Reduced Representation Bisulfite Sequencing Data Research Articles

Transcriptomics and epigenetic data integration learning module on Google Cloud.

Abstract Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. Highlights

Age Prediction Using DNA Methylation Heterogeneity Metrics.

Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.

Abstract LB103: Cell-free DNA methylation as a noninvasive prognostic biomarker in hepatocellular carcinoma

Abstract The current noninvasive prognostic evaluation of hepatocellular carcinoma (HCC) using markers such as imaging features and serum biomarkers like alpha-fetoprotein (AFP) is limited due to low sensitivity. Pathologic tumor features can enhance prognosis beyond imaging and AFP, yet pre-surgical percutaneous biopsies are invasive, not routinely pursued, and may not be representative of the entire tumor. There is a critical unmet need to develop noninvasive blood-based HCC biomarkers to guide therapeutic decisions. Epigenetic changes in circulating cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis. Therefore, we aim to evaluate the potential of cfDNA methylation as a noninvasive predictor for pre-treatment prognostication in HCC patients. Using Illumina HumanMethylation450 array data of 377 HCC tumors and 50 adjacent normal tissues obtained from The Cancer Genome Atlas (TCGA), we systematically identified 158 HCC-related DNA methylation markers associated with overall survival (OS). Functional analyses of these markers indicated their roles in regulating gene expression and revealed biological pathways associated with HCC patient survival. We predicted an OS risk score utilizing the DNA methylation panel, referred to as “methRisk”, and validated its predictive power using the Reduced Representation Bisulfite Sequencing data of HCC tumor tissue samples from 29 surgically resected patients. To assess the noninvasive prognostic value of these markers, we collected plasma cfDNA samples from an independent validation cohort of 52 HCC patients, and profiled the methylation patterns of these samples using our newly developed cost-effective cfDNA methylome sequencing assay, cfMethyl-Seq. We utilized random survival forest models combined with 10-fold cross-validation to predict the methRisk using the circulating markers. The cfDNA-based methRisk effectively stratifies patients into clinically relevant risk groups and predicts patient survival with high accuracy (3-year AUC = 0.81, 95% CI: 0.68-0.94). Integrating the cfDNA-based methRisk with existing risk indices like the Barcelona clinic liver cancer (BCLC) staging greatly improves the noninvasive prognostic assessments (3-year AUC = 0.91, 95% CI: 0.80-0.99). In summary, these findings indicate that cfDNA methylation is a promising noninvasive prognostic biomarker for HCC, providing a proof-of-concept for its potential clinical utility. Incorporating cfDNA methylation analysis into the existing noninvasive prognostic framework for HCC patients holds great potential for enhancing the effectiveness of current staging systems and optimizing patient selection for curative therapies like liver transplantation. Citation Format: Ran Hu, Benjamin Tran, Shuo Li, Mary L. Stackpole, Weihua Zeng, Yonggang Zhou, Andrew Melehy, Xianghong Zhou, Wenyuan Li, Vatche Agopian. Cell-free DNA methylation as a noninvasive prognostic biomarker in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB103.

Strengths and limitations of reduced representation bisulfite sequencing (RRBS) in the perspective of DNA methylation analysis in fish: a case-study on rainbow trout spermatozoa.

DNA methylation in CpG dinucleotides is an important epigenetic mark in fish spermatozoa since it has been shown that some sperm methylome features are transmitted to the offspring. Reduced representation bisulfite sequencing (RRBS) is one genome-scale methods developed to assess DNA methylation at CpG sites. It allows the sequencing of a reduced fraction of the genome expected to be enriched in CpGs. The aim of this study is to characterize the extent of the CpG sites that can be identified in the RRBS-reduced sequenced fraction of rainbow trout spermatozoa, in order to evaluate the potential of RRBS for sperm DNA methylation studies. We observed that RRBS did provide a reduced amount of genomic data, the sum of the CpGs analyzed on 12 males spanning 9% of the total genomic CpGs. CpGs were only slightly enriched in the RRBS data (×1.7 times the sequenced nucleotides), the possible causes being linked to trout genome structure and sequenced fragments size. All genomic functional features were represented in our CpG dataset, with a noticeable enrichment in exons but, strikingly, not in promoters. The number of CpGs shared between biological replicates was low, but this proportion reached workable values from six biological replicates (46% of the analyzed cytosines) on. The choices that are to be made regarding fragment size selection and the options during bioinformatic data processing are discussed. In all, RRBS is a relevant first-approach method to scan the CpG DNA methylation status of spermatozoa along rainbow trout genome, although in a very reduced pattern among biological replicates.

Abstract P390: E-value Over P-value And Its Adjustments: Improving Dna Methylation Analysis With An Application To Hypertension Studies

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data to detect differentially methylated regions (DMRs) that may be involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical literature applications, P-values and their adjustments are among the most widely reported statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism related to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we proposed a method to calculate E-value for DMR detection in RRBS data, which is defined as likelihood ratios falling into the null hypothesis over the entire parameter space. We also provided the corresponding R package ‘ metevalue ’ as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-value, we generated various RRBS benchmarking datasets using our simulator ‘ RRBSsim ’ with 8 samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-value not only significantly improved accuracy, AUC and power, over that of P-value or adjusted P-value, but also reduced false discovery rates and Type I errors. To illustrate the utility of E-value, we applied it to identify DMRs in two real RRBS datasets. One was genome-wide DNA methylome and transcriptome of renal T lymphocytes from Dahl Salt Sensitive rats treated with high- or low-salt diets. The other was genome-wide DNA methylation and gene expression data of human arterioles from our clinical trial in which 10 subjects were placed on 2-week low-salt diet (1200-mg sodium/day). Compared to the classical adjusted P-value, the use of E-value detected biologically more relevant DMRs and improved the negative association between DNA methylation and gene expression.

Abnormal DNA methylation analysis of leucine-rich glioma-inactivated 1 antibody encephalitis reveals novel methylation-driven genes related to prognostic and clinical features

BackgroundAberrant DNA methylation occurs commonly during pathogenesis of neuroimmunological diseases and is of clinical value in various encephalitis subtypes. However, knowledge of the impact of DNA methylation changes on pathogenesis of leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis remains limited.MethodsA total of 44 cytokines and 10 immune checkpoint moleculars (ICMs) in the serum of patients with LGI1 encephalitis and healthy donors (HDs) were measured to evaluate the association of them with clinical parameters. Genome-wide DNA methylation profiles were performed in peripheral blood mononuclear cell (PBMC) from LGI1 encephalitis patients and HDs using reduced representation bisulfite sequencing (RRBS) and validated for the methylation status by pyrosequencing. MicroRNA profiles were acquired in serum exosome by small RNA sequencing. Targeted cytokines expression was assessed at the presence or absence of miR-2467-5p in PBMCs and the culture media, and the binding of miR-2467-5p and its targeted genes was validated by luciferase assay.ResultsThere existed significant difference in 22 cytokines/chemokines and 6 ICMs between LGI1 encephalitis patients and HDs. Decreased PDCD1 with increased ICAM1 could predict unfavorable prognosis in one-year follow-up for LGI1 encephalitis patients. Fifteen of cytokines/chemokines and ICMs presented DNA-methylated changes in the promoter and gene body using RRBS in which five were verified as methylation status by pyrosequencing, and the methylation level of CSF3, CCL2, and ICAM1 was conversely associated with their expression in PBMCs. By combining RRBS data with exosome-derived microRNA sequencing, we found that hypomethylated-driven hsa-miR-2467-5p presented elevated expression in serum exosomes and PBMCs in LGI1 encephalitis. Mechanically, miR-2467-5p significantly induced reduced expression of CSF3 and PDCD1 by binding with their 3`UTR while enhanced CCL15 expression, but not significantly correlated with peripheral blood CD19 + B cell proportion of LGI1 encephalitis patients.ConclusionsOur results provided convincing evidence for DNA methylation changes, microRNA profiles in serum exosome for LGI1 encephalitis, and we also identified several novel cytokines related to clinical features in which some represented epigenetic modification of methylated-driven pattern and microRNA modulation. Our study contributed to develop treatment for epigenetic pathogenesis in LGI1 encephalitis.

E-value: a superior alternative to P-value and its adjustments in DNA methylation studies.

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data for the detection of the differentially methylated regions (DMRs) potentially involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical applications, P-values and their adjustments are among the most standard reporting statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism relating to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we propose a method to calculate E-values, as likelihood ratios falling into the null hypothesis over the entire parameter space, for DMR detection in RRBS data. We also provide the R package 'metevalue' as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-values, we generated various RRBS benchmarking datasets using our simulator 'RRBSsim' with eight samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-values not only significantly improved accuracy, area under ROC curve and power, over that of P-values or adjusted P-values, but also reduced false discovery rates and type I errors. In applications using real RRBS data of CRL rats and a clinical trial on low-salt diet, the use of E-values detected biologically more relevant DMRs and also improved the negative association between DNA methylation and gene expression.

Integrative multi-omics analysis depicts the methylome and hydroxymethylome in recurrent bladder cancers and identifies biomarkers for predicting PD-L1 expression

BackgroundUrinary bladder cancer (UBC) is a common malignancy of the urinary tract; however, the mechanism underlying its high recurrence and responses to immunotherapy remains unclear, making clinical outcome predictions difficult. Epigenetic alterations, especially DNA methylation, play important roles in bladder cancer development and are increasingly being investigated as biomarkers for diagnostic or prognostic predictions. However, little is known about hydroxymethylation since previous studies based on bisulfite-sequencing approaches could not differentiate between 5mC and 5hmC signals, resulting in entangled methylation results.MethodsTissue samples of bladder cancer patients who underwent laparoscopic radical cystectomy (LRC), partial cystectomy (PC), or transurethral resection of bladder tumor (TURBT) were collected. We utilized a multi-omics approach to analyze both primary and recurrent bladder cancer samples. By integrating various techniques including RNA sequencing, oxidative reduced-representation bisulfite sequencing (oxRRBS), reduced-representation bisulfite sequencing (RRBS), and whole exome sequencing, a comprehensive analysis of the genome, transcriptome, methylome, and hydroxymethylome landscape of these cancers was possible.ResultsBy whole exome sequencing, we identified driver mutations involved in the development of UBC, including those in FGFR3, KDMTA, and KDMT2C. However, few of these driver mutations were associated with the down-regulation of programmed death-ligand 1 (PD-L1) or recurrence in UBC. By integrating RRBS and oxRRBS data, we identified fatty acid oxidation-related genes significantly enriched in 5hmC-associated transcription alterations in recurrent bladder cancers. We also observed a series of 5mC hypo differentially methylated regions (DMRs) in the gene body of NFATC1, which is highly involved in T-cell immune responses in bladder cancer samples with high expression of PD-L1. Since 5mC and 5hmC alternations are globally anti-correlated, RRBS-seq-based markers that combine the 5mC and 5hmC signals, attenuate cancer-related signals, and therefore, are not optimal as clinical biomarkers.ConclusionsBy multi-omics profiling of UBC samples, we showed that epigenetic alternations are more involved compared to genetic mutations in the PD-L1 regulation and recurrence of UBC. As proof of principle, we demonstrated that the combined measurement of 5mC and 5hmC levels by the bisulfite-based method compromises the prediction accuracy of epigenetic biomarkers.

MethylC-analyzer: a comprehensive downstream pipeline for the analysis of genome-wide DNA methylation

DNA methylation is a crucial epigenetic modification involved in multiple biological processes and diseases. Current approaches for measuring genome-wide DNA methylation via bisulfite sequencing (BS-seq) include whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and enzymatic methyl-seq (EM-seq). The computational analysis tools available for BS-seq data include customized aligners for mapping bisulfite-converted reads and computational pipelines for downstream data analysis. Current post-alignment methylation tools are specialized for the interpretation of CG methylation, which is known to dominate mammalian genomes, however, non-CG methylation (CHG and CHH, where H refers to A, C, or T) is commonly observed in plants and fungi and is closely associated with gene regulation, transposon silencing, and plant development. Thus, we have developed a MethylC-analyzer to analyze and visualize post-alignment WGBS, RRBS, and EM-seq data focusing on CG. The tool is able to also analyze non-CG sites to enhance deciphering genomes of plants and fungi. By processing aligned data and gene location files, MethylC-analyzer generates a genome-wide view of methylation levels and methylation in user-specified genomic regions. The meta-plot, for example, allows the investigation of DNA methylation within specific genomic elements. Moreover, our tool identifies differentially methylated regions (DMRs) and investigates the enrichment of genomic features associated with variable methylation. MethylC-analyzer functionality is not limited to specific genomes, and we demonstrated its performance on both plant and human BS-seq data. MethylC-analyzer is a Python- and R-based program designed to perform comprehensive downstream analyses of methylation data, providing an intuitive analysis platform for scientists unfamiliar with DNA methylation analysis. It is available as either a standalone version for command-line uses or a graphical user interface (GUI) and is publicly accessible at https://github.com/RitataLU/MethylC-analyzer.

Multiomics Integration at Single-Cell Resolution Using Bayesian Networks: A Case Study in Hepatocellular Carcinoma.

Multiomics data integration is one of the leading frontiers of complex disease research and integrative biology. The advances in single-cell sequencing technologies offer yet another crucial dimension in multiomics research. The single-cell studies enable the study and integration of multiomics data simultaneously in the same cell. We report in this study multiomics data integration in single-cell resolution using Bayesian networks (BNs) in a case study of hepatocellular carcinoma (HCC). A BN encodes the conditional dependencies/independencies of variables using a graphical model with an accompanying joint probability. RNA-seq and Reduced Representation Bisulfite Sequencing data were analyzed separately, and copy number variations were estimated by the hidden Markov model method. Several BN models were constructed to reveal omics' causal and associational relationships. These methods were subjected to a validation study using an independent data set. We show the heterogeneity of the multiple cellular layers of HCC at single-cell omics resolution by identifying best-fitted BN models of 295 genes. We also provide novel insights into the multiomics mechanistic relationships in the human lymphocyte antigen class I genes in HCC. To the best of our knowledge, this is the first study to focus on integrating omics data using a machine learning algorithm, BNs, at the single-cell resolution using a case study of HCC.

Reduced Representation Bisulfite Sequencing (RRBS).

Reduced representation bisulfite sequencing (RRBS) enriches CpG-rich genomic regions using the MspI restriction enzyme-which cuts DNA at all CCGG sites, regardless of their DNA methylation status at the CG site-and enables the measurement of DNA methylation levels at 5%~10% of all CpG sites in the mammalian genome. RRBS has been utilized in a large number of studies as a cost-effective method to investigate DNA methylation patterns, mainly at gene promoters and CpG islands. Here, we describe protocols for gel-free preparation of RRBS libraries, quality control, sequencing, and data analysis. Our protocols typically require nine cycles of polymerase chain reaction (PCR) amplification to obtain a sufficient amount of library for sequencing when 100ng of genomic DNA is used as a starting material; moreover, it takes 3days to complete library preparation and quality control procedures for up to eight samples.

Integrated single-cell RNA-seq and DNA methylation reveal the effects of air pollution in patients with recurrent spontaneous abortion

BackgroundMaternal air pollutants exposure is associated with a number of adverse pregnancy outcomes, including recurrent spontaneous abortion (RSA). However, the underlying mechanisms are still unknown. The present study aimed to understand the mechanism of RSA and its relationship with air pollution exposure. We compared data of decidual tissue from individuals with induced abortions and those with RSA by bulk RNA sequencing (RNA-seq), reduced representation bisulfite sequencing (RRBS), and single-cell RNA sequencing (scRNA-seq). Differentially expressed genes (DEGs) were verified using RT-qPCR and pyrosequencing. A logistic regression model was used to investigate the association between air pollutants exposure and RSA.ResultsWe identified 98 DEGs with aberrant methylation by overlapping the RRBS and RNA-seq data. Nineteen immune cell subsets were identified. Compared with normal controls, NK cells and macrophages accounted for different proportions in the decidua of patients with RSA. We observed that the methylation and expression of IGF2BP1 were different between patients with RSA and controls. Furthermore, we observed significant positive associations between maternal air pollutants exposure during the year prior to pregnancy and in early pregnancy and the risk of RSA. Mediation analyses suggested that 24.5% of the effects of air pollution on the risk of RSA were mediated through IGF2BP1 methylation.ConclusionThese findings reveal a comprehensive cellular and molecular mechanism of RSA and suggest that air pollution might cause pregnancy loss by affecting the methylation level of the IGF2BP1 promoter.

Functional Annotation and Cross‐study Comparison of DNA Methylation Regions uniquely associated with 24‐hour Blood Pressure Phenotypes in African Americans

Epigenetics may reflect the interactions between DNA and environment and lifestyle and DNA methylation marks have been implicated in the development and progression of hypertension. In an African American (AA) cohort of hypertensives and normotensives (n = 281), we have recently discovered up to 72 methylation regions (MRs) in whole blood DNA that were significantly associated with 24‐h BP phenotypes (False Discovery Rate (FDR) < 0.05) via reduced representation bisulfite sequencing (RRBS). One‐time clinic BP measurements were not associated with MRs. Upon adjustments for covariates age, sex, and body mass index, 13 MRs remained associated with 24‐h BP phenotypes (FDR < 0.05), each explaining 6.5% ‐ 9.4% of BP variance. Analysis of the chr19 MR in an independent cohort (n=117) using Bisulfite ULtrapLEx Targeted Sequencing (BULLET‐Seq), a new method that we developed, validated its association with 24‐h avg BP phenotypes (FDR < 0.05).In the current study, we functionally annotated the MRs that we identified as uniquely associated with 24‐h BP phenotypes and examined CpG sites previously reported to be associated with BP. Two MRs were near chr1 linkage disequilibrium (LD) regions within genes, PRDM16 and CASZ1, containing BP‐associated sentinel single nucleotide polymorphisms (SNPs). Although no MRs were found to directly overlap with reported BP‐relevant expression or methylation quantitative trait loci (eQTLs or meQTLs), many overlapped with enhancer regions, CTCF‐binding sites, or genes related to hypertension and stroke. To demonstrate consistency with prior studies, we found associations between reported clinic BP‐associated methylation array chip CpGs and the 24‐h BP phenotypes in our study. Of the 16 BP‐associated CpGs from two reports that were detected in both the methylation chip and our RRBS data, several CpGs were significantly associated with 24‐h and clinic BP phenotypes in our cohort (p < 0.05) but not after adjustment for both covariates and FDR. A chr7 CpG (cg06340364) showed significant negative associations with all studied BP phenotypes except nighttime and clinic DBPs (p < 0.05, unadjusted). These investigations highlight the value of regional DNA methylation and the ability to capture the overall effect of environmental and genetic factors that influence BPs over a 24‐h period which have functional epigenomic relevance.

The DNA methylation haplotype (mHap) format and mHapTools.

Bisulfite sequencing (BS-seq) is currently the gold standard for measuring genome-wide DNA methylation profiles at single-nucleotide resolution. Most analyses focus on mean CpG methylation and ignore methylation states on the same DNA fragments [DNA methylation haplotypes (mHaps)]. Here, we propose mHap, a simple DNA mHap format for storing DNA BS-seq data. This format reduces the size of a BAM file by 40- to 140-fold while retaining complete read-level CpG methylation information. It is also compatible with the Tabix tool for fast and random access. We implemented a command-line tool, mHapTools, for converting BAM/SAM files from existing platforms to mHap files as well as post-processing DNA methylation data in mHap format. With this tool, we processed all publicly available human reduced representation bisulfite sequencing data and provided these data as a comprehensive mHap database. https://jiantaoshi.github.io/mHap/index.html. Supplementary data are available at Bioinformatics online.

GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis.

DNA methylation in gene or gene body could influence gene transcription. Moreover, methylation in gene regions along with CpG island regions could modulate the transcription to undetectable gene expression levels. Therefore, it is necessary to investigate the methylation levels within the gene, gene body, CpG island regions, and their overlapped regions and then identify the gene-based differentially methylated regions (GeneDMRs). In this study, R package GeneDMRs aims to facilitate computing gene-based methylation rate using next-generation sequencing-based methylome data. The user-friendly GeneDMRs package is presented to analyze the methylation levels in each gene/promoter/exon/intron/CpG island/CpG island shore or each overlapped region (e.g., gene-CpG island/promoter-CpG island/exon-CpG island/intron-CpG island/gene-CpG island shore/promoter-CpG island shore/exon-CpG island shore/intron-CpG island shore). GeneDMRs can also interpret complex interplays between methylation levels and gene expression differences or similarities across physiological conditions or disease states. We used the public reduced representation bisulfite sequencing data of mouse (GSE62392) for evaluating software and revealing novel biologically significant results to supplement the previous research. In addition, the whole-genome bisulfite sequencing data of cattle (GSE106538) given the much larger size was used for further evaluation.

A DNA methylation age predictor for zebrafish.

Changes in DNA methylation at specific CpG sites have been used to build predictive models to estimate animal age, predominantly in mammals. Little testing for this effect has been conducted in other vertebrate groups, such as bony fish, the largest vertebrate class. The development of most age-predictive models has relied on a genome-wide sequencing method to obtain a DNA methylation level, which makes it costly to deploy as an assay to estimate age in many samples. Here, we have generated a reduced representation bisulfite sequencing data set of caudal fin tissue from a model fish species, zebrafish (Danio rerio), aged from 11.9-60.1 weeks. We identified changes in methylation at specific CpG sites that correlated strongly with increasing age. Using an optimised unique set of 26 CpG sites we developed a multiplex PCR assay that predicts age with an average median absolute error rate of 3.2 weeks in zebrafish between 10.9-78.1 weeks of age. We also demonstrate the use of multiplex PCR as an efficient quantitative approach to measure DNA methylation for the use of age estimation. This study highlights the potential further use of DNA methylation as an age estimation method in non-mammalian vertebrate species.

A common risk variant in the MTHFR gene contributes to age‐related cerebrovascular dysfunction in VCID

AbstractBackgroundCerebrovascular damage occurs during aging, Alzheimer’s disease and Vascular Dementia, commonly referred to as Vascular Contributions to Cognitive Impairment and Dementia (VCID). However, few mouse models currently recapitulate human cerebrovascular deficiencies in disease. The C677T variant in methylenetetrahydrofolate reductase (MTHFRC677T ) is commonly associated with VCID. MTHFR codes for an enzyme involved in production of folate, homocysteine, and methionine metabolism. The MTHFRC677T polymorphism is predicted to result in hyperhomocysteinemia, a condition that causes vascular inflammation and vascular damage. Additionally, methionine deficiency can result in DNA hypomethylation and has been confirmed in MTHFRC677T carriers. Despite the fact that, in some populations, MTHFRC677T is more common than APOE E4, mechanisms by which MTHFR C677T increases risk for VCID are not known.MethodsCRISPR technology was employed to engineer the Mthfr C677T allele into C57BL/6J (B6) mice. Vascular health is being assessed in 6, 12, 18 and 24 months‐old mice using leakage assays (FITC‐dextran and presence of extravascular fibrin) and immunofluorescence (IF) to examine blood brain barrier integrity in the cerebrovascular unit: endothelium (CD31) smooth muscle (aSMA) pericytes (PDGRFb) basement membrane (ColIV) and tight junctions (Claudin‐5, Occludin, ZO‐1). Functional MRI is used to determine in vivo vascular reactivity. Bulk RNA sequencing and Reduced Representation Bisulfite Sequencing (RRBS) were performed on half brains to assess differential gene expression and DNA methylation, respectively.ResultsMice with the Mthfr C677T risk variant have significantly reduced liver enzymatic activity, significantly increased plasma homocysteine levels and decreased methionine levels, recapitulating the human phenotype. Transcriptional profiling of brain tissue revealed genes regulating circadian rhythm, neurogenesis, ER stress and GABA signaling are differentially expressed (DE) in B6.Mthfr C677T compared to B6 samples. RRBS data demonstrated significant genome‐wide DNA hypomethylation in these mice. Hypomethylated and DE genes are expressed in astrocytes, microglia and endothelial cells and are predicted to regulate apoptosis/proliferation, inflammation and glucose transport across the blood brain barrier. Validation by IF is underway.ConclusionWe have created a novel mouse strain to determine the precise mechanisms by which MTHFR C677T increases risk for VCID. This work aims to identify novel therapeutic approaches that preserve cerebrovascular health throughout aging to prevent VCID.

Robust Discovery of Candidate DNA Methylation Cancer Drivers

Robust Discovery of Candidate DNA Methylation Cancer Drivers

Sperm DNA methylation altered by THC and nicotine: Vulnerability of neurodevelopmental genes with bivalent chromatin

Men consume the most nicotine and cannabis products but impacts on sperm epigenetics are poorly characterized. Evidence suggests that preconception exposure to these drugs alters offspring neurodevelopment. Epigenetics may in part facilitate heritability. We therefore compared effects of exposure to tetrahydrocannabinol (THC) and nicotine on DNA methylation in rat sperm at genes involved in neurodevelopment. Reduced representation bisulfite sequencing data from sperm of rats exposed to THC via oral gavage showed that seven neurodevelopmentally active genes were significantly differentially methylated versus controls. Pyrosequencing data revealed majority overlap in differential methylation in sperm from rats exposed to THC via injection as well as those exposed to nicotine. Neurodevelopmental genes including autism candidates are vulnerable to environmental exposures and common features may mediate this vulnerability. We discovered that autism candidate genes are significantly enriched for bivalent chromatin structure, suggesting this configuration may increase vulnerability of genes in sperm to disrupted methylation.

A comprehensive evaluation of computational tools to identify differential methylation regions using RRBS data

DNA methylation plays a vital role in transcription regulation. Reduced representation bisulfite sequencing (RRBS) is becoming common for analyzing genome-wide methylation profiles at the single nucleotide level. A major goal of RRBS studies is to detect differentially methylated regions (DMRs) between different biological conditions. The previous tools to predict DMRs lack consistency. Here, we simulated RRBS datasets with significant attributes of real sequencing data under a wide range of scenarios, and systematically evaluated seven DMR detection tools in terms of type I error rate, precision/recall (PR), and area under ROC curve (AUC) using different methylation levels, sequencing coverage depth, length of DMRs, read length, and sample sizes. DMRfinder, methylSig, and methylKit were our preferred tools for RRBS data analysis, in terms of their AUC and PR curves. Our comparison highlights the different applicability of DMR detection tools and provides information to guide researchers towards the advancement of sequence-based DMR analysis.