在COVID-19中发现细胞类型特定的免疫调节变体和分子表型,使用结构解析的蛋白质网络
Prabal Chhibbar1, Priyamvada Guha Roy2, Munesh K Harioudh3
1Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Integrative Systems Biology PhD Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Cell reports
|November 6, 2024
概括
识别影响蛋白质相互作用的遗传变异是了解传染病的关键. 我们的网络方法揭示了关键的免疫调节变异,即使是低于全基因组意义的变异,也会影响COVID-19的结果.
科学领域:
- 遗传学 遗传学 是一个
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 影响蛋白相互作用的遗传变异可以改变宿主对传染病的反应.
- 识别这些变异对于理解疾病机制和开发向疗法至关重要.
研究的目的:
- 开发和应用基于网络的方法,将遗传变异与人体蛋白相互作用组相结合,以优先考虑COVID-19中的免疫调节变异.
- 识别那些尽管没有满足全基因组意义的变异,但通过蛋白质-蛋白质相互作用中断显著影响免疫反应的变异.
主要方法:
- 基因变异数据与结构解析的人类蛋白互动组的基于网络的整合.
- 对单细胞表观基因组和转录基因组数据的分析,以确定受影响的细胞子集.
- 根据它们在蛋白质-蛋白质相互作用接口的位置以及对免疫信号通路的影响来确定变异的优先级.
主要成果:
- 在蛋白质-蛋白质相互作用界面上确定了免疫调节变异,包括低于全基因组显著性值的变异.
- 整合多组组的数据突出显示,髓质细胞和T细胞子集在周围血液和肺部的区间中受到高度影响.
- 发现一种常见的编码变体破坏了OAS1-PRMT6相互作用,影响了下游干扰素信号.
结论:
- 基于网络的框架有效地优先考虑免疫调节变异,包括低于传统显著性值的变异.
- 这种方法提高了对COVID-19等传染病遗传贡献的理解.
- 该框架可将其应用于其他传染病环境,用于识别关键免疫调节变异.
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