Methylation Sites Research Articles

Deep learning can predict subgenome dominance in ancient but not in neo/synthetic polyploidized genomes.

Deep learning offers new approaches to investigate the mechanisms underlying complex biological phenomena, such as subgenome dominance. Subgenome dominance refers to the dominant expression and/or biased fractionation of genes in one subgenome of allopolyploids, which has shaped the evolution of a large group of plants. However, the underlying cause of subgenome dominance remains elusive. Here, we adopt deep learning to construct two convolutional neural network (CNN) models, binary expression model (BEM) and homoeolog contrast model (HCM), to investigate the mechanism underlying subgenome dominance using DNA sequence and methylation sites. We apply these CNN models to analyze three representative polyploidization systems, Brassica, Gossypium, and Cucurbitaceae, each with available ancient and neo/synthetic polyploidized genomes. The BEM shows that DNA sequence of the promoter region can accurately predict whether a gene is expressed or not. More importantly, the HCM shows that the DNA sequence of the promoter region predicts dominant expression status between homoeologous gene pairs retained from ancient polyploidizations, thus predicting subgenome dominance associated with these events. However, HCM fails to predict gene expression dominance between new homoeologous gene pairs arising from the neo/synthetic polyploidizations. These results are consistent across the three plant polyploidization systems, indicating broad applicability of our models. Furthermore, the two models based on methylation sites produce similar results. These results show that subgenome dominance is associated with long-term sequence differentiation between the promoters of homoeologs, suggesting that subgenome expression dominance precedes and is the driving force or even the determining factor for sequence divergence between subgenomes following polyploidization.

Genetic effects on the skin methylome in healthy older twins

Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.

Determination of global DNA methylation level by methylation-sensitive comet assay in patients with urinary bladder cancer.

DNA methylation is an important mechanism in the regulation of gene expression and maintenance of genomic integrity. Aberrant DNA methylation is an early event in carcinogenesis. DNA methyltransferase inhibitors are used to restore aberrant DNA methylation and inhibit tumor growth. Evaluation of DNA methylation level is important for an effective anti-cancer therapy. In the present study, the determination of global DNA methylation levels in patients with urinary bladder cancer was proposed. The methylation-sensitive comet assay determined the global DNA methylation level at the level of single cells. McrBC enzyme, a methylation-sensitive restriction endonuclease, was used for enzymatic digestion to generate additional breaks at methylated sites. % DNA methylation level was significantly higher in patients with bladder cancer compared to the control group. The clinical performance of % DNA methylation analysis by methylation-sensitive comet assay was evaluated by ROC curve. Using the cutoff value of 6.5% DNA methylation, 92% sensitivity, and 42% specificity were obtained. In conclusion, global DNA methylation measured by methylation-sensitive comet assay may be a promising noninvasive biomarker that reduces interventional tests required in the diagnosis and follow-up of urinary bladder cancer.

DNA Methylation of PXDN Is Associated with Early-Life Adversity in Adult Mental Disorders.

Early-life adversity (ELA) is characterized by exposure to traumatic events during early periods of life, particularly involving emotional, sexual and/or physical adversities during childhood. Mental disorders are strongly influenced by environmental and lifestyle-related risk factors including ELA. However, the molecular link between ELA and the risk of an adult mental disorder is still not fully understood. Evidence is emerging that long-lasting changes in the epigenetic processes regulating gene expression, such as DNA methylation, play an important role in the biological mechanisms linking ELA and mental disorders. Based on a recent study, we analyzed the DNA methylation of a specific CpG site within the gene PXDN-cg10888111-in blood in the context of ELA across a set of psychiatric disorders, namely Borderline Personality Disorder (BPD), Major Depressive Disorder (MDD) and Social Anxiety Disorder (SAD), and its potential contribution to their pathogenesis. We found significant hypermethylation in mentally ill patients with high levels of ELA compared to patients with low levels of ELA, whereas cg10888111 methylation in healthy control individuals was not affected by ELA. Further investigations revealed that this effect was driven by the MDD cohort. Providing a direct comparison of cg10888111 DNA methylation in blood in the context of ELA across three mental disorders, our results indicate the role of PXDN regulation in the response to ELA in the pathogenesis of mental disorders, especially MDD. Further studies will be needed to validate these results and decipher the corresponding biological network that is involved in the transmission of ELA to an adult mental disorder in general.

WTAP and METTL14 regulate the m6A modification of DKK3 in renal tubular epithelial cells of diabetic nephropathy

Diabetic nephropathy (DN) is an important cause of death in diabetes patients, which is mainly due to its complex pathogenesis. Here, we explored the role of N6-methyladenosine (m6A) RNA methylation in DN development. Renal tubular epithelial cells from DN patients and experimental DN mice treated with streptozotocin (STZ) exhibited a considerable increase in METTL14 and WTAP expression as well as overall m6A methylation. Knocking down the expression of METTL14 and WTAP inhibited the migration and proliferation of tubular epithelial cells. MeRIP-seq analysis of the renal tissues of DN patients revealed that the genes with elevated m6A methylation were concentrated in the Wnt/β-Catenin signaling pathway. Dickkopf homolog 3 (DKK3) was screened out as the gene with the most significant increase in m6A methylation. In addition, the expression change pattern of DKK3 under DN circumstances is in line with those of METTL14 and WTAP. DKK3's m6A methylation sites were confirmed to be located in the 3′UTR region, which is how METTL14 and WTAP improved DKK3's mRNA stability. Finally, YTHDF1, a m6A reader, was demonstrated to recognize m6A-methylated DKK3 and promote DKK3 expression.

Epigenome-wide differential methylation and differential variability as predictors of high-grade cervical intraepithelial neoplasia (cin2+).

CpG site methylation patterns have potential to improve differentiation of high-grade screening-detected cervical abnormalities. We assessed CpG differential methylation (DM) and differential variability (DV) in high-grade (CIN2+) vs. low-grade (≤CIN1) lesions. In ≤CIN1 (n=117) and CIN2+ (n=31) samples, cervical sample DNA underwent testing with Illumina HumanMethylation arrays. We assessed DM and DV of CpG methylation M values among nine cervical cancer-associated genes. We fit CpG-specific linear models and estimated empirical Bayes standard errors and false discovery rates (FDR). An exploratory epigenome-wide association study (EWAS) aimed to detect novel DM and DV CpGs (FDR<0.05) and Gene Ontology (GO) term enrichment. Compared to ≤CIN1, CIN2+ exhibited greater methylation at CCNA1 Cluster 1 (M value difference 0.24; 95% CI 0.04, 0.43) and RARB Cluster 2 (0.16; 95% CI 0.05, 0.28), and lower methylation at CDH1 Cluster 1 (-0.15; 95% CI -0.26, -0.04). CIN2+ exhibited lower variability at CDH1 Cluster 2 (variation difference -0.24; 95% CI -0.41, -0.05) and FHIT Cluster 1 (-0.30; 95% CI -0.50, -0.09). EWAS detected 3,534 DM and 270 DV CpGs. Forty-four GO terms were enriched with DM CpGs related to transcriptional, structural, developmental, and neuronal processes. Methylation patterns may help triage screening-detected cervical abnormalities and inform US screening algorithms.

DNA Methylation Effects on Van der Woude Syndrome Phenotypic Variability.

Van der Woude Syndrome (VWS) presents with combinations of lip pits (LP) and cleft lip and/or cleft palate (CL/P, CPO). VWS phenotypic heterogeneity even amongst relatives, suggests that epigenetic factors may act as modifiers. IRF6, causal for 70% of VWS cases, and TP63 interact in a regulatory loop coordinating epithelial proliferation and differentiation in palatogenesis. We hypothesize that differential DNA methylation within IRF6 and TP63 regulatory regions underlie VWS phenotypic discordance. DNA methylation of CpG sites in IRF6 and TP63 promoters and in an IRF6 enhancer element was compared amongst blood or saliva DNA samples of 78 unrelated cases. Analyses were done separately for blood and saliva, within each sex and in combination, and to address cleft type (CL/P ± LP vs. CPO ± LP) and phenotypic severity (any cleft + LP vs. any cleft only). For cleft type, blood samples showed higher IRF6 and TP63 promoter methylation on males with CPO ± LP compared to CL/P ± LP and on individuals with CPO ± LP compared to those with CL/P ± LP, respectively. Saliva samples showed higher IRF6 enhancer methylation on individuals with CPO ± LP compared to CL/P ± LP and contrary to above, lower TP63 promoter methylation on CPO ± LP compared to CL/P ± LP. For phenotypic severity, blood samples showed no differences; however, saliva samples showed higher IRF6 promoter methylation in individuals with any cleft + LP compared to those without lip pits. We observed differential methylation in IRF6 and TP63 regulatory regions associated with cleft type and phenotypic severity, indicating that epigenetic changes in IRF6 and TP63 can contribute to phenotypic heterogeneity in VWS.

71 Exploring DNA-methylation and DNA-hydroxymethylation in clear cell renal cell carcinoma using Dirichlet multinomial regression

Abstract Background DNA methylation alterations have been found related to clear cell renal cell carcinoma (ccRCC). In addition, DNA methylation has been used to identify cell heterogeneity at the tumor level using cell deconvolution methods. Other cytosine modifications are less studied in the field. In this study, we aim to explore the use of compositional method, Dirichlet multinomial regression1, to simultaneously explore DNA methylation and DNA hydroxymethylation in ccRCC. Methods A total of 243 clear cell renal cell carcinoma (ccRCC) samples were collected from the Dartmouth Renal Tumor Biobank. DNA methylation analysis was conducted using the Infinium MethylationEPIC BeadChip, using tandem bisulfite (BS) and oxidative bisulfite (oxBS) treatments to differentiate between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) states. Beta values were calculated for all the arrayed samples. Quality control was performed using the ENmix pipeline to ensure the inclusion of high-quality samples using a stringent pOOBHA &amp;lt;0.05. The oxBS maximum likelihood estimate (oxBS-MLE) method was applied to assess 5mC and 5hmC levels based on paired BS and oxBS datasets. To determine the inclusion threshold for 5hmC beta values in the Dirichlet analysis, the R package oxBSCut was used to mask values close to zero2. Missing 5hmC values and those values masked below the detection limit were imputed using a compositional approach and a Tobit regression model to retain the model properties. The Dirichlet model was applied to analyze three methylation states: unmethylated, methylated, and hydroxymethylated, providing comprehensive insights into the methylation landscape of ccRCC samples. Results Here, we analyzed the top 1% most variable methylated sites (n=3020). When comparing tumor vs normal adjacent samples we tested the cytosine modifications in the same model adjusting for sex, age at diagnosis, tumor stage, and grade. For this exploratory analysis, we selected those results that passed an FDR&amp;lt;0.01 and an absolute difference &amp;gt;0.2 for further exploration. Four sites were hyperhydroxymethylated related to MYO5A, DLG2, ANKRD33B, and one open sea site. A total of 23 sites were significantly hypermethylated. Using eForge-TF, these sites tracked to transcription factors targeting estrogen receptors, retinoic acid receptors alpha and gamma, and FOXC1 among others3. Conclusions In this preliminary analysis, using a new modeling approach to interrogate cytosine modifications simultaneously, we found some intriguing results consistent with previous literature. We will expand this analysis to evaluate the relation between extreme changes in hydroxymethylation in particular in relation to alternative splicing alterations. Reference 1. Tsagris, M., Stewart, C. A Dirichlet Regression Model for Compositional Data with Zeros. Lobachevskii J Math39, 398–412 (2018). https://doi.org/10.1134/S1995080218030198 2. Zhang Z, Lee MK, Perreard L, Kelsey KT, Christensen BC, Salas LA. Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays. Epigenomics. 2022;14(3):139-152. doi:10.2217/epi-2021-0490 3. Charles E Breeze, Alex P Reynolds, Jenny van Dongen, Ian Dunham, John Lazar, Shane Neph, Jeff Vierstra, Guillaume Bourque, Andrew E Teschendorff, John A Stamatoyannopoulos, Stephan Beck, eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data, Bioinformatics, Volume 35, Issue 22, November 2019, Pages 4767–4769, https://doi.org/10.1093/bioinformatics/btz456

Exploring prognostic DNA methylation genes in bladder cancer: a comprehensive analysis

The current study aimed to investigate the status of genes with prognostic DNA methylation sites in bladder cancer (BLCA). We obtained bulk transcriptome sequencing data, methylation data, and single-cell sequencing data of BLCA from public databases. Initially, Cox survival analysis was conducted for each methylation site, and genes with more than 10 methylation sites demonstrating prognostic significance were identified to form the BLCA prognostic methylation gene set. Subsequently, the intersection of marker genes associated with epithelial cells in single-cell sequencing analysis was obtained to acquire epithelial cell prognostic methylation genes. Utilizing ten machine learning algorithms for multiple combinations, we selected key genes (METRNL, SYT8, COL18A1, TAP1, MEST, AHNAK, RPP21, AKAP13, RNH1) based on the C-index from multiple validation sets. Single-factor and multi-factor Cox analyses were conducted incorporating clinical characteristics and model genes to identify independent prognostic factors (AHNAK, RNH1, TAP1, Age, and Stage) for constructing a Nomogram model, which was validated for its good diagnostic efficacy, prognostic prediction ability, and clinical decision-making benefits. Expression patterns of model genes varied among different clinical features. Seven immune cell infiltration prediction algorithms were used to assess the correlation between immune cell scores and Nomogram scores. Finally, drug sensitivity analysis of Nomogram model genes was conducted based on the CMap database, followed by molecular docking experiments. Our research offers a reference and theoretical basis for prognostic evaluation, drug selection, and understanding the impact of DNA methylation changes on the prognosis of BLCA.

Effects of Naphtho[2,1-a]pyrene Exposure on CYP1A1 Expression: An in Vivo and in Vitro Mechanistic Study Exploring the Role of Posttranscriptional Modification.

Currently, many emerging polycyclic aromatic hydrocarbons (PAHs) have been found to be widely present in the environment. However, little has been reported about their toxicity, particularly in relation to CYP1A1. This study aimed to explore the toxicity of naphtho[2,1-a]pyrene (N21aP) and elucidate the mechanism underlying N21aP-induced expression of CYP1A1. The concentration and sources of N21aP were detected and analyzed by gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) and diagnostic ratio analysis. Then the effects of CYP1A1 on the toxicity of N21aP were conducted in male wild-type (WT) and Cyp1a1 knockout mice exposed to N21aP (0.02, 0.2, and ) through intratracheal instillation. Further, the aryl hydrocarbon receptor (AhR) pathway was examined through luciferase and chromatin immunoprecipitation (ChIP) assays. -methyladenosine () modification levels were measured on global RNA and specifically on CYP1A1 mRNA using dot blotting and methylated RNA immunoprecipitation-quantitative real-time polymerase chain reaction (MeRIP qRT-PCR), with validation by inhibitors, DAA and SAH. sites on CYP1A1 were identified by bioinformatics and luciferase assays, and CYP1A1 mRNA's interaction with IGF2BP3 was confirmed by RNA pull-down, luciferase, and RNA binding protein immunoprecipitation (RIP) assays. N21aP was of the same environmental origin as benzo[a]pyrene (BaP) but was more stably present in the environment. N21aP could be metabolically activated by CYP1A1 to produce epoxides, causing DNA damage and further leading to lung inflammation. Importantly, in addition to the classical AhR pathway (i.e., BaP), N21aP also induced CYP1A1 expression with a posttranscriptional modification of in CYP1A1 mRNA via the METTL14-IGF2BP3-CYP1A1 axis. Specifically, in the two recognition sites of METTL14 on the CYP1A1 mRNA transcript (position at 2700 and 5218), a methylation site (position at 5218) in the 3'-untranslated region (UTR) was recognized by IGF2BP3, enhanced the stability of CYP1A1 mRNA, and finally resulted in an increase in CYP1A1 expression. This study systematically demonstrated that in addition to AhR-mediated transcriptional regulation, N21aP, had a new additional mechanism of -mediated posttranscriptional modification, jointly contributing to CYP1A1 expression. Given that PAHs are the metabolic substrates of CYP1A1, this study not only helps to understand the significance of environment-genetic interactions for the toxicity of PAHs but also helps to better understand the health risks of the emerging PAHs at environmental exposure levels. https://doi.org/10.1289/EHP14055.

CmirC update 2024: a multi-omics database for clustered miRNAs

Clustered miRNAs consist of two or more miRNAs transcribed together and may coordinately regulate gene expression. Differential expression of clustered miRNAs is found to be controlled by crosstalk of genetic or epigenetic mechanisms. It has been demonstrated that clustered miRNA expression patterns greatly impact cancer cell progression. With the CmirC initiative, we initially developed a comprehensive database to identify copy number variation (CNV) driven clustered miRNAs in cancer. Now, we extended the analysis and identified three miRNAs, mir-96, mir-183, and mir-21, were found to be significantly upregulated in 17 cancer types. Further, CmirC is now upgraded to determine the impact of changes in the DNA methylation status at clustered miRNAs by utilizing The Cancer Genomic Atlas (TCGA) cancer datasets. We examined specific methylation datasets from 9,639 samples, pinpointing 215,435 methylation sites and 27,949 CpG islands with miRNA cluster information. The integrated analysis identified 34 clusters exhibiting differentially methylated CpG sites across 14 cancer types. Furthermore, we determined that CpG islands in the promoter region of 20 miRNA clusters could play a regulatory role. Along with ensuring a straightforward and convenient user experience, CmirC has been updated with improved data browsing and analysis functionalities, as well as enabled hyperlinks to literature and miR-cancer databases. The enhanced version of CmirC is anticipated to play an important role in providing information on the regulation of clustered miRNA expression, and their targeted oncogenes and tumor suppressors. The newly updated version of CmirC is available at https://slsdb.manipal.edu/cmirclust/.

Resveratrol Enhances Antioxidant and Anti-Apoptotic Capacities in Chicken Primordial Germ Cells through m6A Methylation: A Preliminary Investigation.

Avian primordial germ cells (PGCs) are essential in avian transgenic research, germplasm conservation, and disease resistance breeding. However, cultured PGCs are prone to fragmentation and apoptosis, regulated at transcriptional and translational levels, with N6-methyladenosine (m6A) being the most common mRNA modification. Resveratrol (RSV) is known for its antioxidant and anti-apoptotic properties, but its effects on PGCs and the underlying mechanisms are not well understood. This study shows that RSV supplementation in cultured PGCs improves cell morphology, significantly enhances total antioxidant capacity (p < 0.01), reduces malondialdehyde levels (p < 0.05), increases anti-apoptotic BCL2 expression, and decreases Caspase-9 expression (p < 0.05). Additionally, RSV upregulates the expression of m6A reader proteins YTHDF1 and YTHDF3 (p < 0.05). m6A methylation sequencing revealed changes in mRNA m6A levels after RSV treatment, identifying 6245 methylation sites, with 1223 unique to the control group and 798 unique to the RSV group. Combined analysis of m6A peaks and mRNA expression identified 65 mRNAs with significantly altered methylation and expression levels. Sixteen candidate genes were selected, and four were randomly chosen for RT-qPCR validation, showing results consistent with the transcriptome data. Notably, FAM129A and SFRP1 are closely related to apoptosis, indicating potential research value. Overall, our study reveals the protective effects and potential mechanisms of RSV on chicken PGCs, providing new insight into its use as a supplement in reproductive stem cell culture.

The association between dietary, physical activity and the DNA methylation of PPARGC1A, HLA-DQA1 and ADCY3 in pregnant women with gestational diabetes mellitus: a nest case-control study

BackgroundGestational diabetes mellitus (GDM) is associated with DNA methylation and lifestyle. The effects of DNA methylation on GDM, and the interaction between DNA methylation and lifestyle factors are not well elucidated. The objective of this study was to explore the association between GDM, DNA methylation and lifestyle factors.MethodsA nest case-control design was performed. Sociodemographic data, dietary intake and daily physical activity information of pregnant women were collected. Bisulfate pyrosequencing was used to detect the DNA methylation level of PPARGC1A, HLA-DQA1, and ADCY3 genes. The differences of DNA methylation levels between the GDM group and the control group were compared. The correlation between clinical characteristics, dietary, physical activity and DNA methylation level was analyzed.ResultsA total of 253 pregnant women were enrolled, of which, 60 participants (GDM: 30; control: 30) were included in the final analysis. There were no significant differences in DNA methylation levels of six methylated sites between the two groups in this study (P > 0.05). Daily intake of potato and poultry were associated with DNA methylation level of the CpG 1 site of the ADCY3 gene in all participants and the control group (P < 0.05). Duration of folic acid intake before pregnancy was correlated with the methylation level of the CpG 1 site of the ADCY3 gene in all participants (r = 0.341, P = 0.04) and the control group (r = 0.431, P = 0.025). Daily oil intake was correlated with the methylation level of CpG 2 (r = 0.627, P = 0.016) and CpG 3 (r = 0.563, P = 0.036) of PPARGC1A in the GDM group.ConclusionThe association between the DNA methylation levels and GDM wasn’t validated. There were associations between dietary and DNA methylation in pregnant women. A large-sample-sized and longitudinal study is warranted to further investigate the impacts of lifestyle on DNA methylation.

Causal Effects of Oxidative Stress on Diabetes Mellitus and Microvascular Complications: Insights Integrating Genome-Wide Mendelian Randomization, DNA Methylation, and Proteome.

Oxidative stress (OS) is involved in the development of diabetes, but the genetic mechanisms are not completely understood. We integrated multi-omics data in order to explore the genetic relations between OS-related genes, diabetes mellitus, and microvascular complications using Mendelian randomization and colocalization analysis. Summary-level data related to OS were acquired from respective studies of methylation, expression, and protein abundance quantitative trait loci. Genetic associations concerning diabetes, diabetic nephropathy (DN), and diabetic retinopathy (DR) were derived from the FinnGen study. Summary-data-based Mendelian randomization (SMR) analysis was conducted to evaluate the correlations between molecular features concerned with OS-related genes and diabetes mellitus, along with its microvascular complications. Additionally, we performed colocalization analysis to determine if the detected signal pairs shared a causal genetic variant. At the genetic level, we identified ten potential causal associations of oxidative stress genes with diabetes, along with microvascular complications, through SMR and colocalization analysis. After integrating the DNA methylation quantitative trait loci (mQTL) and expression QTL (eQTL) data, our analyses revealed a correlation between the methylation site cg26343298 and reduced expression of TP53INP1, supporting the protective role of cg26343298 methylation on type 2 diabetes (T2D) and diabetic nephropathy. Similarly, an inverse association was observed between gene methylation and expression in CHEK1 (cg07110182), confirming the beneficial effect of modification of CHEK1 by cg07110182 in diabetic retinopathy. In addition, upregulation of SUOX expression by cg22580629 was linked to a reduced risk of diabetic retinopathy. At circulating protein levels, genetically predicted a higher level of ICAM1 (OR 1.05, 95%CI 1.03-1.08) was positively connected with the risk of diabetic retinopathy. This SMR study elucidated that the TP53INP1 gene was putatively associated with T2D and DN risk, while the SUOX and CHEK1 genes were associated with DR risk through oxidative stress mechanisms. Additionally, our study showed a positive correlation between the ICAM-1 protein and DR. These findings may enhance our understanding of their pathogenesis and suggest new therapeutic targets for clinical practice.

DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.

As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, plays a vital role in gene expression analyses, cellular differentiation, and the study of disease progression. However, accurately and efficiently identifying DNA methylation sites remains a pivotal concern in the field of bioinformatics. The issue addressed in this paper is the presence of methylation in DNA, which is a binary classification problem. To address this, our research aimed to develop a deep learning algorithm capable of more precisely identifying these sites. The DeepPGD framework combined a dual residual structure involving Temporal convolutional networks (TCNs) and bidirectional long short-term memory (BiLSTM) networks to effectively extract intricate DNA structural and sequence features. Additionally, to meet the practical requirements of DNA methylation identification, extensive experiments were conducted across a variety of biological species. The experimental results highlighted DeepPGD's exceptional performance across multiple evaluation metrics, including accuracy, Matthews' correlation coefficient (MCC), and the area under the curve (AUC). In comparison to other algorithms in the same domain, DeepPGD demonstrated superior classification and predictive capabilities across various biological species datasets. This significant advancement in algorithmic prowess not only offers substantial technical support, but also holds potential for research and practical implementation within the DNA methylation identification domain. Moreover, the DeepPGD framework shows potential for application in genomics research, biomedicine, and disease diagnostics, among other fields.

The correlation between CpG methylation and gene expression is driven by sequence variants

Gene promoter and enhancer sequences are bound by transcription factors and are depleted of methylated CpG sites (cytosines preceding guanines in DNA). The absence of methylated CpGs in these sequences typically correlates with increased gene expression, indicating a regulatory role for methylation. We used nanopore sequencing to determine haplotype-specific methylation rates of 15.3 million CpG units in 7,179 whole-blood genomes. We identified 189,178 methylation depleted sequences where three or more proximal CpGs were unmethylated on at least one haplotype. A total of 77,789 methylation depleted sequences (~41%) associated with 80,503 cis-acting sequence variants, which we termed allele-specific methylation quantitative trait loci (ASM-QTLs). RNA sequencing of 896 samples from the same blood draws used to perform nanopore sequencing showed that the ASM-QTL, that is, DNA sequence variability, drives most of the correlation found between gene expression and CpG methylation. ASM-QTLs were enriched 40.2-fold (95% confidence interval 32.2, 49.9) among sequence variants associating with hematological traits, demonstrating that ASM-QTLs are important functional units in the noncoding genome.

DNA methylation-mediated suppression of TUSC1 expression regulates the malignant progression of esophagogastric junction cancer

BackgroundEsophagogastric junction cancer (EJC) refers to malignant tumors that develop at the junction between the stomach and the esophagus. TUSC1 is a recently identified tumor suppressor gene known for its involvement in various types of cancer. The objective of this investigation was to elucidate the regulatory influence of DNA methylation on TUSC1 expression and its role in the progression of EJC.MethodsBioinformatics software was utilized to analyze the expression of TUSC1, enriched pathways, and highly methylated sites in the promoter region. TUSC1 expression in EJC was assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blot (WB), and immunohistochemistry. Methylation-specific PCR was employed to detect the methylation level of TUSC1. To analyze the effects of TUSC1 and 5-AZA-2 on tumor cell proliferation, migration, invasion, cell cycle, and apoptosis, several assays including CCK-8, colony formation, transwell, and flow cytometry were conducted. The expression of MDM2 was assessed using qRT-PCR and WB. WB detected the expression of p53, and p-p53, markers for EJC cell proliferation, epithelial-mesenchymal transition, and apoptosis. The role of TUSC1 in tumor occurrence in vivo was examined using a xenograft mouse model.ResultsTUSC1 expression was significantly downregulated in EJC. Overexpression of TUSC1 and treatment with 5-AZA-2 inhibited the malignant progression of EJC cells. In EJC, low methylation levels promoted the expression of TUSC1. Upregulation of TUSC1 suppressed the expression of MDM2 and activated the p53 signaling pathway. Inactivation of this pathway attenuated the inhibitory effect of TUSC1 overexpression on EJC cell proliferation, migration, invasion, and other behaviors. Animal experiments demonstrated that TUSC1 overexpression inhibited EJC tumor growth and metastasis in vivo.ConclusionTUSC1 was commonly downregulated in EJC and regulated by methylation. It repressed the malignant progression of EJC tumors by mediating the p53 pathway, suggesting its potential as a diagnostic and therapeutic target for EJC.

Promoter Hypermethylation Downregulates MiR-125b-5p and MiR-199b-5p Targeting of ΔNp63, Resulting in PI3K/AKT/mTOR Pathway Activation and Keratinocyte Differentiation.

Normal keratinocyte differentiation is important for epidermal wound healing. ΔNp63 is a major gene regulating epidermal formation and differentiation. We identified miRNAs targeting ΔNp63 and studied the association between the miRNAs and DNA methylation in keratinocyte differentiation. This study aimed to explore the mechanisms regulating ΔNp63 expression during keratinocyte differentiation. Bioinformatics analysis was performed to screen the miRNAs targeting ΔNp63 and uncover potential pathway mechanisms. The differentiation model of keratinocytes was established by CaCl2 treatment. Furthermore, the effects of the miRNA transgenic technique on Δ Np63 and keratinocyte differentiation were studied. In addition, the RNA FISH experiment was conducted to detect the location of different miRNAs. A double luciferase reporter experiment was carried out to verify the potential bindings between the miRNAs and ΔNp63. A rescue experiment was also performed to assess the effects of different miRNAs targeting ΔNp63 on keratinocyte differentiation. We analyzed the methylation patterns of the promoter regions of miRNAs using keratinocytes treated with 5-Aza-2'-deoxycytidine. Finally, we designed a methylation rescue experiment to verify the effects of miRNA promoter methylation on keratinocyte differentiation. Bioinformatics analysis showed that the miR-125b-5p and miR-199b-5p binding to the ΔNp63 3'UTR region decreased during skin development. Moreover, such binding may downregulate the PI3K/AKT/mTOR pathway. The expression levels of CK10, Inv, TG1, ΔNp63, and PI3K/AKT/mTOR were all significantly increased during keratinocyte differentiation. Both miR- 125b-5p and miR-199b-5p were localized in the cytoplasm. Luciferase assay results showed that both miR-125b-5p and miR-199b-5p can bind to the 3'UTR region of ΔNp63. Overexpression of ΔNp63 can significantly counteract the inhibitory effect of miRNA mimics on keratinocyte differentiation. Moreover, the promoter regions of both miR-125b-5p and miR-199b-5p had methylation sites, and the methylation levels in those promoter regions were significantly increased during keratinocyte differentiation. 5-Aza-2'-Deoxycytidine treatment increased the expression of miR- 125b-5p and miR-199b-5p and inhibited the differentiation of keratinocytes. Finally, miRNA inhibitors reversed the inhibitory effects of 5-Aza-2'-deoxycytidine on keratinocyte differentiation. Promoter hypermethylation in miR-125b-5p and miR-199b-5p seem to promote keratinocyte differentiation via upregulation of ΔNp63 expression and the activation of the PI3K/AKT/mTOR pathway. The findings of this study are helpful for future research on skin development and clinical scar-free healing.

DNA Methylation Profiling in Genetically Selected Clarias magur (Hamilton, 1822) Provides Insights into the Epigenetic Regulation of Growth and Development.

DNA methylation is an epigenetic alteration that impacts gene expression without changing the DNA sequence affecting an organism's phenotype. This study utilized a reduced representation bisulfite sequencing (RRBS) approach to investigate the patterns of DNA methylation in genetically selected Clarias magur stocks. RRBS generated 249.22 million reads, with an average of 490,120 methylation sites detected in various parts of genes, including exons, introns, and intergenic regions. A total of 896 differentially methylated regions (DMRs) were identified; 356 and 540 were detected as hyper-methylated and hypo-methylated regions, respectively. The DMRs and their association with overlapping genes were explored using whole genome data of magur, which revealed 205 genes in exonic, 210 in intronic, and 480 in intergenic regions. The analysis identified the maximum number of genes enriched in biological processes such as RNA biosynthetic process, response to growth factors, nervous system development, neurogenesis, and anatomical structure morphogenesis. Differentially methylated genes (DMGs) such as myrip, mylk3, mafb, egr3, ndnf, meis2a, foxn3, bmp1a, plxna3, fgf6, sipa1l1, mcu, cnot8, trim55b, and myof were associated with growth and development. The selected DMGs were analyzed using real-time PCR, which showed altered mRNA expression levels. This work offers insights into the epigenetic mechanisms governing growth performance regulation in magur stocks. This work provides a valuable resource of epigenetic data that could be integrated into breeding programs to select high-performing individuals.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.