Strategies for analyzing bisulfite sequencing data

Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

Intricacies of aging and Down syndrome

Down syndrome is the most frequently occurring genetic condition, with a substantial escalation in risk associated with advanced maternal age. The syndrome is characterized by a diverse range of phenotypes, affecting to some extent all levels of organization, and its progeroid nature - early manifestation of aspects of the senile phenotype. Despite extensive investigations, many aspects and mechanisms of the disease remain unexplored. The current review aims to provide an overview of the main causes and manifestations of Down syndrome, while also examining the phenomenon of accelerated aging and exploring potential therapeutic strategies.

The genome of Przewalski's horse (Equus ferus przewalskii)

The Przewalski's horse (Equus ferus przewalskii) is an endangered equid native to the steppes of central Asia. After becoming extinct in the wild multiple conservation efforts convened to preserve the species, including captive breeding programs, reintroduction and monitoring systems, protected lands, and cloning. Availability of a highly contiguous reference genome is essential to support these continued efforts. We used Oxford Nanopore sequencing to produce a scaffold-level 2.5 Gb nuclear assembly and 16,002 bp mitogenome from a captive Przewalski's mare. All assembly drafts were generated from 111 Gb of sequence from a single PromethION R10.4.1 flow cell. The mitogenome contained 37 genes in the standard mammalian configuration and was 99.63% identical to the domestic horse (Equus caballus). The nuclear assembly, EquPr2, contained 2,146 scaffolds with an N50 of 85.1 Mb, 43X mean depth, and BUSCO quality score of 98.92%. EquPr2 successfully improves upon the existing Przewalski's horse reference genome (Burgud), with 25-fold fewer scaffolds, a 166-fold larger N50, and phased pseudohaplotypes. Modified basecalls revealed 79.5% DNA methylation and 2.1% hydroxymethylation globally. Allele-specific methylation analysis between pseudohaplotypes revealed 226 differentially methylated regions in known imprinted genes and loci not previously reported as imprinted. The heterozygosity rate of 0.165% matches previous estimates for the species and compares favorably to other endangered animals. This improved Przewalski's horse assembly will serve as a valuable resource for conservation efforts and comparative genomics investigations.

The Ty1 retrotransposon harbors a DNA region that performs dual functions as both a gene silencing and chromatin insulator

In various eukaryotic kingdoms, long terminal repeat (LTR) retrotransposons repress transcription by infiltrating heterochromatin generated within their elements. In contrast, the budding yeast LTR retrotransposon Ty1 does not itself undergo transcriptional repression, although it is capable of repressing the transcription of the inserted genes within it. In this study, we identified a DNA region within Ty1 that exerts its silencing effect via sequence orientation. We identified a DNA region within the Ty1 group-specific antigen (GAG) gene that causes gene silencing, termed GAG silencing (GAGsi), in which the silent chromatin in the GAGsi region is created by euchromatin-specific histone modifications. A characteristic inverted repeat (IR) sequence is present at the 5' end of this region, forming a chromatin boundary between promoter-specific chromatin upstream of the IR sequence and silent chromatin downstream of the IR sequence. In addition, Esc2 and Rad57, which are involved in DNA repair, were required for GAGsi silencing. Finally, the chromatin boundary was required for the transcription of Ty1 itself. Thus, the GAGsi sequence contributes to the creation of a chromatin environment that promotes Ty1 transcription.

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.

Improve-RRBS: a novel tool to correct the 3' trimming of reduced representation sequencing reads

Motivation

Reduced Representation Bisulfite Sequencing (RRBS) is a popular approach to determine DNA methylation of the CpG-rich regions of the genome. However, we observed that false positive differentially methylated sites (DMS) are also identified using the standard computational analysis.

Results

During RRBS library preparation the MspI digested DNA undergo end-repair by a cytosine at the 3' end of the fragments. After sequencing, Trim Galore cuts these end-repaired nucleotides. However, Trim Galore fails to detect end-repair when it overlaps with the 3' end of the sequencing reads. We found that these non-trimmed cytosines bias methylation calling, thus, can identify DMS erroneously. To circumvent this problem, we developed improve-RRBS, which efficiently identifies and hides these cytosines from methylation calling with a false positive rate of maximum 0.5%. To test improve-RRBS, we investigated four datasets from four laboratories and two different species. We found non-trimmed 3' cytosines in all datasets analyzed and as much as >50% of false positive DMS under certain conditions. By applying improve-RRBS, these DMS completely disappeared from all comparisons.

Availability and implementation

Improve-RRBS is a freely available python package https://pypi.org/project/iRRBS/ or https://github.com/fothia/improve-RRBS to be implemented in RRBS pipelines.

A comparison of methods for detecting DNA methylation from long-read sequencing of human genomes

BACKGROUND

Long-read sequencing can enable the detection of base modifications, such as CpG methylation, in single molecules of DNA. The most commonly used methods for long-read sequencing are nanopore developed by Oxford Nanopore Technologies (ONT) and single molecule real-time (SMRT) sequencing developed by Pacific Bioscience (PacBio). In this study, we systematically compare the performance of CpG methylation detection from long-read sequencing.

RESULTS

We demonstrate that CpG methylation detection from 7179 nanopore-sequenced DNA samples is highly accurate and consistent with 132 oxidative bisulfite-sequenced (oxBS) samples, isolated from the same blood draws. We introduce quality filters for CpGs that further enhance the accuracy of CpG methylation detection from nanopore-sequenced DNA, while removing at most 30% of CpGs. We evaluate the per-site performance of CpG methylation detection across different genomic features and CpG methylation rates and demonstrate how the latest R10.4 flowcell chemistry and base-calling algorithms improve methylation detection from nanopore sequencing. Additionally, we show how the methylation detection of 50 SMRT-sequenced genomes compares to nanopore sequencing and oxBS.

CONCLUSIONS

This study provides the first systematic comparison of CpG methylation detection tools for long-read sequencing methods. We compare two commonly used computational methods for the detection of CpG methylation in a large number of nanopore genomes, including samples sequenced using the latest R10.4 nanopore flowcell chemistry and 50 SMRT sequenced samples. We provide insights into the strengths and limitations of each sequencing method as well as recommendations for standardization and evaluation of tools designed for genome-scale modified base detection using long-read sequencing.

CDK5R1, GSE1, HSPG2 and WDFY3 as indirect epigenetic-sensitive genes in atrial fibrillation

BACKGROUND

Although mounting evidence supports that aberrant DNA methylation occurs in the hearts of patients with atrial fibrillation (AF), noninvasive epigenetic characterization of AF has not yet been defined.

METHODS

We investigated DNA methylome changes in peripheral blood CD4+ T cells isolated from 10 patients with AF relative to 11 healthy subjects (HS) who were enrolled in the DIANA clinical trial (NCT04371809) via reduced-representation bisulfite sequencing (RRBS).

RESULTS

An atrial-specific PPI network revealed 18 hub differentially methylated genes (DMGs), wherein ROC curve analysis revealed reasonable diagnostic performance of DNA methylation levels found within CDK5R1 (AUC = 0.76; p = 0.049), HSPG2 (AUC = 0.77; p = 0.038), WDFY3 (AUC = 0.78; p = 0.029), USP49 (AUC = 0.76; p = 0.049), GSE1 (AUC = 0.76; p = 0.049), AIFM1 (AUC = 0.76; p = 0.041), CDK5RAP2 (AUC = 0.81; p = 0.017), COL4A1 (AUC = 0.86; p < 0.001), SEPT8 (AUC = 0.90; p < 0.001), PFDN1 (AUC = 0.90; p < 0.01) and ACOT7 (AUC = 0.78; p = 0.032). Transcriptional profiling of the hub DMGs provided a significant overexpression of PSDM6 (p = 0.004), TFRC (p = 0.01), CDK5R1 (p < 0.001), HSPG2 (p = 0.01), WDFY3 (p < 0.001), USP49 (p = 0.004) and GSE1 (p = 0.021) in AF patients vs HS.

CONCLUSIONS

CDK5R1, GSE1, HSPG2 and WDFY3 resulted the best discriminatory genes both at methylation and gene expression level. Our results provide several candidate diagnostic biomarkers with the potential to advance precision medicine in AF.

The genome of Przewalski's horse (Equus ferus przewalskii)

The Przewalski's horse (Equus ferus przewalskii) is an endangered equid native to the steppes of central Asia. After becoming extinct in the wild, multiple conservation efforts convened to preserve the species including captive breeding programs, reintroduction and monitoring systems, protected lands, and cloning. Availability of a highly contiguous reference genome is essential to support these continued efforts. We used Oxford Nanopore sequencing to produce a scaffold-level 2.5 Gb nuclear assembly and 16,002 bp mitogenome from a captive Przewalski's mare. All assembly drafts were generated from 111 Gb of sequence from a single PromethION R10.4.1 flow cell. The mitogenome contained 37 genes in the standard mammalian configuration and was 99.63% identical to the domestic horse (Equus caballus). The nuclear assembly, EquPr2, contained 2,146 scaffolds with an N50 of 85.1 Mb, 43X mean depth, and BUSCO quality score of 98.92%. EquPr2 successfully improves upon the existing Przewalski's horse reference genome (Burgud), with 25-fold fewer scaffolds, a 166-fold larger N50, and phased pseudohaplotypes. Modified basecalls revealed 79.5% DNA methylation and 2.1% hydroxymethylation globally. Allele-specific methylation analysis between pseudohaplotypes revealed 226 differentially methylated regions (DMRs) in known imprinted genes and loci not previously reported as imprinted. The heterozygosity rate of 0.165% matches previous estimates for the species and compares favorably to other endangered animals. This improved Przewalski's horse assembly will serve as a valuable resource for conservation efforts and comparative genomics investigations.

A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing

Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequencing, allows reduced representation methylation sequencing that can be applied to CpG islands or imprinted regions. Here we present DeepMod2, a comprehensive deep-learning framework for methylation detection using ionic current signal from Nanopore sequencing. DeepMod2 implements both a bidirectional long short-term memory (BiLSTM) model and a Transformer model and can analyze POD5 and FAST5 signal files generated on R9 and R10 flowcells. Additionally, DeepMod2 can run efficiently on central processing unit (CPU) through model pruning and can infer epihaplotypes or haplotype-specific methylation calls from phased reads. We use multiple publicly available and newly generated datasets to evaluate the performance of DeepMod2 under varying scenarios. DeepMod2 has comparable performance to Guppy and Dorado, which are the current state-of-the-art methods from Oxford Nanopore Technologies that remain closed-source. Moreover, we show a high correlation (r = 0.96) between reduced representation and whole-genome Nanopore sequencing. In summary, DeepMod2 is an open-source tool that enables fast and accurate DNA methylation detection from whole-genome or adaptive sequencing data on a diverse range of flowcell types.

A novel approach toward optimal workflow selection for DNA methylation biomarker discovery

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

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.

Profiling neuronal methylome and hydroxymethylome of opioid use disorder in the human orbitofrontal cortex

Opioid use disorder (OUD) is influenced by genetic and environmental factors. While recent research suggests epigenetic disturbances in OUD, this is mostly limited to DNA methylation (5mC). DNA hydroxymethylation (5hmC) has been widely understudied. We conducted a multi-omics profiling of OUD in a male cohort, integrating neuronal-specific 5mC and 5hmC as well as gene expression profiles from human postmortem orbitofrontal cortex (OUD = 12; non-OUD = 26). Single locus methylomic analysis and co-methylation analysis showed a higher number of OUD-associated genes and gene networks for 5hmC compared to 5mC; these were enriched for GPCR, Wnt, neurogenesis, and opioid signaling. 5hmC marks also showed a higher correlation with gene expression patterns and enriched for GWAS of psychiatric traits. Drug interaction analysis revealed interactions with opioid-related drugs, some used as OUD treatments. Our multi-omics findings suggest an important role of 5hmC and reveal loci epigenetically dysregulated in OFC neurons of individuals with OUD.

Next-Generation Sequencing Technology: Current Trends and Advancements

The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models

Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

Single-molecule methylation profiles of cell-free DNA in cancer with nanopore sequencing

Epigenetic characterization of cell-free DNA (cfDNA) is an emerging approach for detecting and characterizing diseases such as cancer. We developed a strategy using nanopore-based single-molecule sequencing to measure cfDNA methylomes. This approach generated up to 200 million reads for a single cfDNA sample from cancer patients, an order of magnitude improvement over existing nanopore sequencing methods. We developed a single-molecule classifier to determine whether individual reads originated from a tumor or immune cells. Leveraging methylomes of matched tumors and immune cells, we characterized cfDNA methylomes of cancer patients for longitudinal monitoring during treatment.

Examining the immunoepigenetic-gut microbiome axis in the context of self-esteem among Native Hawaiians and other Pacific Islanders

Introduction: Native Hawaiian and other Pacific Islander (NHPI) populations experience higher rates of immunometabolic diseases compared to other racial-ethnic groups in Hawaii. As annual NHPI mortality rates for suicide and type 2 diabetes mellitus (T2DM) exceed those of the state as a whole, understanding the social and biological mechanisms underlying these disparities are urgently needed to enable preventive strategies. Methods: A community-based approach was used to investigate the immunoepigenetic-gut microbiome axis in an NHPI-enriched cohort of Oahu residents (N = 68). Self-esteem (SE) data was collected using a modified Rosenberg self-esteem (SE) assessment as a proxy measure for mental wellbeing in consideration for cultural competency. T2DM status was evaluated using point-of-care A1c (%) tests. Stool samples were collected for 16s-based metagenomic sequencing analyses. Plasma from blood samples were isolated by density-gradient centrifugation. Peripheral blood mononuclear cells (PBMCs) were collected from the same samples and enriched for monocytes using negative selection techniques. Flow-cytometry was used for immunoprofiling assays. Monocyte DNA was extracted for Illumina EPIC array-based methylation analysis. Results: Compared to individuals with normal SE (NSE), those with low SE (LSE) exhibited significantly higher plasma concentrations (pg/ml) of proinflammatory cytokines IL-8 (p = 0.051) and TNF-α (p = 0.011). Metagenomic analysis revealed that the relative abundance (%) of specific gut bacteria significantly differed between SE groups - some of which directly correlated with SE scores. Gene ontology analysis revealed that 104 significantly differentially methylated loci (DML) between SE groups were preferentially located at genes involved in immunometabolic processes. Horvath clock analyses indicated epigenetic age (Epi-Age) deceleration in individuals with LSE and acceleration in individuals with NSE (p = 0.042), yet was not reproduced by other clocks. Discussion: These data reveal novel differences in the immunoepigenetic-gut microbiome axis with respect to SE, warranting further investigation into its relationship to brain activity and mental health in NHPI. Unexpected results from Epi-Age analyses warrant further investigation into the relationship between biological age and disparate health outcomes among the NHPI population. The modifiable component of epigenetic processes and the gut microbiome makes this axis an attractive target for potential therapeutics, biomarker discovery, and novel prevention strategies.

Epigenetic Targets and Pathways Linked to SARS-CoV-2 Infection and Pathology

The scale at which the SARS-CoV-2/COVID-19 pandemic has spread remains enormous. Provided the genetic makeup of the virus and humans is readily available, the quest for knowing the mechanism and epidemiology continues to prevail across the entire scientific community. Several aspects, including immunology, molecular biology, and host-pathogen interaction, are continuously being dug into for preparing the human race for future pandemics. The exact reasons for vast differences in symptoms, pathophysiological implications of COVID-infections, and mortality differences remain elusive. Hence, researchers are also looking beyond traditional genomics, proteomics, and transcriptomics approach, especially entrusting the environmental regulation of the genetic landscape of COVID-human interactions. In line with these questions lies a critical process called epigenetics. The epigenetic perturbations in both host and parasites are a matter of great interest to unravel the disparities in COVID-19 mortalities and pathology. This review provides a deeper insight into current research on the epigenetic landscape of SARS-CoV-2 infection in humans and potential targets for augmenting the ongoing investigation. It also explores the potential targets, pathways, and networks associated with the epigenetic regulation of processes involved in SARS-CoV-2 pathology.

DNA Methylation Signature of Aging: Potential Impact on the Pathogenesis of Parkinson's Disease

Regulation of gene expression by epigenetic modifications means lasting and heritable changes in the function of genes without alterations in the DNA sequence. Of all epigenetic mechanisms identified thus far, DNA methylation has been of particular interest in both aging and age-related disease research over the last decade given the consistency of site-specific DNA methylation changes during aging that can predict future health and lifespan. An increasing line of evidence has implied the dynamic nature of DNA (de)methylation events that occur throughout the lifespan has a role in the pathophysiology of aging and age-associated neurodegenerative conditions, including Parkinson's disease (PD). In this regard, PD methylome shows, to some extent, similar genome-wide changes observed in the methylome of healthy individuals of matching age. In this review, we start by providing a brief overview of studies outlining global patterns of DNA methylation, then its mechanisms and regulation, within the context of aging and PD. Considering diverging lines of evidence from different experimental and animal models of neurodegeneration and how they combine to shape our current understanding of tissue-specific changes in DNA methylome in health and disease, we report a high-level comparison of the genomic methylation landscapes of brain, with an emphasis on dopaminergic neurons in PD and in natural aging. We believe this will be particularly useful for systematically dissecting overlapping genome-wide alterations in DNA methylation during PD and healthy aging, and for improving our knowledge of PD-specific changes in methylation patterns independent of aging process.

The Neuroepigenetic Landscape of Vertebrate and Invertebrate Models of Neurodegenerative Diseases

Vertebrate and invertebrate models of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis, have been paramount to our understanding of the pathophysiology of these conditions; however, the brain epigenetic landscape is less well established in these disease models. DNA methylation, histone modifications, and microRNAs are among commonly studied mechanisms of epigenetic regulation. Genome-wide studies and candidate studies of specific methylation marks, histone marks, and microRNAs have demonstrated the dysregulation of these mechanisms in models of neurodegenerative diseases; however, the studies to date are scarce and inconclusive and the implications of many of these changes are still not fully understood. In this review, we summarize epigenetic changes reported to date in the brain of vertebrate and invertebrate models used to study neurodegenerative diseases, specifically diseases affecting the aging population. We also discuss caveats of epigenetic research so far and the use of disease models to understand neurodegenerative diseases, with the aim of improving the use of model organisms in this context in future studies.

Exploitation of epigenetic variation of crop wild relatives for crop improvement and agrobiodiversity preservation

Crop wild relatives (CWRs) are recognized as the best potential source of traits for crop improvement. However, successful crop improvement using CWR relies on identifying variation in genes controlling desired traits in plant germplasms and subsequently incorporating them into cultivars. Epigenetic diversity may provide an additional layer of variation within CWR and can contribute novel epialleles for key traits for crop improvement. There is emerging evidence that epigenetic variants of functional and/or agronomic importance exist in CWR gene pools. This provides a rationale for the conservation of epigenotypes of interest, thus contributing to agrobiodiversity preservation through conservation and (epi)genetic monitoring. Concepts and techniques of classical and modern breeding should consider integrating recent progress in epigenetics, initially by identifying their association with phenotypic variations and then by assessing their heritability and stability in subsequent generations. New tools available for epigenomic analysis offer the opportunity to capture epigenetic variation and integrate it into advanced (epi)breeding programmes. Advances in -omics have provided new insights into the sources and inheritance of epigenetic variation and enabled the efficient introduction of epi-traits from CWR into crops using epigenetic molecular markers, such as epiQTLs.

A multi-omics longitudinal study of the murine retinal response to chronic low-dose irradiation and simulated microgravity

The space environment includes unique hazards like radiation and microgravity which can adversely affect biological systems. We assessed a multi-omics NASA GeneLab dataset where mice were hindlimb unloaded and/or gamma irradiated for 21 days followed by retinal analysis at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles resulting in a total of 4178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation difference was seen across different conditions at the same time point. Nucleotide metabolism biological processes were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. A total of 23 genes showed significant changes in methylation and expression compared to unexposed controls, including genes involved in retinal function and inflammatory response. This multi-omics analysis interrogates the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination and highlights important molecular mechanisms at different post-exposure stages.

Methylated ccfDNA from plasma biomarkers of Alzheimer's disease using targeted bisulfite sequencing

Aim: Noninvasive biomarkers such as methylated ccfDNA from plasma could help to support the diagnosis of Alzheimer's disease (AD). Methods: A targeted sequencing protocol was developed to identify candidate biomarkers of AD in methylated ccfDNA extracted from plasma. Results: The authors identified differentially methylated CpGs, regions of which were the same as those identified in previous AD studies. Specifically, a differentially methylated CpG of the LHX2 gene previously identified in a plasma study of AD was replicated in the study. The MBP and DUSP22 regions have been identified in other brain studies of AD and in the authors' study. Conclusion: Although these biomarkers must be validated in other cohorts, methylated ccfDNA could be a relevant noninvasive biomarker in AD.

Analysis and Performance Assessment of the Whole Genome Bisulfite Sequencing Data Workflow: Currently Available Tools and a Practical Guide to Advance DNA Methylation Studies

DNA methylation is associated with transcriptional repression, genomic imprinting, stem cell differentiation, embryonic development, and inflammation. Aberrant DNA methylation can indicate disease states, including cancer and neurological disorders. Therefore, the prevalence and location of 5-methylcytosine in the human genome is a topic of interest. Whole-genome bisulfite sequencing (WGBS) is a high-throughput method for analyzing DNA methylation. This technique involves library preparation, alignment, and quality control. Advancements in epigenetic technology have led to an increase in DNA methylation studies. This review compares the detailed experimental methodology of WGBS using accessible and up-to-date analysis tools. Practical codes for WGBS data processing are included as a general guide to assist progress in DNA methylation studies through a comprehensive case study.

Machine learning for multi-omics data integration in cancer

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.

The Role of Feed Restriction on DNA Methylation, Feed Efficiency, Metabolome, Biochemical Profile, and Progesterone Patterns in the Female Filial Generation (F1) Obtained From Early Feed Restricted Ewes (F0)

Deficient management of replacement animals in the farm during early developmental windows may promote adverse programming effects on reproductive traits and subsequent transmission to the next generation. In this sense, DNA methylation profiles allow researchers to decode epigenetic regulation mechanisms in mammals and identify novel candidate genes correlated with phenotype differences in both dams and offspring. Therefore, improving knowledge in the field of epigenetics and intergenerational effects caused by prenatal and postnatal early nutritional events (e.g., feed restriction) is crucial for refining strategies dedicated to animal breeding. In this study, we determined differences in the global blood methylation patterns, biochemical profile, and metabolome of ewe lambs (F1) born from either early feed restricted dams (F0-RES) or fed ad libitum (F0-ADL). Our data show that functional categories such as those related to cellular processes, phosphorylation, nervous system, immunity response, or reproductive function were enriched significantly in the F1-RES lambs due to differences in the methylation of genes in these categories. These F1-RES lambs did not show differences in feed efficiency during the replacement period but presented higher levels of insulin and triglycerides and reduced concentration of progesterone, whereas the metabolome profile demonstrated variations in the bile acid composition when compared with the F1-ADL lambs. Taken together, all these results suggest that intergenerational effects caused by early feed restriction of dams (F0) may persist in the F1 female lambs with negative consequences on genes involved in cellular processes and reproductive traits.

LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis

BACKGROUND

DNA methylation is commonly measured using bisulfite sequencing (BS-seq). The quality of a BS-seq library is measured by its bisulfite conversion efficiency. Libraries with low conversion rates are typically excluded from analysis resulting in reduced coverage and increased costs.

RESULTS

We have developed a probabilistic method and software, LuxRep, that implements a general linear model and simultaneously accounts for technical replicates (libraries from the same biological sample) from different bisulfite-converted DNA libraries. Using simulations and actual DNA methylation data, we show that including technical replicates with low bisulfite conversion rates generates more accurate estimates of methylation levels and differentially methylated sites. Moreover, using variational inference speeds up computation time necessary for whole genome analysis.

CONCLUSIONS

In this work we show that taking into account technical replicates (i.e. libraries) of BS-seq data of varying bisulfite conversion rates, with their corresponding experimental parameters, improves methylation level estimation and differential methylation detection.

DNA Methylation and Non-Coding RNAs during Tissue-Injury Associated Pain

While about half of the population experience persistent pain associated with tissue damages during their lifetime, current symptom-based approaches often fail to reduce such pain to a satisfactory level. To provide better patient care, mechanism-based analgesic approaches must be developed, which necessitates a comprehensive understanding of the nociceptive mechanism leading to tissue injury-associated persistent pain. Epigenetic events leading the altered transcription in the nervous system are pivotal in the maintenance of pain in tissue injury. However, the mechanisms through which those events contribute to the persistence of pain are not fully understood. This review provides a summary and critical evaluation of two epigenetic mechanisms, DNA methylation and non-coding RNA expression, on transcriptional modulation in nociceptive pathways during the development of tissue injury-associated pain. We assess the pre-clinical data and their translational implication and evaluate the potential of controlling DNA methylation and non-coding RNA expression as novel analgesic approaches and/or biomarkers of persistent pain.

Analysis of Bisulfite Sequencing Data Using Bismark and DMRcaller to Identify Differentially Methylated Regions

The mechanism of the addition of a methyl group to cytosine has been identified as one of several heritable epigenetic mechanisms. In plants, DNA methylation is involved in mediating response to stress, plant development, polyploidy, and domestication through regulation of gene expression. The correlation of epigenetic variation to phenotypic traits expands our understanding toward plant evolution, and provides new source for targeted manipulation in crop improvement. To address the increasing interest to map methylation landscape in plant species, this chapter describes methods to analyze bisulfite sequencing data and identify epigenetic variation between samples. We also detailed guidelines to highlight possible optimizations, as well as ways to tailor parameters according to data and biological variability.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Advances in mRNA 5-methylcytosine modifications: Detection, effectors, biological functions, and clinical relevance

5-methylcytosine (m⁵C) post-transcriptional modifications affect the maturation, stability, and translation of the mRNA molecule. These modifications play an important role in many physiological and pathological processes, including stress response, tumorigenesis, tumor cell migration, embryogenesis, and viral replication. Recently, there has been a better understanding of the biological implications of m⁵C modification owing to the rapid development and optimization of detection technologies, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and RNA-BisSeq. Further, predictive models (such as PEA-m⁵C, m⁵C-PseDNC, and DeepMRMP) for the identification of potential m⁵C modification sites have also emerged. In this review, we summarize the current experimental detection methods and predictive models for mRNA m⁵C modifications, focusing on their advantages and limitations. We systematically surveyed the latest research on the effectors related to mRNA m⁵C modifications and their biological functions in multiple species. Finally, we discuss the physiological effects and pathological significance of m⁵C modifications in multiple diseases, as well as their therapeutic potential, thereby providing new perspectives for disease treatment and prognosis.

The effect of experimental lead pollution on DNA methylation in a wild bird population

Anthropogenic pollution is known to negatively influence an organism's physiology, behaviour, and fitness. Epigenetic regulation, such as DNA methylation, has been hypothesized as a potential mechanism to mediate such effects, yet studies in wild species are lacking. We first investigated the effects of early-life exposure to the heavy metal lead (Pb) on DNA methylation levels in a wild population of great tits (Parus major), by experimentally exposing nestlings to Pb at environmentally relevant levels. Secondly, we compared nestling DNA methylation from a population exposed to long-term heavy metal pollution (close to a copper smelter), where birds suffer from pollution-related decrease in food quality, and a control population. For both comparisons, the analysis of about one million CpGs covering most of the annotated genes revealed that pollution-related changes in DNA methylation were not genome wide, but enriched for genes underlying developmental processes. However, the results were not consistent when using binomial or beta binomial regression highlighting the difficulty of modelling variance in CpGs. Our study indicates that post-natal anthropogenic heavy metal exposure can affect methylation levels of development related genes in a wild bird population.

Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate

Whole genome bisulphite sequencing (WGBS) permits the genome-wide study of single molecule methylation patterns. One of the key goals of mammalian cell-type identity studies, in both normal differentiation and disease, is to locate differential methylation patterns across the genome. We discuss the most desirable characteristics for DML (differentially methylated locus) and DMR (differentially methylated region) detection tools in a genome-wide context and choose a set of statistical methods that fully or partially satisfy these considerations to compare for benchmarking. Our data simulation strategy is both biologically informed-employing distribution parameters derived from large-scale consortium datasets-and thorough. We report DML detection ability with respect to coverage, group methylation difference, sample size, variability and covariate size, both marginally and jointly, and exhaustively with respect to parameter combination. We also benchmark these methods on FDR control and computational time. We use this result to backend and introduce an expanded version of DMRcate: an existing DMR detection tool for microarray data that we have extended to now call DMRs from WGBS data. We compare DMRcate to a set of alternative DMR callers using a similarly realistic simulation strategy. We find DMRcate and RADmeth are the best predictors of DMRs, and conclusively find DMRcate the fastest.

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 .

Myxosporea (Myxozoa, Cnidaria) Lack DNA Cytosine Methylation

DNA cytosine methylation is central to many biological processes, including regulation of gene expression, cellular differentiation, and development. This DNA modification is conserved across animals, having been found in representatives of sponges, ctenophores, cnidarians, and bilaterians, and with very few known instances of secondary loss in animals. Myxozoans are a group of microscopic, obligate endoparasitic cnidarians that have lost many genes over the course of their evolution from free-living ancestors. Here, we investigated the evolution of the key enzymes involved in DNA cytosine methylation in 29 cnidarians and found that these enzymes were lost in an ancestor of Myxosporea (the most speciose class of Myxozoa). Additionally, using whole-genome bisulfite sequencing, we confirmed that the genomes of two distant species of myxosporeans, Ceratonova shasta and Henneguya salminicola, completely lack DNA cytosine methylation. Our results add a notable and novel taxonomic group, the Myxosporea, to the very short list of animal taxa lacking DNA cytosine methylation, further illuminating the complex evolutionary history of this epigenetic regulatory mechanism.

Riluzole Administration to Rats with Levodopa-Induced Dyskinesia Leads to Loss of DNA Methylation in Neuronal Genes

Dyskinesias are characterized by abnormal repetitive involuntary movements due to dysfunctional neuronal activity. Although levodopa-induced dyskinesia, characterized by tic-like abnormal involuntary movements, has no clinical treatment for Parkinson’s disease patients, animal studies indicate that Riluzole, which interferes with glutamatergic neurotransmission, can improve the phenotype. The rat model of Levodopa-Induced Dyskinesia is a unilateral lesion with 6-hydroxydopamine in the medial forebrain bundle, followed by the repeated administration of levodopa. The molecular pathomechanism of Levodopa-Induced Dyskinesia is still not deciphered; however, the implication of epigenetic mechanisms was suggested. In this study, we investigated the striatum for DNA methylation alterations under chronic levodopa treatment with or without co-treatment with Riluzole. Our data show that the lesioned and contralateral striata have nearly identical DNA methylation profiles. Chronic levodopa and levodopa + Riluzole treatments led to DNA methylation loss, particularly outside of promoters, in gene bodies and CpG poor regions. We observed that several genes involved in the Levodopa-Induced Dyskinesia underwent methylation changes. Furthermore, the Riluzole co-treatment, which improved the phenotype, pinpointed specific methylation targets, with a more than 20% methylation difference relative to levodopa treatment alone. These findings indicate potential new druggable targets for Levodopa-Induced Dyskinesia.

Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning

The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52-81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93-98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91-100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.

Genome-wide DNA methylation of the liver reveals delayed effects of early-life exposure to 17-α-ethinylestradiol in the self-fertilizing mangrove rivulus

Organisms exposed to endocrine disruptors in early life can show altered phenotype later in adulthood. Although the mechanisms underlying these long-term effects remain poorly understood, an increasing body of evidence points towards the potential role of epigenetic processes. In the present study, we exposed hatchlings of an isogenic lineage of the self-fertilizing fish mangrove rivulus for 28 days to 4 and 120 ng/L of 17-α-ethinylestradiol. After a recovery period of 140 days, reduced representation bisulphite sequencing (RRBS) was performed on the liver in order to assess the hepatic genome-wide methylation landscape. Across all treatment comparisons, a total of 146 differentially methylated fragments (DMFs) were reported, mostly for the group exposed to 4 ng/L, suggesting a non-monotonic effect of EE2 exposure. Gene ontology analysis revealed networks involved in lipid metabolism, cellular processes, connective tissue function, molecular transport and inflammation. The highest effect was reported for nipped-B-like protein B (NIPBL) promoter region after exposure to 4 ng/L EE2 (+ 21.9%), suggesting that NIPBL could be an important regulator for long-term effects of EE2. Our results also suggest a significant role of DNA methylation in intergenic regions and potentially in transposable elements. These results support the ability of early exposure to endocrine disruptors of inducing epigenetic alterations during adulthood, providing plausible mechanistic explanations for long-term phenotypic alteration. Additionally, this work demonstrates the usefulness of isogenic lineages of the self-fertilizing mangrove rivulus to better understand the biological significance of long-term alterations of DNA methylation by diminishing the confounding factor of genetic variability.

Clinical epigenomics for cardiovascular disease: Diagnostics and therapies

The study of epigenomics has advanced in recent years to span the regulation of a single genetic locus to the structure and orientation of entire chromosomes within the nucleus. In this review, we focus on the challenges and opportunities of clinical epigenomics in cardiovascular disease. As an integrator of genetic and environmental inputs, and because of advances in measurement techniques that are highly reproducible and provide sequence information, the epigenome is a rich source of potential biosignatures of cardiovascular health and disease. Most of the studies to date have focused on the latter, and herein we discuss observations on epigenomic changes in human cardiovascular disease, examining the role of protein modifiers of chromatin, noncoding RNAs and DNA modification. We provide an overview of cardiovascular epigenomics, discussing the challenges of data sovereignty, data analysis, doctor-patient ethics and innovations necessary to implement precision health.

Estimands in epigenome-wide association studies

Background

In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.

Results

We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.

Conclusions

The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01083-9.

DNA methylation and expression of estrogen receptor alpha in fathead minnows exposed to 17α-ethynylestradiol

The gene expression response thought to underlie the negative apical effects resulting from estrogen exposure have been thoroughly described in fish. Although epigenetics are believed to play a critical role translating environmental exposures into the development of adverse apical effects, they remain poorly characterized in fish species. This study investigated alterations of DNA methylation of estrogen receptor alpha (esr1) in brain and liver tissues from 8 to 10 month old male fathead minnows (Pimephales promelas) after a 2d exposure to either 2.5 ng/L or 10 ng/L 17α-ethynylestradiol (EE2). Changes in the patterns of methylation were evaluated using targeted deep sequencing of bisulfite treated DNA in the 5' region of esr1. Methylation and gene expression were assessed at 2d of exposure and after a 7 and 14d depuration period. After 2d EE2 exposure, males exhibited significant demethylation in the 5' upstream region of esr1 in liver tissue, which was inversely correlated to gene expression. This methylation pattern reflected what was seen in females. No gene body methylation (GBM) was observed for liver of exposed males. Differential methylation was observed for a single upstream CpG site in the liver after the 14d depuration. A less pronounced methylation response was observed in the upstream region in brain tissue, however, several CpGs were necessarily excluded from the analysis. In contrast to the liver, a significant GBM response was observed across the entire gene body, which was sustained until at least 7d post-exposure. No differential expression was observed in the brain, limiting functional interpretation of methylation changes. The identification of EE2-dependent changes in methylation levels strongly suggests the importance of epigenetic mechanisms as a mediator of the organismal response to environmental exposures and the need for further characterization of the epigenome. Further, differential methylation following depuration indicates estrogenic effects persist well after the active exposure, which has implications for the risk posed by repeated exposures..

Heritability of DNA methylation in threespine stickleback (Gasterosteus aculeatus)

Epigenetic mechanisms underlying phenotypic change are hypothesized to contribute to population persistence and adaptation in the face of environmental change. To date, few studies have explored the heritability of intergenerationally stable methylation levels in natural populations, and little is known about the relative contribution of cis- and trans-regulatory changes to methylation variation. Here, we explore the heritability of DNA methylation, and conduct methylation quantitative trait loci (meQTLs) analysis to investigate the genetic architecture underlying methylation variation between marine and freshwater ecotypes of threespine stickleback (Gasterosteus aculeatus). We quantitatively measured genome-wide DNA methylation in fin tissue using reduced representation bisulfite sequencing of F1 and F2 crosses, and their marine and freshwater source populations. We identified cytosines (CpG sites) that exhibited stable methylation levels across generations. We found that additive genetic variance explained an average of 24-35% of the methylation variance, with a number of CpG sites possibly autonomous from genetic control. We also detected both cis- and trans-meQTLs, with only trans-meQTLs overlapping with previously identified genomic regions of high differentiation between marine and freshwater ecotypes. Finally, we identified the genetic architecture underlying two key CpG sites that were differentially methylated between ecotypes. These findings demonstrate a potential role for DNA methylation in facilitating adaptation to divergent environments and improve our understanding of the heritable basis of population epigenomic variation.

Pathophysiological adaptations of resistance arteries in rat offspring exposed in utero to maternal obesity is associated with sex-specific epigenetic alterations

BACKGROUND/OBJECTIVES

Maternal obesity impacts vascular functions linked to metabolic disorders in offspring, leading to cardiovascular diseases during adulthood. Even if the relation between prenatal conditioning of cardiovascular diseases by maternal obesity and vascular function begins to be documented, little is known about resistance arteries. They are of particular interest because of their specific role in the regulation of local blood flow. Then our study aims to determine if maternal obesity can directly program fetal vascular dysfunction of resistance arteries, independently of metabolic disorders.

METHODS

With a model of rats exposed in utero to mild maternal diet-induced obesity (OMO), we investigated third-order mesenteric arteries of 4-month old rats in absence of metabolic disorders. The methylation profile of these vessels was determined by reduced representation bisulfite sequencing (RRBS). Vascular structure and reactivity were investigated using histomorphometry analysis and wire-myography. The metabolic function was evaluated by insulin and glucose tolerance tests, plasma lipid profile, and adipose tissue analysis.

RESULTS

At 4 months of age, small mesenteric arteries of OMO presented specific epigenetic modulations of matrix metalloproteinases (MMPs), collagens, and potassium channels genes in association with an outward remodeling and perturbations in the endothelium-dependent vasodilation pathways (greater contribution of EDHFs pathway in OMO males compared to control rats, and greater implication of PGI₂ in OMO females compared to control rats). These vascular modifications were detected in absence of metabolic disorders.

CONCLUSIONS

Our study reports a specific methylation profile of resistance arteries associated with vascular remodeling and vasodilation balance perturbations in offspring exposed in utero to maternal obesity, in absence of metabolic dysfunctions.

Estimating Copy-Number Proportions: The Comeback of Sanger Sequencing

Determination of the relative copy numbers of mixed molecular species in nucleic acid samples is often the objective of biological experiments, including Single-Nucleotide Polymorphism (SNP), indel and gene copy-number characterization, and quantification of CRISPR-Cas9 base editing, cytosine methylation, and RNA editing. Standard dye-terminator chromatograms are a widely accessible, cost-effective information source from which copy-number proportions can be inferred. However, the rate of incorporation of dye terminators is dependent on the dye type, the adjacent sequence string, and the secondary structure of the sequenced strand. These variable rates complicate inferences and have driven scientists to resort to complex and costly quantification methods. Because these complex methods introduce their own biases, researchers are rethinking whether rectifying distortions in sequencing trace files and using direct sequencing for quantification will enable comparable accurate assessment. Indeed, recent developments in software tools (e.g., TIDE, ICE, EditR, BEEP and BEAT) indicate that quantification based on direct Sanger sequencing is gaining in scientific acceptance. This commentary reviews the common obstacles in quantification and the latest insights and developments relevant to estimating copy-number proportions based on direct Sanger sequencing, concluding that bidirectional sequencing and sophisticated base calling are the keys to identifying and avoiding sequence distortions.

The human aortic endothelium undergoes dose-dependent DNA methylation in response to transient hyperglycemia

BACKGROUND

Glycemic control is a strong predictor of long-term cardiovascular risk in patients with diabetes mellitus, and poor glycemic control influences long-term risk of cardiovascular disease even decades after optimal medical management. This phenomenon, termed glycemic memory, has been proposed to occur due to stable programs of cardiac and endothelial cell gene expression. This transcriptional remodeling has been shown to occur in the vascular endothelium through a yet undefined mechanism of cellular reprogramming.

METHODS

In the current study, we quantified genome-wide DNA methylation of cultured human endothelial aortic cells (HAECs) via reduced-representation bisulfite sequencing (RRBS) following exposure to diabetic (250 mg/dL), pre-diabetic (125 mg/dL), or euglycemic (100 mg/dL) glucose concentrations for 72 h (n = 2).

RESULTS

We discovered glucose-dependent methylation of genomic regions (DMRs) encompassing 2199 genes, with a disproportionate number found among genes associated with angiogenesis and nitric oxide (NO) signaling-related pathways. Multi-omics analysis revealed differential methylation and gene expression of VEGF (↑5.6% DMR, ↑3.6-fold expression), and NOS3 (↓20.3% DMR, ↓1.6-fold expression), nodal regulators of angiogenesis and NO signaling, respectively.

CONCLUSION

In the current exploratory study, we examine glucose-dependent and dose-responsive alterations in endothelial DNA methylation to examine a putative epigenetic mechanism underlying diabetic vasculopathy. Specifically, we uncover the disproportionate glucose-dependent methylation and gene expression of VEGF and NO signaling cascades, a physiologic imbalance known to cause endothelial dysfunction in diabetes. We therefore hypothesize that epigenetic mechanisms encode a glycemic memory within endothelial cells.

Liquid Biopsy of Methylation Biomarkers in Cell-Free DNA

Liquid biopsies, in particular, analysis of cell-free DNA (cfDNA), have emerged as a promising noninvasive diagnostic approach in oncology. Abnormal distribution of DNA methylation is one of the hallmarks of many cancers and methylation changes occur early during carcinogenesis. Systemic analysis of cfDNA methylation profiles is being developed for cancer early detection, monitoring for minimal residual disease (MRD), predicting treatment response and prognosis, and tracing the tissue origin. This review highlights the advantages and disadvantages of ctDNA profiling for noninvasive diagnosis of early-stage cancers and explores recent advances in the clinical application of ctDNA methylation assays. We also summarize the technologies for ctDNA methylation analysis and provide a brief overview of the bioinformatic approaches for analyzing DNA methylation sequencing data.

Challenges and Perspectives in the Epigenetics of Climate Change-Induced Forests Decline

Forest tree species are highly vulnerable to the effects of climate change. As sessile organisms with long generation times, their adaptation to a local changing environment may rely on epigenetic modifications when allele frequencies are not able to shift fast enough. However, the current lack of knowledge on this field is remarkable, due to many challenges that researchers face when studying this issue. Huge genome sizes, absence of reference genomes and annotation, and having to analyze huge amounts of data are among these difficulties, which limit the current ability to understand how climate change drives tree species epigenetic modifications. In spite of this challenging framework, some insights on the relationships among climate change-induced stress and epigenomics are coming. Advances in DNA sequencing technologies and an increasing number of studies dealing with this topic must boost our knowledge on tree adaptive capacity to changing environmental conditions. Here, we discuss challenges and perspectives in the epigenetics of climate change-induced forests decline, aiming to provide a general overview of the state of the art.

Detection of CRISPR-mediated genome modifications through altered methylation patterns of CpG islands

BACKGROUND

The development and application of CRISPR technologies for the modification of the genome are rapidly expanding. Advances in the field describe new CRISPR components that are strategically engineered to improve the precision and reliability of CRISPR editing within the genome sequence. Genome modification using induced genome breaks that are targeted and mediated by CRISPR components leverage cellular mechanisms for repair like homology directed repair (HDR) to incorporate genomic edits with increased precision.

RESULTS

In this report, we describe the gain of methylation at typically hypomethylated CpG island (CGI) locations affected by the CRISPR-mediated incorporation of donor DNA using HDR mechanisms. With characterization of CpG methylation patterns using whole genome bisulfite sequencing, these CGI methylation disruptions trace the insertion of the donor DNA during the genomic edit. These insertions mediated by homology-directed recombination disrupt the generational methylation pattern stability of the edited CGI within the cells and their cellular lineage within the animal strain, persisting across generations. Our approach describes a statistically based workflow for indicating locations of modified CGIs and provides a mechanism for evaluating the directed modification of the methylome of the affected CGI at the CpG-level.

CONCLUSIONS

With advances in genome modification technology comes the need to detect the level and persistence of methylation change that modifications to the genomic sequence impose upon the collaterally edited methylome. Any modification of the methylome of somatic or germline cells could have implications for gene regulation mechanisms governed by the methylation patterns of CGI regions in the application of therapeutic edits of more sensitively regulated genomic regions. The method described here locates the directed modification of the mouse epigenome that persists over generations. While this observance would require supporting molecular observations such as direct sequence changes or gene expression changes, the observation of epigenetic modification provides an indicator that intentionally directed genomic edits can lead to collateral, unintentional epigenomic changes post modification with generational persistence.

Comparison of methylation capture sequencing and Infinium MethylationEPIC array in peripheral blood mononuclear cells

Background

Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium MethylationEPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price.

Methods

Epigenome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (> 1000 ng), medium (300–1000 ng), and low (150 ng–300 ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array.

Results

After quality control, an average of 3,708,550 CpG sites per sample were detected by MC-seq with DNA quantity > 1000 ng. Reproducibility of DNA methylation in MC-seq-detected CpG sites was high among samples with high, medium, and low DNA inputs (r > 0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98–0.99). However, methylation for a small proportion of CpGs (N = 235) differed significantly between the two platforms, with differences in beta values of greater than 0.5.

Conclusions

Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.