An in vitro approach reveals molecular mechanisms underlying endocrine disruptor-induced epimutagenesis.

Endocrine disrupting chemicals (EDCs) such as bisphenol S (BPS) are xenobiotic compounds that can disrupt endocrine signaling due to steric similarities to endogenous hormones. EDCs have been shown to induce disruptions in normal epigenetic programming (epimutations) and differentially expressed genes (DEGs) that predispose disease states. Most interestingly, the prevalence of epimutations following exposure to many EDCs persists over multiple generations. Many studies have described direct and prolonged effects of EDC exposure in animal models, but many questions remain about molecular mechanisms by which EDC-induced epimutations are introduced or subsequently propagated, whether there are cell type-specific susceptibilities to the same EDC, and whether this correlates with differential expression of relevant hormone receptors. We exposed cultured pluripotent (iPS), somatic (Sertoli and granulosa), and primordial germ cell-like (PGCLC) cells to BPS and found that differential incidences of BPS-induced epimutations and DEGs correlated with differential expression of relevant hormone receptors inducing epimutations near relevant hormone response elements in somatic and pluripotent, but not germ cell types. Most interestingly, we found that when iPS cells were exposed to BPS and then induced to differentiate into PGCLCs, the prevalence of epimutations and DEGs was largely retained, however, >90% of the specific epimutations and DEGs were replaced by novel epimutations and DEGs. These results suggest a unique mechanism by which an EDC-induced epimutated state may be propagated transgenerationally.

DNA demethylation triggers cell free DNA release in colorectal cancer cells.

BACKGROUND: Liquid biopsy based on cell-free DNA (cfDNA) analysis holds significant promise as a minimally invasive approach for the diagnosis, genotyping, and monitoring of solid malignancies. Human tumors release cfDNA in the bloodstream through a combination of events, including cell death, active and passive release. However, the precise mechanisms leading to cfDNA shedding remain to be characterized. Addressing this question in patients is confounded by several factors, such as tumor burden extent, anatomical and vasculature barriers, and release of nucleic acids from normal cells. In this work, we exploited cancer models to dissect basic mechanisms of DNA release. METHODS: We measured cell loss ratio, doubling time, and cfDNA release in the supernatant of a colorectal cancer (CRC) cell line collection (N = 76) representative of the molecular subtypes previously identified in cancer patients. Association analyses between quantitative parameters of cfDNA release, cell proliferation, and molecular features were evaluated. Functional experiments were performed to test the impact of modulating DNA methylation on cfDNA release. RESULTS: Higher levels of supernatant cfDNA were significantly associated with slower cell cycling and increased cell death. In addition, a higher cfDNA shedding was found in non-CpG Island Methylator Phenotype (CIMP) models. These results indicate a positive correlation between lower methylation and increased cfDNA levels. To explore this further, we exploited methylation microarrays to identify a subset of probes significantly associated with cfDNA shedding and derive a methylation signature capable of discriminating high from low cfDNA releasers. We applied this signature to an independent set of 176 CRC cell lines and patient derived organoids to select 14 models predicted to be low or high releasers. The methylation profile successfully predicted the amount of cfDNA released in the supernatant. At the functional level, genetic ablation of DNA methyl-transferases increased chromatin accessibility and DNA fragmentation, leading to increased cfDNA release in isogenic CRC cell lines. Furthermore, in vitro treatment of five low releaser CRC cells with a demethylating agent was able to induce a significant increase in cfDNA shedding. CONCLUSIONS: Methylation status of cancer cell lines contributes to the variability of cfDNA shedding in vitro. Changes in methylation pattern are associated with cfDNA release levels and might be exploited to increase sensitivity of liquid biopsy assays.

STARD7 could be an immunological and prognostic biomarker: from pan-cancer analysis to hepatocellular carcinoma validation.

BACKGROUND: As the emergence of technologies such as sequencing and gene mapping, significant advancements have been made in understanding the landscape of tumors. However, the effective treatment of tumors continues to pose a tremendous challenge in clinical practice, which highlights the importance of predicting tumor markers and studying drug resistance mechanisms. The prognosis and differential expression of STARD7 in human pan-cancer were investigated by bioinformatic methods and experimental verification. METHODS: The expression, diagnostic, and prognostic significance of the STARD7 gene were comprehensive analyzed using bioinformatics techniques. Furthermore, we validated our projected outcomes in liver cancer through experimental methodologies, including the use of qRT-PCR, CCK8 and transwell assays. RESULTS: The STARD7 gene exhibits differential expression in 25 tumors, with high expression observed in 22 tumors. These distinct expression patterns within different tumor types are closely associated with poor prognosis and diagnosis. Furthermore, the STARD7 gene plays a role in regulating the tumor immune microenvironment. Methylation levels of STARD7 vary among 20 types of tumors and are correlated with survival outcomes. Furthermore, the experiment results demonstrated that STARD7 is highly expressed in hepatocellular carcinoma cells. Suppression of STARD7 significantly impedes the proliferation, migration, and invasion of HepG-2 and SMMC-7721 cells. CONCLUSIONS: STARD7 has the potential to function as a crucial prognostic biomarker and exhibit correlation with tumor immunity in various types of human cancers. The implications of our findings extend to informing cancer immune-therapy and promoting the advancement of precision immune-oncology.

DNA methylation drives hematopoietic stem cell aging phenotypes after proliferative stress.

Aging of hematopoietic stem cells (HSCs) is implicated in various aging phenotypes, including immune dysfunction, anemia, and malignancies. The role of HSC proliferation in driving these aging phenotypes, particularly under stress conditions, remains unclear. Therefore, we induced forced replications of HSCs in vivo by a cyclical treatment with low-dose fluorouracil (5FU) and examined the impact on HSC aging. Our findings show that proliferative stress induces several aging phenotypes, including altered leukocyte counts, decreased lymphoid progenitors, accumulation of HSCs with high expression of Slamf1, and reduced reconstitution potential, without affecting stem cell self-renewal capacity. The divisional history of HSCs was imprinted in the DNA methylome, consistent with functional decline. Specifically, DNA methylation changes included global hypermethylation in non-coding regions and similar frequencies of hypo- and hyper-methylation at promoter regions, particularly affecting genes targeted by the PRC2 complex. Importantly, initial forced replication promoted DNA damage repair accumulated with age, but continuous proliferative stress led to the accumulation of double-strand breaks, independent of functional decline. Overall, our results suggest that HSC proliferation can drive some aging phenotypes primarily through epigenetic mechanisms, including DNA methylation changes.

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

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

A roadmap to cure CHD2-related disorders.

Coalition to Cure CHD2 (CCC) is a patient advocacy group dedicated to improving the lives of those affected by CHD2-related disorders (CHD2-RD) by increasing education, building community, and accelerating research to uncover a cure. CHD2 is a chromatin remodeler that was identified in 2013 as being a genetic cause for developmental and epileptic encephalopathies. Pathogenic changes in CHD2 can cause treatment-resistant epilepsy, intellectual and developmental delays, and autism, and some individuals experience neurodevelopmental regression. There are currently no targeted therapies available for CHD2-related disorders. Haploinsufficiency of CHD2 is a causative mechanism of disease for individuals with pathogenic variants (primarily truncating) in CHD2. Recently, identification of individuals with deletion of nearby gene CHASERR, a regulator of CHD2 gene expression, has established dosage sensitivity in CHD2 and solidified the CHASERR gene as a potential therapeutic target for CHD2 levels. Through collaboration with our community and our scientific advisory board, CCC has created a Roadmap to Cure CHD2 as our guide toward a targeted cure that can benefit our community, with steps including (1) identifying and defining patients, (2) developing models of CHD2, (3) studying models of CHD2, (4) testing therapies, (5) involving patients, and (6) reaching a cure. Despite some of the challenges inherent in CHD2 research including establishing animal and cellular models that recapitulate the CHD2 clinical phenotype, identifying measurable outcomes and reliable biomarkers, or testing emerging therapeutic approaches, CCC continues to engage with our community to support ongoing research that aligns with our priorities. CCC sees new and exciting opportunities for additional research that can move our community toward our common goal of a cure that will improve the lives of individuals and their families now and in the future.

A roadmap to cure disorders caused by the CHD2 and CHASERR genes Coalition to Cure CHD2 (CCC) is a nonprofit founded in October 2020 to fund research towards a cure for individuals with CHD2-related disorders. The CHD2 gene was discovered as a genetic cause for epilepsy in 2013. Individuals with CHD2 typically experience seizures that can be resistant to treatment, intellectual disability, delayed development, autism, and other symptoms. The nearby CHASERR gene has been found to regulate CHD2 and is a possible therapeutic target. Individuals with a deletion of CHASERR have been identified - these individuals have too much CHD2 and more severe symptoms. CCC has created a Roadmap to Cure CHD2 as a guide for their journey towards a targeted cure for CHD2-related disorders. The steps in the roadmap include: (1) identify and define patients, (2) develop models of CHD2, (3) study models of CHD2, (4) test therapies, (5) involve patients, (6) reach a cure. CCC has worked with CHD2 families to identify family-level priorities for therapeutic development (e.g. seizures, behavior, etc), to capture the impact of disease through qualitative research, and to collect patient health data and tissue samples for scientific analysis. The development of CHD2 models, mouse models in particular, has been challenging as the mice do not develop seizures. Additional models are underway including frogs, zebrafish, and patient-derived cells. These models have provided crucial insight into the biology of CHD2 but scientific questions remain unanswered. A variety of therapeutic approaches have been proposed including novel treatments that directly target CHD2 biology as well as the repurposing of existing FDA-approved compounds. Establishing measurable outcomes, including biomarkers, and finding treatments that can reach the brain will be important. By continuing to follow this roadmap, the CCC believes that one day there will be a cure for CHD2-related disorders.

PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation.

Despite advanced diagnostics, 3%-5% of cases remain classified as cancer of unknown primary (CUP). DNA methylation, an important epigenetic feature, is essential for determining the origin of metastatic tumors. We presented PathMethy, a novel Transformer model integrated with functional categories and crosstalk of pathways, to accurately trace the origin of tumors in CUP samples based on DNA methylation. PathMethy outperformed seven competing methods in F1-score across nine cancer datasets and predicted accurately the molecular subtypes within nine primary tumor types. It not only excelled at tracing the origins of both primary and metastatic tumors but also demonstrated a high degree of agreement with previously diagnosed sites in cases of CUP. PathMethy provided biological insights by highlighting key pathways, functional categories, and their interactions. Using functional categories of pathways, we gained a global understanding of biological processes. For broader access, a user-friendly web server for researchers and clinicians is available at https://cup.pathmethy.com.

DNA methylation analysis of the SDC2, SEPT9 and VIM genes in fecal DNA for colorectal cancer diagnosis.

BACKGROUND: Colorectal cancer is one of the most common cancers worldwide. DNA methylation sites may serve as a new gene signature for colorectal cancer diagnosis. The search for representative DNA methylation sites is urgently needed. This study aimed to systematically identify a methylation gene panel for colorectal cancer diagnosis via tissue and fecal samples. METHODS: A total of 181 fecal and 50 tumor tissue samples were collected. They were obtained from 83 colorectal cancer patients and 98 healthy subjects. These samples were evaluated for DNA methylation of 9 target genes via quantitative bisulfite next-generation sequencing. We employed the rank-sum test to screen the colorectal cancer-specific methylation sites in the tissue and fecal cohorts. A data model was subsequently constructed and validated via the dedicated validation dataset. RESULTS: Compared with the fecal and negative control samples, the colorectal cancer tissue samples presented significantly higher methylation rates for all the selected gene sites. The methylation rates of the tissue and preoperative fecal samples showed the same high and low rates at the same sites. After screening, a panel of 29 loci in the SDC2, SEPT9, and VIM genes proved to be reliable biomarkers for colorectal cancer diagnosis in fecal samples. Logistic regression models were then constructed and validated using this panel. The sensitivity of the model was 91.43% (95% CI = [89.69, 93.17]), the specificity was 100% (95% CI = [100,100]), and the AUC value is 99.31% (95% CI = [99,99.62]). The diagnostic accuracy of the model for stage I and stage II colorectal cancer was 100% (11/11) and 91.3% (21/23), respectively. Overall, this study confirms that the gene locus panel and the model can be used to diagnose colorectal cancer effectively through feces. CONCLUSIONS: Our study identified a set of key methylation sites for colorectal cancer diagnosis from fecal samples, highlighting the importance of using tissue and fecal samples to accurately assess DNA methylation levels to screen for methylation sites, and developing an effective diagnostic model for colorectal cancer.

Epigenetic landscape reorganisation and reactivation of embryonic development genes are associated with malignancy in IDH-mutant astrocytoma.

Accurate grading of IDH-mutant gliomas defines patient prognosis and guides the treatment path. Histological grading is challenging, and aside from CDKN2A/B homozygous deletions in IDH-mutant astrocytomas, there are no other objective molecular markers used for grading. RNA-sequencing was conducted on primary IDH-mutant astrocytomas (n = 138) included in the prospective CATNON trial, which was performed to assess the prognostic effect of adjuvant and concurrent temozolomide. We integrated the RNA-sequencing data with matched DNA-methylation and NGS data. We also used multi-omics data from IDH-mutant astrocytomas included in the TCGA dataset and validated results on matched primary and recurrent samples from the GLASS-NL study. Since discrete classes do not adequately capture grading of these tumours, we utilised DNA-methylation profiles to generate a Continuous Grading Coefficient (CGC) based on classification scores from a CNS-tumour classifier. CGC was an independent predictor of survival outperforming current WHO-CNS5 and methylation-based classification. Our RNA-sequencing analysis revealed four distinct transcription clusters that were associated with (i) upregulation of cell cycling genes; (ii) downregulation of glial differentiation genes; (iii) upregulation of embryonic development genes (e.g. HOX, PAX, and TBX) and (iv) upregulation of extracellular matrix genes. The upregulation of embryonic development genes was associated with a specific increase of CpG island methylation near these genes. Higher grade IDH-mutant astrocytomas have DNA-methylation signatures that, on the RNA level, are associated with increased cell cycling, tumour cell de-differentiation and extracellular matrix remodelling. These combined molecular signatures can serve as an objective marker for grading of IDH-mutant astrocytomas.

The prognostic role of circulating tumour DNA detected prior to clinical diagnosis of colorectal cancer in the HUNT study.

BACKGROUND: Today, the prognostic tools available at the time of diagnosis in colorectal cancer (CRC) are limited. Better prognostic tools are a prerequisite for personalised treatment. This study aimed to investigate whether circulating tumour DNA (ctDNA) markers found in plasma before clinical diagnosis of CRC could contribute to the prediction of poor prognosis. METHODS: This observational cohort study included patients diagnosed with CRC stage I-III within 24 months following participation in the Trøndelag Health Study (n = 85). Known methylated ctDNA biomarkers of CRC were analysed by PCR in plasma. Outcomes were overall survival (OS), recurrence-free survival (RFS) and poor prognosis (PP). Candidate clinical and methylated ctDNA predictors of the outcomes were identified by Cox regression analyses. RESULTS: Methylated GRIA4 (HR 1.96 (1.06-3.63)), RARB (HR 9.48 (3.00-30.00)), SLC8A1 (HR 1.97 (1.03-3.77)), VIM (HR 2.95 (1.22-7.14)) and WNT5A (HR 5.83 (2.33-14.56)) were independent predictors of OS, methylated RARB (HR 9.67 (2.54-36.81)), SDC2 (HR 3.38 (1.07-10.66)), SLC8A1 (HR 2.93 (1.01-8.51)) and WNT5A (HR 6.95 (1.81-26.68)) were independent predictors of RFS and methylated RARB (HR 6.11 (1.69-22.18)), SDC2 (HR 2.79 (1.20-6.49)) and WNT5A (HR 5.57 (3.04-15.26)) were independent predictors of PP (p < 0.05). CONCLUSIONS: Prediagnostic ctDNA markers are promising contributors to predicting poor prognosis in CRC, potentially becoming one of the tools guiding more personalised treatment.

Methylation patterns at the adjacent CpG sites within enhancers are a part of cell identity.

BACKGROUND: It is generally accepted that methylation status of CpG sites spaced up to 50 bp apart is correlated, and accumulation of locally disordered methylation at adjacent CpG sites is involved in neoplastic transformation, acting in similar way as stochastic accumulation of mutations. RESULTS: We used EPIC microarray data from 596 samples, representing 12 healthy tissue and cell types, as well as 572 blood cancer specimens to analyze methylation status of adjacent CpG sites across human genome, and subsequently validated our findings with NGS and Sanger sequencing. Our analysis showed that there is a subset of the adjacent CpG sites in human genome, with cytosine at one CpG site methylated and the other devoid of methyl group. These loci map to enhancers that are targeted by families of transcription factors involved in cell differentiation. Moreover, our results suggest that the methylation at these loci differ between alleles within a cell, what allows for remarkable level of heterogeneity of methylation patterns. However, different types of specialized cells acquire only one specific and stable pattern of methylation at each of these loci and that pattern is to a large extent lost during neoplastic transformation. CONCLUSIONS: We identified a substantial number of adjacent CpG loci in human genome that display remarkably stable and cell type specific methylation pattern. The methylation pattern at these loci appears to reflect different methylation of alleles in cells. Furthermore, we showed that changes of methylation status at those loci are likely to be involved in regulation of the activity of enhancers and contribute to neoplastic transformation.

Personality and COMT gene: molecular-genetic and epigenetic associations with NEO-PI-R personality domains and facets in monozygotic twins.

Background: This study investigates the relationship between MB-COMT DNA methylation (DNAm) and the personality traits outlined in the NEO-PI-R model through an epigenetic study of monozygotic twins. DNAm, a critical epigenetic mechanism, regulates gene expression and has been linked to various biological processes and disorders. By leveraging the genetic similarities of monozygotic twins, this research explores how epigenetic variations influenced by environmental factors correlate with personality differences. Methods: The study utilized the Five-Factor Model (FFM) to categorize personality traits into five domains: Neuroticism, Extraversion, Conscientiousness, Agreeableness, and Openness to Experience. Each domain comprises six facets, providing a granular view of personality. The research centered on the catechol-O-methyltransferase (COMT) gene, focusing on its role in dopamine metabolism, which is hypothesized to influence personality traits through the dopaminergic system. DNAm status in the MB-COMT promoter region was examined to determine its association with personality facets. Results: Preliminary findings suggest a complex interaction between MB-COMT DNAm patterns and personality traits. Specific methylation patterns at different CpG sites were linked to varying expressions of traits such as impulsivity and aggression, highlighting the nuanced impact of epigenetics on personality. Conclusion: This study underscores the potential of integrating genetic, epigenetic, and environmental data to enhance our understanding of personality formation. The results contribute to a broader understanding of how genetic predispositions shaped by environmental factors manifest in complex trait differences, paving the way for future research in genetic psychiatry and personalized medicine.

Ozone stress-induced DNA methylation variations and their transgenerational inheritance in foxtail millet.

Elevated near-surface ozone (O3) concentrations have surpassed the tolerance limits of plants, significantly impacting crop growth and yield. To mitigate ozone pollution, plants must evolve a rapid and effective defense mechanism to alleviate ozone-induced damage. DNA methylation, as one of the most crucial epigenetic modifications, plays a pivotal role in maintaining gene stability, regulating gene expression, and enhancing plant resilience to environmental stressors. However, the epigenetic response of plants to O3 stress, particularly DNA methylation variations and their intergenerational transmission, remains poorly understood. This study aims to explore the epigenetic mechanisms underlying plant responses to ozone stress across generations and to identify potential epigenetic modification sites or genes crucial in response to ozone stress. Using Open Top Chambers (OTCs), we simulated ozone conditions and subjected foxtail millet to continuous ozone stress at 200 nmol mol-1 for two consecutive generations (S0 and S1). Results revealed that under high-concentration ozone stress, foxtail millet leaves exhibited symptoms ranging from yellowing and curling to desiccation, but the damage in the S1 generation was not more severe than that in the S0 generation. Methylation Sensitive Amplified Polymorphism (MSAP) analysis of the two generations indicated that ozone stress-induced methylation variations ranging from 10.82% to 13.59%, with demethylation events ranged from 0.52% to 5.58%, while hypermethylation occurred between 0.35% and 2.76%. Reproductive growth stages were more sensitive to ozone than vegetative stages. Notably, the S1 generation exhibited widespread demethylation variations, primarily at CNG sites, compared to S0 under similar stress conditions. The inheritance pattern between S0 and S1 generations was mainly of the A-A-B-A type. By recovering and sequencing methylation variant bands, we identified six stress-related differential amplification sequences, implicating these variants in various biological processes. These findings underscore the potential significance of DNA methylation variations as a critical mechanism in plants' response to ozone stress, providing theoretical insights and references for a comprehensive understanding of plant adaptation mechanisms to ozone stress and the epigenetic role of DNA methylation in abiotic stress regulation.

DNA methylation of serotonin genes as predictive biomarkers of antidepressant treatment response.

Selective serotonin reuptake inhibitors (SSRI) are frequently ineffective in treating depressive episodes and biomarkers are needed to optimize antidepressant treatment outcomes. DNA methylation levels of serotonin transporter (SLC6A4) and tryptophan hydroxylase 2 genes (TPH2) have been suggested to predict antidepressant clinical outcomes but their applicability remains uncertain. In this study, we: 1) evaluated SLC6A4/TPH2 methylation biomarker potential for predicting clinical outcomes after escitalopram treatment; 2) evaluated whether changes in SLC6A4/TPH2 methylation are informative of treatment mechanisms. We used a cohort of 90 unmedicated patients with major depressive disorder that were part of a 12-week open-label longitudinal trial and compared our observations with previous findings. Depressive symptoms were measured at baseline and after 8 and 12 weeks of treatment using the Hamilton Depression Rating Scale (HAMD6/17). We found an association between baseline TPH2 methylation and both clinical response (ß:3.43; p = 0.01; 95 % CI:[0.80; 6.06]) and change in depressive symptoms after 8 weeks (ß:-45.44; p = 0.01; 95 %CI:[- -78.58; -12.30]). However, we found no evidence for predictive value of any gene (TPH2 AUC: 0.74 95 % CI:[0.42;0.79]; SLC6A4: AUC: 0.61; 95 % CI: [0.48-0.78]). Methylation levels changed at the trend level for CpG sites of SLC6A4 and TPH2 over the course of 12 weeks of treatment. In addition, similar to previous observations, we found a trend for an association between methylation of SLC6A4 CpG2 (chr17:30,236,083) and HAMD17 change after 12 weeks. Our findings suggest that although TPH2 and SLC6A4 methylation may be informative of antidepressant treatment outcome, they are unlikely to prove useful as clinical predictor tools.

Rapid genome-wide profiling of DNA methylation and genetic variation using guide positioning sequencing (GPS).

Whole-genome bisulfite sequencing (WGBS) has been extensively utilized for DNA methylation profiling over the past decade. However, it has shown limitations in terms of high costs and inefficiencies. The productivity and accuracy of DNA methylation detection rely critically on the optimization of methodologies and the continuous refinements of related sequencing platforms. Here, we describe a detailed protocol of guide positioning sequencing (GPS), a bisulfite-based, location-specific sequencing technology designed for comprehensive DNA methylation characterization across the genome. The fundamental principle of GPS lies in the substitution of dCTP with 5-methyl-dCTP (5 mC) at the 3'-end of DNA fragments by T4 DNA polymerase, which protects cytosines from bisulfite conversion to preserve the integrity of the base composition. This alteration allows the 3'-end to independently facilitate genetic variation profiling and guides the 5'-end, enriched with methylation information, to align more rapidly to the reference genome. Hence, GPS enables the concurrent detection of both genetic and epigenetic variations. Additionally, we provide an accessible description of the data processing, specifically involving certain software and scripts. Overall, the entire GPS procedure can be completed within a maximum of 15 days, starting with a low initial DNA input of 100-500 ng, followed by 4-5 days for library construction, 8-10 days for high-throughput sequencing (HTS) and data analysis, which can greatly facilitate the promotion and application of DNA methylation detection, especially for the rapid clinical diagnosis of diverse disease pathologies associated with concurrent genetic and epigenetic variations.

Sarcoma diagnosis by DNA methylation classifier: A systematic review, current status and future prospects.

Sarcomas, a diverse group of malignant tumors originating from connective tissues, present substantial diagnostic challenges due to their histological heterogeneity. Traditional diagnostic methods include histomorphology along with immunohistochemistry is necessary for primary evaluation. Fluorescence in situ hybridization (FISH) is a supplementary tool that helps with additional findings. However it is very difficult sometimes to accurately classify sarcoma subtypes despite all these tools. Recent advancements in DNA methylation profiling have emerged as a promising approach to enhance the precision of sarcoma diagnosis. This paper delves into the role of DNA methylation classifiers in diagnosing sarcomas, emphasizing their potential to improve diagnostic accuracy, inform treatment decisions, and ultimately enhance patient outcomes.

MethylSeqLogo: DNA methylation smart sequence logos.

BACKGROUND: Some transcription factors, MYC for example, bind sites of potentially methylated DNA. This may increase binding specificity as such sites are (1) highly under-represented in the genome, and (2) offer additional, tissue specific information in the form of hypo- or hyper-methylation. Fortunately, bisulfite sequencing data can be used to investigate this phenomenon. METHOD: We developed MethylSeqLogo, an extension of sequence logos which includes new elements to indicate DNA methylation and under-represented dimers in each position of a set binding sites. Our method displays information from both DNA strands, and takes into account the sequence context (CpG or other) and genome region (promoter versus whole genome) appropriate to properly assess the expected background dimer frequency and level of methylation. MethylSeqLogo preserves sequence logo semantics-the relative height of nucleotides within a column represents their proportion in the binding sites, while the absolute height of each column represents information (relative entropy) and the height of all columns added together represents total information RESULTS: We present figures illustrating the utility of using MethylSeqLogo to summarize data from several CpG binding transcription factors. The logos show that unmethylated CpG binding sites are a feature of transcription factors such as MYC and ZBTB33, while some other CpG binding transcription factors, such as CEBPB, appear methylation neutral. CONCLUSIONS: Our software enables users to explore bisulfite and ChIP sequencing data sets-and in the process obtain publication quality figures.

Genome-wide DNA methylation and their transgenerational pattern differ in Arabidopsis thaliana populations originated along the elevation of West Himalaya.

Methylation at 5' cytosine of DNA molecule is an important epigenetic mark. It is known to play critical role in adaptation of organisms under different biotic and abiotic stressors via modulating gene expression and/or chromatin architecture. Plant populations evolved under variable climatic conditions may have evolved different epigenetic marks including DNA methylation. Here we, describe the genome-wide DNA methylation pattern under native field, F1 and F6 generation followed by their association with phenotypes, climate and global gene expression in the three Arabidopsis thaliana populations originated at different elevation ranges of Indian West Himalaya. We show that the global methyl cytosine (mC) content is more or less similar in the three populations but differ in their distribution across genome. There was an increase in differential methylation between the populations as elevation increased. The methylation divergence was the highest between the low and the high elevation populations. The high elevation populations were hypo-methylated than the low elevation population. The methylation in the genes was associated with population specific phenotypes and climate of the region. The genes which were differentially methylated as well as differentially expressed between the low and high elevation populations were mostly related to abiotic stresses. When grown under controlled condition, there was gain of differential methylation over native condition and the maximum percent changes was observed in CHH-sequence context. Further ~ 99.8% methylated cytosines were stably passed on from F1 to F6 generation. Overall, our data suggest that high elevation population is epigenetically more plastic under changing environmental condition.Background Arabidopsis thaliana is the model plant species and has been extensively studied to understand plants life processes. There are numerous reports on its origin, demography, evolution, epigenomes and adaptation etc. however, Indian populations of Arabidopsis thaliana evolved along wide elevation ranging from ~ 700 m amsl to ~ 3400 m amsl not explored yet. Here we, describe the genome-wide DNA methylation pattern under native field, F1 and F6 generation followed by their association with phenotypes, climate and global gene expression in the three Arabidopsis thaliana populations originated at different elevation ranges of Indian West Himalaya.Results In our study we found that total mCs percent was more or less similar in the three populations but differ in their distribution across genome. The proportion of CG-mCs was the highest, followed by CHH-mCs and CHG-mCs in all the three populations. Under native field condition the methylation divergence was more prominent between low and high elevation populations and the high elevation populations were hypo-methylated than the low elevation population. The methylation in the genes was linked to population-specific phenotypes and the regional climate. The genes that showed differential methylation and expression between low and high elevation populations were primarily associated with abiotic stress responses. When grown under controlled condition, there was gain of differential methylation compared to the native condition and the maximum percent changes was observed in CHH-sequence context. Further 99.8% methylated cytosines were stably passed on from F1 to F6 generation.Conclusions The populations of A. thaliana adapted at different climatic conditions were significantly differentially methylated both under native and controlled condition. However, the magnitude and extent of gain or loss of methylation were most significant between the low and the high elevation populations. Overall, our data suggest that high elevation population is epigenetically more plastic under changing environmental condition.