埃皮雷古隆:单细胞转录因子活动推断以预测药物反应和细胞状态的驱动因素
Tomasz Włodarczyk1, Aaron Lun1, Diana Wu2
1gRED Computational Sciences, Genentech Inc, South San Francisco, CA, USA.
Nature communications
|August 2, 2025
概括
埃皮雷古隆集成基因表达和染色质可访问性数据来预测转录因子活性,从而为转录失调驱动的疾病提供更好的药物发现.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 转录因子 (TF) 和转录辅调器是关键的治疗点.
- 现有的基因调节网络 (GRN) 方法经常忽视转录后TF调节.
- 准确的GRN构造对于了解疾病机制和药物疗效至关重要.
研究的目的:
- 介绍Epiregulon,一种用于构建GRNs的新型计算方法.
- 通过整合各种基因组数据,使TF活动的准确预测成为可能.
- 促进发现针对转录调节者的新疗法.
主要方法:
- 埃皮雷古隆通过从单细胞ATAC-seq和RNA-seq数据中分析TF表达和染色质可访问性在TF结合部位的同时发生来构建GRNs.
- 包含ChIP-seq数据用于对转录系调节器或TF活动的动机无关推断.
- 应用该方法来预测药物效应并识别疾病驱动因素.
主要成果:
- 埃皮雷古隆准确地预测了使用各种药物抑制雄激素受体 (AR) 的影响.
- 该方法通过确定特定背景的相互作用伙伴来阐明SMARCA4降解剂作用的机制.
- 埃皮雷古隆成功地优先考虑了血统重编程和瘤发生的关键驱动因素.
结论:
- 埃皮雷古隆提供了一种强大的方法,可以在细胞环境中映射基因调节.
- 这种方法加速了针对涉及转录失调的疾病的向治疗方法的发现.
- 整合表观遗传和表达数据可以提高TF活性和治疗反应的预测.
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