Epigenomics最新文献

Gliomas, highly aggressive tumors of the central nervous system, present overwhelming challenges due to their heterogeneity and therapeutic resistance. Glioblastoma multiforme (GBM), the most malignant form, underscores this clinical urgency due to dismal prognosis despite aggressive treatment regimens. Recent advances in cancer research revealed signaling pathways and epigenetic mechanisms that intricately govern glioma progression, offering multifaceted targets for therapeutic intervention. This review explores the dynamic interplay between signaling events and epigenetic regulation in the context of glioma, with a particular focus on the crucial roles played by non-coding RNAs (ncRNAs). Through direct and indirect epigenetic targeting, ncRNAs emerge as key regulators shaping the molecular landscape of glioblastoma across its various stages. By dissecting these intricate regulatory networks, novel and patient-tailored therapeutic strategies could be devised to improve patient outcomes with this devastating disease.

Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age. Machine learning technologies offer promising avenues to enhance our understanding of the biological mechanisms governing aging by further elucidating the gap between biological and chronological ages. This perspective article examines current algorithmic approaches to epigenetic clocks, explores the use of machine learning for age estimation from DNA methylation, and discusses how refining the interpretation of ML methods and tailoring their inferences for specific patient populations and cell types can amplify the utility of these technologies in age prediction. By harnessing insights from machine learning, we are well-positioned to effectively adapt, customize and personalize interventions aimed at aging.

Aim: This study aims to examine the gene expression and DNA methylation patterns of angiogenic factors in the placentae of Indian women who underwent assisted reproductive technology (ART) procedures and their association with maternal one-carbon metabolites and birth outcome.

Methods: Placental gene expression and DNA methylation of angiogenic factors (VEGF, PlGF, FLT-1, KDR) in Indian women who underwent ART procedures (n = 64) and women who conceived naturally (Non-ART) (n = 93) was investigated using RT-qPCR and Epitect Methyl-II PCR assay kits. Maternal plasma one-carbon metabolites were assessed by CMIA technology.

Result: Gene expression of FLT-1 and KDR was higher (p < 0.05) in the ART placentae. Placental global DNA methylation levels were higher (p < 0.01) and DNA methylation levels of VEGF promoter were lower (p < 0.05) in ART compared to non-ART women. Maternal plasma folate and vitamin B₁₂ levels were higher (p < 0.01) in the ART group. Gene expression of PlGF was negatively associated with maternal plasma folate (p < 0.05) whereas KDR was positively associated with maternal plasma homocysteine (p < 0.05). Gene expression of KDR was positively associated with chest circumference of the baby (p < 0.05).

Conclusion: Hypomethylation of VEGF and increased expression of FLT-1 and KDR was observed in the placentae of women who underwent ART procedure.

Aim: Diabetic cardiomyopathy (DbCM), a complex metabolic disease, greatly threatens human health due to therapeutic limitations. Multi-omics approaches facilitate the elucidation of its intrinsic pathological changes.

Methods: Metabolomics, RNA-seq, proteomics, and assay of transposase-accessible chromatin (ATAC-seq) were utilized to elucidate multidimensional molecular alterations in DbCM.

Results: In the heart and plasma of mice with DbCM, metabolomic analysis demonstrated significant differences in branched-chain amino acids (BCAAs) and lipids. Subsequent RNA-seq and proteomics showed that the key genes, including BCKDHB, PPM1K, Cpt1b, Fabp4, Acadm, Acadl, Acadvl, HADH, HADHA, HADHB, Eci1, Eci2, PDK4, and HMGCS2, were aberrantly regulated, contributing to the disorder of BCAAs and fatty acids. ATAC-seq analysis underscored the pivotal role of epigenetic regulation by revealing dynamic shifts in chromatin accessibility and a robust positive correlation with gene expression patterns in diabetic cardiomyopathy mice. Furthermore, motif analysis identified that KLF15 as a critical transcription factor in DbCM, regulating the core genes implicated with BCAAs metabolism.

Conclusion: Our research delved into the metabolic alterations and epigenetic landscape and revealed that KLF15 may be a promising candidate for therapeutic intervention in DbCM.

Epigenetic dysregulation is an important nexus in the development and maintenance of human cancers. This review provides an overview of how understanding epigenetic dysregulation in cancers has led to insights for novel cancer therapy development. Over the past two decades, significant strides have been made in drug discovery efforts targeting cancer epigenetic mechanisms, leading to successes in clinical development and approval of cancer epigenetic therapeutics. This article will discuss the current therapeutic rationale guiding the discovery and development of epigenetic therapeutics, key learnings from clinical experiences and new opportunities on the horizon.

Aims: Epigenomics has significantly advanced through the incorporation of Systems Biology approaches. This study aims to investigate the human lysine methylome as a system, using a data-science approach to reveal its emergent properties, particularly focusing on histone mimicry and the broader implications of lysine methylation across the proteome.

Methods: We employed a data-science-driven OMICS approach, leveraging high-dimensional proteomic data to study the lysine methylome. The analysis focused on identifying sequence-based recognition motifs of lysine methyltransferases and evaluating the prevalence and distribution of lysine methylation across the human proteome.

Results: Our analysis revealed that lysine methylation impacts 15% of the known proteome, with a notable bias toward mono-methylation. We identified sequence-based recognition motifs of 13 lysine methyltransferases, highlighting candidates for histone mimicry. These findings suggest that the selective inhibition of individual lysine methyltransferases could have systemic effects rather than merely targeting histone methylation.

Conclusions: The lysine methylome has significant mechanistic value and should be considered in the design and testing of therapeutic strategies, particularly in precision oncology. The study underscores the importance of considering non-histone proteins involved in DNA damage and repair, cell signaling, metabolism, and cell cycle pathways when targeting lysine methyltransferases.

Background: Existing analyses with conventional assays have generated significant insights into static states of DNA methylation but were unable to visualize the dynamics of epigenetic regulation.

Materials & results: We utilized a genomic DNA methylation reporter (GMR) system carrying Snrpn minimal promoter and CpG regions of Cdk1 (Cyclin-dependent kinase 1) or Sox2 (SRY-Box Transcription Factor 2). Mouse Sox2 GMR iPSCs rapidly lost fluorescent reporter signal upon the induction of cardiac differentiation. Cdk1 GMR reporter signal was strong in undifferentiated iPSCs, and gradually decreased during cardiomyocyte differentiation. RT-qPCR and pyrosequencing demonstrated that the reduction of Sox2 and Cdk1 was regulated by hypermethylation of their promoters' CpG regions during cardiac differentiation.

Conclusion: The GMR reporter system can be useful for monitoring real-time epigenetic DNA modification at single-cell resolution.

Aging presents a significant challenge to health and social care systems due to the increasing proportion of the elderly population. The identification of reliable biomarkers to assess the progression of aging remains an unresolved question. Circular RNAs (circRNAs) are single-stranded covalently closed RNAs. They have been found to regulate various biological processes. CircRNAs are present in human biological fluids, are relatively stable, and accumulate with age, making them promising as biomarkers of aging. Current information on the expression of circRNAs in aging was analyzed using scientific databases. In this review, we have identified key stages in the study of circRNAs during aging and summarized the current understanding of their biogenesis. By focusing on the role of circRNAs in processes that contribute to aging - such as genomic stability, metabolism, cell death, and signaling pathways - we hypothesize that circRNAs may drive the aging process through their age-related accumulation and resultant deregulation. Examples of age-related differential expression of circRNAs in various species, including humans, are provided. This review highlights the importance of finding novel epigenetic biomarkers of aging, beyond the already identified molecules (circFOXO3, circRNA100783, circPVT1), and highlights circRNAs as a potential therapeutic target for the treatment of age-associated diseases.