DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis

DNA Methylation in the Adaptive Response to Exercise

Emerging evidence published over the past decade has highlighted the role of DNA methylation in skeletal muscle function and health, including as an epigenetic transducer of the adaptive response to exercise. In this review, we aim to synthesize the latest findings in this field to highlight: (1) the shifting understanding of the genomic localization of altered DNA methylation in response to acute and chronic aerobic and resistance exercise in skeletal muscle (e.g., promoter, gene bodies, enhancers, intergenic regions, un-annotated regions, and genome-wide methylation); (2) how these global/regional methylation changes relate to transcriptional activity following exercise; and (3) the factors (e.g., individual demographic or genetic features, dietary, training history, exercise parameters, local epigenetic characteristics, circulating hormones) demonstrated to alter both the pattern of DNA methylation after exercise, and the relationship between DNA methylation and gene expression. Finally, we discuss the changes in non-CpG methylation and 5-hydroxymethylation after exercise, as well as the importance of emerging single-cell analyses to future studies-areas of increasing focus in the field of epigenetics. We anticipate that this review will help generate a framework for clinicians and researchers to begin developing and testing exercise interventions designed to generate targeted changes in DNA methylation as part of a personalized exercise regimen.

An update on the cell-free DNA-derived methylome as a non-invasive biomarker for coronary artery disease

Cardiovascular diseases are the foremost contributor to global mortality, presenting a complex etiology and an expanding array of risk factors. Coronary artery disease characterized by atherosclerotic plaque build-up in the coronary arteries, imposes significant mortality and financial burdens, especially in low- and middle-income nations. The pathogenesis of coronary artery disease involves a multifaceted interplay of genetic, environmental, and epigenetic factors. Epigenetic regulation contributes to the dynamic control of gene expression without altering the underlying DNA sequence. The mounting evidence that highlights the pivotal role of epigenetic regulation in coronary artery disease development and progression, offering potential avenues for the development of novel diagnostic biomarkers and therapeutic targets. Abnormal DNA methylation patterns are linked to the modulation of gene expression involved in crucial processes like lipid metabolism, inflammation, and vascular function in the context of coronary artery disease. Cell-free DNA has become invaluable in tumor biology as a liquid biopsy, while its applications in coronary artery disease are limited, but intriguing. Atherosclerotic plaque rupture causes myocardial infarction, by depriving heart muscles of oxygen, releasing cell-free DNA from dead cardiac cells, and providing a minimally invasive source to explore tissue-specific epigenetic alterations. We discussed the methodologies for studying the global methylome and hydroxy-methylome landscape, their advantages, and limitations. It explores methylome alterations in coronary artery disease, considering risk factors and their relevance in coronary artery disease genesis. The review also details the implications of MI-derived cell-free DNA for developing minimally invasive biomarkers and associated challenges.

Computational deconvolution of DNA methylation data from mixed DNA samples

In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.

Circulating tumor DNA methylation: a promising clinical tool for cancer diagnosis and management

Cancer continues to pose significant challenges to the medical community. Early detection, accurate molecular profiling, and adequate assessment of treatment response are critical factors in improving the quality of life and survival of cancer patients. Accumulating evidence shows that circulating tumor DNA (ctDNA) shed by tumors into the peripheral blood preserves the genetic and epigenetic information of primary tumors. Notably, DNA methylation, an essential and stable epigenetic modification, exhibits both cancer- and tissue-specific patterns. As a result, ctDNA methylation has emerged as a promising molecular marker for noninvasive testing in cancer clinics. In this review, we summarize the existing techniques for ctDNA methylation detection, describe the current research status of ctDNA methylation, and present the potential applications of ctDNA-based assays in the clinic. The insights presented in this article could serve as a roadmap for future research and clinical applications of ctDNA methylation.

Integrative Epigenetic and Molecular Analysis Reveals a Novel Promoter for a New Isoform of the Transcription Factor TEAD4

TEAD4 is a transcription factor that plays a crucial role in the Hippo pathway by regulating the expression of genes related to proliferation and apoptosis. It is also involved in the maintenance and differentiation of the trophectoderm during pre- and post-implantation embryonic development. An alternative promoter for the TEAD4 gene was identified through epigenetic profile analysis, and a new transcript from the intronic region of TEAD4 was discovered using the 5'RACE method. The transcript of the novel promoter encodes a TEAD4 isoform (TEAD4-ΔN) that lacks the DNA-binding domain but retains the C-terminal protein-protein interaction domain. Gene expression studies, including end-point PCR and Western blotting, showed that full-length TEAD4 was present in all investigated tissues. However, TEAD4-ΔN was only detectable in certain cell types. The TEAD4-ΔN promoter is conserved throughout evolution and demonstrates transcriptional activity in transient-expression experiments. Our study reveals that TEAD4 interacts with the alternative promoter and increases the expression of the truncated isoform. DNA methylation plays a crucial function in the restricted expression of the TEAD4-ΔN isoform in specific tissues, including the umbilical cord and the placenta. The data presented indicate that the DNA-methylation status of the TEAD4-ΔN promoter plays a critical role in regulating organ size, cancer development, and placenta differentiation.

PCDHGB7 hypermethylation-based Cervical cancer Methylation (CerMe) detection for the triage of high-risk human papillomavirus-positive women: a prospective cohort study

BACKGROUND

Implementation of high-risk human papillomavirus (hrHPV) screening has greatly reduced the incidence and mortality of cervical cancer. However, a triage strategy that is effective, noninvasive, and independent from the subjective interpretation of pathologists is urgently required to decrease unnecessary colposcopy referrals in hrHPV-positive women.

METHODS

A total of 3251 hrHPV-positive women aged 30-82 years (median = 41 years) from International Peace Maternity and Child Health Hospital were included in the training set (n = 2116) and the validation set (n = 1135) to establish Cervical cancer Methylation (CerMe) detection. The performance of CerMe as a triage for hrHPV-positive women was evaluated.

RESULTS

CerMe detection efficiently distinguished cervical intraepithelial neoplasia grade 2 or worse (CIN2 +) from cervical intraepithelial neoplasia grade 1 or normal (CIN1 -) women with excellent sensitivity of 82.4% (95% CI = 72.6 ~ 89.8%) and specificity of 91.1% (95% CI = 89.2 ~ 92.7%). Importantly, CerMe showed improved specificity (92.1% vs. 74.9%) in other 12 hrHPV type-positive women as well as superior sensitivity (80.8% vs. 61.5%) and specificity (88.9% vs. 75.3%) in HPV16/18 type-positive women compared with cytology testing. CerMe performed well in the triage of hrHPV-positive women with ASC-US (sensitivity = 74.4%, specificity = 87.5%) or LSIL cytology (sensitivity = 84.4%, specificity = 83.9%).

CONCLUSIONS

PCDHGB7 hypermethylation-based CerMe detection can be used as a triage strategy for hrHPV-positive women to reduce unnecessary over-referrals.

TRIAL REGISTRATION

ChiCTR2100048972. Registered on 19 July 2021.

Epigenetic Methylation Changes in Pregnant Women: Bisphenol Exposure and Atopic Dermatitis

Bisphenol is a chemical substance widely used in plastic products and food containers. In this study, we observed a relationship between DNA methylation and atopic dermatitis (AD) in the peripheral blood mononuclear cells (PBMCs) of pregnant women exposed to bisphenol A (BPA) and its alternatives, bisphenol S (BPS) and bisphenol F (BPF). DNA methylation is an epigenetic mechanism that regulates gene expression, which can be altered by environmental factors, and affects the onset and progression of diseases. We found that genes belonging to the JAK-STAT and PI3K-AKT signaling pathways were hypomethylated in the blood of pregnant women exposed to bisphenols. These genes play important roles in skin barrier function and immune responses, and may influence AD. Therefore, we suggest that not only BPA, but also BPS and BPF, which are used as alternatives, can have a negative impact on AD through epigenetic mechanisms.

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

Synchrotron-based FTIR microspectroscopy reveals DNA methylation profile in DNA-HALO structure

Fourier transform infrared (FTIR) spectroscopy is a rapid, non-destructive and label-free technique for identifying subtle changes in all bio-macromolecules, and has been used as a method of choice for studying DNA conformation, secondary DNA structure transition and DNA damage. In addition, the specific level of chromatin complexity is introduced via epigenetic modifications forcing the technological upgrade in the analysis of such an intricacy. As the most studied epigenetic mechanism, DNA methylation is a major regulator of transcriptional activity, involved in the suppression of a broad spectrum of genes and its deregulation is involved in all non-communicable diseases. The present study was designed to explore the use of synchrotron-based FTIR analysis to monitor the subtle changes in molecule bases regarding the DNA methylation status of cytosine in the whole genome. In order to reveal the conformation-related best sample for FTIR-based DNA methylation analysis in situ, we used methodology for nuclear HALO preparations and slightly modified it to isolated DNA in HALO formations. Nuclear DNA-HALOs represent samples with preserved higher-order chromatin structure liberated of any protein residues that are closer to native DNA conformation than genomic DNA (gDNA) isolated by the standard batch procedure. Using FTIR spectroscopy we analyzed the DNA methylation profile of isolated gDNA and compared it with the DNA-HALOs. This study demonstrated the potential of FTIR microspectroscopy to detect DNA methylation marks in analyzed DNA-HALO specimens more precisely in comparison with classical DNA extraction procedures that yield unstructured whole genomic DNA. In addition, we used different cell types to assess their global DNA methylation profile, as well as defined specific infrared peaks that can be used for screening DNA methylation.

Climate change and epigenetic biomarkers in allergic and airway diseases

Human epigenetic variation is associated with both environmental exposures and allergic diseases and can potentially serve as a biomarker connecting climate change with allergy and airway diseases. In this narrative review, we summarize recent human epigenetic studies examining exposure to temperature, precipitation, extreme weather events, and malnutrition to discuss findings as they relate to allergic and airway diseases. Temperature has been the most widely studied exposure, with the studies implicating both short-term and long-term exposures with epigenetic alterations and epigenetic aging. Few studies have examined natural disasters or extreme weather events. The studies available have reported differential DNA methylation of multiple genes and pathways, some of which were previously associated with asthma or allergy. Few studies have integrated climate-related events, epigenetic biomarkers, and allergic disease together. Prospective longitudinal studies are needed along with the collection of target tissues beyond blood samples, such as nasal and skin cells. Finally, global collaboration to increase diverse representation of study participants, particularly those most affected by climate injustice, as well as strengthen replication, validation, and harmonization of measurements will be needed to elucidate the impacts of climate change on the human epigenome.

DNA methylation: The epigenetic mechanism of Alzheimer's disease

Nowadays, with the development of the social health care system, there is an increasing trend towards an aging society. The incidence of Alzheimer's disease (AD) is also on the rise. AD is a kind of neurodegenerative disease that can be found in any age group. For years, scientists have been committing to discovering the cause of AD. DNA methylation is one of the most common epigenetic mechanisms in mammals and plays a vital role in the pathogenesis of several diseases, including tumors. Studying chemical changes in the epigenome, or DNA methylation can help us understand the effects of our environment and life on diseases, such as smoking, depression, and menopause, which may affect people's chances of developing Alzheimer's or other diseases. Recent studies have identified some crucial genes like ANK1, RHBDF2, ABCA7, and BIN1, linking DNA methylation to AD. This review focuses on elucidating the relationship between DNA methylation and the pathogenesis of AD and provides an outlook on possible targeted therapeutic modalities.

Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network

DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of chromosomes. DNA methylation modification sites should be identified, which lays a solid basis for gaining more insights into their biological functions. Existing machine learning-based methods of predicting DNA methylation have not fully exploited the hidden multidimensional information in DNA gene sequences, such that the prediction accuracy of models is significantly limited. Besides, most models have been built in terms of a single methylation type. To address the above-mentioned issues, a deep learning-based method was proposed in this study for DNA methylation site prediction, termed the MEDCNN model. The MEDCNN model is capable of extracting feature information from gene sequences in three dimensions (i.e., positional information, biological information, and chemical information). Moreover, the proposed method employs a convolutional neural network model with double convolutional layers and double fully connected layers while iteratively updating the gradient descent algorithm using the cross-entropy loss function to increase the prediction accuracy of the model. Besides, the MEDCNN model can predict different types of DNA methylation sites. As indicated by the experimental results,the deep learning method based on coding from multiple dimensions outperformed single coding methods, and the MEDCNN model was highly applicable and outperformed existing models in predicting DNA methylation between different species. As revealed by the above-described findings, the MEDCNN model can be effective in predicting DNA methylation sites.

Epigenetic analytical approaches in ecotoxicological aquatic research

Environmental epigenetics has become a key research focus in global climate change studies and environmental pollutant investigations impacting aquatic ecosystems. Specifically, triggered by environmental stress conditions, intergenerational DNA methylation changes contribute to biological adaptive responses and survival of organisms to increase their tolerance towards these conditions. To critically review epigenetic analytical approaches in ecotoxicological aquatic research, we evaluated 78 publications reported over the past five years (2016-2021) that applied these methods to investigate the responses of aquatic organisms to environmental changes and pollution. The results show that DNA methylation appears to be the most robust epigenetic regulatory mark studied in aquatic animals. As such, multiple DNA methylation analysis methods have been developed in aquatic organisms, including enzyme restriction digestion-based and methyl-specific immunoprecipitation methods, and bisulfite (in)dependent sequencing strategies. In contrast, only a handful of aquatic studies, i.e. about 15%, have been focusing on histone variants and post-translational modifications due to the lack of species-specific affinity based immunological reagents, such as specific antibodies for chromatin immunoprecipitation applications. Similarly, ncRNA regulation remains as the least popular method used in the field of environmental epigenetics. Insights into the opportunities and challenges of the DNA methylation and histone variant analysis methods as well as decreasing costs of next generation sequencing approaches suggest that large-scale epigenetic environmental studies in model and non-model organisms will soon become available in the near future. Moreover, antibody-dependent and independent methods, such as mass spectrometry-based methods, can be used as an alternative epigenetic approach to characterize global changes of chromatin histone modifications in future aquatic research. Finally, a systematic guide for DNA methylation and histone variant methods is offered for ecotoxicological aquatic researchers to select the most relevant epigenetic analytical approach in their research.

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

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

DNA-Methylation Signatures of Tobacco Smoking in a High Cardiovascular Risk Population: Modulation by the Mediterranean Diet

Biomarkers based on DNA methylation are relevant in the field of environmental health for precision health. Although tobacco smoking is one of the factors with a strong and consistent impact on DNA methylation, there are very few studies analyzing its methylation signature in southern European populations and none examining its modulation by the Mediterranean diet at the epigenome-wide level. We examined blood methylation smoking signatures on the EPIC 850 K array in this population (n = 414 high cardiovascular risk subjects). Epigenome-wide methylation studies (EWASs) were performed analyzing differential methylation CpG sites by smoking status (never, former, and current smokers) and the modulation by adherence to a Mediterranean diet score was explored. Gene-set enrichment analysis was performed for biological and functional interpretation. The predictive value of the top differentially methylated CpGs was analyzed using receiver operative curves. We characterized the DNA methylation signature of smoking in this Mediterranean population by identifying 46 differentially methylated CpGs at the EWAS level in the whole population. The strongest association was observed at the cg21566642 (p = 2.2 × 10-32) in the 2q37.1 region. We also detected other CpGs that have been consistently reported in prior research and discovered some novel differentially methylated CpG sites in subgroup analyses. In addition, we found distinct methylation profiles based on the adherence to the Mediterranean diet. Particularly, we obtained a significant interaction between smoking and diet modulating the cg5575921 methylation in the AHRR gene. In conclusion, we have characterized biomarkers of the methylation signature of tobacco smoking in this population, and suggest that the Mediterranean diet can increase methylation of certain hypomethylated sites.

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.

Monitoring EGFR-lung cancer evolution: a possible beginning of a "methylation era" in TKI resistance prediction

The advances in scientific knowledge on biological therapies of the last two decades have impressively oriented the clinical management of non-small-cell lung cancer (NSCLC) patients. The treatment with tyrosine kinase inhibitors (TKIs) in patients harboring Epidermal Growth Factor Receptor (EGFR)-activating mutations is dramatically associated with an improvement in disease control. Anyhow, the prognosis for this selected group of patients remains unfavorable, due to the innate and/or acquired resistance to biological therapies. The methylome analysis of many tumors revealed multiple patterns of methylation at single/multiple cytosine-phosphate-guanine (CpG) sites that are linked to the modulation of several cellular pathways involved in cancer onset and progression. In lung cancer patients, ever increasing evidences also suggest that the association between DNA methylation changes at promoter/intergenic regions and the consequent alteration of gene-expression signatures could be related to the acquisition of resistance to biological therapies. Despite this intriguing hypothesis, large confirmatory studies are demanded to consolidate and finalize many preliminary observations made in this field. In this review, we will summarize the available knowledge about the dynamic role of DNA methylation in EGFR-mutated NSCLC patients.

MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation, and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep learning framework for predicting DNA methylation sites, which is based on 5 popular transformer-based language models. The framework identifies methylation sites for 3 different types of DNA methylation: N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pretrain and fine-tune" paradigm. Pretraining is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA methylation status of each type. The 5 models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source, and we provide a web server that implements the approach.

Environmental risk factors in puppies and kittens for developing chronic disorders in adulthood: A call for research on developmental programming

Many dogs and cats are affected by chronic diseases that significantly impact their health and welfare and relationships with humans. Some of these diseases can be challenging to treat, and a better understanding of early-life risk factors for diseases occurring in adulthood is key to improving preventive veterinary care and husbandry practices. This article reviews early-life risk factors for obesity and chronic enteropathy, and for chronic behavioral problems, which can also be intractable with life-changing consequences. Aspects of early life in puppies and kittens that can impact the risk of adult disorders include maternal nutrition, establishment of the gut microbiome, maternal behavior, weaning, nutrition during growth, growth rate, socialization with conspecifics and humans, rehoming and neutering. Despite evidence in some species that the disorders reviewed here reflect the developmental origins of health and disease (DOHaD), developmental programming has rarely been studied in dogs and cats. Priorities and strategies to increase knowledge of early-life risk factors and DOHaD in dogs and cats are discussed. Critical windows of development are proposed: preconception, gestation, the suckling period, early growth pre-neutering or pre-puberty, and growth post-neutering or post-puberty to adult size, the durations of which depend upon species and breed. Challenges to DOHaD research in these species include a large number of breeds with wide genetic and phenotypic variability, and the existence of many mixed-breed individuals. Moreover, difficulties in conducting prospective lifelong cohort studies are exacerbated by discontinuity in pet husbandry between breeders and subsequent owners, and by the dispersed nature of pet ownership.

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.

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.

MeDIP-seq and RNA-seq analysis during porcine testis development reveals functional DMRs at the promoter of LDHC

Testis development requires tight regulation of gene expression programmed by epigenetic modifiers. However, their mechanism remains to be elucidated. Here, we investigated the genome-wide DNA methylation landscape in the Duroc and Meishan boar testes using methylated DNA immunoprecipitation sequencing (MeDIP-seq). We identified over 1100 promoter differential methylation genes (DMGs) before and after puberty, most of which are associated with testis development. Furthermore, we discovered that the expression of lactate dehydrogenase C (LDHC) gene during testis development is regulated by DNA methylation. The promoter of LDHC in pre-pubertal testes is substantially methylated, whereas considerably demethylated in post-pubertal testes. Artificial demethylation with the demethylating agent 5-Aza-CdR induced LDHC expression in immature Sertoli cells (SCs). Mechanistically, we confirmed the transcription factor SP1 was recruited to bind in hypomethylated differentially methylated regions (DMRs) in LDHC promoter, which upregulated the expression of LDHC. Functionally, we demonstrated that LDHC gene was activated in mature SCs (mSCs) and its overexpression significantly increases lactate secretion in SCs. In conclusion, our results highlight the function and regulation of dynamic DNA methylation in testis development.

Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.

Benchmarking DNA Methylation Analysis of 14 Alignment Algorithms for Whole Genome Bisulfite Sequencing in Mammals

Whole genome bisulfite sequencing (WGBS) is an essential technique for methylome studies. Although a series of tools have been developed to overcome the mapping challenges caused by bisulfite treatment, the latest available tools have not been evaluated on the performance of reads mapping as well as on biological insights in multiple mammals. Herein, based on the real and simulated WGBS data of 14.77 billion reads, we undertook 936 mappings to benchmark and evaluate 14 wildly utilized alignment algorithms from reads mapping to biological interpretation in humans, cattle and pigs: Bwa-meth, BSBolt, BSMAP, Walt, Abismal, Batmeth2, Hisat_3n, Hisat_3n_repeat, Bismark-bwt2-e2e, Bismark-his2, BSSeeker2-bwt, BSSeeker2-soap2, BSSeeker2-bwt2-e2e and BSSeeker2-bwt2-local. Specifically, Bwa-meth, BSBolt, BSMAP, Bismark-bwt2-e2e and Walt exhibited higher uniquely mapped reads, mapped precision, recall and F1 score than other nine alignment algorithms, and the influences of distinct alignment algorithms on the methylomes varied considerably at the numbers and methylation levels of CpG sites, the calling of differentially methylated CpGs (DMCs) and regions (DMRs). Moreover, we reported that BSMAP showed the highest accuracy at the detection of CpG coordinates and methylation levels, the calling of DMCs, DMRs, DMR-related genes and signaling pathways. These results suggested that careful selection of algorithms to profile the genome-wide DNA methylation is required, and our works provided investigators with useful information on the choice of alignment algorithms to effectively improve the DNA methylation detection accuracy in mammals.

Completing Single-Cell DNA Methylome Profiles via Transfer Learning Together With KL-Divergence

The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with very low coverage (below 2%). Transfer learning was used to leverage less sparse profiles that are typically available for different tissues for the same species, while KL divergence was employed to maximize the usage of information carried in the input data. A deep neural network was adopted to extract both DNA sequence and local methylation patterns for imputation. Our study of training models for completing methylome profiles of bovine oocytes and early embryos demonstrates the effectiveness of transfer learning and KL divergence, with individual increase of 29.98 and 29.43%, respectively, in prediction performance and 38.70% increase when the two were used together. The drastically increased data coverage (43.80-73.6%) after imputation powers downstream analyses involving methylomes that cannot be effectively done using the very low coverage profiles (0.06-1.47%) before imputation.

Evaluation of different computational methods for DNA methylation-based biological age

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

Simple Summary

The rise of Big Data, the widespread use of Machine Learning, and the cheapening of omics techniques have allowed for the creation of more sophisticated and accurate models in biomedical research. This article presents the state-of-the-art predictive models of cancer prognosis that use multimodal data, considering clinical, molecular (omics and non-omics), and image data. The subject of study, the data modalities used, the data processing and modelling methods applied, the validation strategies involved, the integration strategies encompassed, and the evolution of prognostic predictive models are discussed. Finally, we discuss challenges and opportunities in this field of cancer research, with great potential impact on the clinical management of patients and, by extension, on the implementation of personalised and precision medicine.

Abstract

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.

DNA Hypermethylation and a Specific Methylation Spectrum on the X Chromosome in Turner Syndrome as Determined by Nanopore Sequencing

The molecular genetic mechanism of Turner syndrome (TS) still leaves much to be discovered. Methods: TS (45X0) patients and age-matched controls (46XX and 46XY) were selected. The nanopore sequencing combined with trio-whole exome sequencing (trio-WES) were used for the first time to investigate TS. Results: Thirteen TS (45X0) patients and eight controls were enrolled. Trio-WES analysis did not find any pathogenetic or likely pathogenic variants except X chromosome (chrX) deletion. The average methylation levels and patterns of chrX in 45X0 and 46XY were similar, and significantly higher than in 46XX (p = 2.22 × 10⁻¹⁶). Both hyper-methylation and hypo-methylation were detected in the CpG island (CGI), CGI_shore, promoter, genebody, and PAR1-region, while in the transposon element inactivation regions of the chrX and hypermethylation were predominant. A total of 125 differentially methylated genes were identified in 45X0 compared to 46XX, including 8 and 117 hypermethylated and hypomethylated genes, respectively, with the enrichment terms of mitophagy, regulation of DNA-binding transcription factor activity, etc. Conclusions: The results suggest that the methylation profile in patients with TS might be determined by the number of X chromosomes; the patterns of methylation in TS were precisely associated with the maintenance of genomic stability and improvement of gene expression. Differentially methylated genes/pathways might reveal the potential epigenetic modulation and lead to better understanding of TS.

Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health

The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene–environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.

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.

Computational challenges in detection of cancer using cell-free DNA methylation

Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

Harnessing CRISPR-Cas9 for Epigenetic Engineering

Epigenome editing has become more precise and effective by coupling epigenetic effectors to the dCas9 protein and targeting regulatory regions such as promoters and enhancers. Here, we describe a basic methodology for performing an epigenome editing experiment, starting from gRNA design and cloning to transiently transfecting the gRNA plasmid and the CRISPR/dCas9-based epigenetic effector and finalizing with chromatin immunoprecipitation (ChIP) to validate changes in epigenetic state at a targeted genomic region.

Molecular markers for cervical cancer screening

INTRODUCTION

Cervical cancer remains a significant healthcare problem, notably in low- to middle-income countries. While a negative test for hrHPV has a predictive value of more than 99.5%, its positive predictive value is less than 10% for CIN2+ stages. This makes the use of a so-called triage test indispensable for population-based screening to avoid referring women, that are ultimately at low risk of developing cervical cancer, to a gynecologist. This review will give an overview of tests that are based on epigenetic marker panels and protein markers.

AREAS COVERED

There is a medical need for molecular markers with a better predictive value to discriminate hrHPV-positive women that are at risk of developing cervical cancer from those that are not. Areas covered are epigenetic and protein markers as well as health economic considerations in view of the fact that most cases of cervical cancer arise in low-to-middle-income countries.

EXPERT OPINION

While there are biomarker assays based on changes at the nucleic acid (DNA methylation patterns, miRNAs) and at the protein level, they are not widely used in population screening. Combining nucleic acid-based and protein-based tests could improve the overall specificity for discriminating CIN2+ lesions that carry a low risk of progressing to cervical cancer within the screening interval from those that carry an elevated risk. The challenge is to reduce unnecessary referrals without an undesired increase in false-negative diagnoses resulting in cases of cervical cancer that could have been prevented. A further challenge is to develop tests for low-and middle-income countries, which is critical to reduce the worldwide burden of cervical cancer.

Detection of Genomic Uracil Patterns

The appearance of uracil in the deoxyuridine moiety of DNA is among the most frequently occurring genomic modifications. Three different routes can result in genomic uracil, two of which do not require specific enzymes: spontaneous cytosine deamination due to the inherent chemical reactivity of living cells, and thymine-replacing incorporation upon nucleotide pool imbalances. There is also an enzymatic pathway of cytosine deamination with multiple DNA (cytosine) deaminases involved in this process. In order to describe potential roles of genomic uracil, it is of key importance to utilize efficient uracil-DNA detection methods. In this review, we provide a comprehensive and critical assessment of currently available uracil detection methods with special focus on genome-wide mapping solutions. Recent developments in PCR-based and in situ detection as well as the quantitation of genomic uracil are also discussed.

Current Advances in DNA Methylation Analysis Methods

DNA methylation is one of the epigenetic changes, which plays a major role in regulating gene expression and, thus, many biological processes and diseases. There are several methods for determining the methylation of DNA samples. However, selecting the most appropriate method for answering biological questions appears to be a challenging task. The primary methods in DNA methylation focused on identifying the state of methylation of the examined genes and determining the total amount of 5-methyl cytosine. The study of DNA methylation at a large scale of genomic levels became possible following the use of microarray hybridization technology. The new generation of sequencing platforms now allows the preparation of genomic maps of DNA methylation at the single-open level. This review includes the majority of methods available to date, introducing the most widely used methods, the bisulfite treatment, biological identification, and chemical cutting along with their advantages and disadvantages. The techniques are then scrutinized according to their robustness, high throughput capabilities, and cost.

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.

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

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

Perspectives on the Role of Histone Modification in Breast Cancer Progression and the Advanced Technological Tools to Study Epigenetic Determinants of Metastasis

Metastasis is a complex process that involved in various genetic and epigenetic alterations during the progression of breast cancer. Recent evidences have indicated that the mutation in the genome sequence may not be the key factor for increasing metastatic potential. Epigenetic changes were revealed to be important for metastatic phenotypes transition with the development in understanding the epigenetic basis of breast cancer. Herein, we aim to present the potential epigenetic drivers that induce dysregulation of genes related to breast tumor growth and metastasis, with a particular focus on histone modification including histone acetylation and methylation. The pervasive role of major histone modification enzymes in cancer metastasis such as histone acetyltransferases (HAT), histone deacetylases (HDACs), DNA methyltransferases (DNMTs), and so on are demonstrated and further discussed. In addition, we summarize the recent advances of next-generation sequencing technologies and microfluidic-based devices for enhancing the study of epigenomic landscapes of breast cancer. This feature also introduces several important biotechnologists for identifying robust epigenetic biomarkers and enabling the translation of epigenetic analyses to the clinic. In summary, a comprehensive understanding of epigenetic determinants in metastasis will offer new insights of breast cancer progression and can be achieved in the near future with the development of innovative epigenomic mapping tools.

Differences in DNA Methylation Reprogramming Underlie the Sexual Dimorphism of Behavioral Disorder Caused by Prenatal Stress in Rats

Prenatal stress (PS) can lead to neuroendocrine and emotional disorders later in adolescence. Sexual dimorphism in these neurodevelopmental outcomes have been observed; however, the underlying mechanisms are not fully understood. To address this issue, we investigated whether there are sex differences in epigenetic reprogramming in rats exposed to PS. Pregnant female rats were subjected to chronic restraint stress from gestational day (G)12 to G18. From postnatal day (P)38 to P45, subgroups of offspring including both males and females were subjected to behavioral testing and brain tissue specimens were analyzed by DNA pyrosequencing, western blotting, and Golgi staining to assess changes in methylation pattern of glucocorticoid receptor (GR) gene, expression of DNA methyltransferase (DNMT) and DNA demethylase, and dendrite morphology, respectively. The DNA methyltransferase inhibitor decitabine was administered to rats prior to PS to further evaluate the role of methylation in the sexually dimorphic effects of PS. The results showed that PS increased anxiety-like behavior in offspring, especially in females, while depression-like behavior was increased in male offspring compared to control littermates. The methylation pattern in the promoter region of the GR gene differed between males and females. Sex-specific changes in the expression of DNMTs (DNMT1 and DNMT3a) and DNA demethylase (Tet methylcytosine dioxygenase 2) were also observed. Interestingly, decitabine alleviated the behavioral disorder caused by PS and restored dendrite density and morphology in female but not male rats. These findings suggest that different change patterns of DNMT and demethylase in the two sexes after PS are responsible for the sexually dimorphism, which could have implications for the clinical management of stress-related disorders.

LncRNA regulation: New frontiers in epigenetic solutions to drug chemoresistance

Long-noncoding RNAs (lncRNAs) have been shown to participate in sensitizing or de-sensitizing cancer cells to chemical drugs during cancer therapeutics. Notably, a plethora of lncRNAs have been confirmed to be associated with epigenetic controllers and regulate histone protein modification or DNA methylation states in the process of gene transcription. This correlation between lncRNAs and epigenetic regulators can induce the expression of core genes to trigger drug resistance. In addition, epigenetic signatures are considered to be effective and attractive biomarkers for monitoring drug therapeutic effects because they are inheritable, dynamic, and reversible. Therefore, the regulatory mechanism between lncRNAs and epigenetic machinery can serve as a novel indicator and target to overcome or reverse drug resistance in cancer therapy. In this review, we also presented a curated selection of computational tools (including online databases and network analysis) in the area of epigenetics. A classic workflow for lncRNA expression network analysis is presented, providing guidance for non-bioinformaticians to identify significant correlation between lncRNAs and other biomolecules.

Modern epigenetics methods in biological research

The definition of epigenetics refers that molecular modifications on DNA that can regulate gene activity are independent of DNA sequence and mitotically stable. Notably, epigenetics studies have grown exponentially in the past few years. Recent progresses that lead to exciting discoveries and groundbreaking nature of this area demand thorough methodologies and advanced technologies to move epigenetics to the forefront of molecular biology. The most recognized epigenetic regulations are DNA methylation, histone modifications, and non-coding RNAs (ncRNAs). This review will discuss the modern techniques that are available to detect locus-specific and genome-wide changes for all epigenetic codes. Furthermore, updated analysis of technologies, newly developed methods, recent breakthroughs and bioinformatics pipelines in epigenetic analysis will be presented. These methods, as well as many others presented in this specific issue, provide comprehensive guidelines in the area of epigenetics that facilitate further developments in this promising and rapidly developing field.