Genome-wide DNA methylation dynamics following recent polyploidy in the allotetraploid Tragopogon miscellus (Asteraceae)

Polyploidy is an important evolutionary force, yet epigenetic mechanisms, such as DNA methylation, that regulate genome-wide expression of duplicated genes remain largely unknown. Here, we use Tragopogon (Asteraceae) as a model system to discover patterns and temporal dynamics of DNA methylation in recently formed polyploids. The naturally occurring allotetraploid Tragopogon miscellus formed in the last 95-100 yr from parental diploids Tragopogon dubius and T. pratensis. We profiled the DNA methylomes of these three species using whole-genome bisulfite sequencing. Genome-wide methylation levels in T. miscellus were intermediate between its diploid parents. However, nonadditive CG and CHG methylation occurred in transposable elements (TEs), with variation among TE types. Most differentially methylated regions (DMRs) showed parental legacy, but some novel DMRs were detected in the polyploid. Differentially methylated genes (DMGs) were also identified and characterized. This study provides the first assessment of both overall and locus-specific patterns of DNA methylation in a recent natural allopolyploid and shows that novel methylation variants can be generated rapidly after polyploid formation. Together, these results demonstrate that mechanisms to regulate duplicate gene expression may arise soon after allopolyploid formation and that these mechanisms vary among genes.

The landscape of allelic expression and DNA methylation at the bovine SGCE/PEG10 locus

Genomic imprinting is an epigenetic regulation in mammals in which a small subset of genes is monoallelically expressed dependent on their parental origin. A large imprinted domain, SGCE/PEG10 locus, is located on human chromosome 7q21s and mouse proximal chromosome 6. However, genomic imprinting of bovine SGCE/PEG10 cluster has not been systematically studied. In this study, we investigated allele expression of 14 genes of the SGCE/PEG10 locus in bovine somatic tissues and term placenta using a single nucleotide polymorphism (SNP)-based sequencing method. In addition to SGCE and PEG10, two conserved paternally expressed genes in human and mice, five other genes (TFPI2, GNG11, ASB4, PON1, and PON3) were paternally expressed. Three genes, BET1, COL1A2, and CASD1, exhibited tissue-specific monoallelic expression. CALCR showed monoallelic expression in tissues but biallelic expression in the placenta. Three genes, GNGT1, PPP1R9A, and PON2, showed biallelic expression in cattle. Five differentially methylated regions (DMRs) were found to be associated with the allelic expression of TFPI2, COL1A2, SGCE/PEG10, PON3, and ASB4 genes, respectively. The SGCE/PEG10 DMR is a maternally hypermethylated germline DMR, but TFPI2, COL1A2, PON3, and ASB4 DMRs are secondary DMRs. In summary, we identified five novel bovine imprinted genes (GNG11, BET1, COL1A2, CASD1, and PON1) and four secondary DMRs at the SGCE/PEG10 locus.

Promoter DNA methylation patterns in oral, laryngeal and oropharyngeal anatomical regions are associated with tumor differentiation, nodal involvement and survival

Differentially methylated regions (DMRs) can be used as head and neck squamous cell carcinoma (HNSCC) diagnostic, prognostic and therapeutic targets in precision medicine workflows. DNA from 21 HNSCC and 10 healthy oral tissue samples was hybridized to a genome-wide tiling array to identify DMRs in a discovery cohort. Downstream analyses identified differences in promoter DNA methylation patterns in oral, laryngeal and oropharyngeal anatomical regions associated with tumor differentiation, nodal involvement and survival. Genome-wide DMR analysis showed 2,565 DMRs common to the three subsites. A total of 738 DMRs were unique to laryngeal cancer (n=7), 889 DMRs were unique to oral cavity cancer (n=10) and 363 DMRs were unique to pharyngeal cancer (n=6). Based on the genome-wide analysis and a Gene Ontology analysis, 10 candidate genes were selected to test for prognostic value and association with clinicopathological features. TIMP3 was associated with tumor differentiation in oral cavity cancer (P=0.039), DAPK1 was associated with nodal involvement in pharyngeal cancer (P=0.017) and PAX1 was associated with tumor differentiation in laryngeal cancer (P=0.040). A total of five candidate genes were selected, DAPK1, CDH1, PAX1, CALCA and TIMP3, for a prevalence study in a larger validation cohort: Oral cavity cancer samples (n=42), pharyngeal cancer tissues (n=25) and laryngeal cancer samples (n=52). PAX1 hypermethylation differed across HNSCC anatomic subsites (P=0.029), and was predominantly detected in laryngeal cancer. Kaplan-Meier survival analysis (P=0.043) and Cox regression analysis of overall survival (P=0.001) showed that DAPK1 methylation is associated with better prognosis in HNSCC. The findings of the present study showed that the HNSCC subsites oral cavity, pharynx and larynx display substantial differences in aberrant DNA methylation patterns, which may serve as prognostic biomarkers and therapeutic targets.

High-resolution DNA methylation screening of the major histocompatibility complex in multiple sclerosis

Background

The HLA-DRB1 gene in the major histocompatibility complex (MHC) region in chromosome 6p21 is the strongest genetic factor identified as influencing multiple sclerosis (MS) susceptibility. DNA methylation changes associated with MS have been consistently detected at the MHC region. However, understanding the full scope of epigenetic regulations of the MHC remains incomplete, due in part to the limited coverage of this region by standard whole genome bisulfite sequencing or array-based methods.

Methods

We developed and validated an MHC capture protocol coupled with bisulfite sequencing and conducted a comprehensive analysis of the MHC methylation landscape in blood samples from 147 treatment naïve MS study participants and 129 healthy controls.

Results

We identified 132 differentially methylated region (DMRs) within MHC region associated with disease status. The DMRs overlapped with established MS risk loci. Integration of the MHC methylome with human leukocyte antigen (HLA) genetic data indicate that the methylation changes are significantly associated with HLA genotypes. Using DNA methylation quantitative trait loci (mQTL) mapping and the causal inference test (CIT), we identified 643 cis-mQTL-DMRs paired associations, including 71 DMRs possibly mediating causal relationships between 55 single nucleotide polymorphisms (SNPs) and MS risk.

Results

The results describe MS-associated methylation changes in MHC region and highlight the association between HLA genotypes and methylation changes. Results from the mQTL and CIT analyses provide evidence linking MHC region variations, methylation changes, and disease risk for MS.

Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions

We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data.

Analysis of DNA Methylation Differences during the JIII Formation of Bursaphelenchus xylophilus

DNA methylation is a pivotal process that regulates gene expression and facilitates rapid adaptation to challenging environments. The pinewood nematode (PWN; Bursaphelenchus xylophilus), the causative agent of pine wilt disease, survives at low temperatures through third-stage dispersal juvenile, making it a major pathogen for pines in Asia. To comprehend the impact of DNA methylation on the formation and environmental adaptation of third-stage dispersal juvenile, we conducted whole-genome bisulfite sequencing and transcriptional sequencing on both the third-stage dispersal juvenile and three other propagative juvenile stages of PWN. Our findings revealed that the average methylation rate of cytosine in the samples ranged from 0.89% to 0.99%. Moreover, we observed significant DNA methylation changes in the third-stage dispersal juvenile and the second-stage propagative juvenile of PWN, including differentially methylated cytosine (DMCs, n = 435) and regions (DMRs, n = 72). In the joint analysis of methylation-associated transcription, we observed that 23 genes exhibited overlap between differentially methylated regions and differential gene expression during the formation of the third-stage dispersal juvenile of PWN. Further functional analysis of these genes revealed enrichment in processes related to lipid metabolism and fatty acid synthesis. These findings emphasize the significance of DNA methylation in the development of third-stage dispersal juvenile of PWN, as it regulates transcription to enhance the probability of rapid expansion in PWN.

Reduced non-CpG methylation is a potential epigenetic target after spinal cord injury

DNA methylation is a critical epigenetic regulator in the occurrence and development of diseases and is closely related to various functional responses in relation to spinal cord injury. To investigate the role of DNA methylation in spinal cord injury, we constructed a library with reduced-representation bisulfite sequencing data obtained at various time points (day 0-42) after spinal cord injury in mice. Global DNA methylation levels, specifically non-CpG (CHG and CHH) methylation levels, decreased modestly following spinal cord injury. Stages post-spinal cord injury were classified as early (day 0-3), intermediate (day 7-14), and late (day 28-42) based on similarity and hierarchical clustering of global DNA methylation patterns. The non-CpG methylation level, which included CHG and CHH methylation levels, was markedly reduced despite accounting for a minor proportion of total methylation abundance. At multiple genomic sites, including the 5' untranslated regions, promoter, exon, intron, and 3' untranslated regions, the non-CpG methylation level was markedly decreased following spinal cord injury, whereas the CpG methylation level remained unchanged at these locations. Approximately one-half of the differentially methylated regions were located in intergenic areas; the other differentially methylated regions in both CpG and non-CpG regions were clustered in intron regions, where the DNA methylation level was highest. The function of genes associated with differentially methylated regions in promoter regions was also investigated. From Gene Ontology analysis results, DNA methylation was implicated in a number of essential functional responses to spinal cord injury, including neuronal synaptic connection creation and axon regeneration. Notably, neither CpG methylation nor non-CpG methylation was implicated in the functional response of glial or inflammatory cells. In summary, our work elucidated the dynamic pattern of DNA methylation in the spinal cord following injury and identified reduced non-CpG methylation as an epigenetic target after spinal cord injury in mice.

Methylation is maintained specifically at imprinting control regions but not other DMRs associated with imprinted genes in mice bearing a mutation in the Dnmt1 intrinsically disordered domain

Differential methylation of imprinting control regions in mammals is essential for distinguishing the parental alleles from each other and regulating their expression accordingly. To ensure parent of origin-specific expression of imprinted genes and thereby normal developmental progression, the differentially methylated states that are inherited at fertilization must be stably maintained by DNA methyltransferase 1 throughout subsequent somatic cell division. Further epigenetic modifications, such as the acquisition of secondary regions of differential methylation, are dependent on the methylation status of imprinting control regions and are important for achieving the monoallelic expression of imprinted genes, but little is known about how imprinting control regions direct the acquisition and maintenance of methylation at these secondary sites. Recent analysis has identified mutations that reduce DNA methyltransferase 1 fidelity at some genomic sequences but not at others, suggesting that it may function differently at different loci. We examined the impact of the mutant DNA methyltransferase 1 P allele on methylation at imprinting control regions as well as at secondary differentially methylated regions and non-imprinted sequences. We found that while the P allele results in a major reduction in DNA methylation levels across the mouse genome, methylation is specifically maintained at imprinting control regions but not at their corresponding secondary DMRs. This result suggests that DNA methyltransferase 1 may work differently at imprinting control regions or that there is an alternate mechanism for maintaining methylation at these critical regulatory regions and that maintenance of methylation at secondary DMRs is not solely dependent on the methylation status of the ICR.

Promoter Methylation-Regulated Differentially Expressed Genes in Breast Cancer

Background

Breast cancer is one of the most common malignancies among women. Recent studies revealed that differentially methylated regions (DMRs) are implicated in regulating gene expression. The goal of this research was to determine which genes and pathways are dysregulated in breast cancer when their promoters are methylated in an abnormal way, leading to differential expression. Whole-genome bisulfite sequencing was applied to analyze DMRs for eight peripheral blood samples collected from five Saudi females diagnosed with stages I and II of breast cancer aligned with three normal females. Three of those patients and three normal samples were used to determine differentially expressed genes (DEG) using Illumina platform NovaSeq PE150.

Results

Based on ontology (GO) and KEGG pathways, the analysis indicated that DMGs and DEG are closely related to associated processes, such as ubiquitin-protein transferase activity, ubiquitin-mediated proteolysis, and oxidative phosphorylation. The findings indicated a potentially significant association between global hypomethylation and breast cancer in Saudi patients. Our results revealed 81 differentially promoter-methylated and expressed genes. The most significant differentially methylated and expressed genes found in gene ontology (GO) are pumilio RNA binding family member 1 (PUM1) and zinc finger AN1-type containing 2B (ZFAND2B) also known as (AIRAPL).

Conclusion

The essential outcomes of this study suggested that aberrant hypermethylation at crucial genes that have significant parts in the molecular pathways of breast cancer could be used as a potential prognostic biomarker for breast cancer.

Nursing Informatics and Epigenetics: Methodological Considerations for Big Data Analysis

Nursing informatics requires an understanding of patient-centered data and clinical workflow, and epigenetic research requires an understanding of data analysis. The purpose of this article is to document the methodology that nursing informatics specialists can use to conduct epigenetic research and subsequently strengthen patient-centered care. A pilot study of a secondary methylation data analysis using The Cancer Genome Atlas data from individuals with colon cancer is utilized to illustrate the methodology. The steps for conducting the study using public and free resources are discussed. These steps include finding a data source; downloading and analyzing differentially methylated regions; annotating differentially methylated region, gene ontology and function analysis; and reporting results. A model of epigenetic testing workflow is provided, as is a list of publicly available data and analysis sources that can be used to conduct epigenetic research.

Heritable changes of epialleles in maize can be triggered in the absence of DNA methylation

Trans-chromosomal interactions resulting in changes in DNA methylation during hybridization have been observed in several plant species. However, very little is known about the causes or consequences of these interactions. Here, we compared DNA methylomes of F1 hybrids that are mutant for a small RNA biogenesis gene, Mop1 (mediator of paramutation1) with that of their parents, wild type siblings, and backcrossed progeny in maize. Our data show that hybridization triggers global changes in both trans-chromosomal methylation (TCM) and trans-chromosomal demethylation (TCdM), most of which involved changes in CHH methylation. In more than 60% of these TCM differentially methylated regions (DMRs) in which small RNAs are available, no significant changes in the quantity of small RNAs were observed. Methylation at the CHH TCM DMRs was largely lost in the mop1 mutant, although the effects of this mutant varied depending on the location of the CHH DMRs. Interestingly, an increase in CHH at TCM DMRs was associated with enhanced expression of a subset of highly expressed genes and suppressed expression of a small number of lowly expressed genes. Examination of the methylation levels in backcrossed plants demonstrates that TCM and TCdM can be maintained in the subsequent generation, but that TCdM is more stable than TCM. Surprisingly, although increased CHH methylation in F1 plants did require Mop1, initiation of the changes in the epigenetic state of TCM DMRs did not require a functional copy of this gene, suggesting that initiation of these changes is not dependent on RNA-directed DNA methylation.

Epigenomic signature of major congenital heart defects in newborns with Down syndrome

Background

Congenital heart defects (CHDs) affect approximately half of individuals with Down syndrome (DS) but the molecular reasons for incomplete penetrance are unknown. Previous studies have largely focused on identifying genetic risk factors associated with CHDs in individuals with DS, but comprehensive studies of the contribution of epigenetic marks are lacking. We aimed to identify and characterize DNA methylation differences from newborn dried blood spots (NDBS) of DS individuals with major CHDs compared to DS individuals without CHDs.

Methods

We used the Illumina EPIC array and whole-genome bisulfite sequencing (WGBS) to quantitate DNA methylation for 86 NDBS samples from the California Biobank Program: 1) 45 DS-CHD (27 female, 18 male) and 2) 41 DS non-CHD (27 female, 14 male). We analyzed global CpG methylation and identified differentially methylated regions (DMRs) in DS-CHD vs DS non-CHD comparisons (both sex-combined and sex-stratified) corrected for sex, age of blood collection, and cell type proportions. CHD DMRs were analyzed for enrichment in CpG and genic contexts, chromatin states, and histone modifications by genomic coordinates and for gene ontology enrichment by gene mapping. DMRs were also tested in a replication dataset and compared to methylation levels in DS vs typical development (TD) WGBS NDBS samples.

Results

We found global CpG hypomethylation in DS-CHD males compared to DS non-CHD males, which was attributable to elevated levels of nucleated red blood cells and not seen in females. At a regional level, we identified 58, 341, and 3,938 CHD-associated DMRs in the Sex Combined, Females Only, and Males Only groups, respectively, and used machine learning algorithms to select 19 Males Only loci that could distinguish CHD from non-CHD. DMRs in all comparisons were enriched for gene exons, CpG islands, and bivalent chromatin and mapped to genes enriched for terms related to cardiac and immune functions. Lastly, a greater percentage of CHD-associated DMRs than background regions were differentially methylated in DS vs TD samples.

Conclusions

A sex-specific signature of DNA methylation was detected in NDBS of DS-CHD compared to DS non-CHD individuals. This supports the hypothesis that epigenetics can reflect the variability of phenotypes in DS, particularly CHDs.

Dynamics of the Equine Placental DNA Methylome and Transcriptome from Mid- to Late Gestation

The placenta is a temporary organ that is essential for the survival of the fetus, with a lifelong effect on the health of both the offspring and the dam. The functions of the placenta are controlled by its dynamic gene expression during gestation. In this study, we aimed to investigate the equine placental DNA methylome as one of the fundamental mechanisms that controls the gene expression dynamic. Chorioallantois samples from four (4M), six (6M), and ten (10M) months of gestation were used to map the methylation pattern of the placenta. Globally, methylation levels increased toward the end of gestation. We identified 921 differentially methylated regions (DMRs) between 4M and 6M, 1225 DMRs between 4M and 10M, and 1026 DMRs between 6M and 10M. A total of 817 genes carried DMRs comparing 4M and 6M, 978 comparing 4M and 10M, and 804 comparing 6M and 10M. We compared the transcriptomes between the samples and found 1381 differentially expressed genes (DEGs) when comparing 4M and 6M, 1428 DEGs between 4M and 10M, and 741 DEGs between 6M and 10M. Finally, we overlapped the DEGs and genes carrying DMRs (DMRs-DEGs). Genes exhibiting (a) higher expression, low methylation and (b) low expression, high methylation at different time points were identified. The majority of these DMRs-DEGs were located in introns (48.4%), promoters (25.8%), and exons (17.7%) and were involved in changes in the extracellular matrix; regulation of epithelial cell migration; vascularization; and regulation of minerals, glucose, and metabolites, among other factors. Overall, this is the first report highlighting the dynamics in the equine placenta methylome during normal pregnancy. The findings presented serve as a foundation for future studies on the impact of abnormal methylation on the outcomes of equine pregnancies.

Placental Mesenchymal Dysplasia and Beckwith-Wiedemann Syndrome

Placental mesenchymal dysplasia (PMD) is characterized by placentomegaly, aneurysmally dilated chorionic plate vessels, thrombosis of the dilated vessels, and large grapelike vesicles, and is often mistaken for partial or complete hydatidiform mole with a coexisting normal fetus. Androgenetic/biparental mosaicism (ABM) has been found in many PMD cases. Beckwith-Wiedemann syndrome (BWS) is an imprinting disorder with complex and diverse phenotypes and an increased risk of developing embryonal tumors. There are five major causative alterations: loss of methylation of imprinting control region 2 (KCNQ1OT1:TSS-DMR) (ICR2-LOM), gain of methylation at ICR1 (H19/IGF2:IG-DMR) (ICR1-GOM), paternal uniparental disomy of 11 (pUPD11), loss-of-function variants of the CDKN1C gene, and paternal duplication of 11p15. Additional minor alterations include genetic variants within ICR1, paternal uniparental diploidy/biparental diploidy mosaicism (PUDM, also called ABM), and genetic variants of KCNQ1. ABM (PUDM) is found in both conditions, and approximately 20% of fetuses from PMD cases are BWS and vice versa, suggesting a molecular link. PMD and BWS share some molecular characteristics in some cases, but not in others. These findings raise questions concerning the timing of the occurrence of the molecularly abnormal cells during the postfertilization period and the effects of these abnormalities on cell fates after implantation.

Different Mechanisms Cause Hypomethylation of Both H19 and KCNQ1OT1 Imprinted Differentially Methylated Regions in Two Cases of Silver-Russell Syndrome Spectrum

Silver–Russell syndrome is an imprinting disorder characterised by pre- and post-natal growth retardation and several heterogeneous molecular defects affecting different human genomic loci. In the majority of cases, the molecular defect is the loss of methylation (LOM) of the H19/IGF2 differentially methylated region (DMR, also known as IC1) at the telomeric domain of the 11p15.5 imprinted genes cluster, which causes the altered expression of the growth controlling genes, IGF2 and H19. Very rarely, the LOM also affects the KCNQ1OT1 DMR (also known as IC2) at the centromeric domain, resulting in an SRS phenotype by an unknown mechanism. In this study, we report on two cases with SRS features and a LOM of either IC1 and IC2. In one case, this rare and complex epimutation was secondary to a de novo mosaic in cis maternal duplication, involving the entire telomeric 11p15.5 domain and part of the centromeric domain but lacking CDKN1C. In the second case, neither the no 11p15.5 copy number variant nor the maternal-effect subcortical maternal complex (SCMC) variant were found to be associated with the epimutation, suggesting that it arose as a primary event. Our findings further add to the complexity of the molecular genetics of SRS and indicate how the LOM in both 11p15.5 DMRs may result from different molecular mechanisms.

Dynamic DNA methylation changes reveal tissue-specific gene expression in sugarcane

DNA methylation is an important mechanism for the dynamic regulation of gene expression and silencing of transposons during plant developmental processes. Here, we analyzed genome-wide methylation patterns in sugarcane (Saccharum officinarum) leaves, roots, rinds, and piths at single-base resolution. DNA methylation patterns were similar among the different sugarcane tissues, whereas DNA methylation levels differed. We also found that DNA methylation in different genic regions or sequence contexts plays different roles in gene expression. Differences in methylation among tissues resulted in many differentially methylated regions (DMRs) between tissues, particularly CHH DMRs. Genes overlapping with DMRs tended to be differentially expressed (DEGs) between tissues, and these DMR-associated DEGs were enriched in biological pathways related to tissue function, such as photosynthesis, sucrose synthesis, stress response, transport, and metabolism. Moreover, we observed many DNA methylation valleys (DMVs), which always overlapped with transcription factors (TFs) and sucrose-related genes, such as WRKY, bZIP, WOX, SPS, and FBPase. Collectively, these findings provide significant insights into the complicated interplay between DNA methylation and gene expression and shed light on the epigenetic regulation of sucrose-related genes in sugarcane.

Clomifene and Assisted Reproductive Technology in Humans Are Associated with Sex-Specific Offspring Epigenetic Alterations in Imprinted Control Regions

Children conceived with assisted reproductive technology (ART) have an increased risk of adverse outcomes, including congenital malformations and imprinted gene disorders. In a retrospective North Carolina-based-birth-cohort, we examined the effect of ovulation drugs and ART on CpG methylation in differentially methylated CpGs in known imprint control regions (ICRs). Nine ICRs containing 48 CpGs were assessed for methylation status by pyrosequencing in mixed leukocytes from cord blood. After restricting to non-smoking, college-educated participants who agreed to follow-up, ART-exposed (n = 27), clomifene-only-exposed (n = 22), and non-exposed (n = 516) groups were defined. Associations of clomifene and ART with ICR CpG methylation were assessed with linear regression and stratifying by offspring sex. In males, ART was associated with hypomethylation of the PEG3 ICR [β(95% CI) = -1.46 (-2.81, -0.12)] and hypermethylation of the MEG3 ICR [3.71 (0.01, 7.40)]; clomifene-only was associated with hypomethylation of the NNAT ICR [-5.25 (-10.12, -0.38)]. In female offspring, ART was associated with hypomethylation of the IGF2 ICR [-3.67 (-6.79, -0.55)]. Aberrant methylation of these ICRs has been associated with cardiovascular disease and metabolic and behavioral outcomes in children. The results suggest that the increased risk of adverse outcomes in offspring conceived through ART may be due in part to altered methylation of ICRs. Larger studies utilizing epigenome-wide interrogation are warranted.

Annexin-A1 deficiency attenuates stress-induced tumor growth via fatty acid metabolism in mice: an Integrated multiple omics analysis on the stress- microbiome-metabolite-epigenetic-oncology (SMMEO) axis

Background: High emotional or psychophysical stress levels have been correlated with an increased risk and progression of various diseases. How stress impacts the gut microbiota to influence metabolism and subsequent cancer progression is unclear.

Methods: Feces and serum samples from BALB/c ANXA1^+/+ and ANXA1^-/- mice with or without chronic restraint stress were used for 16S rRNA gene sequencing and GC-MS metabolomics analysis to investigate the effect of stress on microbiome and metabolomics during stress and breast tumorigenesis. Breast tumors samples from stressed and non-stressed mice were used to perform Whole-Genome Bisulfite Sequencing (WGBS) and RNAseq analysis to construct the potential network from candidate hub genes. Finally, machine learning and integrated analysis were used to map the axis from chronic restraint stress to breast cancer development.

Results: We report that chronic stress promotes breast tumor growth via a stress-microbiome-metabolite-epigenetic-oncology (SMMEO) axis. Chronic restraint stress in mice alters the microbiome composition and fatty acids metabolism and induces an epigenetic signature in tumors xenografted after stress. Subsequent machine learning and systemic modeling analyses identified a significant correlation among microbiome composition, metabolites, and differentially methylated regions in stressed tumors. Moreover, silencing Annexin-A1 inhibits the changes in the gut microbiome and fatty acid metabolism after stress as well as basal and stress-induced tumor growth.

Conclusions: These data support a physiological axis linking the microbiome and metabolites to cancer epigenetics and inflammation. The identification of this axis could propel the next phase of experimental discovery in further understanding the underlying molecular mechanism of tumorigenesis caused by physiological stress.

Novel deoxyribonucleic acid methylation perturbations in workers exposed to vinyl chloride

To explore the epigenetic mechanism of deoxyribonucleic acid (DNA) damage induced by vinyl chloride (VC), we studied the micronuclei of peripheral blood lymphocytes in 193 subjects (92 in a VC exposure group employed in a chlorine-alkali plant; 101 in a control group employed in a power plant) and selected three pairs from the subjects (exposed and control) for whole-genome bisulfite sequencing (WGBS). The results showed that the rate of micronucleus formation in the VC exposure group was higher than that of control group (6.05 ± 3.28‰ vs. 2.01 ± 1.79‰). A total of 9534 differentially methylated regions (DMRs) were identified by WGBS, of which 4816 were hypomethylated and 4718 were hypermethylated. The Kyoto encyclopedia of genes and genomes (KEGG) pathway and gene ontology (GO) analyses showed the top three KEGG pathways were cancer , neuroactive ligand-receptor interaction, and axon guidance, and the top three GO-BP pathways enriched were multicellular organismal process, developmental process, and anatomical structure development. In the most enriched DMR pathway (pathways in cancer), we found that BCL2, TJP2, TAOK1, PFKFB3, LIPI, and LIPH were hypermethylated, and the methylation levels of BNIP1 and GRPEL2 were decreased. The methylation of differentially methylated genes (DMGs) mentioned above were verified by methylation-specific PCR (MSP) and agarose gel electrophoresis (AGE) in 50 pairs of subjects, where the coincidence rate was 60-100%. In conclusion, the epigenetic perturbations of specific DMGs (BCL2, TJP2, TAOK1, PFKFB3, LIPI, LIPH, BNIP1, and GRPEL2) may be associated with DNA damage from vinyl chloride exposure.

DNA methylation profile of liver tissue in end-stage cholestatic liver disease

Aims:

In this methylome-wide association study of cholestatic liver diseases (primary sclerosing cholangitis and primary biliary cholangitis), the authors aimed to elucidate changes in methylome and pathway enrichment to identify candidate genes.

Patients & methods:

Reduced representation bisulfite sequencing was performed on liver tissue from 58 patients with primary sclerosing cholangitis (n = 13), primary biliary cholangitis (n = 20), alcoholic liver disease (n = 21) and live liver donors (n = 4). Pathway enrichment and network analysis were used to explore key genes/pathways.

Results:

Both cholestatic liver diseases were characterized by global hypomethylation, with pathway enrichment demonstrating distinct genes and pathways associated with the methylome.

Conclusions:

This novel study demonstrated that differential methylation in cholestatic liver disease was associated with unique pathways, suggesting it may drive disease pathogenesis.

While DNA is the permanent code that defines each living being, the epigenome comprises sequences attached to DNA that can change with the environment. This means that abnormal changes to the epigenome may lead to disease and that finding and treating these abnormalities may in turn help treat disease. In this study of liver tissue from individuals with two rare liver diseases, primary sclerosing cholangitis and primary biliary cholangitis, the authors found that the epigenome of these two conditions is distinct, suggesting that the epigenome is linked to the development of these conditions and may be the key to treating them.

Novel study in rare cholestatic liver diseases (primary sclerosing cholangitis and primary biliary cholangitis) shows unique methylome changes, which may lead to novel treatment opportunities.

Development of a Droplet Digital™ PCR DNA methylation detection and quantification assay of prenatal tobacco exposure

DNA methylation is a labile modification associated with gene expression control and environmental adaptations. High throughput, scalable and quantitative assessments of specific DNA methylation modifications in complex genomic regions for use in large population studies are needed. The performance of Droplet Digital™ PCR (ddPCR™) was investigated for DNA methylation detection against next-generation bisulfite sequencing (NGS) to demonstrate the ability of ddPCR to detect and validate DNA methylation levels and complex patterns among neighboring CpGs in regions associated with prenatal tobacco exposure. While both techniques are reproducible, ddPCR demonstrates a unique advantage for high-throughput DNA methylation analysis in large-scale population studies and provides the specificity to accurately measure DNA methylation of target CpGs in complex regions.

[Epigenetic Regulation Disturbances on Gene Expression in Imprinting Diseases]

Epigenetic regulation is hereditary and non-hereditary changes in the expression of a particular gene without any corresponding structural changes in its nucleotide sequence. Genomic imprinting is an epigenetic mechanism for regulating the expression of homologous genes depending on parental origin, i.e., they are expressed monoallelically in the mammalian diploid cell. Being genetically imprinted, only the maternal or only the paternal genome is unable to ensure normal embryonic development. The most studied epigenetic modification, which plays one of the main roles in the maintenance of imprinting processes, is the specific methylation of cytosine in CpG-dinucleotides. All known imprinted genes contain differential DNA methylation regions on homologous parent chromosomes, which are necessary for their monoallelic expression. However, it is now known that not only DNA methylation, but chromatin remodeling, histone modifications, and non-coding RNAs also ensure the proper functioning of imprinted genes in the human body. Structural and functional disturbances of epigenetic mechanisms lead to imprinting diseases.

Metrics for evaluating differentially methylated region sets predicted from BS-seq data

Investigating differentially methylated regions (DMRs) presented in different tissues or cell types can help to reveal the mechanisms behind the tissue-specific gene expression. The identified tissue-/disease-specific DMRs also can be used as feature markers for spotting the tissues-of-origins of cell-free DNA (cfDNA) in noninvasive diagnosis. In recent years, many methods have been proposed to detect DMRs. However, due to the lack of benchmark DMRs, it is difficult for researchers to choose proper methods and select desirable DMR sets for downstream studies. The application of DMRs, used as feature markers, can be benefited by the longer length of DMRs containing more CpG sites when a threshold is given for the methylation differences of DMRs. According to this, two metrics ($Qn$ and $Ql$), in which the CpG numbers and lengths of DMRs with different methylation differences are weighted differently, are proposed in this paper to evaluate the DMR sets predicted by different methods on BS-seq data. DMR sets predicted by eight methods on both simulated datasets and real BS-seq datasets are evaluated by the proposed metrics, the benchmark-based metrics, and the enrichment analysis of biological data, including genomic features, transcription factors and histones. The rank correlation analysis shows that the $Qn$ and $Ql$ are highly correlated to the benchmark metrics for simulated datasets and the biological data enrichment analysis for real BS-seq data. Therefore, with no need for additional biological data, the proposed metrics can help researchers selecting a more suitable DMR set on a certain BS-seq dataset.

Whole-Genome Methylation Analysis Revealed ART-Specific DNA Methylation Pattern of Neuro- and Immune-System Pathways in Chinese Human Neonates

The DNA methylation of human offspring can change due to the use of assisted reproductive technology (ART). In order to find the differentially methylated regions (DMRs) in ART newborns, cord blood maternal cell contamination and parent DNA methylation background, which will add noise to the real difference, must be removed. We analyzed newborns’ heel blood from six families to identify the DMRs between ART and natural pregnancy newborns, and the genetic model of methylation was explored, meanwhile we analyzed 32 samples of umbilical cord blood of infants born with ART and those of normal pregnancy to confirm which differences are consistent with cord blood data. The DNA methylation level was lower in ART-assisted offspring at the whole genome-wide level. Differentially methylated sites, DMRs, and cord blood differentially expressed genes were enriched in the important pathways of the immune system and nervous system, the genetic patterns of DNA methylation could be changed in the ART group. A total of three imprinted genes and 28 housekeeping genes which were involved in the nervous and immune systems were significant different between the two groups, six of them were detected both in heel blood and cord blood. We concluded that there is an ART-specific DNA methylation pattern involved in neuro- and immune-system pathways of human ART neonates, providing an epigenetic basis for the potential long-term health risks in ART-conceived neonates.

DNA Methylation Level Changes in Transgenic Chinese Cabbage (Brassica rapa ssp. pekinensis) Plants and Their Effects on Corresponding Gene Expression Patterns

Plant tissue culture is an in vitro technique used to manipulate cells, tissues, or organs, and plays an important role in genetic transformation. However, plants cultured in vitro often exhibit unintended genetic and epigenetic variations. Since it is important to secure the stability of endogenous and exogenous gene expressions in transgenic plants, it is preferable to avoid the occurrence of such variations. In this study, we focused on epigenetic variations, exclusively on methylation level changes of DNA, in transgenic Chinese cabbage (Brassica rapa ssp. pekinensis) plants. To detect these methylation level changes of DNA, bisulfite sequencing was performed and the obtained sequences were compared with the ‘CT001’ reference genome. Differentially methylated regions (DMRs) of DNA between the non-transgenic and transgenic lines were detected by bisulfite sequencing, and ten DMRs located in exonic regions were identified. The regions with methylation variations that were inherited and consistently maintained in the next generation lines were selected and validated. We also analyzed the relationship between methylation status and expression levels of transformant-conserved DMR (TCD) genes by quantitative reverse transcription-PCR. These results suggested that the changes in methylation levels of these DMRs might have been related to the plant transformation process, affecting subsequent gene expression. Our findings can be used in fundamental research on methylation variations in transgenic plants and suggest that these variations affect the expression of the associated genes.

Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals

High levels of anti-citrullinated protein antibodies (ACPA) are often observed prior to a diagnosis of rheumatoid arthritis (RA). We undertook a replication study to confirm CpG sites showing evidence of differential methylation in subjects positive vs. negative for ACPA, in a new subset of 112 individuals sampled from the population cohort and biobank CARTaGENE in Quebec, Canada. Targeted custom capture bisulfite sequencing was conducted at approximately 5.3 million CpGs located in regulatory or hypomethylated regions from whole blood; library and protocol improvements had been instituted between the original and this replication study, enabling better coverage and additional identification of differentially methylated regions (DMRs). Using binomial regression models, we identified 19,472 ACPA-associated differentially methylated cytosines (DMCs), of which 430 overlapped with the 1909 DMCs reported by the original study; 814 DMRs of relevance were clustered by grouping adjacent DMCs into regions. Furthermore, we performed an additional integrative analysis by looking at the DMRs that overlap with RA related loci published in the GWAS Catalog, and protein-coding genes associated with these DMRs were enriched in the biological process of cell adhesion and involved in immune-related pathways.

Comprehensive Evaluation of Differential Methylation Analysis Methods for Bisulfite Sequencing Data

Background: With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methylated regions in bisulfite sequencing data, and more are being developed continuously. Results: Here, we focused on a comprehensive evaluation of commonly used differential methylation analysis methods and describe the potential strengths and limitations of each method. We found that there are large differences among methods, and no single method consistently ranked first in all benchmarking. Moreover, smoothing seemed not to improve the performance greatly, and a small number of replicates created more difficulties in the computational analysis of BS-seq data than low sequencing depth. Conclusions: Data analysis and interpretation should be performed with great care, especially when the number of replicates or sequencing depth is limited.

Growth Restriction and Genomic Imprinting-Overlapping Phenotypes Support the Concept of an Imprinting Network

Intrauterine and postnatal growth disturbances are major clinical features of imprinting disorders, a molecularly defined group of congenital syndromes caused by molecular alterations affecting parentally imprinted genes. These genes are expressed monoallelically and in a parent-of-origin manner, and they have an impact on human growth and development. In fact, several genes with an exclusive expression from the paternal allele have been shown to promote foetal growth, whereas maternally expressed genes suppress it. The evolution of this correlation might be explained by the different interests of the maternal and paternal genomes, aiming for the conservation of maternal resources for multiple offspring versus extracting maximal maternal resources. Since not all imprinted genes in higher mammals show the same imprinting pattern in different species, the findings from animal models are not always transferable to human. Therefore, human imprinting disorders might serve as models to understand the complex regulation and interaction of imprinted loci. This knowledge is a prerequisite for the development of precise diagnostic tools and therapeutic strategies for patients affected by imprinting disorders. In this review we will specifically overview the current knowledge on imprinting disorders associated with growth retardation, and its increasing relevance in a personalised medicine direction and the need for a multidisciplinary therapeutic approach.

Comparative epigenome-wide analysis highlights placenta-specific differentially methylated regions

Aim: The placenta undergoes DNA methylation (DNAm) programming that is unique compared with all other fetal tissues. We aim to decipher some of the physiologic roles of the placenta by comparing its DNAm profile with that of another fetal tissue. Materials & methods: We performed a comparative analysis of genome-wide DNAm of 444 placentas paired with cord blood samples collected at birth. Gene ontology term analyses were conducted on the resulting differentially methylated regions. Results: Genomic regions upstream of transcription start sites showing lower DNAm in the placenta were enriched with terms related to miRNA functions and genes encoding G-protein-coupled receptors. Conclusion: These results highlight genomic regions that are differentially methylated in the placenta in contrast to fetal blood.

Epigenetic Variability Among Saffron Crocus (Crocus sativus L.) Accessions Characterized by Different Phenotypes

This work represents the first epigenomic study carried out on saffron crocus. Five accessions of saffron, showing differences in tepal pigmentation, yield of saffron and flowering time, were analyzed at the epigenetic level by applying a methylation-sensitive restriction enzyme-sequencing (MRE-seq) approach. Five accession-specific hypomethylomes plus a reference hypomethylome, generated by combining the sequence data from the single accessions, were obtained. Assembled sequences were annotated against existing online databases. In the absence of the Crocus genome, the rice genome was mainly used as the reference as it is the best annotated genome among monocot plants. Comparison of the hypomethylomes revealed many differentially methylated regions, confirming the high epigenetic variability present among saffron accessions, including sequences encoding for proteins that could be good candidates to explain the accessions' alternative phenotypes. In particular, transcription factors involved in flowering process (MADS-box and TFL) and for the production of pigments (MYB) were detected. Finally, by comparing the generated sequences of the different accessions, a high number of SNPs, likely having arisen as a consequence of the prolonged vegetative propagation, were detected, demonstrating surprisingly high genetic variability. Gene ontology (GO) was performed to map and visualize sequence polymorphisms located within the GOs and to compare their distributions among different accessions. As well as suggesting the possible existence of alternative phenotypes with a genetic basis, a clear difference in polymorphic GO is present among accessions based on their geographic origin, supporting a possible signature of selection in the Indian accession with respect to the Spanish ones.

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.

Hypermethylation of DRD5 Promoter Is a Biomarker Across 12 Cancer Types

Aberrant DNA methylation is thought to be an early event in cancer development. Thus, identification of DNA methylation-based markers may provide valuable evidence in clinical decision-making. In this study, a DNA methylation dataset from 514 normal-tumor pairs is used to explore possible shared differentially methylated regions (DMRs) across 12 cancer types. Results showed that DMR in Dopamine receptor D5 (DRD5) promoter may be serviced as a good candidate biomarker across different cancer types. We further validated the extended DMR (292bp) in DRD5 promoter using SEQUENOM MassARRAY platform. Detection of DRD5 promoter dynamic methylation will allow rapid risk assessment at diagnosis, for suspicious tumor with the tissue biopsies.

DNA methylation analysis in plants: review of computational tools and future perspectives

Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.

Development of a new methylation-based fetal fraction estimation assay using multiplex ddPCR

Background

Non‐invasive prenatal testing (NIPT) for fetal aneuploidies has rapidly been incorporated into clinical practice. Current NGS‐based methods can reliably detect fetal aneuploidies non‐invasively with fetal fraction of at least 4%. Inaccurate fetal fraction assessment can compromise the accuracy of the test as affected samples with low fetal fraction have an increased risk for misdiagnosis. Using a novel set of fetal‐specific differentially methylated regions (DMRs) and methylation sensitive restriction digestion (MSRD), we developed a multiplex ddPCR assay for accurate detection of fetal fraction in maternal plasma.

Methods

We initially performed MSRD followed by methylation DNA immunoprecipitation (MeDIP) and NGS on fetal and non‐pregnant female tissues to identify fetal‐specific DMRs. DMRs with the highest methylation difference between the two tissues were selected for fetal fraction estimation employing MSRD and multiplex ddPCR. Chromosome Y multiplex ddPCR assay (YMM) was used as a reference standard, to develop our fetal fraction estimation model in male pregnancy samples. Additional 123 samples were tested to examine whether the model is sex dependent and/or ploidy dependent.

Results

In all, 93 DMRs were identified of which seven were selected for fetal fraction estimation. Statistical analysis resulted in the final model which included four DMRs (FFMM). High correlation with YMM‐based fetal fractions was observed using 85 male pregnancies (r = 0.86 95% CI: 0.80–0.91). The model was confirmed using an independent set of 53 male pregnancies.

Conclusion

By employing a set of well‐characterized DMRs, we developed a SNP‐, sex‐ and ploidy‐independent methylation‐based multiplex ddPCR assay for accurate fetal fraction estimation.

Divergence Analyses of Sperm DNA Methylomes between Monozygotic Twin AI Bulls

Semen quality is critical for fertility. However, it is easily influenced by environmental factors and can induce subfertility in the next generations. Here, we aimed to assess the impacts of differentially methylated regions and genes on semen quality and offspring fertility. A specific pair of monozygotic (MZ) twin artificial insemination (AI) Holstein bulls with moderately different sperm qualities (Bull1 > Bull2) was used in the study, and each twin bull had produced ~6000 recorded daughters nationwide in China. Using whole genome bisulfite sequencing, we profiled the landscape of the twin bulls' sperm methylomes, and we observed markedly higher sperm methylation levels in Bull1 than in Bull2. Furthermore, we found 528 differentially methylated regions (DMR) between the MZ twin bulls, which spanned or overlapped with 309 differentially methylated genes (DMG). These DMG were particularly associated with embryo development, organ development, reproduction, and the nervous system. Several DMG were also shown to be differentially expressed in the sperm cells. Moreover, the significant differences in DNA methylation on gene INSL3 between the MZ twin bulls were confirmed at three different age points. Our results provided new insights into the impacts of AI bull sperm methylomes on offspring fertility.

Dysregulation of DNA methylation patterns may identify patients with breast cancer resistant to endocrine therapy: A predictive classifier based on differentially methylated regions

Endocrine therapy (ET) is one of a number of targeted therapies for estrogen receptor-positive breast cancer (BRCA); however, resistance to ET has become the primary issue affecting treatment outcome. In the present study, a predictive classifier was created using a DNA methylation dataset to identify patients susceptible to endocrine resistance. DNA methylation and RNA sequencing data, and the clinicopathological features of BRCA, were obtained from The Cancer Genome Atlas. Stringent criteria were set to select and classify patients into two groups, namely those resistant to ET (n=11) and sensitive to ET (n=21) groups. Bump hunting analysis revealed that 502 out of 135,418 genomic regions were differentially methylated between these two groups; these regions were differentially methylated regions (DMRs). The majority of the CpG sites contained in the DMRs mapped to the promoter region. Functional enrichment analyses indicated that a total of 562 specific genes encompassing these DMRs were primarily associated with 'biological progress of organ morphogenesis and development' and 'cell-cell adhesion' gene ontologies. Logistic regression and Pearson's correlation analysis were conducted to construct a predictive classifier for distinguishing patients resistant or sensitive to ET. The highest areas under the curve and relatively low Akaike information criterion values were associated with a total of 60 DMRs; a risk score retained from this classifier was revealed to be an unfavorable predictor of survival in two additional independent datasets. Furthermore, the majority of genes (55/63) exhibited a statistically significant association between DNA methylation and mRNA expression (P<0.05). The association between the mRNA expression of a number of genes (namely calcium release activated channel regulator 2A, Schlafen family member 12, chromosome 3 open reading frame 18, zinc finger protein 880, dual oxidase 1, major histocompatibility complex, class II, DP β1, C-terminal binding protein 1, ALG13 UDP-N-acetylglucosaminyltransferase subunit and RAS protein activator like 2) and the prognosis of patients with estrogen receptor-positive BRCA and ET resistance was determined using Kaplan-Meier Plotter. In summary, the predictive classifier proposed in the present study may aid the identification of patients sensitive or resistant to ET, and numerous genes maybe potential therapeutic targets to delay the development of resistance to ET.

Epigenetic association analysis of clinical sub-phenotypes in patients with polycystic ovary syndrome (PCOS)

Polycystic ovarian syndrome (PCOS) is a complex disorder affecting up to 15-20% of reproductive women. PCOS has recently been investigated using genome-wide association studies revealing important mutations and DNA methylation sites associated with the syndrome. As a clinically highly heterogenous condition, studying the molecular basis of the differential manifestation of PCOS is both meaningful concerning individualized management and important for understanding the biology of PCOS. Using genome-wide DNA methylation data collected from PCOS patients, we performed a powerful region-based analysis to detect differentially methylated regions (DMR) by correlating DNA methylation pattern in a genomic region with the level of each PCOS clinical sub-phenotype. We identified seven significant DMRs on chromosome 19 (12877188-12876846 bp) and chromosome 6 (MHC region) associated with prolactin level, as well as chromosomes 11 and 2 associated with metabolic attributes. Functional annotation linked significant DNA methylation patterns to functional genes (HOOK2, BDNFl, HLA-G, HLA-H, HLA-J, RNF39, etc) of metabolic disorders and immunity or novel associations to serve as targets for validation and replication.

Whole genome bisulfite sequencing of Down syndrome brain reveals regional DNA hypermethylation and novel disorder insights

Down Syndrome (DS) is the most common genetic cause of intellectual disability, in which an extra copy of human chromosome 21 (HSA21) affects regional DNA methylation profiles across the genome. Although DNA methylation has been previously examined at select regulatory regions across the genome in a variety of DS tissues and cells, differentially methylated regions (DMRs) have yet to be examined in an unbiased sequencing-based approach. Here, we present the first analysis of DMRs from whole genome bisulfite sequencing (WGBS) data of human DS and matched control brain, specifically frontal cortex. While no global differences in DNA methylation were observed, we identified 3,152 DS-DMRs across the entire genome, the majority of which were hypermethylated in DS. DS-DMRs were significantly enriched at CpG islands and de-enriched at specific gene body and regulatory regions. Functionally, the hypermethylated DS-DMRs were enriched for one-carbon metabolism, membrane transport, and glutamatergic synaptic signalling, while the hypomethylated DMRs were enriched for proline isomerization, glial immune response, and apoptosis. Furthermore, in a cross-tissue comparison to previous studies of DNA methylation from diverse DS tissues and reference epigenomes, hypermethylated DS-DMRs showed a strong cross-tissue concordance, while a more tissue-specific pattern was observed for the hypomethylated DS-DMRs. Overall, this approach highlights that low-coverage WGBS of clinical samples can identify epigenetic alterations to known biological pathways, which are potentially relevant to therapeutic treatments and include metabolic pathways. These results also provide new insights into the genome-wide effects of genetic alterations on DNA methylation profiles indicative of altered neurodevelopment and brain function.

A non-randomized procedure for large-scale heterogeneous multiple discrete testing based on randomized tests

In the analysis of next-generation sequencing technology, massive discrete data are generated from short read counts with varying biological coverage. Conducting conditional hypothesis testing such as Fisher's Exact Test at every genomic region of interest thus leads to a heterogeneous multiple discrete testing problem. However, most existing multiple testing procedures for controlling the false discovery rate (FDR) assume that test statistics are continuous and become conservative for discrete tests. To overcome the conservativeness, in this article, we propose a novel multiple testing procedure for better FDR control on heterogeneous discrete tests. Our procedure makes decisions based on the marginal critical function (MCF) of randomized tests, which enables achieving a powerful and non-randomized multiple testing procedure. We provide upper bounds of the positive FDR (pFDR) and the positive false non-discovery rate (pFNR) corresponding to our procedure. We also prove that the set of detections made by our method contains every detection made by a naive application of the widely-used q-value method. We further demonstrate the improvement of our method over other existing multiple testing procedures by simulations and a real example of differentially methylated region (DMR) detection using whole-genome bisulfite sequencing (WGBS) data.

The Cord Blood Insulin and Mitochondrial DNA Content Related Methylome

Mitochondrial dysfunction seems to play a key role in the etiology of insulin resistance. At birth, a link has already been established between mitochondrial DNA (mtDNA) content and insulin levels in cord blood. In this study, we explore shared epigenetic mechanisms of the association between mtDNA content and insulin levels, supporting the developmental origins of this link. First, the association between cord blood insulin and mtDNA content in 882 newborns of the ENVIRONAGE birth cohort was assessed. Cord blood mtDNA content was established via qPCR, while cord blood levels of insulin were determined using electrochemiluminescence immunoassays. Then the cord blood DNA methylome and transcriptome were determined in 179 newborns, using the human 450K methylation Illumina and Agilent Whole Human Genome 8 × 60 K microarrays, respectively. Subsequently, we performed an epigenome-wide association study (EWAS) adjusted for different maternal and neonatal variables. Afterward, we focused on the 20 strongest associations based on p-values to assign transcriptomic correlates and allocate corresponding pathways employing the R packages ReactomePA and RDAVIDWebService. On the regional level, we examined differential methylation using the DMRcate and Bumphunter packages in R. Cord blood mtDNA content and insulin were significantly correlated (r = 0.074, p = 0.028), still showing a trend after additional adjustment for maternal and neonatal variables (p = 0.062). We found an overlap of 33 pathways which were in common between the association with cord blood mtDNA content and insulin levels, including pathways of neurodevelopment, histone modification, cytochromes P450 (CYP)-metabolism, and biological aging. We further identified a DMR annotated to Repulsive Guidance Molecule BMP Co-Receptor A (RGMA) linked to cord blood insulin as well as mtDNA content. Metabolic variation in early life represented by neonatal insulin levels and mtDNA content might reflect or accommodate alterations in neurodevelopment, histone modification, CYP-metabolism, and aging, indicating etiological origins in epigenetic programming. Variation in metabolic hormones at birth, reflected by molecular changes, might via these alterations predispose children to metabolic diseases later in life. The results of this study may provide important markers for following targeted studies.

A Bayesian hidden Markov model for detecting differentially methylated regions

Alterations in DNA methylation have been linked to the development and progression of many diseases. The bisulfite sequencing technique presents methylation profiles at base resolution. Count data on methylated and unmethylated reads provide information on the methylation level at each CpG site. As more bisulfite sequencing data become available, these data are increasingly needed to infer methylation aberrations in diseases. Automated and powerful algorithms also need to be developed to accurately identify differentially methylated regions between treatment groups. This study adopts a Bayesian approach using the hidden Markov model to account for inherent dependence in read count data. Given the expense of sequencing experiments, few replicates are available for each treatment group. A Bayesian approach that borrows information across an entire chromosome improves the reliability of statistical inferences. The proposed hidden Markov model considers location dependence among genomic loci by incorporating correlation structures as a function of genomic distance. An iterative algorithm based on expectation-maximization is designed for parameter estimation. Methylation states are inferred by identifying the optimal sequence of latent states from observations. Real datasets and simulation studies that mimic the real datasets are used to illustrate the reliability and success of the proposed method.

Discovery and development of differentially methylated regions in human papillomavirus-related oropharyngeal squamous cell carcinoma

Human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (OPSCC) exhibits a different composition of epigenetic alterations. In this study, we identified differentially methylated regions (DMRs) with potential utility in screening for HPV-positive OPSCC. Genome wide DNA methylation was measured using methyl-CpG binding domain protein-enriched genome sequencing (MBD-seq) in 50 HPV-positive OPSCC tissues and 25 normal tissues. Fifty-one DMRs were defined with maximal methylation specificity to cancer samples. The Cancer Genome Atlas (TCGA) methylation array data was used to evaluate the performance of the proposed candidates. Supervised hierarchical clustering of 51 DMRs found that HPV-positive OPSCC had significantly higher DNA methylation levels compared to normal samples, and non-HPV-related head and neck squamous cell carcinoma (HNSCC). The methylation levels of all top 20 DNA methylation biomarkers in HPV-positive OPSCC were significantly higher than those in normal samples. Further confirmation using quantitative methylation specific PCR (QMSP) in an independent set of 24 HPV-related OPSCCs and 22 controls showed that 16 of the 20 candidates had significant higher methylation levels in HPV-positive OPSCC samples compared with controls. One candidate, OR6S1, had a sensitivity of 100%, while 17 candidates (KCNA3, EMBP1, CCDC181, DPP4, ITGA4, BEND4, ELMO1, SFMBT2, C1QL3, MIR129-2, NID2, HOXB4, ZNF439, ZNF93, VSTM2B, ZNF137P and ZNF773) had specificities of 100%. The prediction accuracy of the 20 candidates rang from 56.2% to 99.8% by receiver operating characteristic analysis. We have defined 20 highly specific DMRs in HPV-related OPSCC, which can potentially be applied to molecular-based detection tests and improve disease management.

Assessing genome-wide significance for the detection of differentially methylated regions

DNA methylation plays an important role in human health and disease, and methods for the identification of differently methylated regions are of increasing interest. There is currently a lack of statistical methods which properly address multiple testing, i.e. control genome-wide significance for differentially methylated regions. We introduce a scan statistic (DMRScan), which overcomes these limitations. We benchmark DMRScan against two well established methods (bumphunter, DMRcate), using a simulation study based on real methylation data. An implementation of DMRScan is available from Bioconductor. Our method has higher power than alternative methods across different simulation scenarios, particularly for small effect sizes. DMRScan exhibits greater flexibility in statistical modeling and can be used with more complex designs than current methods. DMRScan is the first dynamic approach which properly addresses the multiple-testing challenges for the identification of differently methylated regions. DMRScan outperformed alternative methods in terms of power, while keeping the false discovery rate controlled.

Winter-dormant shoot apical meristem in poplar trees shows environmental epigenetic memory

Trees have a long lifespan and must continually adapt to environmental pressures, notably in the context of climate change. Epigenetic mechanisms are doubtless involved in phenotypic plasticity and in stress memory; however, little evidence of the role of epigenetic processes is available for trees growing in fields. Here, we analyzed the possible involvement of epigenetic mechanisms in the winter-dormant shoot apical meristem of Populus × euramericana clones in memory of the growing conditions faced during the vegetative period. We aimed to estimate the range of genetic and environmentally induced variations in global DNA methylation and to evaluate their correlation with changes in biomass production, identify differentially methylated regions (DMRs), and characterize common DMRs between experiments. We showed that the variations in global DNA methylation between conditions were genotype dependent and correlated with biomass production capacity. Microarray chip analysis allowed detection of DMRs 6 months after the stressful summer period. The 161 DMRs identified as common to three independent experiments most notably targeted abiotic stress and developmental response genes. Results are consistent with a winter-dormant shoot apical meristem epigenetic memory of stressful environmental conditions that occurred during the preceding summer period. This memory may facilitate tree acclimation.

Emerging Technologies for the Diagnosis of Perihilar Cholangiocarcinoma

The diagnosis of malignant biliary strictures remains problematic, especially in the perihilar region and in primary sclerosing cholangitis (PSC). Conventional cytology obtained during endoscopic retrograde cholangiography (ERC)-guided brushings of biliary strictures is suboptimal due to limited sensitivity, albeit it remains the gold standard with a high specificity. Emerging technologies are being developed and validated to address this pressing unmet patient need. Such technologies include enhanced visualization of the biliary tree by cholangioscopy, intraductal ultrasound, and confocal laser endomicroscopy. Conventional cytology can be aided by employing complementary and advanced cytologic techniques such as fluorescent in situ hybridization (FISH), and this technique should be widely adapted. Interrogation of bile and serum by examining extracellular vesicle number and cargo, and exploiting next-generation sequencing and proteomic technologies, is also being explored. Examination of circulating cell-free deoxyribonucleic acid (cfDNA) for differentially methylated regions is a promising test which is being rigorously validated. The special expertise required for these analyses has to date hampered their validation and adaptation. Herein, we will review these emerging technologies to inform the reader of the progress made and encourage further studies, as well as adaptation of validated approaches.

Genomic DNA Methylation-Derived Algorithm Enables Accurate Detection of Malignant Prostate Tissues

Introduction

The current methodology involving diagnosis of prostate cancer (PCa) relies on the pathology examination of prostate needle biopsies, a method with high false negative rates partly due to temporospatial, molecular, and morphological heterogeneity of prostate adenocarcinoma. It is postulated that molecular markers have a potential to assign diagnosis to a considerable portion of undetected prostate tumors. This study examines the genome-wide DNA methylation changes in PCa in search of genomic markers for the development of a diagnostic algorithm for PCa screening.

Methods

Archival PCa and normal tissues were assessed using genomic DNA methylation arrays. Differentially methylated sites and regions (DMRs) were used for functional assessment, gene-set enrichment and protein interaction analyses, and examination of transcription factor-binding patterns. Raw signal intensity data were used for identification of recurrent copy number variations (CNVs). Non-redundant fully differentiating cytosine-phosphate-guanine sites (CpGs), which did not overlap CNV segments, were used in an L1 regularized logistic regression model (LASSO) to train a classification algorithm. Validation of this algorithm was performed using a large external cohort of benign and tumor prostate arrays.

Results

Approximately 6,000 probes and 600 genomic regions showed significant DNA methylation changes, primarily involving hypermethylation. Gene-set enrichment and protein interaction analyses found an overrepresentation of genes related to cell communications, neurogenesis, and proliferation. Motif enrichment analysis demonstrated enrichment of tumor suppressor-binding sites nearby DMRs. Several of these regions were also found to contain copy number amplifications. Using four non-redundant fully differentiating CpGs, we trained a classification model with 100% accuracy in discriminating tumors from benign samples. Validation of this algorithm using an external cohort of 234 tumors and 92 benign samples yielded 96% sensitivity and 98% specificity. The model was found to be highly sensitive to detect metastatic lesions in bone, lymph node, and soft tissue, while being specific enough to differentiate the benign hyperplasia of prostate from tumor.

Conclusion

A considerable component of PCa DNA methylation profile represent driver events potentially established/maintained by disruption of tumor suppressor activity. As few as four CpGs from this profile can be used for screening of PCa.

Insights into Epigenome Evolution from Animal and Plant Methylomes

Evolutionary studies of DNA methylation offer insights into the mechanisms governing the variation of genomic DNA methylation across different species. Comparisons of gross levels of DNA methylation between distantly related species indicate that the size of the genome and the level of genomic DNA methylation are positively correlated. In plant genomes, this can be reliably explained by the genomic contents of repetitive sequences. In animal genomes, the role of repetitive sequences on genomic DNA methylation is less clear. On a shorter timescale, population-level comparisons demonstrate that genetic variation can explain the observed variability of DNA methylation to some degree. The amount of DNA methylation variation that has been attributed to genetic variation in the human population studies so far is substantially lower than that from Arabidopsis population studies, but this disparity might reflect the differences in the computational and experimental techniques used. The effect of genetic variation on DNA methylation has been directly examined in mammalian systems, revealing several causative factors that govern DNA methylation. On the other hand, studies from Arabidopsis have furthered our understanding of spontaneous mutations of DNA methylation, termed “epimutations.” Arabidopsis has an extremely high rate of spontaneous epimutations, which may play a major role in shaping the global DNA methylation landscape in this genome. Key missing information includes the frequencies of spontaneous epimutations in other lineages, in particular animal genomes, and how population-level variation of DNA methylation leads to species-level differences.

Identification of Differentially Methylated Sites with Weak Methylation Effects

Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments. Most statistical methods for DMC detection do not consider the dependency of methylation patterns across the genome, thus possibly inflating type I error. Furthermore, small sample sizes and weak methylation effects among different phenotype categories make it difficult for these statistical methods to accurately detect DMCs. To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs. To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit. Analyses of simulated data that replicated the effects of the herbicide glyphosate on DNA methylation in Arabidopsis thaliana show that WFMM results in higher sensitivity and specificity in detecting DMCs compared to methylKit, especially when the methylation differences among phenotype groups are small. Moreover, the performance of WFMM is robust with respect to small sample sizes, making it particularly attractive considering the current high costs of bisulfite sequencing. Analysis of empirical Arabidopsis thaliana data under varying glyphosate dosages, and the analysis of monozygotic (MZ) twins who have different pain sensitivities-both datasets have weak methylation effects of <1%-show that WFMM can identify more relevant DMCs related to the phenotype of interest than methylKit. Differentially methylated regions (DMRs) are genomic regions with different DNA methylation status across biological samples. DMRs and DMCs are essentially the same concepts, with the only difference being how methylation information across the genome is summarized. If methylation levels are determined by grouping neighboring cytosine sites, then they are DMRs; if methylation levels are calculated based on single cytosines, they are DMCs.

Changes in the epigenome and transcriptome of the poplar shoot apical meristem in response to water availability affect preferentially hormone pathways

The adaptive capacity of long-lived organisms such as trees to the predicted climate changes, including severe and successive drought episodes, will depend on the presence of genetic diversity and phenotypic plasticity. Here, the involvement of epigenetic mechanisms in phenotypic plasticity toward soil water availability was examined in Populus×euramericana. This work aimed at characterizing (i) the transcriptome plasticity, (ii) the genome-wide plasticity of DNA methylation, and (iii) the function of genes affected by a drought-rewatering cycle in the shoot apical meristem. Using microarray chips, differentially expressed genes (DEGs) and differentially methylated regions (DMRs) were identified for each water regime. The rewatering condition was associated with the highest variations of both gene expression and DNA methylation. Changes in methylation were observed particularly in the body of expressed genes and to a lesser extent in transposable elements. Together, DEGs and DMRs were significantly enriched in genes related to phytohormone metabolism or signaling pathways. Altogether, shoot apical meristem responses to changes in water availability involved coordinated variations in DNA methylation, as well as in gene expression, with a specific targeting of genes involved in hormone pathways, a factor that may enable phenotypic plasticity.

Comparative Methylome Analyses Identify Epigenetic Regulatory Loci of Human Brain Evolution

How do epigenetic modifications change across species and how do these modifications affect evolution? These are fundamental questions at the forefront of our evolutionary epigenomic understanding. Our previous work investigated human and chimpanzee brain methylomes, but it was limited by the lack of outgroup data which is critical for comparative (epi)genomic studies. Here, we compared whole genome DNA methylation maps from brains of humans, chimpanzees and also rhesus macaques (outgroup) to elucidate DNA methylation changes during human brain evolution. Moreover, we validated that our approach is highly robust by further examining 38 human-specific DMRs using targeted deep genomic and bisulfite sequencing in an independent panel of 37 individuals from five primate species. Our unbiased genome-scan identified human brain differentially methylated regions (DMRs), irrespective of their associations with annotated genes. Remarkably, over half of the newly identified DMRs locate in intergenic regions or gene bodies. Nevertheless, their regulatory potential is on par with those of promoter DMRs. An intriguing observation is that DMRs are enriched in active chromatin loops, suggesting human-specific evolutionary remodeling at a higher-order chromatin structure. These findings indicate that there is substantial reprogramming of epigenomic landscapes during human brain evolution involving noncoding regions.