循环核细胞体的基因组修饰与无细胞DNA碎片化模式的变化有关
Jinyue Bai1,2,3, Peiyong Jiang1,2,3,4, Lu Ji1,2,3
1Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong Special Administrative Region, China.
概括
基于FRAGmentomics的基因组修饰分析 (FRAGHA) 将无细胞DNA (cfDNA) 碎片化模式与基因组修饰联系起来,使得用于诊断的准确原生组织分析成为可能. 这种新的方法可以提高各种疾病的液体活检准确度.
科学领域:
- 分子生物学分子生物学
- 基因组学就是基因组学.
- 生物化学 生物化学
背景情况:
- 分析非细胞DNA (cfDNA) 源组织对于研究和诊断至关重要.
- 现有的方法,如双硫酸盐治疗或免疫沉,往往导致DNA损失.
- cfDNA碎片组学提供了一个有希望的途径,但需要评估组织贡献的工具.
研究的目的:
- 开发一种新的方法来分析cfDNA组织贡献,使用碎片的特征.
- 为了确定与特定的基因素修饰相关的特征性碎片化模式.
- 建立基于FRAGmentomics的基因组修饰分析 (FRAGHA) 以改善液体活检.
主要方法:
- 开发了基于FRAGmentomics的基因组修饰分析 (FRAGHA) 来分析cfDNA碎片化模式.
- 识别了特定组织的素H3素27乙化 (H3K27ac) 和H3素4三甲基化 (H3K4me3) 相关信号.
- 利用具有碎片化模式的机器学习算法来增强疾病检测.
主要成果:
- FRAGHA显示,胎盘特异的H3K27ac信号与胎儿DNA分数 (r=0.96) 之间存在很强的相关性.
- 肝脏特异性的H3K27ac信号与移植受体的供体DNA相关 (r=0.92),并且在肝细胞癌 (HCC) 中升高.
- 在 β-thalassemia主要和结肠直肠癌中分别观察到红细胞和结肠特异性H3K27ac信号的升高.
- 使用H3K27ac模式的机器学习模型实现了HCC检测的AUC为0.97.
- 对于来自H3K27ac和H3K4me3区域的cfDNA,观察到明显的片段性模式.
结论:
- 通过将碎片组学与基因组修饰联系起来,FRAGHA有效地分析cfDNA组织贡献.
- 这种方法表明了在各种疾病中进行非侵入性诊断的潜力,包括癌症和移植.
- 这项研究扩大了cfDNA碎片学和基因组修饰分析在液体活检应用中的实用性.
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