Epigenomic Dysregulation in Youth Vapers: Implications for Disease Risk Assessment.

Despite the ongoing epidemic of youth vaping, the long-term health consequences of electronic cigarette use are largely unknown. We report the effects of vaping versus smoking on the oral cell methylome of healthy young vapers and smokers relative to non-users. Whereas vapers and smokers differ in number of differentially methylated regions (DMRs) (831 vs 2,863), they share striking similarities in the distribution and patterns of DNA methylation, chromatin states, transcription factor binding motifs, and pathways. There is substantial overlap in DMR-associated genes between vapers and smokers, with the shared subset of genes enriched for transcriptional regulation, signaling, tobacco use disorders, and cancer-related pathways. Of significance is the identification of a common hypermethylated DMR at the promoter of "Hypermethylated In Cancer 1" (HIC1), a tumor suppressor gene frequently silenced in smoking-related cancers. Our data support a potential link between epigenomic dysregulation in youth vapers and disease risk. These novel findings have significant implications for public health and tobacco product regulation.

Discrimination of pancreato-biliary cancer and pancreatitis patients by non-invasive liquid biopsy.

BACKGROUND: Current diagnostics for the detection of pancreato-biliary cancers (PBCs) need to be optimized. We therefore propose that methylated cell-free DNA (cfDNA) derived from non-invasive liquid biopsies serves as a novel biomarker with the ability to discriminate pancreato-biliary cancers from non-cancer pancreatitis patients. METHODS: Differentially methylated regions (DMRs) from plasma cfDNA between PBCs, pancreatitis and clinical control samples conditions were identified by next-generation sequencing after enrichment using methyl-binding domains and database searches to generate a discriminatory panel for a hybridization and capture assay with subsequent targeted high throughput sequencing. RESULTS: The hybridization and capture panel, covering around 74 kb in total, was applied to sequence a cohort of 25 PBCs, 25 pancreatitis patients, 25 clinical controls, and seven cases of Intraductal Papillary Mucinous Neoplasia (IPMN). An unbiased machine learning approach identified the 50 most discriminatory methylation markers for the discrimination of PBC from pancreatitis and controls resulting in an AUROC of 0.85 and 0.88 for a training (n = 45) and a validation (n = 37) data set, respectively. The panel was also able to distinguish high grade from low grade IPMN samples. CONCLUSIONS: We present a proof of concept for a methylation biomarker panel with better performance and improved discriminatory power than the current clinical marker CA19-9 for the discrimination of pancreato-biliary cancers from non-cancerous pancreatitis patients and clinical controls. This workflow might be used in future diagnostics for the detection of precancerous lesions, e.g. the identification of high grade IPMNs vs. low grade IPMNs.

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling.

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

Universal dynamic fitting of magnetic resonance spectroscopy.

PURPOSE: Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type. METHODS: A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting. RESULTS: Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data. CONCLUSION: A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.

Genome-wide analysis toward the epigenetic aetiology of myelodysplastic syndrome disease progression and pharmacoepigenomic basis of hypomethylating agents drug treatment response.

Myelodysplastic syndromes (MDS) consist of a group of hematological malignancies characterized by ineffective hematopoiesis, cytogenetic abnormalities, and often a high risk of transformation to acute myeloid leukemia (AML). So far, there have been only a very limited number of studies assessing the epigenetics component contributing to the pathophysiology of these disorders, but not a single study assessing this at a genome-wide level. Here, we implemented a generic high throughput epigenomics approach, using methylated DNA sequencing (MeD-seq) of LpnPI digested fragments to identify potential epigenomic targets associated with MDS subtypes. Our results highlighted that PCDHG and ZNF gene families harbor potential epigenomic targets, which have been shown to be differentially methylated in a variety of comparisons between different MDS subtypes. Specifically, CpG islands, transcription start sites and post-transcriptional start sites within ZNF124, ZNF497 and PCDHG family are differentially methylated with fold change above 3,5. Overall, these findings highlight important aspects of the epigenomic component of MDS syndromes pathogenesis and the pharmacoepigenomic basis to the hypomethylating agents drug treatment response, while this generic high throughput whole epigenome sequencing approach could be readily implemented to other genetic diseases with a strong epigenetic component.

Diffusion magnetic resonance spectroscopy captures microglial reactivity related to gut-derived systemic lipopolysaccharide: A preliminary study.

Neuroinflammation is a key component underlying multiple neurological disorders, yet non-invasive and cost-effective assessment of in vivo neuroinflammatory processes in the central nervous system remains challenging. Diffusion weighted magnetic resonance spectroscopy (dMRS) has shown promise in addressing these challenges by measuring diffusivity properties of different neurometabolites, which can reflect cell-specific morphologies. Prior work has demonstrated dMRS utility in capturing microglial reactivity in the context of lipopolysaccharide (LPS) challenges and serious neurological disorders, detected as changes of microglial metabolite diffusivity properties. However, the extent to which such dMRS metrics are capable of detecting subtler and more nuanced levels of neuroinflammation in populations without overt neuropathology is unknown. Here we examined the relationship between intrinsic, gut-derived levels of systemic LPS and dMRS-based apparent diffusion coefficients (ADC) of choline, creatine, and N-acetylaspartate (NAA) in two brain regions: the thalamus and the corona radiata. Higher plasma LPS concentrations were significantly associated with increased ADC of choline and NAA in the thalamic region, with no such relationships observed in the corona radiata for any of the metabolites examined. As such, dMRS may have the sensitivity to measure microglial reactivity across populations with highly variable levels of neuroinflammation, and holds promising potential for widespread applications in both research and clinical settings.

Long-term epigenetic and metabolomic changes in the mouse ventricular myocardium after exertional heat stroke.

Evidence from human epidemiological studies suggests that exertional heat stroke (EHS) results in an elevated risk of long-term cardiovascular and systemic disease. Previous results using a preclinical mouse model of EHS demonstrated severe metabolic imbalances in ventricular myocardium developing at 9-14 days of recovery. Whether this resolves over time is unknown. We hypothesized that the long-term effects of EHS on the heart reflect retained maladaptive epigenetic responses. In this study, we evaluated genome-wide DNA methylation, RNA-Seq, and metabolomic profiles of the left ventricular myocardium in female C57BL/6 mice, 30 days after EHS (exercise in 37.5°C; n = 7-8), compared with exercise controls. EHS mice ran to loss of consciousness, reaching core temperatures of 42.4 ± 0.2°C. All mice recovered quickly. After 30 days, the left ventricles were rapidly frozen for DNA methyl sequencing, RNA-Seq, and untargeted metabolomics. Ventricular DNA from EHS mice revealed >13,000 differentially methylated cytosines (DMCs) and >900 differentially methylated regions (DMRs; ≥5 DMCs with ≤300 bp between each CpG). Pathway analysis using DMRs revealed alterations in genes regulating basic cell functions, DNA binding, transcription, and metabolism. Metabolomics and mRNA expression revealed modest changes that are consistent with a return to homeostasis. Methylation status did not predict RNA expression or metabolic state at 30 days. We conclude that EHS induces a sustained DNA methylation memory lasting over 30 days of recovery, but ventricular gene expression and metabolism return to a relative homeostasis at rest. Such long-lasting alterations to the DNA methylation landscape could alter responsiveness to environmental or clinical challenges later in life.

Single-base cytosine methylation analysis in fruits of three Capsicum species.

Single-base cytosine methylation analysis across fruits of Capsicum annuum, C. chinense and C. frutescens showed global average methylation ranging from 82.8-89.1%, 77.6-83.9%, and 22.4-25% at CG, CHG and CHH contexts, respectively. High gene-body methylation at CG and CHG was observed across Capsicum species. The C. annuum showed the highest proportion (>80%) of mCs at different genomic regions compared to C. chinense and C. frutescens. Cytosine methylation for transposable-elements were lower in C. frutescens compared to C. annuum and C. chinense. A total of 510,165 CG, 583112 CHG and 277,897 CHH DMRs were identified across three Capsicum species. The differentially methylated regions (DMRs) distribution analysis revealed C. frutescens as more hypo-methylated compared to C. annuum and C. chinense, and also the presence of more intergenic DMRs in Capsicum genome. At CG and CHG context, gene expression and promoter methylation showed inverse correlations. Furthermore, the observed correlation between methylation and expression of genes suggested the potential role of methylation in Capsicum fruit development/ripening.

A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons.

BACKGROUND: Researchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized. RESULTS: A-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names. CONCLUSIONS: A-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools.

Tissue-specific DNA demethylation is required for proper B-cell differentiation and function.

There is ample evidence that somatic cell differentiation during development is accompanied by extensive DNA demethylation of specific sites that vary between cell types. Although the mechanism of this process has not yet been elucidated, it is likely to involve the conversion of 5mC to 5hmC by Tet enzymes. We show that a Tet2/Tet3 conditional knockout at early stages of B-cell development largely prevents lineage-specific programmed demethylation events. This lack of demethylation affects the expression of nearby B-cell lineage genes by impairing enhancer activity, thus causing defects in B-cell differentiation and function. Thus, tissue-specific DNA demethylation appears to be necessary for proper somatic cell development in vivo.

Low Probability of Intercept-Based Radar Waveform Design for Spectral Coexistence of Distributed Multiple-Radar and Wireless Communication Systems in Clutter.

In this paper, the problem of low probability of intercept (LPI)-based radar waveform design for distributed multiple-radar system (DMRS) is studied, which consists of multiple radars coexisting with a wireless communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the total transmitted energy by optimizing the transmission waveform of each radar with the communication signals acting as interference to the radar system, while meeting a desired target detection/characterization performance. Firstly, signal-to-clutter-plus-noise ratio (SCNR) and mutual information (MI) are used as the practical metrics to evaluate target detection and characterization performance, respectively. Then, the SCNR- and MI-based optimal radar waveform optimization methods are formulated. The resulting waveform optimization problems are solved through the well-known bisection search technique. Simulation results demonstrate utilizing various examples and scenarios that the proposed radar waveform design schemes can evidently improve the LPI performance of DMRS without interfering with friendly communications.

Methods for discovering genomic loci exhibiting complex patterns of differential methylation.

BACKGROUND: Cytosine methylation is widespread in most eukaryotic genomes and is known to play a substantial role in various regulatory pathways. Unmethylated cytosines may be converted to uracil through the addition of sodium bisulphite, allowing genome-wide quantification of cytosine methylation via high-throughput sequencing. The data thus acquired allows the discovery of methylation 'loci'; contiguous regions of methylation consistently methylated across biological replicates. The mapping of these loci allows for associations with other genomic factors to be identified, and for analyses of differential methylation to take place. RESULTS: The segmentSeq R package is extended to identify methylation loci from high-throughput sequencing data from multiple experimental conditions. A statistical model is then developed that accounts for biological replication and variable rates of non-conversion of cytosines in each sample to compute posterior likelihoods of methylation at each locus within an empirical Bayesian framework. The same model is used as a basis for analysis of differential methylation between multiple experimental conditions with the baySeq R package. We demonstrate the capability of this method to analyse complex data sets in an analysis of data derived from multiple Dicer-like mutants in Arabidopsis. This reveals several novel behaviours at distinct sets of loci in response to loss of one or more of the Dicer-like proteins that indicate an antagonistic relationship between the Dicer-like proteins at at least some methylation loci. Finally, we show in simulation studies that this approach can be significantly more powerful in the detection of differential methylation than many existing methods in data derived from both mammalian and plant systems. CONCLUSIONS: The methods developed here make it possible to analyse high-throughput sequencing of the methylome of any given organism under a diverse set of experimental conditions. The methods are able to identify methylation loci and evaluate the likelihood that a region is truly methylated under any given experimental condition, allowing for downstream analyses that characterise differences between methylated and non-methylated regions of the genome. Futhermore, diverse patterns of differential methylation may also be characterised from these data.

Divergent cytosine DNA methylation patterns in single-cell, soybean root hairs.

Chromatin modifications, such as cytosine methylation of DNA, play a significant role in mediating gene expression in plants, which affects growth, development, and cell differentiation. As root hairs are single-cell extensions of the root epidermis and the primary organs for water uptake and nutrients, we sought to use root hairs as a single-cell model system to measure the impact of environmental stress. We measured changes in cytosine DNA methylation in single-cell root hairs as compared with multicellular stripped roots, as well as in response to heat stress. Differentially methylated regions (DMRs) in each methylation context showed very distinct methylation patterns between cell types and in response to heat stress. Intriguingly, at normal temperature, root hairs were more hypermethylated than were stripped roots. However, in response to heat stress, both root hairs and stripped roots showed hypomethylation in each context, especially in the CHH context. Moreover, expression analysis of mRNA from similar tissues and treatments identified some associations between DMRs, genes and transposons. Taken together, the data indicate that changes in DNA methylation are directly or indirectly associated with expression of genes and transposons within the context of either specific tissues/cells or stress (heat).

A novel method for detecting association between DNA methylation and diseases using spatial information.

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.

Novel sex-dependent differentially methylated regions are demethylated in adult male mouse livers.

In mammalian livers, sexual dimorphisms are observed in tissue-specific functions and diseases such as hepatocellular carcinoma. We identified sex-dependent differentially methylated regions (S-DMRs) which had been previously been characterized as growth hormone- STAT5 dependent. In this study, we performed genome-wide screening and identified ten additional hypomethylated S-DMR gene regions in male livers. Of these S-DMRs, Uggt2 and Sarnp were hypomethylated in both male and female livers compared to brain and embryonic stem (ES) cells. Similarly, Adam2, Uggt2, and Scp2 were hypomethylated in female embryonic germ (EG) cells and not in male EG cells, indicating that these S-DMRs are liver-specific male hypo-S-DMRs. Interestingly, the five S-DMRs were free from STAT5 chromatin immunoprecipitation (ChIP) signals, suggesting that S-DMRs are independent of the growth hormone-STAT5-pathway. Instead, the DNA methylation statuses of the S-DMRs of Adam2, Snx29, Uggt2, Sarnp, and Rnpc3 genes were under the control of testosterone. Importantly, the hypomethylated S-DMRs of the Adam2 and Snx29 regions showed chromatin decondensation. Epigenetic factors could be responsible for the sexual dimorphisms in DNA methylation status and chromatin structure, as the expression of Dnmt1, Dnmt3b, and Tet2 genes was lower in male mice compared to female mice and TET2 expression recovered following orchidectomy by testosterone treatment. In conclusion, we identified novel male-specific hypomethylated S-DMRs that contribute to chromatin decondensation in the liver. S-DMRs were tissue-specific and the hypomethylation is testosterone-dependent.

Defensive functioning in individuals with depressive disorders: A systematic review and meta-analysis.

BACKGROUND: This systematic review and meta-analysis aimed to address the limited generalizability of studies on defense mechanisms in depression by comparing depressive individuals with non-clinical controls (aim a) and examining changes throughout psychological interventions (aim b) (PROSPERO CRD42023442620). METHODS: We followed PRISMA 2020 guidelines, searching PubMed/Web of Science/(EBSCO)PsycINFO until 13/04/2023 for studies evaluating defense mechanisms with measures based on the hierarchical model in depressive patients versus non-clinical controls or throughout psychological intervention. We conducted random-effect meta-analyses for mature defenses/non-mature (neurotic/immature) defenses/overall defensive functioning (ODF), with standardized mean difference (SMD) as outcome measure metric. Meta-regression/sub-group/sensitivity analyses were conducted. Study quality was appraised using the Newcastle-Ottawa Scale (NOS), and certainty of evidence for aim b outcomes was evaluated using GRADE (Grading of Recommendations, Assessment, Development and Evaluations). RESULTS: 18 studies were included (mean NOS score = 5.56). Depressive patients used significantly more non-mature defenses than non-clinical controls (SMD = 0.74; k = 13). Non-clinical controls did not significantly differ in use of mature defenses compared to depressive patients (SMD = 0.33; k = 14). Significant moderators were publication year/NOS score/geographical distribution/mean age for non-mature defenses and NOS score/geographical distribution for mature defenses. Throughout psychological interventions, only ODF significantly increased (SMD = 0.55; k = 2) (GRADE = very low). LIMITATIONS: Quality of many studies was medium/sub-optimal, and longitudinal studies were scarce. CONCLUSION: Individuals with depressive disorders show a high use of non-mature defenses that could be assessed and targeted in psychological interventions, especially in younger patients.

Construction of methylation driver gene-related prognostic signature and development of a new prognostic stratification strategy in neuroblastoma.

BACKGROUND: Aberrant DNA methylation is one of the major epigenetic alterations in neuroblastoma. OBJECTIVE: Exploring the prognostic significance of methylation driver genes in neuroblastoma could help to comprehensively assess patient prognosis. METHODS: After identifying methylation driver genes (MDGs), we used the LASSO algorithm and stepwise Cox regression to construct methylation driver gene-related risk score (MDGRS), and evaluated its predictive performance by multiple methods. By combining risk grouping and MDGRS grouping, we developed a new prognostic stratification strategy and explored the intrinsic differences between the different groupings. RESULTS: We identified 44 stably expressed MDGs in neuroblastoma. MDGRS showed superior predictive performance in both internal and external cohorts and was strongly correlated with immune-related scores. MDGRS can be an independent prognostic factor for neuroblastoma, and we constructed the nomogram to facilitate clinical application. Based on the new prognostic stratification strategy, we divided the patients into three groups and found significant differences in overall prognosis, clinical characteristics, and immune infiltration between the different subgroups. CONCLUSION: MDGRS was an accurate and promising tool to facilitate comprehensive pre-treatment assessment. And the new prognostic stratification strategy could be helpful for clinical decision making.

Initial validation of the Turkish version of the defense mechanisms rating scales-self-report-30.

The Defense Mechanisms Rating Scales-Self Report-30 (DMRS-SR-30) was recently developed to add a self-report alternative to the assessment of defenses, reflecting their generally accepted hierarchical organization. In this study, we aimed to examine psychometric properties and factor structure of the Turkish language version of the DMRS-SR-30. The sample consisted of 1.002 participants who filled out a survey comprising the DMRS-SR-30, the Brief Symptom Inventory, and the Inventory of Personality Organization through Qualtrics. Confirmatory Factor Analysis indicated a three-factor structure (CFI = 0.89, RMSEA = 0.05) that confirms the DMRS theoretical frame with a relatively acceptable fit. Defensive categories and total scale scores showed good to excellent reliability (α values ranging from 0.64 to 0.89). Correlations between defenses, symptoms, and personality functioning demonstrated good convergent and discriminant validity. The individuals with clinically significant BSI scores (T-score ≥ 63) differed on the DMRS-SR-30 scores from the individuals in the non-clinical range. The Turkish version of the DMRS-SR-30 is a reliable and valid instrument to self-assess the hierarchy of defense mechanisms and overall defensive functioning. Moreover, the current study supports the validity of the tripartite model of defenses in a language and culture different from the origins of the DMRS and DMRS-SR-30.

Smoking-Induced DNA Hydroxymethylation Signature Is Less Pronounced than True DNA Methylation: The Population-Based KORA Fit Cohort.

Despite extensive research on 5-methylcytosine (5mC) in relation to smoking, there has been limited exploration into the interaction between smoking and 5-hydroxymethylcytosine (5hmC). In this study, total DNA methylation (5mC+5hmC), true DNA methylation (5mC) and hydroxymethylation (5hmC) levels were profiled utilizing conventional bisulphite (BS) and oxidative bisulphite (oxBS) treatment, measured with the Illumina Infinium Methylation EPIC BeadChip. An epigenome-wide association study (EWAS) of 5mC+5hmC methylation revealed a total of 38,575 differentially methylated positions (DMPs) and 2023 differentially methylated regions (DMRs) associated with current smoking, along with 82 DMPs and 76 DMRs associated with former smoking (FDR-adjusted p < 0.05). Additionally, a focused examination of 5mC identified 33 DMPs linked to current smoking and 1 DMP associated with former smoking (FDR-adjusted p < 0.05). In the 5hmC category, eight DMPs related to current smoking and two DMPs tied to former smoking were identified, each meeting a suggestive threshold (p < 1 × 10-5). The substantial number of recognized DMPs, including 5mC+5hmC (7069/38,575, 2/82), 5mC (0/33, 1/1), and 5hmC (2/8, 0/2), have not been previously reported. Our findings corroborated previously established methylation positions and revealed novel candidates linked to tobacco smoking. Moreover, the identification of hydroxymethylated CpG sites with suggestive links provides avenues for future research.

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer.

Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.