIC) and the HumanMethylation450 (HM450) BeadChip.

RESULTS

We present mLiftOver, a user-friendly tool that harmonizes probe ID, methylation level, and signal intensity data across different Infinium platforms. It manages probe replicates, missing data imputation, and platform-specific bias for accurate data conversion. We validated the tool by applying HM450-based cancer classifiers to EPICv2 cancer data, achieving high accuracy. Additionally, we successfully integrated EPICv2 healthy tissue data with legacy HM450 data for tissue identity analysis and produced consistent copy number profiles in cancer cells.

AVAILABILITY AND IMPLEMENTATION

mLiftOver is implemented R and available in the Bioconductor package SeSAMe (version 1.21.13+): https://bioconductor.org/packages/release/bioc/html/sesame.html. Analysis of EPIC and EPICv2 platform-specific bias and high-confidence mapping is available at https://github.com/zhou-lab/InfiniumAnnotationV1/raw/main/Anno/EPICv2/EPICv2ToEPIC_conversion.tsv.gz. The source code is available at https://github.com/zwdzwd/sesame/blob/devel/R/mLiftOver.R under the MIT license.

Aberrant cell adhesiveness due to DNA hypermethylation of KLF11 in papillary urothelial carcinomas

PURPOSE

The aim of this study was to clarify DNA methylation profiles determining the clinicopathological diversity of urothelial carcinomas.

METHODS

Genome-wide DNA methylation analysis was performed using the Infinium HumanMethylation450 BeadChip in 46 paired samples of non-cancerous urothelium (N) and corresponding cancerous tissue (T), and 26 samples of normal control urothelium obtained from patients without urothelial carcinomas (C). For genes of interest, correlation between DNA methylation and mRNA expression was examined using the Cancer Genome Atlas database. In addition, the role of a selected target for cancer-relevant endpoints was further examined in urothelial carcinoma cell lines.

RESULTS

The genes showing significant differences in DNA methylation levels between papillary carcinomas and more aggressive non-papillary (nodular) carcinomas were accumulated in signaling pathways participating in cell adhesion and cytoskeletal remodeling. Five hundred ninety-six methylation sites showed differences in DNA methylation levels between papillary and nodular carcinomas. Of those sites, that were located in CpG-islands around transcription start site, 5'-untranslated region or 1st exon, 16 genes exhibited inverse correlations between DNA methylation and mRNA expression levels. Among the latter, only the KLF11 gene showed papillary T sample-specific DNA hypermethylation in comparison to C and N samples. The DNA methylation levels of KLF11 were not significantly different between T samples and N samples or T samples and C samples for patients with papillo-nodular or nodular carcinomas. Knockdown experiments using the urothelial carcinoma cell lines HT1376 and 5637, which are considered models for papillary carcinoma, revealed that KLF11 participates in altering the adhesiveness of cells to laminin-coated dishes, although cell growth was not affected.

CONCLUSION

These data indicate that DNA hypermethylation of KLF11 may participate in the generation of papillary urothelial carcinomas through induction of aberrant cancer cell adhesion to the basement membrane.

Low-input and single-cell methods for Infinium DNA methylation BeadChips

The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection P-value calculation achieved higher sensitivities for low-input datasets and was validated in over 100 000 public diverse methylome profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.

On epigenetic stochasticity, entropy and cancer risk

Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Molecular pathological approach to cancer epigenomics and its clinical application

Careful microscopic observation of histopathological specimens, accumulation of large numbers of high-quality tissue specimens, and analysis of molecular pathology in relation to morphological features are considered to yield realistic data on the nature of multistage carcinogenesis. Since the morphological hallmark of cancer is disruption of the normal histological structure maintained through cell-cell adhesiveness and cellular polarity, attempts have been made to investigate abnormalities of the cadherin-catenin cell adhesion system in human cancer cells. It has been shown that the CDH1 tumor suppressor gene encoding E-cadherin is silenced by DNA methylation, suggesting that a "double hit" involving DNA methylation and loss of heterozygosity leads to carcinogenesis. Therefore, in the 1990s, we focused on epigenomic mechanisms, which until then had not received much attention. In chronic hepatitis and liver cirrhosis associated with hepatitis virus infection, DNA methylation abnormalities were found to occur frequently, being one of the earliest indications that such abnormalities are present even in precancerous tissue. Aberrant expression and splicing of DNA methyltransferases, such as DNMT1 and DNMT3B, was found to underlie the mechanism of DNA methylation alterations in various organs. The CpG island methylator phenotype in renal cell carcinoma was identified for the first time, and its therapeutic targets were identified by multilayer omics analysis. Furthermore, the DNA methylation profile of nonalcoholic steatohepatitis (NASH)-related hepatocellular carcinoma was clarified in groundbreaking studies. Since then, we have developed diagnostic markers for carcinogenesis risk in NASH patients and noninvasive diagnostic markers for upper urinary tract cancer, as well as developing a new high-performance liquid chromatography-based diagnostic system for DNA methylation diagnosis. Research on the cancer epigenome has revealed that DNA methylation alterations occur from the precancerous stage as a result of exposure to carcinogenic factors such as inflammation, smoking, and viral infections, and continuously contribute to multistage carcinogenesis through aberrant expression of cancer-related genes and genomic instability. DNA methylation alterations at the precancerous stages are inherited by or strengthened in cancers themselves and determine the clinicopathological aggressiveness of cancers as well as patient outcome. DNA methylation alterations have applications as biomarkers, and are expected to contribute to diagnosis, as well as preventive and preemptive medicine.

Select EZH2 inhibitors enhance viral mimicry effects of DNMT inhibition through a mechanism involving NFAT:AP-1 signaling

DNA methyltransferase inhibitor (DNMTi) efficacy in solid tumors is limited. Colon cancer cells exposed to DNMTi accumulate lysine-27 trimethylation on histone H3 (H3K27me3). We propose this Enhancer of Zeste Homolog 2 (EZH2)-dependent repressive modification limits DNMTi efficacy. Here, we show that low-dose DNMTi treatment sensitizes colon cancer cells to select EZH2 inhibitors (EZH2is). Integrative epigenomic analysis reveals that DNMTi-induced H3K27me3 accumulates at genomic regions poised with EZH2. Notably, combined EZH2i and DNMTi alters the epigenomic landscape to transcriptionally up-regulate the calcium-induced nuclear factor of activated T cells (NFAT):activating protein 1 (AP-1) signaling pathway. Blocking this pathway limits transcriptional activating effects of these drugs, including transposable element and innate immune response gene expression involved in viral defense. Analysis of primary human colon cancer specimens reveals positive correlations between DNMTi-, innate immune response-, and calcium signaling-associated transcription profiles. Collectively, we show that compensatory EZH2 activity limits DNMTi efficacy in colon cancer and link NFAT:AP-1 signaling to epigenetic therapy-induced viral mimicry.

Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling

BACKGROUND

The Illumina family of Infinium Methylation BeadChip microarrays has been widely used over the last 15 years for genome-wide DNA methylation profiling, including large-scale and population-based studies, due to their ease of use and cost effectiveness. Succeeding the popular HumanMethylationEPIC BeadChip (EPICv1), the recently released Infinium MethylationEPIC v2.0 BeadChip (EPICv2) claims to extend genomic coverage to more than 935,000 CpG sites. Here, we comprehensively characterise the reproducibility, reliability and annotation of the EPICv2 array, based on bioinformatic analysis of both manifest data and new EPICv2 data from diverse biological samples.

RESULTS

We find a high degree of reproducibility with EPICv1, evidenced by comparable sensitivity and precision from empirical cross-platform comparison incorporating whole genome bisulphite sequencing (WGBS), and high correlation between technical sample replicates, including between samples with DNA input levels below the manufacturer's recommendation. We provide a full assessment of probe content, evaluating genomic distribution and changes from previous array versions. We characterise EPICv2's new feature of replicated probes and provide recommendations as to the superior probes. In silico analysis of probe sequences demonstrates that probe cross-hybridisation remains a significant problem in EPICv2. By mapping the off-target sites at single nucleotide resolution and comparing with WGBS we show empirical evidence for preferential off-target binding.

CONCLUSIONS

Overall, we find EPICv2 a worthy successor to the previous Infinium methylation microarrays, however some technical issues remain. To support optimal EPICv2 data analysis we provide an expanded version of the EPICv2 manifest to aid researchers in understanding probe design, data processing, choosing appropriate probes for analysis and for integration with methylation datasets from previous versions of the Infinium Methylation BeadChip.

DNA methylation alterations of ADCY5, MICAL2, and PLEKHG2 during the developmental stage of cryptogenic hepatocellular carcinoma

AIM

The aim of this study was to clarify the significance of DNA methylation alterations of cryptogenic hepatocellular carcinomas (HCCs).

METHODS

Using the Infinium assay, we performed genome-wide DNA methylation analysis of 250 liver tissue samples, including noncancerous liver tissue (U-N) and corresponding cancerous tissue (U-T) from patients with cryptogenic HCC without a history of excessive alcohol use and hepatitis virus infection, and whose U-N samples showed no remarkable histological features (no microscopic evidence of simple steatosis, any form of hepatitis including nonalcoholic steatohepatitis, or liver cirrhosis).

RESULTS

We identified 3272 probes that showed significant differences of DNA methylation levels between U-N and normal liver tissue samples from patients without HCC, indicating that a distinct DNA methylation profile had already been established at the precancerous U-N stage. U-Ns have a DNA methylation profile differing from that of noncancerous liver tissue of patients with nonalcoholic steatohepatitis-related, viral hepatitis-related, and alcoholic liver disease-related HCCs. Such DNA methylation alterations in U-Ns were inherited by U-Ts. The U-Ns showed DNA methylation alteration of ADCY5, resulting in alteration of its mRNA expression, whereas noncancerous liver tissue of patients with nonalcoholic steatohepatitis-, viral hepatitis-, or alcoholic liver disease-related HCCs did not. DNA methylation levels of MICAL2 and PLEKHG2 in U-Ts were correlated with larger tumor diameter and portal vein involvement, respectively.

CONCLUSIONS

U-N-specific DNA hypermethylation of ADCY5 may have significance, even from the precancerous stage in liver showing no remarkable histological features. DNA hypomethylation of MICAL2 and PLEKHG2 may determine the clinicopathological features of cryptogenic HCC.

Quantitative transcriptomic and epigenomic data analysis: a primer

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.

Creation and Validation of the First Infinium DNA Methylation Array for the Human Imprintome

Background

Differentially methylated imprint control regions (ICRs) regulate the monoallelic expression of imprinted genes. Their epigenetic dysregulation by environmental exposures throughout life results in the formation of common chronic diseases. Unfortunately, existing Infinium methylation arrays lack the ability to profile these regions adequately. Whole genome bisulfite sequencing (WGBS) is the unique method able to profile these regions, but it is very expensive and it requires not only a high coverage but it is also computationally intensive to assess those regions.

Findings

To address this deficiency, we developed a custom methylation array containing 22,819 probes. Among them, 9,757 probes map to 1,088 out of the 1,488 candidate ICRs recently described. To assess the performance of the array, we created matched samples processed with the Human Imprintome array and WGBS, which is the current standard method for assessing the methylation of the Human Imprintome. We compared the methylation levels from the shared CpG sites and obtained a mean R 2 = 0.569. We also created matched samples processed with the Human Imprintome array and the Infinium Methylation EPIC v2 array and obtained a mean R 2 = 0.796. Furthermore, replication experiments demonstrated high reliability (ICC: 0.799-0.945).

Conclusions

Our custom array will be useful for replicable and accurate assessment, mechanistic insight, and targeted investigation of ICRs. This tool should accelerate the discovery of ICRs associated with a wide range of diseases and exposures, and advance our understanding of genomic imprinting and its relevance in development and disease formation throughout the life course.

Advances in Molecular Pathology, Diagnosis and Treatment of Spinal Cord Astrocytomas

Spinal cord astrocytoma (SCA) is a rare subtype of astrocytoma, posing challenges in diagnosis and treatment. Low-grade SCA can achieve long-term survival solely through surgery, while high-grade has a disappointing prognosis even with comprehensive treatment. Diagnostic criteria and standard treatment of intracranial astrocytoma have shown obvious limitations in SCA. Research on the molecular mechanism in SCA is lagging far behind that on intracranial astrocytoma. In recent years, huge breakthroughs have been made in molecular pathology of astrocytoma, and novel techniques have emerged, including DNA methylation analysis and radiomics. These advances are now making it possible to provide a precise diagnosis and develop corresponding treatment strategies in SCA. Our aim is to review the current status of diagnosis and treatment of SCA, and summarize the latest research advancement, including tumor subtype, molecular characteristics, diagnostic technology, and potential therapy strategies, thus deepening our understanding of this uncommon tumor type and providing guidance for accurate diagnosis and treatment.

Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients

Background and aims

Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).

Methods

DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.

Results

Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.

Conclusion

Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.

Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits

DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more 'aged methylome' for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein-protein interaction enrichment analyses identified the hepatic nuclear factor, Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.

Alcohol consumption and epigenetic age acceleration across human adulthood

The alcohol-associated biological aging remains to be studied across adulthood. We conducted linear regression analyses to investigate the associations between alcohol consumption and two DNA methylation-based biological age acceleration metrics in 3823 Framingham Heart Study participants (24-92 years and 53.8% women) adjusting for covariates. We also investigated whether the two epigenetic aging metrics mediated the association of alcohol consumption with hypertension. We found that higher long-term average alcohol consumption was significantly associated with biological age acceleration assessed by GrimAge acceleration (GAA) and PhenoAge acceleration (PAA) in middle-aged (45-64 years, n = 1866) and older (65-92 years, n = 1267) participants while not in young participants (24-44 years, n = 690). For example, one additional standard drink of alcohol (~14 grams of ethanol per day) was associated with a 0.71 ± 0.15-year (p = 2.1e-6) and 0.60 ± 0.18-year (p = 7.5e-4) increase in PAA in middle-aged and older participants, respectively, but the association was not significant in young participants (p = 0.23). One additional standard serving of liquor (~14 grams of ethanol) was associated with a greater increase in GAA (0.82-year, p = 4.8e-4) and PAA (1.45-year, p = 7.4e-5) than beer (GAA: 0.45-year, p = 5.2e-4; PAA: 0.48-year, p = 0.02) and wine (GAA: 0.51-year, p = 0.02; PAA: 0.91-year, p = 0.008) in middle-aged participant group. We observed that up to 28% of the association between alcohol consumption and hypertension was mediated by GAA or PAA in the pooled sample. Our findings suggest that alcohol consumption is associated with greater biological aging quantified by epigenetic aging metrics, which may mediate the association of alcohol consumption with quantitative traits, such as hypertension.

Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications

BACKGROUND

DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals.

OBJECTIVE

This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application.

METHODS

The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics.

CONCLUSION

The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.

Genome-wide DNA methylation landscape of four Chinese populations and epigenetic variation linked to Tibetan high-altitude adaptation

DNA methylation (DNAm) is one of the major epigenetic mechanisms in humans and is important in diverse cellular processes. The variation of DNAm in the human population is related to both genetic and environmental factors. However, the DNAm profiles have not been investigated in the Chinese population of diverse ethnicities. Here, we performed double-strand bisulfite sequencing (DSBS) for 32 Chinese individuals representing four major ethnic groups including Han Chinese, Tibetan, Zhuang, and Mongolian. We identified a total of 604,649 SNPs and quantified DNAm at more than 14 million CpGs in the population. We found global DNAm-based epigenetic structure is different from the genetic structure of the population, and ethnic difference only partially explains the variation of DNAm. Surprisingly, non-ethnic-specific DNAm variations showed stronger correlation with the global genetic divergence than these ethnic-specific DNAm. Differentially methylated regions (DMRs) among these ethnic groups were found around genes in diverse biological processes. Especially, these DMR-genes between Tibetan and non-Tibetans were enriched around high-altitude genes including EPAS1 and EGLN1, suggesting DNAm alteration plays an important role in high-altitude adaptation. Our results provide the first batch of epigenetic maps for Chinese populations and the first evidence of the association of epigenetic changes with Tibetans' high-altitude adaptation.

Comprehensive Evaluation of The Infinium Human MethylationEPIC v2 BeadChip

Infinium Methylation BeadChips are widely used to profile DNA cytosine modifications in large cohort studies for reasons of cost-effectiveness, accurate quantification, and user-friendly data analysis in characterizing these canonical epigenetic marks. In this work, we conducted a comprehensive evaluation of the updated Infinium MethylationEPIC v2 BeadChip (EPICv2). Our evaluation revealed that EPICv2 offers significant improvements over its predecessors, including expanded enhancer coverage, applicability to diverse ancestry groups, support for low-input DNA down to one nanogram, coverage of existing epigenetic clocks, cell type deconvolution panels, and human trait associations, while maintaining accuracy and reproducibility. Using EPICv2, we were able to identify epigenome and sequence signatures in cell line models of DNMT and SETD2 loss and/or hypomorphism. Furthermore, we provided probe-wise evaluation and annotation to facilitate the use of new features on this array for studying the interplay between somatic mutations and epigenetic landscape in cancer genomics. In conclusion, EPICv2 provides researchers with a valuable tool for studying epigenetic modifications and their role in development and disease.

Low-input and single-cell methods for Infinium DNA methylation BeadChips

The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection p -values calculation achieved higher sensitivities for low-input datasets and was validated in over 100,000 public datasets with diverse methylation profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.

A Review of the Epigenetic Clock: Emerging Biomarkers for Asthma and Allergic Disease

DNA methylation (DNAm) is a dynamic, age-dependent epigenetic modification that can be used to study interactions between genetic and environmental factors. Environmental exposures during critical periods of growth and development may alter DNAm patterns, leading to increased susceptibility to diseases such as asthma and allergies. One method to study the role of DNAm is the epigenetic clock-an algorithm that uses DNAm levels at select age-informative Cytosine-phosphate-Guanine (CpG) dinucleotides to predict epigenetic age (EA). The difference between EA and calendar age (CA) is termed epigenetic age acceleration (EAA) and reveals information about the biological capacity of an individual. Associations between EAA and disease susceptibility have been demonstrated for a variety of age-related conditions and, more recently, phenotypes such as asthma and allergic diseases, which often begin in childhood and progress throughout the lifespan. In this review, we explore different epigenetic clocks and how they have been applied, particularly as related to childhood asthma. We delve into how in utero and early life exposures (e.g., smoking, air pollution, maternal BMI) result in methylation changes. Furthermore, we explore the potential for EAA to be used as a biomarker for asthma and allergic diseases and identify areas for further study.

DNA methylation status of the SPHK1 and LTB genes underlies the clinicopathological diversity of non-alcoholic steatohepatitis-related hepatocellular carcinomas

PURPOSE

This study was performed to identify the DNA methylation profiles underlying the clinicopathological diversity of non-alcoholic steatohepatitis (NASH)-related hepatocellular carcinomas (HCCs).  METHODS: Genome-wide DNA methylation analysis of 88 liver tissue samples was performed using the Infinium assay.

RESULTS

Principal component analysis revealed that distinct DNA methylation profiles differing from such profiles in normal control liver tissue had already been established in non-cancerous liver tissue showing NASH, which is considered to be a precancerous condition. Hierarchical clustering separated 26 NASH-related HCCs into Cluster I (n = 8) and Cluster II (n = 18). Such epigenetic clustering was significantly correlated with histopathological diversity, i.e. poorer tumor differentiation, tumor steatosis and development of a scirrhous HCC component. Significant differences in DNA methylation levels between the two clusters were accumulated in molecular pathways participating in cell adhesion and cytoskeletal remodeling, as well as cell proliferation and apoptosis. Among tumor-related genes characterizing Clusters I and II, differences in the levels of DNA methylation and mRNA expression for the SPHK1, INHBA, LTB and PDE3B genes were correlated with poorer tumor differentiation. 5-Aza-2'-deoxycytidine treatment of HCC cells revealed epigenetic regulation of the SPHK1 and LTB genes. Knockdown experiments showed that SPHK1 promotes cell proliferation, represses apoptosis and enhances migration, whereas LTB enhances migration of HCC cells. DNA hypomethylation resulting in increased expression of SPHK1 and LTB in poorly differentiated HCCs may underlie the aggressive phenotype of such HCCs.

CONCLUSION

These data indicate that DNA methylation profiles may determine the clinicopathological heterogeneity of NASH-related HCCs via alterations of tumor-related gene expression.

meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans

Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans.

DNA Methylation Description of Hippocampus, Cortex, Amygdala, and Blood of Drug-Resistant Temporal Lobe Epilepsy

Epigenetic changes such as DNA methylation were observed in drug-resistant temporal lobe epilepsy (DR-TLE), a disease that affects 25-30% of epilepsy patients. The main objective is to simultaneously describe DNA methylation patterns associated with DR-TLE in hippocampus, amygdala, surrounding cortex to the epileptogenic zone (SCEZ), and peripheral blood. An Illumina Infinium MethylationEPIC BeadChip array was performed in 19 DR-TLE patients and 10 postmortem non-epileptic controls. Overall, 32, 59, and 3210 differentially methylated probes (DMPs) were associated with DR-TLE in the hippocampus, amygdala, and SCEZ, respectively. These DMP-affected genes were involved in neurotrophic and calcium signaling in the hippocampus and voltage-gated channels in SCEZ, among others. One of the hippocampus DMPs (cg26834418 (CHORDC1)) showed a strong blood-brain correlation with BECon and IMAGE-CpG, suggesting that it could be a potential surrogate peripheral biomarker of DR-TLE. Moreover, in three of the top SCEZ's DMPs (SHANK3, SBF1, and MCF2L), methylation status was verified with methylation-specific qPCR. The differentially methylated CpGs were classified in DMRs: 2 in the hippocampus, 12 in the amygdala, and 531 in the SCEZ. We identified genes that had not been associated to DR-TLE so far such as TBX5, EXOC7, and WRHN. The area with more DMPs associated with DR-TLE was the SCEZ, some of them related to voltage-gated channels. The DMPs found in the amygdala were involved in inflammatory processes. We also found a potential surrogate peripheral biomarker of DR-TLE. Thus, these results provide new insights into epigenetic modifications involved in DR-TLE.

DNA Methylation in Autoimmune Thyroid Disease

Graves disease and Hashimoto disease form part of the spectrum of autoimmune thyroid disease (AITD), to which genetic and environmental factors are recognized contributors. Epigenetics provides a potential link between environmental influences, gene expression, and thyroid autoimmunity. DNA methylation (DNAm) is the best studied epigenetic process, and global hypomethylation of leukocyte DNA is reported in several autoimmune disorders. This review summarizes the current understanding of DNAm in AITD. Targeted DNAm studies of blood samples from AITD patients have reported differential DNAm in the promoter regions of several genes implicated in AITD, including TNF, IFNG, IL2RA, IL6, ICAM1, and PTPN22. In many cases, however, the findings await replication and are unsupported by functional studies to support causal roles in AITD pathogenesis. Furthermore, thyroid hormones affect DNAm, and in many studies confounding by reverse causation has not been considered. Recent studies have shown that DNAm patterns in candidate genes including ITGA6, PRKAA2, and DAPK1 differ between AITD patients from regions with different iodine status, providing a potential mechanism for associations between iodine and AITD. Research focus in the field is moving from candidate gene studies to an epigenome-wide approach. Genome-wide methylation studies of AITD patients have demonstrated multiple differentially methylated positions, including some in immunoregulatory genes such as NOTCH1, HLA-DRB1, TNF, and ICAM1. Large, epigenome-wide studies are required to elucidate the pathophysiological role of DNAm in AITD, with the potential to provide novel diagnostic and prognostic biomarkers as well as therapeutic targets.

Co-occurrence of mutations in NF1 and other susceptibility genes in pheochromocytoma and paraganglioma

Introduction

The percentage of patients diagnosed with pheochromocytoma and paraganglioma (altogether PPGL) carrying known germline mutations in one of the over fifteen susceptibility genes identified to date has dramatically increased during the last two decades, accounting for up to 35-40% of PPGL patients. Moreover, the application of NGS to the diagnosis of PPGL detects unexpected co-occurrences of pathogenic allelic variants in different susceptibility genes.

Methods

Herein we uncover several cases with dual mutations in NF1 and other PPGL genes by targeted sequencing. We studied the molecular characteristics of the tumours with co-occurrent mutations, using omic tools to gain insight into the role of these events in tumour development.

Results

Amongst 23 patients carrying germline NF1 mutations, targeted sequencing revealed additional pathogenic germline variants in DLST (n=1) and MDH2 (n=2), and two somatic mutations in H3-3A and PRKAR1A. Three additional patients, with somatic mutations in NF1 were found carrying germline pathogenic mutations in SDHB or DLST, and a somatic truncating mutation in ATRX. Two of the cases with dual germline mutations showed multiple pheochromocytomas or extra-adrenal paragangliomas - an extremely rare clinical finding in NF1 patients. Transcriptional and methylation profiling and metabolite assessment showed an "intermediate signature" to suggest that both variants had a pathological role in tumour development.

Discussion

In conclusion, mutations affecting genes involved in different pathways (pseudohypoxic and receptor tyrosine kinase signalling) co-occurring in the same patient could provide a selective advantage for the development of PPGL, and explain the variable expressivity and incomplete penetrance observed in some patients.

Multiomics Integration at Single-Cell Resolution Using Bayesian Networks: A Case Study in Hepatocellular Carcinoma

Multiomics data integration is one of the leading frontiers of complex disease research and integrative biology. The advances in single-cell sequencing technologies offer yet another crucial dimension in multiomics research. The single-cell studies enable the study and integration of multiomics data simultaneously in the same cell. We report in this study multiomics data integration in single-cell resolution using Bayesian networks (BNs) in a case study of hepatocellular carcinoma (HCC). A BN encodes the conditional dependencies/independencies of variables using a graphical model with an accompanying joint probability. RNA-seq and Reduced Representation Bisulfite Sequencing data were analyzed separately, and copy number variations were estimated by the hidden Markov model method. Several BN models were constructed to reveal omics' causal and associational relationships. These methods were subjected to a validation study using an independent data set. We show the heterogeneity of the multiple cellular layers of HCC at single-cell omics resolution by identifying best-fitted BN models of 295 genes. We also provide novel insights into the multiomics mechanistic relationships in the human lymphocyte antigen class I genes in HCC. To the best of our knowledge, this is the first study to focus on integrating omics data using a machine learning algorithm, BNs, at the single-cell resolution using a case study of HCC.

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.

Quantification of DNA methylation for carcinogenic risk estimation in patients with non-alcoholic steatohepatitis

BACKGROUND

In recent years, non-alcoholic steatohepatitis (NASH) has become the main cause of hepatocellular carcinoma (HCC). As a means of improving the treatment of NASH-related HCCs based on early detection, this study investigated the feasibility of carcinogenic risk estimation in patients with NASH.

RESULTS

Normal liver tissue (NLT), non-cancerous liver tissue showing histological findings compatible with non-alcoholic fatty liver from patients without HCC (NAFL-O), non-cancerous liver tissue showing NASH from patients without HCC (NASH-O), non-cancerous liver tissue showing non-alcoholic fatty liver from patients with HCC (NAFL-W), non-cancerous liver tissue showing NASH from patients with HCC (NASH-W) and NASH-related HCC were analyzed. An initial cohort of 171 tissue samples and a validation cohort of 55 tissue samples were used. Genome-wide DNA methylation screening using the Infinium HumanMethylation450 BeadChip and DNA methylation quantification using high-performance liquid chromatography (HPLC) with a newly developed anion-exchange column were performed. Based on the Infinium assay, 4050 CpG sites showed alterations of DNA methylation in NASH-W samples relative to NLT samples. Such alterations at the precancerous NASH stage were inherited by or strengthened in HCC samples. Receiver operating characteristic curve analysis identified 415 CpG sites discriminating NASH-W from NLT samples with area under the curve values of more than 0.95. Among them, we focused on 21 CpG sites showing more than 85% specificity, even for discrimination of NASH-W from NASH-O samples. The DNA methylation status of these 21 CpG sites was able to predict the coincidence of HCC independently from histopathological findings such as ballooning and fibrosis stage. The methylation status of 5 candidate marker CpG sites was assessed using a HPLC-based system, and for 3 of them sufficient sensitivity and specificity were successfully validated in the validation cohort. By combining these 3 CpG sites including the ZC3H3 gene, NAFL-W and NASH-W samples from which HCCs had already arisen were confirmed to show carcinogenic risk with 95% sensitivity in the validation cohort.

CONCLUSIONS

After a further prospective validation study using a larger cohort, carcinogenic risk estimation in liver biopsy specimens of patients with NASH may become clinically applicable using this HPLC-based system for quantification of DNA methylation.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

Recognition of 5-methyl-CG and CG base pairs in duplex DNA with high stability using antiparallel-type triplex-forming oligonucleotides with 2-guanidinoethyl-2'-deoxynebularine

The formation of triplex DNA is a site-specific recognition method that directly targets duplex DNA. However, triplex DNA formation is generally formed for the GC and AT base pairs of duplex DNA, and there are no natural nucleotides that recognize the CG and TA base pairs, or even the 5-methyl-CG (5mCG) base pair. Moreover, duplex DNA, including 5mCG base pairs, epigenetically regulates gene expression in vivo, and thus targeting strategies are of biological importance. Therefore, the development of triplex-forming oligonucleotides (TFOs) with artificial nucleosides that selectively recognize these base pairs with high affinity is needed. We recently reported that 2'-deoxy-2-aminonebularine derivatives exhibited the ability to recognize 5mCG and CG base pairs in triplex formation; however, this ability was dependent on sequences. Therefore, we designed and synthesized new nucleoside derivatives based on the 2'-deoxy-nebularine (dN) skeleton to shorten the linker length connecting to the hydrogen-bonding unit in formation of the antiparallel motif triplex. We successfully demonstrated that TFOs with 2-guanidinoethyl-2'-deoxynebularine (guanidino-dN) recognized 5mCG and CG base pairs with very high affinity in all four DNA sequences with different adjacent nucleobases of guanidino-dN as well as in the promoter sequences of human genes containing 5mCG base pairs with a high DNA methylation frequency.

Deciphering the role of aberrant DNA methylation in NAFLD and NASH

The global incidence of nonalcoholic fatty liver disease (NAFLD) is mounting incessantly, and it is emerging as the most frequent cause of chronic and end stage liver disorders. It is the starting point for a range of conditions from simple steatosis to more progressive nonalcoholic steatohepatitis (NASH) and associated hepatocellular carcinoma (HCC). Dysregulation of insulin secretion and dyslipidemia due to obesity and other lifestyle variables are the primary contributors to establishment of NAFLD. Onset and progression of NAFLD is orchestrated by an interplay of metabolic environment with genetic and epigenetic factors. An incompletely understood mechanism of NAFLD progression has greatly hampered the progress in identification of novel prognostic and therapeutic strategies. Emerging evidence suggests altered DNA methylation pattern as a key determinant of NAFLD pathogenesis. Environmental and lifestyle factors can manipulate DNA methylation patterns in a reversible manner, which manifests as changes in gene expression. In this review we attempt to highlight the importance of DNA methylation in establishment and progression of NAFLD. Development of novel diagnostic, prognostic and therapeutic strategies centered around DNA methylation signatures and modifiers has also been explored.

COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration.

Genome-wide methylation analysis of post-mortem cerebellum samples supports the role of peroxisomes in autism spectrum disorder

Aim: We tested the hypothesis that a subset of patients with autism spectrum disorder (ASD) contains candidate genes with high DNA methylation differences (effective values) that potentially affect one of the two alleles. Materials & methods: Genome-wide DNA methylation comparisons were made on cerebellum samples from 30 patients and 45 controls. Results: 12 genes with high effective values, including GSDMD, MMACHC, SLC6A5 and NKX6-2, implicated in ASD and other neuropsychiatric disorders were identified. Monoallelic promoter methylation and downregulation were observed for SERHL (serine hydrolase-like) and CAT (catalase) genes associated with peroxisome function. Conclusion: These data are consistent with the hypothesis implicating impaired peroxisome function/biogenesis for ASD. A similar approach holds promise for identifying rare epimutations in ASD and other complex disorders.

The role of maternal DNA methylation in pregnancies complicated by gestational diabetes

Diabetes in pregnancy is associated with adverse pregnancy outcomes and poses a serious threat to the health of mother and child. Although the pathophysiological mechanisms that underlie the association between maternal diabetes and pregnancy complications have not yet been elucidated, it has been suggested that the frequency and severity of pregnancy complications are linked to the degree of hyperglycemia. Epigenetic mechanisms reflect gene-environment interactions and have emerged as key players in metabolic adaptation to pregnancy and the development of complications. DNA methylation, the best characterized epigenetic mechanism, has been reported to be dysregulated during various pregnancy complications, including pre-eclampsia, hypertension, diabetes, early pregnancy loss and preterm birth. The identification of altered DNA methylation patterns may serve to elucidate the pathophysiological mechanisms that underlie the different types of maternal diabetes during pregnancy. This review aims to provide a summary of existing knowledge on DNA methylation patterns in pregnancies complicated by pregestational type 1 (T1DM) and type 2 diabetes mellitus (T2DM), and gestational diabetes mellitus (GDM). Four databases, CINAHL, Scopus, PubMed and Google Scholar, were searched for studies on DNA methylation profiling in pregnancies complicated with diabetes. A total of 1985 articles were identified, of which 32 met the inclusion criteria and are included in this review. All studies profiled DNA methylation during GDM or impaired glucose tolerance (IGT), while no studies investigated T1DM or T2DM. We highlight the increased methylation of two genes, Hypoxia‐inducible Factor‐3α (HIF3α) and Peroxisome Proliferator-activated Receptor Gamma-coactivator-Alpha (PGC1-α), and the decreased methylation of one gene, Peroxisome Proliferator Activated Receptor Alpha (PPARα), in women with GDM compared to pregnant women with normoglycemia that were consistently methylated across diverse populations with varying pregnancy durations, and using different diagnostic criteria, methodologies and biological sources. These findings support the candidacy of these three differentially methylated genes as biomarkers for GDM. Furthermore, these genes may provide insight into the pathways that are epigenetically influenced during maternal diabetes and which should be prioritized and replicated in longitudinal studies and in larger populations to ensure their clinical applicability. Finally, we discuss the challenges and limitations of DNA methylation analysis, and the need for DNA methylation profiling to be conducted in different types of maternal diabetes in pregnancy.

Genome-wide DNA methylation profiling of stomach cancer in the ethnic population of Mizoram, North East India

Stomach cancer is the fifth most common cancer in terms of prevalence and incidence and the fourth leading cause of mortality in men and women worldwide. It is well-established that aberrant DNA methylation in cells can lead to carcinogenesis. The primary objective of our study was to investigate the aberrant DNA methylation status of genes associated with stomach cancer with a particular reference to the ethnic population of Mizoram, North East India. The site-level analysis identified 2883 CpG sites differentially methylated, representing ~922 genes. Out of which 476 Differentially Methylated Positions (DMPs) were promoter-associated, 452 DMPs were hypermethylated, and 24 were hypomethylated. The region-level analysis identified 462 Differentially Methylated Regions (DMRs) corresponding to ~320 genes, of which ~281 genes were hypermethylated and ~ 40 genes were hypomethylated. TCGA analysis showed that some of the genes had been previously implicated in other cancers including stomach cancer. Five hypermethylated genes were selected as candidate genes for further investigations and they have shown to be novel and could serve as candidate hypermethylation biomarkers for stomach cancer in this particular ethnic group.

Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing

Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.

DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome

Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.

Prognostication of early-onset endometrioid endometrial cancer based on genome-wide DNA methylation profiles

OBJECTIVE

The aim of this study was to establish criteria that would indicate whether fertility preservation therapy would likely be safe for patients aged 40 years or less with endometrioid endometrial cancer based on their DNA methylation profile.

METHODS

Forty-nine fresh-frozen tissue samples from patients with endometrial cancer from an initial cohort and 31 formalin-fixed paraffin-embedded tissue samples from a second cohort were subjected to genome-wide DNA methylation analysis using the Infinium MethylationEPIC BeadChip.

RESULTS

Epigenomic clustering of early-onset endometrial cancer was correlated with the widely used recurrence risk classification. Genes showing differences in DNA methylation levels between the low-recurrence-risk category and intermediate- and high-risk categories were accumulated in pathways related to fibroblast growth factor and nuclear factor-κB signaling. DNA hypomethylation and overexpression of ZBTB38 were frequently observed in the low-risk category. Eight hundred thirty-one marker CpG probes showed area under the curve values of >0.7 on the receiver operating characteristic curve for discrimination of patients belonging to the low-risk category. By combining marker CpG sites, seven panels for placing patients into the low-risk category with 91.3% or more sensitivity and specificity in both the initial and second cohorts were established.

CONCLUSIONS

DNA methylation diagnostics criteria using up to 6 of 8 CpG sites for LPP, FOXO1, RNF4, EXOC6B, CCPG1, RREB1 and ZBTB38 may be applicable to recurrence risk estimation for patients aged 40 years or less with endometrial cancer, regardless of tumor cell content, even if formalin-fixed paraffin-embedded biopsy or curettage materials are used.