Whole-genome Bisulfite Sequencing Data Research Articles

Multimodal analysis of cfDNA methylomes for early detecting esophageal squamous cell carcinoma and precancerous lesions

Detecting early-stage esophageal squamous cell carcinoma (ESCC) and precancerous lesions is critical for improving survival. Here, we conduct whole-genome bisulfite sequencing (WGBS) on 460 cfDNA samples from patients with non-metastatic ESCC or precancerous lesions and matched healthy controls. We develop an expanded multimodal analysis (EMMA) framework to simultaneously identify cfDNA methylation, copy number variants (CNVs), and fragmentation markers in cfDNA WGBS data. cfDNA methylation markers are the earliest and most sensitive, detectable in 70% of ESCCs and 50% of precancerous lesions, and associated with molecular subtypes and tumor microenvironments. CNVs and fragmentation features show high specificity but are linked to late-stage disease. EMMA significantly improves detection rates, increasing AUCs from 0.90 to 0.99, and detects 87% of ESCCs and 62% of precancerous lesions with >95% specificity in validation cohorts. Our findings demonstrate the potential of multimodal analysis of cfDNA methylome for early detection and monitoring of molecular characteristics in ESCC.

Profiling age and body fluid DNA methylation markers using nanopore adaptive sampling

DNA methylation plays essential roles in regulating physiological processes, from tissue and organ development to gene expression and aging processes and has emerged as a widely used biomarker for the identification of body fluids and age prediction. Currently, methylation markers are targeted independently at specific CpG sites as part of a multiplexed assay rather than through a unified assay. Methylation detection is also dependent on divergent methodologies, ranging from enzyme digestion and affinity enrichment to bisulfite treatment, alongside various technologies for high-throughput profiling, including microarray and sequencing. In this pilot study, we test the simultaneous identification of age-associated and body fluid-specific methylation markers using a single technology, nanopore adaptive sampling. This innovative approach enables the profiling of multiple CpG marker sites across entire gene regions from a single sample without the need for specialized DNA preparation or additional biochemical treatments. Our study demonstrates that adaptive sampling achieves sufficient coverage in regions of interest to accurately determine the methylation status, shows a robust consistency with whole-genome bisulfite sequencing data, and corroborates known CpG markers of age and body fluids. Our work also resulted in the identification of new sites strongly correlated with age, suggesting new possible age methylation markers. This study lays the groundwork for the systematic development of nanopore-based methodologies in both age prediction and body fluid identification, highlighting the feasibility and potential of nanopore adaptive sampling while acknowledging the need for further validation and expansion in future research.

Multi‐omics panoramic analysis of HBV integration, transcriptional regulation, and epigenetic modifications in PLC/PRF/5 cell line

AbstractThe clearance or transcriptional silencing of integrated HBV DNA is crucial for achieving a functional cure in patients with chronic hepatitis B and reducing the risk of hepatocellular carcinoma development. The PLC/PRF/5 cell line is commonly used as an in vitro model for studying HBV integration. In this study, we employed a range of multi‐omics techniques to gain a panoramic understanding of the characteristics of HBV integration in PLC/PRF/5 cells and to reveal the transcriptional regulatory mechanisms of integrated HBV DNA. Transcriptome long‐read sequencing (ONT) was conducted to analyze and characterize the transcriptional activity of different HBV DNA integration sites in PLC/PRF/5 cells. Additionally, we collected data related to epigenetic regulation, including whole‐genome bisulfite sequencing (WGBS), histone chromatin immunoprecipitation sequencing (ChIP‐seq), and assays for transposase‐accessible chromatin using sequencing (ATAC‐seq), to explore the potential mechanisms involved in the transcriptional regulation of integrated HBV DNA. Long‐read RNA sequencing analysis revealed significant transcriptional differences at various integration sites in the PLC/PRF/5 cell line, with higher HBV DNA transcription levels at integration sites on chr11, chr13, and the chr13/chr5 fusion chromosome t (13:5). Combining long‐read DNA and RNA sequencing results, we found that transcription of integrated HBV DNA generally starts downstream of the SP1, SP2, or XP promoters. ATAC‐seq data confirmed that chromatin accessibility has limited influence on the transcription of integrated HBV DNA in the PLC/PRF/5 cell line. Analysis of WGBS data showed that the methylation intensity of integrated HBV DNA was highly negatively correlated with its transcription level (r = −0.8929, p = 0.0123). After AzaD treatment, the transcription level of integrated HBV DNA significantly increased, especially for the integration chr17, which had the highest level of methylation. Through ChIP‐seq data, we observed the association between histone modification of H3K4me3 and H3K9me3 with the transcription of integrated HBV DNA. Our findings suggest that the SP1, SP2 and XP in integrated HBV DNA, methylation level of surrounding host chromosome, and histone modifications affect the transcription of integrated HBV DNA in PLC/PRF/5 cells. This provides important clues for future studies on the expression and regulatory mechanisms of integrated HBV.

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA reveals clinically relevant biomarkers but requires specialized protocols such as whole-genome bisulfite sequencing. Meanwhile, millions of cell-free DNA samples are being profiled by whole-genome sequencing. Here, we develop FinaleMe, a non-homogeneous Hidden Markov Model, to predict DNA methylation of cell-free DNA and, therefore, tissues-of-origin, directly from plasma whole-genome sequencing. We validate the performance with 80 pairs of deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing data.

Using methylation data to improve transcription factor binding prediction

ABSTRACT Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

CpG island definition and methylation mapping of the T2T-YAO genome

Abstract Precisely defining and mapping all cytosine positions and their clusters, known as CpG islands (CGIs), as well as their methylation status are pivotal for genome-wide epigenetic studies, especially when population-centric reference genomes are ready for timely application. Here we first align the two high-quality reference genomes, T2T-YAO and T2T-CHM13, from different ethnic backgrounds in a base-by-base fashion and compute their genome-wide density-defined and position-defined CGIs. Second, mapping some representative genome-wide methylation data from selected organs onto the two genomes, we find that there are about 4.7–5.8% sequence divergency of variable categories depending on quality cutoffs. Genes among the divergent sequences are mostly associated with neurological functions. Moreover, CGIs associated with the divergent sequences are significantly different with respect to CpG density and observed CpG/expected CpG (O/E) ratio between the two genomes. Finally, we find that the T2T-YAO genome not only has a greater CpG site coverage than that of the T2T-CHM13 genome when whole-genome bisulfite sequencing (WGBS) data from the European and American populations are mapped to each reference, but also show more hyper-methylated CpG sites as compared to the T2T-CHM13 genome. Our study suggests that future genome-wide epigenetic studies of the Chinese populations rely on both acquisition of high-quality methylation data and subsequent precision CGI mapping based on the Chinese T2T reference.

Comprehensive analysis of differentially methylated regions in colorectal cancer (CRC).

73 Background: Epigenetic alterations have been shown to govern many disease states, including cancer. A key epigenetic mark is the DNA methylation of CpGs. Large studies, like The Cancer Genome Atlas, have shown that DNA methylation can differentiate between cancer status and type. Here, differentially methylated genomic patterns were defined between patients with CRC and healthy individuals by utilizing patterns of co-methylation of adjacent CpG loci. Methods: Deep, whole genome bisulfite sequencing (WGBS) was conducted on DNA samples from 100 healthy individuals (median effective coverage of 352x±38x) and from 75 patients with CRC (median effective coverage of 48x±18x). The two cohorts were chosen to be balanced in terms of age (CRC: 58±11 years; healthy: 57±8 years) and to represent major ethnicities in the USA, and the CRC cohort was chosen to represent all stages, histologies and morphologies, and microsatellite instability (MSI, 23%). A novel machine learning model was developed to discover differentially methylated regions between the healthy and CRC cohorts as well as between subpopulations within the CRC cohort, including age, sex, stage, histology, and MSI vs microsatellite stability (MSS). Results: In total, we discovered 132,911 differentially methylated regions between healthy and CRC cohorts, spanning more than 1.14 million CpGs. 76% of all discovered regions were hypermethylated (70% CpG islands, 1.3% CpG shelves, 21% CpG shores) and the rest were hypomethylated (7.3% CpG islands, 8.6% CpG shelves, 25% CpG shores). We also discovered 220 hypermethylated regions that were differentially methylated between CRC MSI samples and CRC MSS samples and healthy cfDNA samples. Furthermore, we discovered subtype-specific differentially methylated regions, such as 62 hyper-methylated regions that were differentially methylated between mucinous adenocarcinoma samples and the rest of the cohort. Conclusions: Based on deep WGBS data from heterogeneous cohorts, representative of the US population eligible for CRC screening, we identified many differentially methylated regions as strong biomarker candidates for the early detection of cancer. Compared to recurrently mutated driver genes in CRC, we find that differential methylation profiles of CRC samples are generally much more homogenous and are therefore ideal biomarkers for targeted sequencing panels with the purpose of early cancer detection. Further, analysis of these patterns based on clinicopathologic factors enabled discovery of subtype specific markers, which can be incorporated to ensure equivalent performance for underrepresented subtypes or subpopulations.

Methylated Whole Genome Cell-Free DNA Sequencing before Chimeric Antigen Receptor T-Cell Therapy for Large B-Cell Lymphoma Predicts Treatment Outcomes

Introduction More than half of patients with relapsed or refractory large B-cell lymphoma (R/R LBCL) will experience progressive disease after CD19 chimeric antigen receptor T-cell therapy (CART). Circulating tumor DNA profiling has been used to prognosticate patients before CART, but mainly has focused on identification of somatic genomic aberrations. Epigenetic changes, specifically aberrant DNA methylation, can alter transcriptional regulation of genes important for treatment resistance. Altered methylation features can be detected from cell-free DNA (cfDNA) in patients with LBCL and are associated with outcomes after frontline immunochemotherapy. In the current study, we used whole genome bisulfite sequencing (WGBS) to analyze the methylation landscape in cfDNA from patient plasma before CART and identified novel methylation signatures associated with poor treatment outcomes. Methods Patients with R/R LBCL who received standard-of-care CART with plasma samples drawn before lymphodepletion (LD) or at cell infusion were identified. The primary endpoint was 3-month overall response after CART. cfDNA was extracted from 3-5 ml of plasma and cfDNA libraries were prepared using a Zymo-Seq cell free WGBS kit. Each library was sequenced to 300 - 600 million read pairs at a read length of 150 bp, enabling an average CpG coverage of 7-13X per detected CpG. Unique alignment rate among all libraries were about or above 82%. Reads were adapter trimmed with Trim Galore and aligned to reference genome hg19 using Bismark. Methylation calling was performed by MethylDackel. Differentially methylated cytosines (DMC, FDR ≤0.05) and regions (DMR) from WGBS data with an absolute methylation difference of ≥ 0.1 between 3-month responders and non-responders were identified using DSS. Functional enrichment analysis of genes overlapped by DMRs was performed by gprofiler2. Results A total of 22 patients with plasma collected before LD (n=21) or at cell infusion (n=1) who received CART were identified. Median follow-up was 21.5 months (95% CI 12.5-NR). Three-month response was evaluable in 21 patients (1 pending evaluation). Overall response rate (ORR) was 77% (8 CR, 9 PR) and 55% (9 CR, 2 PR) at one- and 3-months post-CART, respectively. Median PFS was 6.5 months (95% CI 3.0-NR), and median OS was 57.7 months (95% CI 7.7-NR). Patient and disease characteristics are described in Table 1. Differential methylation analysis identified 22,815 DMCs, of which 1,043 were hypomethylated and 21,772 were hypermethylated in non-responders. The global average methylation values across 25 million cytosines were 0.807 and 0.786 for non-responders and responders, respectively (p=0.265). A total of 582 DMRs were identified, of which 34 were hypomethylated and 548 were hypermethylated in non-responders (Figure 1). Of these, 281 (48%) overlapped intergenic regions, 265 (46%) gene bodies, and 33 (6%) promoter regions. DMRs overlapping DNA repair gene PARP1 (areaStat 88.2), B-cell receptor signaling genes SYK (27.2), FYN (21.4), and PTPN6 (20.9), polycomb repressor complex genes JARID2 (25.2) and BCOR (21.4), tumor suppressor WT1 (47.1), immune mediators NRP1 (80.0), TRAF3 (56.7), PAK3 (28.4), IRAK2 (28.7), and TNFS14 (23.6), and non-coding RNAs MIR4520 (95.8) and VTRNA-2 (23.1) were hypermethylated in non-responders and IL17RA (-33.2) hypomethylated. Functional enrichment analysis on overlapped genes also identified common molecular functions involving guanyl nucleotide exchange factors (p=0.0007), GTPase regulation (p=0.001), and cGMP binding (p=0.007). Discussion This proof-of-concept study is the first to perform WGBS on pretreatment plasma from patients with R/R LBCL to associate methylation signatures with post-CART outcomes. We identified that non-responders were enriched in hypermethylated DMRs compared to responders, overlapping with genes important to lymphomagenesis, immune function, and GTP energy metabolism. Further work will expand this cohort, compare with matched-tissue to find tumor-specific markers, and identify candidate DMR targets for targeted bisulfite sequencing from pre- and post-treatment plasma samples to assess response. By identifying high-risk methylation signatures in the plasma of CART recipients, we may be able to identify patients to be prioritized for novel treatment strategies such as combination with hypomethylating agents to improve outcomes.

Transposon wave remodeled the epigenomic landscape in the rapid evolution of X-Chromosome dosage compensation.

Sex chromosome dosage compensation is a model to understand the coordinated evolution of transcription; however, the advanced age of the sex chromosomes in model systems makes it difficult to study how the complex regulatory mechanisms underlying chromosome-wide dosage compensation can evolve. The sex chromosomes of Poecilia picta have undergone recent and rapid divergence, resulting in widespread gene loss on the male Y, coupled with complete X Chromosome dosage compensation, the first case reported in a fish. The recent de novo origin of dosage compensation presents a unique opportunity to understand the genetic and evolutionary basis of coordinated chromosomal gene regulation. By combining a new chromosome-level assembly of P. picta with whole-genome bisulfite sequencing and RNA-seq data, we determine that the YY1 transcription factor (YY1) DNA binding motif is associated with male-specific hypomethylated regions on the X, but not the autosomes. These YY1 motifs are the result of a recent and rapid repetitive element expansion on the P. picta X Chromosome, which is absent in closely related species that lack dosage compensation. Taken together, our results present compelling support that a disruptive wave of repetitive element insertions carrying YY1 motifs resulted in the remodeling of the X Chromosome epigenomic landscape and the rapid de novo origin of a dosage compensation system.

MethMarkerDB: acomprehensive cancer DNA methylation biomarker database.

DNA methylation plays a crucial role in tumorigenesis and tumor progression, sparking substantial interest in the clinical applications of cancer DNA methylation biomarkers. Cancer-related whole-genome bisulfite sequencing (WGBS) data offers a promising approach to precisely identify these biomarkers with differentially methylated regions (DMRs). However, currently there is no dedicated resource for cancer DNA methylation biomarkers with WGBS data. Here, we developed a comprehensive cancer DNA methylation biomarker database (MethMarkerDB, https://methmarkerdb.hzau.edu.cn/), which integrated 658 WGBS datasets, incorporating 724 curated DNA methylation biomarker genes from 1425 PubMed published articles. Based on WGBS data, we documented 5.4 million DMRs from 13 common types of cancer as candidate DNA methylation biomarkers. We provided search and annotation functions for these DMRs with different resources, such as enhancers and SNPs, and developed diagnostic and prognostic models for further biomarker evaluation. With the database, we not only identified known DNA methylation biomarkers, but also identified 781 hypermethylated and 5245 hypomethylated pan-cancer DMRs, corresponding to 693 and 2172 genes, respectively. These novel potential pan-cancer DNA methylation biomarkers hold significant clinical translational value. We hope that MethMarkerDB will help identify novel cancer DNA methylation biomarkers and propel the clinical application of these biomarkers.

Genome-wide methylation profiling identified methylated KCNA3 and OTOP2 as promising diagnostic markers for esophageal squamous cell carcinoma.

Early detection of esophageal squamous cell carcinoma (ESCC) can considerably improve the prognosis of patients. Aberrant cell-free DNA (cfDNA) methylation signatures are a promising tool for detecting ESCC. However, available markers based on cell-free DNA methylation are still inadequate. This study aimed to identify ESCC-specific cfDNA methylation markers and evaluate the diagnostic performance in the early detection of ESCC. We performed whole-genome bisulfite sequencing (WGBS) for 24 ESCC tissues and their normal adjacent tissues. Based on the WGBS data, we identified 21,469,837 eligible CpG sites (CpGs). By integrating several methylation datasets, we identified several promising ESCC-specific cell-free DNA methylation markers. Finally, we developed a dual-marker panel based on methylated KCNA3 and OTOP2, and then, we evaluated its performance in our training and validation cohorts. The ESCC diagnostic model constructed based on KCNA3 and OTOP2 had an AUC of 0.91 [95% CI: 0.85-0.95], and an optimal sensitivity and specificity of 84.91% and 94.32%, respectively, in the training cohort. In the independent validation cohort, the AUC was 0.88 [95% CI: 0.83-0.92], along with an optimal sensitivity of 81.5% and specificity of 92.9%. The model sensitivity for stage I-II ESCC was 78.4%, which was slightly lower than the sensitivity of the model (85.7%) for stage III-IV ESCC. The dual-target panel based on cfDNA showed excellent performance for detecting ESCC and might be an alternative strategy for screening ESCC.

Comprehensive analyses of partially methylated domains and differentially methylated regions in esophageal cancer reveal both cell-type- and cancer-specific epigenetic regulation

BackgroundAs one of the most common malignancies, esophageal cancer has two subtypes, squamous cell carcinoma and adenocarcinoma, arising from distinct cells-of-origin. Distinguishing cell-type-specific molecular features from cancer-specific characteristics is challenging.ResultsWe analyze whole-genome bisulfite sequencing data on 45 esophageal tumor and nonmalignant samples from both subtypes. We develop a novel sequence-aware method to identify large partially methylated domains (PMDs), revealing profound heterogeneity at both methylation level and genomic distribution of PMDs across tumor samples. We identify subtype-specific PMDs that are associated with repressive transcription, chromatin B compartments and high somatic mutation rate. While genomic locations of these PMDs are pre-established in normal cells, the degree of loss is significantly higher in tumors. We find that cell-type-specific deposition of H3K36me2 may underlie genomic distribution of PMDs. At a smaller genomic scale, both cell-type- and cancer-specific differentially methylated regions (DMRs) are identified for each subtype. Using binding motif analysis within these DMRs, we show that a cell-type-specific transcription factor HNF4A maintains the binding sites that it generates in normal cells, while establishing new binding sites cooperatively with novel partners such as FOSL1 in esophageal adenocarcinoma. Finally, leveraging pan-tissue single-cell and pan-cancer epigenomic datasets, we demonstrate that a substantial fraction of cell-type-specific PMDs and DMRs identified here in esophageal cancer are actually markers that co-occur in other cancers originating from related cell types.ConclusionsThese findings advance our understanding of DNA methylation dynamics at various genomic scales in normal and malignant states, providing novel mechanistic insights into cell-type- and cancer-specific epigenetic regulations.

Preliminary Study of Whole-Genome Bisulfite Sequencing and Transcriptome Sequencing in VHL Disease-Associated ccRCC.

Von Hippel-Lindau (VHL) disease is an autosomal dominant hereditary tumor syndrome with an incidence of approximately 1/36,000. VHL disease-associated clear cell renal cell carcinoma (ccRCC) is the most common congenital RCC. Although recent advances in treating RCC have improved the long-term prognosis of patients with VHL disease, kidney cancer is still the leading cause of death in these patients. Therefore, finding new targets for diagnosing and treating VHL disease-associated ccRCC is still essential. In this study, we collected matched tumor tissues and normal samples from 25 patients with VHL disease-associated ccRCC, diagnosed and surgically treated in the Department of Urology, Peking University First Hospital. After screening, we performed whole genome bisulfite sequencing (WGBS) on 23 pairs of tissues and RNA-seq on 6 pairs of tissues. And we also compared the VHL disease-associated ccRCC transcriptome data with the sporadic ccRCC transcriptome data from the The Cancer Genome Atlas (TCGA) public database RESULTS: We found that the methylation level of VHL disease-associated ccRCC tumor tissues was significantly lower than that of normal tissues. The tumor tissues showed a difference in the copy number of 3p loss and 5q and 7q gain compared with normal tissues. We integrated RNA-seq and WGBS data to reveal methylation candidate genes associated with VHL disease-associated ccRCC; our results showed 124 hypermethylated and downregulated genes, and 245 hypomethylated and upregulated genes. By comparing the VHL disease-associated ccRCC transcriptome data with the sporadic ccRCC transcriptome data from the TCGA public database, we found that the major pathways of differential gene enrichment differed between them. Our study mapped the multiomics of copy number variation, methylation and mRNA level changes in tumor and normal tissues of clear cell renal cell carcinoma with VHL syndrome, which provides a solid foundation for the mechanistic study, biomarker screening, and therapeutic target discovery of clear cell renal cell carcinoma.

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

BackgroundChanges in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.MethodsHere we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes.ResultsOur meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67–0.72), which increased to 0.83 (0.80–0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity.ConclusionsThis work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

A pipeline for sample tagging of whole genome bisulfite sequencing data using genotypes of whole genome sequencing

BackgroundIn large-scale high-throughput sequencing projects and biobank construction, sample tagging is essential to prevent sample mix-ups. Despite the availability of fingerprint panels for DNA data, little research has been conducted on sample tagging of whole genome bisulfite sequencing (WGBS) data. This study aims to construct a pipeline and identify applicable fingerprint panels to address this problem.ResultsUsing autosome-wide A/T polymorphic single nucleotide variants (SNVs) obtained from whole genome sequencing (WGS) and WGBS of individuals from the Third China National Stroke Registry, we designed a fingerprint panel and constructed an optimized pipeline for tagging WGBS data. This pipeline used Bis-SNP to call genotypes from the WGBS data, and optimized genotype comparison by eliminating wildtype homozygous and missing genotypes, and retaining variants with identical genomic coordinates and reference/alternative alleles. WGS-based and WGBS-based genotypes called from identical or different samples were extensively compared using hap.py. In the first batch of 94 samples, the genotype consistency rates were between 71.01%-84.23% and 51.43%-60.50% for the matched and mismatched WGS and WGBS data using the autosome-wide A/T polymorphic SNV panel. This capability to tag WGBS data was validated among the second batch of 240 samples, with genotype consistency rates ranging from 70.61%-84.65% to 49.58%-61.42% for the matched and mismatched data, respectively. We also determined that the number of genetic variants required to correctly tag WGBS data was on the order of thousands through testing six fingerprint panels with different orders for the number of variants. Additionally, we affirmed this result with two self-designed panels of 1351 and 1278 SNVs, respectively. Furthermore, this study confirmed that using the number of genetic variants with identical coordinates and ref/alt alleles, or identical genotypes could not correctly tag WGBS data.ConclusionThis study proposed an optimized pipeline, applicable fingerprint panels, and a lower boundary for the number of fingerprint genetic variants needed for correct sample tagging of WGBS data, which are valuable for tagging WGBS data and integrating multi-omics data for biobanks.

Methylation-Regulated Long Non-Coding RNA Expression in Ulcerative Colitis

Long non-coding RNAs (lncRNAs) have been shown to play a role in the pathogenesis of ulcerative colitis (UC). Although epigenetic processes such as DNA methylation and lncRNA expression are well studied in UC, the importance of the interplay between the two processes has not yet been fully explored. It is, therefore, believed that interactions between environmental factors and epigenetics contribute to disease development. Mucosal biopsies from 11 treatment-naïve UC patients and 13 normal controls were used in this study. From each individual sample, both whole-genome bisulfite sequencing data (WGBS) and lncRNA expression data were analyzed. Correlation analysis between lncRNA expression and upstream differentially methylated regions (DMRs) was used to identify lncRNAs that might be regulated by DMRs. Furthermore, proximal protein-coding genes associated with DMR-regulated lncRNAs were identified by correlating their expression. The study identified UC-associated lncRNAs such as MIR4435-2HG, ZFAS1, IL6-AS1, and Pvt1, which may be regulated by DMRs. Several genes that are involved in inflammatory immune responses were found downstream of DMR-regulated lncRNAs, including SERPINB1, CCL18, and SLC15A4. The interplay between lncRNA expression regulated by DNA methylation in UC might improve our understanding of UC pathogenesis.

Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data

BackgroundAbnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods.MethodsPurified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter.ResultsOf 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05–06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders.ConclusionsSOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.

Gene body hypomethylation of pyroptosis-related genes NLRP7, NLRP2, and NLRP3 facilitate non-invasive surveillance of hepatocellular carcinoma.

Programmed cell death (PCD) resistance is a key driver of cancer occurrence and development. The prognostic relevance of PCD-related genes in hepatocellular carcinoma (HCC) has attracted considerable attention in recent years. However, there is still a lack of efforts to compare the methylation status of different types of PCD genes in HCC and their roles in its surveillance. The methylation status of genes related to pyroptosis, apoptosis, autophagy, necroptosis, ferroptosis, and cuproptosis was analyzed in tumor and non-tumor tissues from TCGA. Whole-genome bisulfite sequencing (WGBS) data of paired tumor tissue and buffy coat samples were used to filter the potential interference of blood leukocytes in cell-free DNA (cfDNA). The WGBS data of healthy individuals' and early-stage HCC patients' cfDNA were analyzed to evaluate the distinguishing ability. The average gene body methylation (gbDNAme) of pyroptosis-related genes (PRGs) was significantly altered in HCC tissues relative to normal tissues, and their distinguishing ability was higher compared to the other types of PCD-related genes. The gbDNAme of NLRP7, NLRP2, and NLRP3 was reflective of the hypomethylation in HCC tissues, and methylation levels of NLRP3 correlated positively with its expression level (r=0.51). The candidate hypomethylated PRGs could discriminate between early HCC patients and healthy controls in cfDNA analysis with high accuracy (area under the receiver operation curve, AUC=0.94). Furthermore, the hypomethylation of PRGs was associated with poor prognosis of HCC. Gene body hypomethylation of PRGs is a promising biomarker for early HCC detection, monitoring of tumor recurrence, and prognosis prediction.

Partial erosion on under-methylated regions and chromatin reprogramming contribute to oncogene activation in IDH mutant gliomas

BackgroundIDH1/2 hotspot mutations are well known to drive oncogenic mutations in gliomas and are well-defined in the WHO 2021 classification of central nervous system tumors. Specifically, IDH mutations lead to aberrant hypermethylation of under-methylated regions (UMRs) in normal tissues through the disruption of TET enzymes. However, the chromatin reprogramming and transcriptional changes induced by IDH-related hypermethylation in gliomas remain unclear.ResultsHere, we have developed a precise computational framework based on Hidden Markov Model to identify altered methylation states of UMRs at single-base resolution. By applying this framework to whole-genome bisulfite sequencing data from 75 normal brain tissues and 15 IDH mutant glioma tissues, we identified two distinct types of hypermethylated UMRs in IDH mutant gliomas. We named them partially hypermethylated UMRs (phUMRs) and fully hypermethylated UMRs (fhUMRs), respectively. We found that the phUMRs and fhUMRs exhibit distinct genomic features and chromatin states. Genes related to fhUMRs were more likely to be repressed in IDH mutant gliomas. In contrast, genes related to phUMRs were prone to be up-regulated in IDH mutant gliomas. Such activation of phUMR genes is associated with the accumulation of active H3K4me3 and the loss of H3K27me3, as well as H3K36me3 accumulation in gene bodies to maintain gene expression stability. In summary, partial erosion on UMRs was accompanied by locus-specific changes in key chromatin marks, which may contribute to oncogene activation.ConclusionsOur study provides a computational strategy for precise decoding of methylation encroachment patterns in IDH mutant gliomas, revealing potential mechanistic insights into chromatin reprogramming that contribute to oncogenesis.

Study on detection of CNVs using human whole genome bisulfite sequencing data.

It has been reported that the aberrant DNA methylation may result in copy number variations (CNVs), and the CNVs may alter the levels of DNA methylation. Whole genome bisulfite sequencing (WGBS) is able to generate the sequencing data of DNAs, and shows the potential ability to detect CNVs. However, the evaluations and performances on the detections of CNVs using WGBS data is still unclear. In this study, five software with different strategies for CNV detections, e.g., BreakDancer, cn.mops, CNVnator, DELLY and Pindel, were selected to explore and benchmark the performances of CNV detections with WGBS data. Based on the real (2.62 billion reads) and simulated (12.35 billion reads) WGBS data of humans, we calculated the number, precision, recall, relative ability, memory usage, and running time of CNV detections by 150 times, and tried to figure out the optimal strategy for CNV detections with WGBS data. Based on the real WGBS data, Pindel detected the most deletions and duplications, CNVnator detected the deletions with the highest precision, cn.mops detected the duplications with the highest precision, Pindel detected the deletions with the highest recall, and cn.mops detected the duplications with the highest recall. Based on the simulated WGBS data, BreakDancer detected the most deletions, and cn.mops detected the most duplications. The CNVnator showed the highest precision and recall for both deletions and duplications. In real and simulated WGBS data, the ability of CNVnator to detect CNVs was likely to overtake that in the whole genome sequencing data. Additionally, DELLY and BreakDancer displayed the lowest peak of memory usage and the lest CPU runtime, while CNVnator expressed the highest peak of memory usage and the most CPU runtime. Taken together, CNVnator and cn.mops showed the excellent performances of CNV detections with WGBS data. These results suggested that it was feasible to detect CNVs using WGBS data, and provided the useful information to further investigate both CNVs and DNA methylation using WGBS data alone.