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Background Epigenetic changes, particularly DNA methylation, are crucial to breast cancer development. Tumor-adjacent normal (AN) tissue frequently serves as a reference for characterizing genomic alterations but is reported to share some characteristics with tumors. However, it is unclear whether AN’s epigenetic profiles reflect a predisposition to cancer or a response to the presence of the tumor. We address this gap by systematically comparing methylation profiles of tumor, AN, and matched-benign tissues from both breasts, as well as to healthy donated breast tissue. Methods We studied four different sample categories from 69 cancer cases: tumor (TU), AN, ipsilateral opposite quadrant (OQ), and contralateral unaffected breast (CUB); and healthy donated breast (HDB) tissue from 182 cancer-unaffected donors. These constitute a “tumor proximity axis” (TPxA): HDB→CUB→OQ→AN→TU. Methylation profiles were assayed using Illumina’s Infinium Methylation EPICv1.0 BeadChip. Differential methylation (DM) analysis was conducted, and the significantly DM CpGs were analyzed for enrichment of transcription factor binding sites (TFBS) and other features. Results Following data processing and quality control, there were 69 TU, 60 AN, 67 OQ, 68 CUB, and 182 HDB samples for analysis. DM analysis showed distinct methylation profiles of TU relative to benign tissues, whereas case-benign tissues were similar to each other but distinct from HDB. Hypomethylated sites in case-benign versus HDB were enriched for TF binding sites of TP63, GATA3, ESR1, PR, AR, NR3C1, and GREB1. TU hypermethylation events were enriched for Polycomb-repressive complex 2 (PRC2) binding, including EZH2, SUZ12, and JARID2, with hypermethylation enrichment for PRC2-related binding motifs in both ER + and ER- tumors. TU methylation profiles were otherwise highly distinct by ER status: TFBS enrichment of hypomethylation events for hormone receptor-related pathways in ER + tumors and for hematopoiesis/immune-related pathways in ER- tumors. We found no differential methylation between benign tissues from patients with ER + vs. ER- tumors. Conclusions DNA methylation profiles differ profoundly at two points: tumor to case-benign and case-benign to HDB, with clear distinction between ER + and ER- tumors. Case-benign tissues are not epigenetically “normal”, are similar across both breasts, and do not differ by ER status of paired tumors.

Cytosine DNA methylation is a heritable epigenetic mark present in many eukaryotic organisms. Although DNA methylation likely has a conserved role in gene silencing, the levels and patterns of DNA methylation appear to vary drastically among different organisms. Here we used shotgun genomic bisulfite sequencing (BS-Seq) to compare DNA methylation in eight diverse plant and animal genomes. We found that patterns of methylation are very similar in flowering plants with methylated cytosines detected in all sequence contexts, whereas CG methylation predominates in animals. Vertebrates have methylation throughout the genome except for CpG islands. Gene body methylation is conserved with clear preference for exons in most organisms. Furthermore, genes appear to be the major target of methylation in Ciona and honey bee. Among the eight organisms, the green alga Chlamydomonas has the most unusual pattern of methylation, having non-CG methylation enriched in exons of genes rather than in repeats and transposons. In addition, the Dnmt1 cofactor Uhrf1 has a conserved function in maintaining CG methylation in both transposons and gene bodies in the mouse, Arabidopsis, and zebrafish genomes.

Telomerase reverse transcriptase (TERT) promoter (pTERT) mutation has often been described as a late event in gliomagenesis and it has been suggested as a prognostic biomarker in gliomas other than 1p19q codeleted tumors. However, the characteristics of isocitrate dehydrogenase (IDH) wild type (wt) (IDHwt), pTERTwt glioblastomas are not well known. We recruited 72 adult IDHwt, pTERTwt glioblastomas and performed methylation profiling, targeted sequencing, and fluorescence in situ hybridization (FISH) for TERT structural rearrangement and ALT (alternative lengthening of telomeres). There was no significant difference in overall survival (OS) between our cohort and a the Cancer Genome Atlas (TCGA) cohort of IDHwt, pTERT mutant (mut) glioblastomas, suggesting that pTERT mutation on its own is not a prognostic factor among IDHwt glioblastomas. Epigenetically, the tumors clustered into classic‐like (11%), mesenchymal‐like (32%), and LGm6‐glioblastoma (GBM) (57%), the latter far exceeding the corresponding proportion seen in the TCGA cohort of IDHwt, pTERTmut glioblastomas. LGm6‐GBM‐clustered tumors were enriched for platelet derived growth factor receptor alpha (PDGFRA) amplification or mutation (p = 0.008), and contained far fewer epidermal growth factor receptor (EGFR) amplification (p < 0.01), 10p loss (p = 0.001) and 10q loss (p < 0.001) compared with cases not clustered to this group. LGm6‐GBM cases predominantly showed ALT (p = 0.038). In the whole cohort, only 35% cases showed EGFR amplification and no case showed combined chromosome +7/−10. Since the cases were already pTERTwt, so the three molecular properties of EGFR amplification, +7/−10, and pTERT mutation may not cover all IDHwt glioblastomas. Instead, EGFR and PDGFRA amplifications covered 67% and together with their mutations covered 71% of cases of this cohort. Homozygous deletion of cyclin dependent kinase inhibitor 2A (CDKN2A)/B was associated with a worse OS (p = 0.031) and was an independent prognosticator in multivariate analysis (p = 0.032). In conclusion, adult IDHwt, pTERTwt glioblastomas show epigenetic clustering different from IDHwt, pTERTmut glioblastomas, and IDHwt glioblastomas which are pTERTwt may however not show EGFR amplification or +7/−10 in a significant proportion of cases. CDKN2A/B deletion is a poor prognostic biomarker in this group.

DNA methylation plays vital roles in both prokaryotes and eukaryotes. There are three forms of DNA methylation in prokaryotes: N 6 -methyladenine (6mA), N 4 -methylcytosine (4mC), and 5-methylcytosine (5mC). Although many sequencing methods have been developed to sequence specific types of methylation, few technologies can be used for efficiently mapping multiple types of methylation. Here, we present NT-seq for mapping all three types of methylation simultaneously. NT-seq reliably detects all known methylation motifs in two bacterial genomes and can be used for identifying de novo methylation motifs. NT-seq provides a simple and efficient solution for detecting multiple types of DNA methylation.

Background Comprehensive profiling of epigenetic states is essential for understanding gene regulation and disease mechanisms. Sequencing-based methods such as ChIP-seq, Hi-C, and RNA-seq provide genome-wide views of histone modifications and 3D genome organization, but lack spatial resolution within single nuclei. Results Here we present an image-based epigenetic profiling framework that combines high-speed super-resolution microscopy with deep learning. Using models of (i) histone deacetylase inhibition in HEK293T cells and (ii) Rett syndrome iPS cells carrying MECP2 mutations, our approach accurately discriminated their epigenetic states (99.6% and 96.1% accuracy, respectively) and identified the nuclear periphery as a hotspot of H3K27ac and CTCF redistribution. Sequencing-based analyses showed compartment switching and lamina-associated domain alterations consistent with the image-based features. These results demonstrate that high-speed super-resolution imaging, when combined with deep learning, provides a powerful tool for epigenetic profiling. Conclusions Our framework offers a generalizable strategy for image-based epigenetic profiling to uncover chromatin alterations in development, disease, and therapeutic response.

More than a decade ago, the ENCODE and NIH Epigenomics Roadmap consortia organized large multilaboratory efforts to profile the epigenomes of >110 different mammalian somatic cell types. This generated valuable publicly accessible datasets that are being mined to reveal genome-wide patterns of a variety of different epigenetic parameters. This consortia approach facilitated the powerful and comprehensive multiparametric integrative analysis of the epigenomes in each cell type. However, no germ cell types were included among the cell types characterized by either of these consortia. Thus, comprehensive epigenetic profiling data are not generally available for the most evolutionarily important cells, male and female germ cells. We discuss the need for reproductive biologists to generate similar multiparametric epigenomic profiling datasets for both male and female germ cells at different developmental stages and summarize our recent effort to derive such data for mammalian spermatogonial stem cells and progenitor spermatogonia. Summary sentence The ENCODE and NIH Epigenome Roadmap consortia have generated valuable datasets profiling epigenomes of >110 different mammalian somatic cell types, but none of these have included profiles of even a single germ cell type.

Background Uveal melanoma (UM) is a highly aggressive malignancy with a metastatic risk that depends on the molecular subclass. This subclass can be determined through molecular characterization of tumor-derived tissue. With eye-sparing treatments, tumor tissue is rarely available for molecular testing. We hypothesized that minimal invasive biomarkers such as methylated cell-free DNA (cfDNA) or circulating tumor DNA (ctDNA) can be used for prognosis and monitoring of patients. Methods Plasma cfDNA was isolated from healthy blood donors (HBDs, N  = 19) and UM patients ( N  = 22). Plasma was collected at baseline (localized disease, N  = 13) and during follow-up (metastatic disease, N  = 9) from independent patients with high metastatic risk (HR, N  = 11) (monosomy 3 and/or BAP1 -mutated tumor) or intermediate metastatic risk (IR, N  = 11) (disomy 3 and/or SF3B1 -mutated tumor). Methylation signatures were determined using genome-wide LpnPI-based methylated DNA sequencing (MeD-seq). Samples with a CpG/reads ratio < 20% ( N  = 3) were excluded. IchorCNA was used to estimate the tumor fraction. cfDNA samples with detectable tumor fraction ( N  = 2) were analyzed separately from the other cfDNA samples without detectable tumor fraction ( N  = 18) to reduce noise in downstream analyses. Differentially methylated regions (DMRs) were identified between the following predefined subgroups: UM ( N  = 11) vs. HBDs ( N  = 19), and HR ( N  = 10) vs. IR ( N  = 7). To visualize clustering, principal component analysis (PCA) and hierarchical clustering was performed on the DMRs with fold change > 2.0. Gene set enrichment analysis (GSEA, Z-score > 2.0 and p  < 0.05) was performed to evaluate biological relevance. Results Distinct clustering was observed between UM and HBDs samples, and between HR and IR samples, although outliers were present in the latter comparison. GSEA implicated eight canonical pathways including the S100 Family Signaling Pathway and RAF/MAP kinase cascade, which are linked to tumorigenesis and immune processes. Conclusion This pilot study reports on cfDNA methylation signatures that differentiates UM patients from HBDs, and may distinguish between intermediate and high risk UM subgroups, supporting its prognostic potential. However, its role in monitoring disease progression requires further validation. Independent replication studies are warranted to confirm our findings and evaluate the clinical applicability in UM.

Body mass index (BMI) has a notable genetic component, yet the majority of genetic variants remain elusive. Their identification may be hindered as weight also stems from exogenous factors and may be shaped by lifestyle influences. Since DNA methylation is partially controlled by genes and also captures environmental exposures, epigenetic biomarkers have been successfully employed in BMI prediction. In this study, we integrated SNP and DNA methylation data to independently validate existing epigenetic models and polygenic risk scores for predicting BMI. In a sample of 624 adult Polish individuals, the top ten CpG sites accounted for nearly twice the variance in phenotypic BMI compared to the top ten SNPs. The polygenic risk score formulated by Yengo et al. demonstrated a highly significant, low-to-moderate correlation with BMI ( r  = 0.249, p  = 2.7 × 10 − 10 ). Four published epigenetic predictors, trained on up to 2,000 CpG sites, were evaluated in our sample, with a significant moderate correlation with BMI observed across all models ( r  = 0.4–0.5). Next, predicted values were transformed onto the absolute BMI scale and applied to an independent validation set ( N  = 112). The mean absolute error (MAE) of BMI prediction was comparable across all models, averaging ± 3.1 units, with the Smith model achieving the highest predictive accuracy (MAE = 2.7). Notably, the predictor developed by Do et al. showed the most consistent association between the deviation in epigenetic versus phenotypic BMI and multiple health-related epigenetic biomarkers. The strongest association was observed for body fat percentage (β > 0.6), while moderate effect sizes were found for waist-to-hip ratio, alcohol consumption, epigenetic CRP, HDL cholesterol, and smoking. This study provides new insights into the relative roles of genetic and epigenetic markers in BMI prediction. While known SNPs contribute to the explained variance, CpG markers exhibited stronger effect sizes in the studied population. The BMI values returned by the models showed associations with multiple epigenetic health indicators, underscoring their potential translational value for metabolic health assessment. This study can also inform the selection of BMI predictors suitable for forensic genetic analyses, supporting the reconstruction of an individual’s appearance from secured biological evidence.

A myriad of diseases is caused or characterized by alteration of epigenetic patterns, including changes in DNA methylation, post-translational histone modifications, or chromatin structure. These changes of the epigenome represent a highly interesting layer of information for disease stratification and for personalized medicine. Traditionally, epigenomic profiling required large amounts of cells, which are rarely available with clinical samples. Also, the cellular heterogeneity complicates analysis when profiling clinical samples for unbiased genome-wide biomarker discovery. Recent years saw great progress in miniaturization of genome-wide epigenomic profiling, enabling large-scale epigenetic biomarker screens for disease diagnosis, prognosis, and stratification on patient-derived samples. All main genome-wide profiling technologies have now been scaled down and/or are compatible with single-cell readout, including: (i) Bisulfite sequencing to determine DNA methylation at base-pair resolution, (ii) ChIP-Seq to identify protein binding sites on the genome, (iii) DNaseI-Seq/ATAC-Seq to profile open chromatin, and (iv) 4C-Seq and HiC-Seq to determine the spatial organization of chromosomes. In this review we provide an overview of current genome-wide epigenomic profiling technologies and main technological advances that allowed miniaturization of these assays down to single-cell level. For each of these technologies we evaluate their application for future biomarker discovery. We will focus on (i) compatibility of these technologies with methods used for clinical sample preservation, including methods used by biobanks that store large numbers of patient samples, and (ii) automation of these technologies for robust sample preparation and increased throughput.