Differentially Methylated Regions Research Articles

Dynamics of DNA methylation during osteogenic differentiation of porcine synovial membrane mesenchymal stem cells from two metabolically distinct breeds

ABSTRACT Mesenchymal stem cells (MSCs), with the ability to differentiate into osteoblasts, adipocytes, or chondrocytes, show evidence that the donor cell’s metabolic type influences the osteogenic process. Limited knowledge exists on DNA methylation changes during osteogenic differentiation and the impact of diverse donor genetic backgrounds on MSC differentiation. In this study, synovial membrane mesenchymal stem cells (SMSCs) from two pig breeds (Angeln Saddleback, AS; German Landrace, DL) with distinct metabolic phenotypes were isolated, and the methylation pattern of SMSCs during osteogenic induction was investigated. Results showed that most differentially methylated regions (DMRs) were hypomethylated in osteogenic-induced SMSC group. These DMRs were enriched with genes of different osteogenic signalling pathways at different time points including Wnt, ECM, TGFB and BMP signalling pathways. AS pigs consistently exhibited a higher number of hypermethylated DMRs than DL pigs, particularly during the peak of osteogenesis (day 21). Predicting transcription factor motifs in regions of DMRs linked to osteogenic processes and donor breeds revealed influential motifs, including KLF1, NFATC3, ZNF148, ASCL1, FOXI1, and KLF5. These findings contribute to understanding the pattern of methylation changes promoting osteogenic differentiation, emphasizing the substantial role of donor the metabolic type and epigenetic memory of different donors on SMSC differentiation.

Skeletal muscle feature of different populations in large yellow croaker (Larimichthys crocea): from an epigenetic point of view.

Fish skeletal muscle is composed of well-defined fiber types. In order to identify potential candidate genes affecting muscle growth and development under epigenetic regulation. Bisulfite sequencing was utilized to analyze and compare the muscle DNA methylation profiles of Larimichthys crocea inhabiting different environments. The results revealed that DNA methylation in L. crocea was predominantly CG methylation, with 2,396 differentially methylated regions (DMRs) identified through comparisons among different populations. The largest difference in methylation was observed between the ZhouShan and JinMen wild populations, suggesting that L. crocea may have undergone selection and domestication. Additionally, GO and KEGG enrichment analysis of differentially methylated genes (DMGs) revealed 626 enriched GO functional categories, including various muscle-related genes such as myh10, myf5, myf6, ndufv1, klhl31, map3k4, syn2b, sostdc1a, bag4, and hsp90ab. However, significant enrichment in KEGG pathways was observed only in the JinMen and XiangShan populations of L. crocea. Therefore, this study provides a theoretical foundation for a better understanding of the epigenetic regulation of skeletal muscle growth and development in L. crocea under different environmental conditions.

A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data

Background: Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison. Methods: In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval. Result & Discussion: By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer. Conclusion: Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).

Genome-wide methylation landscape during somatic embryogenesis in Medicago truncatula reveals correlation between Tnt1 retrotransposition and hyperactive methylation regions.

Medicago truncatula is a model legume for fundamental research on legume biology and symbiotic nitrogen fixation. Tnt1, a retrotransposon from tobacco, was used to generate insertion mutants in M. truncatula R108. Approximately 21 000 insertion lines have been generated and publicly available. Tnt1 retro-transposition event occurs during somatic embryogenesis (SE), a pivotal process that triggers massive methylation changes. We studied the SE of M. truncatula R108 using leaf explants and explored the dynamic shifts in the methylation landscape from leaf explants to callus formation and finally embryogenesis. Higher cytosine methylation in all three contexts of CG, CHG, and CHH patterns was observed during SE compared to the controls. Higher methylation patterns were observed in assumed promoter regions (~2-kb upstream regions of transcription start site) of the genes, while lowest was recorded in the untranslated regions. Differentially methylated promoter region analysis showed a higher CHH methylation in embryogenesis tissue samples when compared to CG and CHG methylation. Strong correlation (89.71%) was identified between the differentially methylated regions (DMRs) and the site of Tnt1 insertions in M.truncatula R108 and stronger hypermethylation of genes correlated with higher number of Tnt1 insertions in all contexts of CG, CHG, and CHH methylation. Gene ontology enrichment and KEGG pathway enrichment analysis identified genes and pathways enriched in the signal peptide processing, ATP hydrolysis, RNA polymerase activity, transport, secondary metabolites, and nitrogen metabolism pathways. Combined gene expression analysis and methylation profiling showed an inverse relationship between methylation in the DMRs (regions spanning genes) and the expression of genes. Our results show that a dynamic shift in methylation happens during the SE process in the context of CG, CHH and CHG methylation, and the Tnt1 retrotransposition correlates with the hyperactive methylation regions.

Prenatal exposure to environmental phenols and phthalates and altered patterns of DNA methylation in childhood

IntroductionEpigenetic marks are key biomarkers linking the prenatal environment to health and development. However, DNA methylation associations and persistence of marks for prenatal exposure to multiple Endocrine Disrupting Chemicals (EDCs) in human populations have not been examined in great detail. MethodsWe measured Bisphenol-A (BPA), triclosan, benzophenone-3 (BP3), methyl-paraben, propyl-paraben, and butyl-paraben, as well as 11 phthalate metabolites, in two pregnancy urine samples, at approximately 13 and 26 weeks of gestation in participants of the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) study (N = 309). DNA methylation of cord blood at birth and child peripheral blood at ages 9 and 14 years was measured with 450K and EPIC arrays. Robust linear regression was used to identify differentially methylated probes (DMPs), and comb-p was used to identify differentially methylated regions (DMRs) in association with pregnancy-averaged EDC concentrations. Quantile g-computation was used to assess associations of the whole phenol/phthalate mixture with DMPs and DMRs. ResultsPrenatal BPA exposure was associated with 1 CpG among males and Parabens were associated with 10 CpGs among females at Bonferroni-level significance in cord blood. Other suggestive DMPs (unadjusted p-value < 1 × 10−6) and several DMRs associated with the individual phenols and whole mixture were also identified. A total of 10 CpG sites at least suggestively associated with BPA, Triclosan, BP3, Parabens, and the whole mixture in cord blood were found to persist into adolescence in peripheral blood. ConclusionsWe found sex-specific associations between prenatal phenol exposure and DNA methylation, particularly with BPA in males and Parabens in females. Additionally, we found several DMPs that maintained significant associations with prenatal EDC exposures at age 9 and age 14 years.

Identifying drug candidates for pancreatic ductal adenocarcinoma based on integrative multiomics analysis.

Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis. We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes. Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered. Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.

Differential methylation region detection via an array-adaptive normalized kernel-weighted model

Differential methylation region detection via an array-adaptive normalized kernel-weighted model

Genome-wide Methylation Dynamics and Context-dependent Gene Expression Variability in Differentiating Preadipocytes.

Obesity, characterized by the accumulation of excess fat, is a complex condition resulting from the combination of genetic and epigenetic factors. Recent studies have found correspondence between DNA methylation and cell differentiation, suggesting a role of the former in cell fate determination. There is a lack of comprehensive understanding concerning the underpinnings of preadipocyte differentiation, specifically when cells are undergoing terminal differentiation (TD). To gain insight into dynamic genome-wide methylation, 3T3 L1 preadipocyte cells were differentiated by a hormone cocktail. The genomic DNA was isolated from undifferentiated cells and 4 hours, 2 days postdifferentiated cells, and 15 days TD cells. We employed whole-genome bisulfite sequencing (WGBS) to ascertain global genomic DNA methylation alterations at single base resolution as preadipocyte cells differentiate. The genome-wide distribution of DNA methylation showed similar overall patterns in pre-, post-, and terminally differentiated adipocytes, according to WGBS analysis. DNA methylation decreases at 4 hours after differentiation initiation, followed by methylation gain as cells approach TD. Studies revealed novel differentially methylated regions (DMRs) associated with adipogenesis. DMR analysis suggested that though DNA methylation is global, noticeable changes are observed at specific sites known as "hotspots." Hotspots are genomic regions rich in transcription factor (TF) binding sites and exhibit methylation-dependent TF binding. Subsequent analysis indicated hotspots as part of DMRs. The gene expression profile of key adipogenic genes in differentiating adipocytes is context-dependent, as we found a direct and inverse relationship between promoter DNA methylation and gene expression.

Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma

Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

DNA methylation patterns in patients with asthenospermia and oligoasthenospermia

BackgroundSpermatogenesis is a highly regulated and complex process in which DNA methylation plays a crucial role. This study aimed to explore the differential methylation profiles in sperm DNA between patients with asthenospermia (AS) and healthy controls (HCs), those with oligoasthenospermia (OAS) and HCs, and patients with AS and those with OAS.ResultsSemen samples and clinical data were collected from five patients with AS, five patients with OAS, and six age-matched HCs. Reduced representation bisulfite sequencing (RRBS) was performed to identify differentially methylated regions (DMRs) in sperm cells among the different types of patients and HCs. A total of 6520, 28,019, and 16,432 DMRs were detected between AS and HC, OAS and HC, and AS and OAS groups, respectively. These DMRs were predominantly located within gene bodies and mapped to 2868, 9296, and 9090 genes in the respective groups. Of note, 12, 9, and 8 DMRs in each group were closely associated with spermatogenesis and male infertility. Furthermore, BDNF, SMARCB1, PIK3CA, and DDX27; RBMX and SPATA17; ASZ1, CDH1, and CHDH were identified as strong differentially methylated candidate genes in each group, respectively. Meanwhile, the GO analysis of DMR-associated genes in the AS vs. HC groups revealed that protein binding, cytoplasm, and transcription (DNA-templated) were the most enriched terms in the biological process (BP), cellular component (CC), and molecular function (MF), respectively. Likewise, in both the OAS vs. HC and AS vs. OAS groups, GO analysis revealed protein binding, nucleus, and transcription (DNA-templated) as the most enriched terms in BP, CC, and MF, respectively. Finally, the KEGG analysis of DMR-annotated genes and these genes at promoters suggested that metabolic pathways were the most significantly associated across all three groups.ConclusionsThe current study results revealed distinctive sperm DNA methylation patterns in the AS vs. HC and OAS vs. HC groups, particularly between patients with AS and those with OAS. The identification of key genes associated with spermatogenesis and male infertility in addition to the differentially enriched metabolic pathways may contribute to uncovering the potential pathogenesis in different types of abnormal sperm parameters.

Whole-genome DNA methylation profiling revealed epigenetic regulation of NF-κB signaling pathway involved in response to Vibrio alginolyticus infection in the Pacific oyster, Crassostrea gigas

DNA methylation, an essential epigenetic alteration, is tightly linked to a variety of biological processes, such as immune response. To identify the epigenetic regulatory mechanism in Pacific oyster (Crassostrea gigas), whole-genome bisulfite sequencing (WGBS) was conducted on C. gigas at 0 h, 6 h, and 48 h after infection with Vibrio alginolyticus. At 6 h and 48 h, a total of 11,502 and 14,196 differentially methylated regions (DMRs) were identified (p<0.05, FDR<0.001) compared to 0 h, respectively. Gene ontology (GO) analysis showed that differentially methylated genes (DMGs) were significantly enriched in various biological pathways including immunity, cytoskeleton, epigenetic modification, and metabolic processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that transcription machinery (ko03021) is one of the most important pathways. Integrated transcriptome and methylome analyses allowed the identification of 167 and 379 DMG-related DEGs at 6 h and 48 h, respectively. These genes were significantly enriched in immune-related pathways, including nuclear factor kappa B (NF-κB) signaling pathway (ko04064) and tumor necrosis factor (TNF) signaling pathway (ko04668). Interestingly, it's observed that the NF-κB pathway could be activated jointly by TNF Receptor Associated Factor 2 (TRAF2) and Baculoviral IAP Repeat Containing 3 (BIRC3, the homolog of human BIRC2) which were regulated by DNA methylation in response to the challenge posed by V. alginolyticus infection. Through this study, we provided insightful information about the epigenetic regulation of immunity-related genes in the C. gigas, which will be valuable for the understanding of the innate immune system modulation and defense mechanism against bacterial infection in invertebrates.

Environmental influence on the Atlantic salmon transcriptome and methylome during sea lice infestations

The fish's immune response is affected by different factors, including a wide range of environmental conditions that can also disrupt or promote changes in the host-pathogen interactions. How environmental conditions modulate the salmon genome during parasitism is poorly understood here. This study aimed to explore the environmental influence on the Salmo salar transcriptome and methylome infected with the sea louse Caligus rogercresseyi. Atlantic salmon were experimentally infected with lice at two temperatures (8 and 16 °C) and salinity conditions (32 and 26PSU). Fish tissues were collected from the infected Atlantic salmon for reduced representation bisulfite sequencing (RRBS) and whole transcriptome sequencing (RNA-seq) analysis. The parasitic load was highly divergent in the evaluated environmental conditions, where the lowest lice abundance was observed in fish infected at 8 °C/26PSU. Notably, transcriptome profile differences were statistically associated with the number of alternative splicing events in fish exposed to low temperature/salinity conditions. Furthermore, the temperature significantly affected the methylation level, where high values of differential methylation regions were observed at 16 °C. Also, the association between expression levels of spliced transcripts and their methylation levels was determined, revealing significant correlations with Ferroptosis and TLR KEEG pathways. This study supports the relevance of the environmental conditions during host-parasite interactions in marine ecosystems. The discovery of alternative splicing transcripts associated with DMRs is also discussed as a novel player in fish biology.

Differential methylation of OPRK1 in borderline personality disorder is associated with childhood trauma.

According to a growing body of neurobiological evidence, the core symptoms of borderline personality disorder (BPD) may be linked to an opioidergic imbalance between the hedonic and stimulatory activity of mu opioid receptors (MOR) and the reward system inhibiting effects of kappa opioid receptors (KOR). Childhood trauma (CT), which is etiologically relevant to BPD, is also likely to lead to epigenetic and neurobiological adaptations by extensive activation of the stress and endogenous opioid systems. In this study, we investigated the methylation differences in the promoter of the KOR gene (OPRK1) in subjects with BPD (N = 47) and healthy controls (N = 48). Comparing the average methylation rates of regulatorily relevant subregions (specified regions CGI-1, CGI-2, EH1), we found no differences between BPD and HC. Analyzing individual CG nucleotides (N = 175), we found eight differentially methylated CG sites, all of which were less methylated in BPD, with five showing highly interrelated methylation rates. This differentially methylated region (DMR) was found on the falling slope (5') of the promoter methylation gap, whose effect is enhanced by the DMR hypomethylation in BPD. A dimensional assessment of the correlation between disease severity and DMR methylation rate revealed DMR hypomethylation to be negatively associated with BPD symptom severity (measured by BSL-23). Finally, analyzing the influence of CT on DMR methylation, we found DMR hypomethylation to correlate with physical and emotional neglect in childhood (quantified by CTQ). Thus, the newly identified DMR may be a biomarker of the risks caused by CT, which likely epigenetically contribute to the development of BPD.

Epigenetic and Genomic Hallmarks of PARP-Inhibitor Resistance in Ovarian Cancer Patients.

Patients with advanced-stage epithelial ovarian cancer (EOC) receive treatment with a poly-ADP ribose-polymerase (PARP) inhibitor (PARPi) as maintenance therapy after surgery and chemotherapy. Unfortunately, many patients experience disease progression because of acquired therapy resistance. This study aims to characterize epigenetic and genomic changes in cell-free DNA (cfDNA) associated with PARPi resistance. Blood was taken from 31 EOC patients receiving PARPi therapy before treatment and at disease progression during/after treatment. Resistance was defined as disease progression within 6 months after starting PARPi and was seen in fifteen patients, while sixteen patients responded for 6 to 42 months. Blood cfDNA was evaluated via Modified Fast Aneuploidy Screening Test-Sequencing System (mFast-SeqS to detect aneuploidy, via Methylated DNA Sequencing (MeD-seq) to find differentially methylated regions (DMRs), and via shallow whole-genome and -exome sequencing (shWGS, exome-seq) to define tumor fractions and mutational signatures. Aneuploid cfDNA was undetectable pre-treatment but observed in six patients post-treatment, in five resistant and one responding patient. Post-treatment ichorCNA analyses demonstrated in shWGS and exome-seq higher median tumor fractions in resistant (7% and 9%) than in sensitive patients (7% and 5%). SigMiner analyses detected predominantly mutational signatures linked to mismatch repair and chemotherapy. DeSeq2 analyses of MeD-seq data revealed three methylation signatures and more tumor-specific DMRs in resistant than in responding patients in both pre- and post-treatment samples (274 vs. 30 DMRs, 190 vs. 57 DMRs, Χ2-test p < 0.001). Our genome-wide Next-Generation Sequencing (NGS) analyses in PARPi-resistant patients identified epigenetic differences in blood before treatment, whereas genomic alterations were more frequently observed after progression. The epigenetic differences at baseline are especially interesting for further exploration as putative predictive biomarkers for PARPi resistance.

DNA methylation and stroke prognosis: an epigenome-wide association study

Background and aimsStroke is the leading cause of adult-onset disability. Although clinical factors influence stroke outcome, there is a significant variability among individuals that may be attributed to genetics and epigenetics, including DNA methylation (DNAm). We aimed to study the association between DNAm and stroke prognosis.Methods and resultsTo that aim, we conducted a two-phase study (discovery-replication and meta-analysis) in Caucasian patients with ischemic stroke from two independent centers (BasicMar [discovery, N = 316] and St. Pau [replication, N = 92]). Functional outcome was assessed using the modified Rankin Scale (mRS) at three months after stroke, being poor outcome defined as mRS > 2. DNAm was determined using the 450K and EPIC BeadChips in whole-blood samples collected within the first 24 h. We searched for differentially methylated positions (DMPs) in 370,344 CpGs, and candidates below p-value < 10–5 were subsequently tested in the replication cohort. We then meta-analyzed DMP results from both cohorts and used them to identify differentially methylated regions (DMRs).After doing the epigenome-wide association study, we found 29 DMPs at p-value < 10–5 and one of them was replicated: cg24391982, annotated to thrombospondin-2 (THBS2) gene (p-valuediscovery = 1.54·10–6; p-valuereplication = 9.17·10–4; p-valuemeta-analysis = 6.39·10–9). Besides, four DMRs were identified in patients with poor outcome annotated to zinc finger protein 57 homolog (ZFP57), Arachidonate 12-Lipoxygenase 12S Type (ALOX12), ABI Family Member 3 (ABI3) and Allantoicase (ALLC) genes (p-value < 1·10–9 in all cases).DiscussionPatients with poor outcome showed a DMP at THBS2 and four DMRs annotated to ZFP57, ALOX12, ABI3 and ALLC genes. This suggests an association between stroke outcome and DNAm, which may help identify new stroke recovery mechanisms.

Smoking-Induced DNA Hydroxymethylation Signature Is Less Pronounced than True DNA Methylation: The Population-Based KORA Fit Cohort.

Despite extensive research on 5-methylcytosine (5mC) in relation to smoking, there has been limited exploration into the interaction between smoking and 5-hydroxymethylcytosine (5hmC). In this study, total DNA methylation (5mC+5hmC), true DNA methylation (5mC) and hydroxymethylation (5hmC) levels were profiled utilizing conventional bisulphite (BS) and oxidative bisulphite (oxBS) treatment, measured with the Illumina Infinium Methylation EPIC BeadChip. An epigenome-wide association study (EWAS) of 5mC+5hmC methylation revealed a total of 38,575 differentially methylated positions (DMPs) and 2023 differentially methylated regions (DMRs) associated with current smoking, along with 82 DMPs and 76 DMRs associated with former smoking (FDR-adjusted p < 0.05). Additionally, a focused examination of 5mC identified 33 DMPs linked to current smoking and 1 DMP associated with former smoking (FDR-adjusted p < 0.05). In the 5hmC category, eight DMPs related to current smoking and two DMPs tied to former smoking were identified, each meeting a suggestive threshold (p < 1 × 10-5). The substantial number of recognized DMPs, including 5mC+5hmC (7069/38,575, 2/82), 5mC (0/33, 1/1), and 5hmC (2/8, 0/2), have not been previously reported. Our findings corroborated previously established methylation positions and revealed novel candidates linked to tobacco smoking. Moreover, the identification of hydroxymethylated CpG sites with suggestive links provides avenues for future research.

5 mC modification of steroid hormone biosynthesis-related genes orchestrates feminization of channel catfish induced by high-temperature

To elucidate the mechanism behind channel catfish feminization induced by high temperature, gonad samples were collected from XY pseudo-females and wild-type females and subjected to high-throughput sequencing for Whole-Genome-Bisulfite-Seq (WGBS) and transcriptome sequencing (RNA-Seq). The analysis revealed 50 differentially methylated genes between wild-type females and XY pseudo-females, identified through the analysis of KEGG pathways and GO enrichment in the promoter of the genome and differentially methylated regions (DMRs). Among these genes, multiple differential methylation sites observed within the srd5a2 gene. Repeatability tests confirmed 7 differential methylation sites in the srd5a2 gene in XY pseudo-females compared to normal males, with 1 specific differential methylation site (16608174) distinguishing XY pseudo-females from normal females. Interestingly, the expression of these genes in the transcriptome showed no difference between wild-type females and XY pseudo-females. Our study concluded that methylation of the srd5a2 gene sequence leads to decreased expression, which inhibits testosterone synthesis while promoting the synthesis of 17β-estradiol from testosterone. This underscores the significance of the srd5a2 gene in the sexual differentiation of channel catfish, as indicated by the ipu00140 KEGG pathway analysis.

DMR_Kmeans: Identifying Differentially Methylated Regions Based on k-means Clustering and Read Methylation Haplotype Filtering

Introduction:: Differentially methylated regions (DMRs), including tissue-specific DMRs and disease-specific DMRs, can be used in revealing the mechanisms of gene regulation and screening diseases. Up until now, many methods have been proposed to detect DMRs from bisulfite sequencing data. In these methods, differentially methylated CpG sites and DMRs are usually identified based on statistical tests or distribution models, which neglect the joint methylation statuses provided in each read and result in inaccurate boundaries of DMRs. Methods:: In this paper, a method, named DMR_Kmeans, is proposed to detect DMRs based on kmeans clustering and read methylation haplotype filtering. In DMR_Kmeans, for each CpG site, the k-means algorithm is used to cluster the methylation levels from two groups, and the methylation difference of the CpG is measured based on the different distributions in clusters. Methylation haplotypes of reads are employed to extract the methylation patterns in a candidate region. Finally, DMRs are identified based on the methylation differences and the methylation patterns in candidate regions. Result:: Comparing the performance of DMR_Kmeans and eight DMR detection methods on the whole genome bisulfite sequencing data of six pairs of tissues, the results show that DMR_Kmeans achieves higher Qn and Ql, and more overlapped promoters than other methods when given a certain threshold of methylation difference greater than 0.4, which indicates that the DMRs predicted by DMR_Kmeans with accurate boundaries contain less CpGs with small methylation differences than those by other methods. Conclusion:: Furthermore, it suggests that DMR_Kmeans can provide a DMR set with high quality for downstream analysis since the total length of DMRs predicted by DMR_Kmeans is longer and the total number of CpG sites in the DMRs is greater than those of other methods.

Effects of Developmental Lead and Phthalate Exposures on DNA Methylation in Adult Mouse Blood, Brain, and Liver: A Focus on Genomic Imprinting by Tissue and Sex.

Maternal exposure to environmental chemicals can cause adverse health effects in offspring. Mounting evidence supports that these effects are influenced, at least in part, by epigenetic modifications. It is unknown whether epigenetic changes in surrogate tissues such as the blood are reflective of similar changes in target tissues such as cortex or liver. We examined tissue- and sex-specific changes in DNA methylation (DNAm) associated with human-relevant lead (Pb) and di(2-ethylhexyl) phthalate (DEHP) exposure during perinatal development in cerebral cortex, blood, and liver. Female mice were exposed to human relevant doses of either Pb () via drinking water or DEHP () via chow for 2 weeks prior to mating through offspring weaning. Whole genome bisulfite sequencing (WGBS) was utilized to examine DNAm changes in offspring cortex, blood, and liver at 5 months of age. Metilene and methylSig were used to identify differentially methylated regions (DMRs). Annotatr and ChIP-enrich were used for genomic annotations and gene set enrichment tests of DMRs, respectively. The cortex contained the majority of DMRs associated with Pb (66%) and DEHP (57%) exposure. The cortex also contained the greatest degree of overlap in DMR signatures between sexes ( and 8 DMRs with Pb and DEHP exposure, respectively) and exposure types ( and 39 DMRs in males and females, respectively). In all tissues, detected DMRs were preferentially found at genomic regions associated with gene expression regulation (e.g., CpG islands and shores, 5' UTRs, promoters, and exons). An analysis of GO terms associated with DMR-containing genes identified imprinted genes to be impacted by both Pb and DEHP exposure. Of these, Gnas and Grb10 contained DMRs across tissues, sexes, and exposures, with some signatures replicated between target and surrogate tissues. DMRs were enriched in the imprinting control regions (ICRs) of Gnas and Grb10, and we again observed a replication of DMR signatures between blood and target tissues. Specifically, we observed hypermethylation of the Grb10 ICR in both blood and liver of Pb-exposed male animals. These data provide preliminary evidence that imprinted genes may be viable candidates in the search for epigenetic biomarkers of toxicant exposure in target tissues. Additional research is needed on allele- and developmental stage-specific effects, as well as whether other imprinted genes provide additional examples of this relationship. https://doi.org/10.1289/EHP14074.

A blood-based screening test for colorectal cancer (CRC) using methylation sequencing.

e15089 Background: Diagnosis of CRC is biased towards later stages in India (3.8% Stage I, 16.7% Stage II, 50.7% Stage III, 28.8% Stage IV), and five-year survival at &lt; 40% is one of the lowest in the world. A blood-based non-invasive screening test for CRC using cell-free DNA (cfDNA) methylation sequencing is developed here from the blood of 212 controls and 67 treatment naive CRC patients from 21 sites across India and processed in Strand’s reference lab in Bangalore. Methods: Steps involved cfDNA extraction, NEB Enzymatic Methyl-Seq library preparation, Twist Human Methylome hybridisation capture, 2x150bp sequencing on NovaSeq 6000/X. Methylation + fragmentomic features were calculated for each target region. Samples were split randomly into a leave-in set of 170 controls + 53 cancers (I: 8, II 16, III: 23, IV: 6) and a leave-out set of 42 controls + 14 cancers, (I: 5, II: 4, III: 2, IV: 3) with 20 rounds of 4-fold cross-validation done on the leave-in set (random splits). Feature selection per fold was performed using the KS test without access to ¼ of the leave-in set and to the entire leave-out set. Gradient boosted trees with monotonic constraints reflecting the expected association of the scores with cancer were used to build “explainable” models. Test robustness was assessed using differentially methylated regions (DMRs) from other studies, and by assessing predictability using sample metadata alone. Results: At a 91% specificity level, the ensemble model had a median sensitivity of 62.5% for Stage I (95%CI 38%-86%), 87% for Stage II (95% CI 75%-95%), 87% for Stage III (95% CI 75%-95%) and 83.4% for Stage IV (95% CI 84%-100%) in cross-validation, and 60% for Stage I (95%CI 60%-80%), 100% for Stage II (95% CI 80%-100%), 100% for Stage III (95% CI 100%-100%) and 100% for Stage IV (95% CI 100%-100%) on the leave-out set. At a ~98% specificity level, the model had a median sensitivity of 37.5% for Stage I (95%CI 37%-50%), 69% for Stage II (95% CI 56%-75%), 69% for Stage III (95% CI 60%-84%) and 67% for Stage IV (95% CI 50%-84%) in cross-validation, and 40% for Stage I (95%CI 20%-60%), 75% for Stage II (95% CI 75%-100%), 100% for Stage III (95% CI 100%-100%) and 100% for Stage IV (95% CI 66%-100%) with a slight decrease in specificity to 95.2% on the leave-out set. Using DMRs derived from TCGA data and other publications, yielded a comparable (to our models) cross-validation area under the curve (AUC: 0.93-0.95). Cross-validation performance using only the sample metadata in table 1 and without access to the data was significantly poorer (AUC 0.75-0.81). Conclusions: cfDNA-based methylation profiles are consistent across studies and ethnicities, leading to robust and “explainable” CRC screening predictions. [Table: see text]