全基因组分析反映了与糖尿病肾病有关的新型5-羟甲基胞嘧啶和生物标志物潜力。

Ying Yang, C. Zeng, Kun Yang, Shaohua Xu, Zhou Zhang, Qinyun Cai, Chuan He, Wei Zhang, Song-Mei Liu
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引用次数: 3

摘要

在大多数国家,糖尿病肾病(DN)已成为终末期肾病(ESRD)最常见的病因。阐明新的表观遗传因素不仅可以增强我们对这种复杂疾病的理解,而且可以为未来开发更有效的监测工具和预防干预奠定基础,从而有助于我们改善患者护理的最终目标。方法采用高选择性化学标记技术5hmC- seal,对中南医院循环无细胞DNA (cfDNA)样本进行全基因组5-羟甲基胞嘧啶(5hmC)分析,该基因是一种稳定的胞嘧啶修饰型标记基因激活物,该样本包括合并肾病的T2D患者(DN, n = 12)、合并非DN血管并发症的T2D患者(非DN, n = 29)和无并发症的T2D患者(对照组,n = 14)。进行差异分析以发现DN相关的5hmC特征,然后使用机器学习方法探索cfDNA中5hmC对DN的生物标志物潜力。结果与非DN个体和对照组相比,cfDNA中5hmC的全基因组分析发现了427和336个与DN相关的差异5hmC修饰,并提示了nod样受体信号通路和酪氨酸代谢等相关途径。我们使用机器学习方法的探索揭示了一个由十个5hmC基因组成的探索性模型,显示了区分DN与非DN个体或对照的可能性。结论全基因组分析表明,利用患者源性cfDNA中的新型5hmC作为未来监测高风险T2D患者DN的无创工具是可能的。
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Genome-wide Analysis Reflects Novel 5-Hydroxymethylcytosines Implicated in Diabetic Nephropathy and the Biomarker Potential.
Aim Diabetic nephropathy (DN) has become the most common cause of end-stage renal disease (ESRD) in most countries. Elucidating novel epigenetic contributors to DN can not only enhance our understanding of this complex disorder, but also lay the foundation for developing more effective monitoring tools and preventive interventions in the future, thus contributing to our ultimate goal of improving patient care. Methods The 5hmC-Seal, a highly selective, chemical labeling technique, was used to profile genome-wide 5-hydroxymethylcytosines (5hmC), a stable cytosine modification type marking gene activation, in circulating cell-free DNA (cfDNA) samples from a cohort of patients recruited at Zhongnan Hospital, including T2D patients with nephropathy (DN, n = 12), T2D patients with non-DN vascular complications (non-DN, n = 29), and T2D patients without any complication (controls, n = 14). Differentially analysis was performed to find DN-associated 5hmC features, followed by the exploration of biomarker potential of 5hmC in cfDNA for DN using a machine learning approach. Results Genome-wide analyses of 5hmC in cfDNA detected 427 and 336 differential 5hmC modifications associated with DN, compared with non-DN individuals and controls, and suggested relevant pathways such as NOD-like receptor signaling pathway and tyrosine metabolism. Our exploration using a machine learning approach revealed an exploratory model comprised of ten 5hmC genes showing the possibility to distinguish DN from non-DN individuals or controls. Conclusion Genome-wide analysis suggests the possibility of exploiting novel 5hmC in patient-derived cfDNA as a non-invasive tool for monitoring DN in high risk T2D patients in the future.
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