在阿尔茨海默氏症疾病中的上下文依赖的调节变异
bioRxiv : the preprint server for biology
|July 17, 2025
概括
在晚发性阿尔茨海默病 (LOAD) 中识别非编码基因变异是具有挑战性的. 这项研究使用了大量并行记者分析和机器学习,揭示了变异如何在细胞类型和状态之间不同影响基因调节.
科学领域:
- 基因组学和生物信息学
- 神经科学是一个神经科学.
- 分子生物学分子生物学
背景情况:
- 非编码的遗传变异有助于复杂的疾病,如晚发性阿尔茨海默病 (LOAD).
- 解释这些变体的功能影响是困难的,因为它们的上下文依赖的监管作用.
- LOAD与免疫细胞中的常见变异以及影响神经发育的罕见变异有关.
研究的目的:
- 在骨髓和神经环境中系统地描述常见和罕见的非编码变体的功能影响.
- 了解细胞类型和细胞状态如何影响基因调节的变异效应.
- 为解释与疾病相关的非编码变异开发一个可概括的框架.
主要方法:
- 结合体外和体内大量并行报告员测定 (MPRAs).
- 利用可解释的深度学习模型进行序列到函数分析.
- 采用CRISPR干扰来功能验证特定疾病相关的位置.
主要成果:
- 个体变异在免疫和神经细胞之间表现出差异性和上下文依赖的调节功能.
- 常见的LOAD变体在免疫环境中具有更强的效果,而罕见的变体在大脑环境中具有更大的影响.
- 深度学习模型将动机破坏和上下文调整确定为变异效应的机制.
- 在SEC63-OSTM1位点静止增强剂调节了炎症和氨基基基因生成.
结论:
- 在LOAD中,非编码变异效应高度依赖上下文,因细胞类型和状态而异.
- 该研究为解释复杂疾病中遗传风险变异的功能后果提供了一个框架.
- 监管要素可以作为疾病相关的生物过程的守门人.
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