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DNA methylation is an important regulator on the mRNA expression. However, a genome-wide correlation pattern between DNA methylation and mRNA expression in human peripheral blood mononuclear cells (PBMCs) is largely unknown. The comprehensive relationship between mRNA and DNA methylation was explored by using four types of correlation analyses and a genome-wide methylation-mRNA expression quantitative trait locus (eQTL) analysis in PBMCs in 46 unrelated female subjects. An enrichment analysis was performed to detect biological function for the detected genes. Single pair correlation coefficient (r ) between methylation level and mRNA is moderate (-0.63-0.62) in intensity, and the negative and positive correlations are nearly equal in quantity. Correlation analysis on each gene (T4) found 60.1% genes showed correlations between mRNA and gene-based methylation at P < 0.05 and more than 5.96% genes presented very strong correlation (R > 0.8). Methylation sites have regulation effects on mRNA expression in eQTL analysis, with more often observations in region of transcription start site (TSS). The genes under significant methylation regulation both in correlation analysis and eQTL analysis tend to cluster to the categories (e.g., transcription, translation, regulation of transcription) that are essential for maintaining the basic life activities of cells. Our findings indicated that DNA methylation has predictive regulation effect on mRNA with a very complex pattern in PBMCs. The results increased our understanding on correlation of methylation and mRNA and also provided useful clues for future epigenetic studies in exploring biological and disease-related regulatory mechanisms in PBMC.
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http://dx.doi.org/10.1007/s10142-017-0568-6 | DOI Listing |
Clin Epigenetics
September 2025
Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany.
Background: Work-related stress is a well-established contributor to mental health decline, particularly in the context of burnout, a state of prolonged exhaustion. Epigenetic clocks, which estimate biological age based on DNA methylation (DNAm) patterns, have been proposed as potential biomarkers of chronic stress and its impact on biological aging and health. However, their role in mediating the relationship between work-related stress, physiological stress markers, and burnout remains unclear.
View Article and Find Full Text PDFImmunol Cell Biol
September 2025
Department of Biotechnology, Indian Institute of Technology Hyderabad (IITH), Sangareddy, Telangana, India.
The immune system uses a variety of DNA sensors, including endo-lysosomal Toll-like receptors 9 (TLR9) and cytosolic DNA sensor cyclic GMP-AMP (cGAMP) synthase (cGAS). These sensors activate immune responses by inducing the production of a variety of cytokines, including type I interferons (IFN). Activation of cGAS requires DNA-cGAS interaction.
View Article and Find Full Text PDFBiochem Pharmacol
September 2025
Department of Biosciences, JIS University, 81, Nilgunj Road, Agarpara, Kolkata, West Bengal 700109, India. Electronic address:
The malignant manifestation of breast cancer is driven by complex molecular alterations that extend beyond genetic mutations to include epigenetic dysregulation. Among these, DNA methylation is a critical and reversible epigenetic modification that significantly influences breast cancer initiation, progression, and therapeutic resistance. This process, mediated by DNA methyltransferases (DNMTs), involves the addition of methyl groups to cytosine residues within CpG dinucleotides, resulting in transcriptional repression of genes.
View Article and Find Full Text PDFMol Hum Reprod
September 2025
Department of Obstetrics and Gynecology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
Infertility impacts up to 17.5% of reproductive-aged couples worldwide. To aid in conception, many couples turn to assisted reproductive technology, such as IVF.
View Article and Find Full Text PDFEpigenomics
September 2025
College of Physical Education, Yangzhou University, Yangzhou, China.
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder lacking objective biomarkers for early diagnosis. DNA methylation is a promising epigenetic marker, and machine learning offers a data-driven classification approach. However, few studies have examined whole-blood, genome-wide DNA methylation profiles for ASD diagnosis in school-aged children.
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