Epigenomics最新文献

Aim: To assess the associations between genome-wide DNA methylation (DNAm) and glucose metabolism among a Chinese population, in particular the multisite correlation. Materials & methods: Epigenome-wide associations with fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) were analyzed among 100 Shanghai monozygotic (MZ) twin pairs using the Infinium HumanMethylationEPIC v2.0 BeadChip. We conducted a Pearson's correlation test, hierarchical cluster and pairwise analysis to examine the differential methylation patterns from clusters. Results: Cg01358804 (TXNIP) was identified as the most significant site associated with FPG and HbA1c. Two clusters with hypermethylated and hypomethylated patterns were observed for both FPG and HbA1c. Conclusion: Differential methylation patterns from clusters may provide new clues for epigenetic changes and biological mechanisms in glucose metabolism.

Objective: To elucidate the role of the competitive endogenous RNA (ceRNA) network in immune infiltration of diabetic retinopathy (DR). Methods: We obtained differentially expressed (DE) circRNAs, miRNAs and mRNAs from the Gene Expression Omnibus database. Then, we identified immune infiltration by CIBERSORT and single-sample gene set enrichment analysis and discovered co-expression genes by weighted gene co-expression network analysis. Furthermore, STAT1-mediated Th1 differentiation was determined in DR cell models, DR patients and DR mouse models. Results: hsa_circ_0087100/hsa-miR-6743-5p/STAT1 was involved in immune infiltration of Th1 cells. Aberrant expression of the ceRNA network and STAT1-mediated Th1 differentiation was thus verified in vitro and in vivo. Conclusion: hsa_circ_0087100/hsa-miR-6743-5p/STAT1 may affect Th1 cell differentiation in DR.

Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13-1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6, CTSK). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.

Cervical cancer (CC) poses a significant health threat in women globally. MicroRNA clusters (MCs), comprising multiple miRNA-encoding genes, are pivotal in gene regulation. Various factors, including circular RNA and DNA methylation, govern MC expression. Dysregulated MC expression correlates strongly with CC development via promoting the acquisition of cancer hallmarks. Certain MCs show promise for diagnosis, prognosis and therapy selection due to their distinct expression patterns in normal, premalignant and tumor tissues. This review explains the regulation and biological functions of MCs and highlights the clinical relevance of abnormal MC expression in CC.

Neurological diseases are multifactorial, genetic and environmental. Environmental factors such as diet, physical activity and emotional state are epigenetic factors. Environmental markers are responsible for epigenetic modifications. The effect of epigenetic changes is increased inflammation of the nervous system and neuronal damage. In recent years, it has been shown that epigenetic changes may cause an increased risk of neurological disorders but, currently, the relationship between epigenetic modifications and neurodegeneration remains unclear. This review summarizes current knowledge about neurological disorders caused by epigenetic changes in diseases such as Alzheimer's disease, Parkinson's disease, stroke and epilepsy. Advances in epigenetic techniques may be key to understanding the epigenetics of central changes in neurological diseases.

R-loops, intricate three-stranded structures formed by RNA-DNA hybrids and an exposed non-template DNA strand, are fundamental to various biological phenomena. They carry out essential and contrasting functions within cellular mechanisms, underlining their critical role in maintaining cellular homeostasis. The specific cellular context that dictates R-loop formation determines their function, particularly emphasizing the necessity for their meticulous genomic regulation. Notably, the aberrant formation or misregulation of R-loops is implicated in numerous neurological disorders. This review focuses on the complex interactions between R-loops and double-strand DNA breaks, exploring how R-loop dysregulation potentially contributes to the pathogenesis of various brain disorders, which could provide novel insights into the molecular mechanisms underpinning neurological disease progression and identify potential therapeutic targets by highlighting these aspects.

Aim: Castration-resistant prostate cancer (CRPC) eventually becomes resistant to androgen receptor pathway inhibitors like enzalutamide. Immunotherapy also fails in CRPC. We propose a new approach to simultaneously revert enzalutamide resistance and rewire anti-tumor immunity. Methods: We investigated in vitro and in subcutaneous and spontaneous mouse models the effects of combining enzalutamide and GSK-126, a drug inhibiting the epigenetic modulator EZH2. Results: Enzalutamide and GSK-126 synergized to reduce CRPC growth, also restraining tumor neuroendocrine differentiation. The anti-tumor activity was lost in immunodeficient mice. Indeed, the combination treatment awoke cytotoxic activity and IFN-γ production of tumor-specific CD8⁺ T lymphocytes. Conclusion: These results promote the combination of enzalutamide and GSK-126 in CRPC, also offering new avenues for immunotherapy in prostate cancer.

Aim: The present study was designed to investigate the coregulatory effects of multiple histone modifications (HMs) on gene expression in lung adenocarcinoma (LUAD). Materials & methods: Ten histones for LUAD were analyzed using ChIP-seq and RNA-seq data. An innovative computational method is proposed to quantify the coregulatory effects of multiple HMs on gene expression to identify strong coregulatory genes and regions. This method was applied to explore the coregulatory mechanisms of key ferroptosis-related genes in LUAD. Results: Nine strong coregulatory regions were identified for six ferroptosis-related genes with diverse coregulatory patterns (CA9, PGD, CDKN2A, PML, OTUB1 and NFE2L2). Conclusion: This quantitative method could be used to identify important HM coregulatory genes and regions that may be epigenetic regulatory targets in cancers.

Aim: The current work was designed to demonstrate the application of the exposome framework in examining associations between exposures and children's long-term neurodevelopmental and behavioral outcomes. Methods: Longitudinal data were collected from birth through age 6 from 402 preterm infants. Three statistical methods were utilized to demonstrate the exposome framework: exposome-wide association study, cumulative exposure and machine learning models, with and without epigenetic data. Results: Each statistical approach answered a distinct research question regarding the impact of exposures on longitudinal child outcomes. Findings highlight associations between exposures, epigenetics and executive function. Conclusion: Findings demonstrate how an exposome-based approach can be utilized to understand relationships between internal (e.g., DNA methylation) and external (e.g., prenatal risk) exposures and long-term developmental outcomes in preterm children.

Aim: To predict base-resolution DNA methylation in cancerous and paracancerous tissues. Material & methods: We collected six cancer DNA methylation datasets from The Cancer Genome Atlas and five cancer datasets from Gene Expression Omnibus and established machine learning models using paired cancerous and paracancerous tissues. Tenfold cross-validation and independent validation were performed to demonstrate the effectiveness of the proposed method. Results: The developed cross-tissue prediction models can substantially increase the accuracy at more than 68% of CpG sites and contribute to enhancing the statistical power of differential methylation analyses. An XGBoost model leveraging multiple correlating CpGs may elevate the prediction accuracy. Conclusion: This study provides a powerful tool for DNA methylation analysis and has the potential to gain new insights into cancer research from epigenetics.