瑞登:数据驱动的从转录组数据推断受体活性.
Szilvia Barsi1,2, Eszter Varga2, Daniel Dimitrov3
1Institute of Molecular Life Sciences, Centre of Excellence of the Hungarian Academy of Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary.
PLoS computational biology
|June 16, 2025
概括
RIDDEN通过分析基因表达变化来预测受体活性,而不是连接体或受体水平. 这种计算工具有助于识别细胞特异性受体变化,并了解疾病中的细胞通信.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 受体信号传递对生理调节和疾病至关重要,使受体成为关键药物点.
- 对于联体受体相互作用的现有计算方法通常侧重于联体或基因共同表达,这可能不反映功能活动.
- 需要工具直接推断受体活性从下游基因表达变化.
研究的目的:
- 开发一种计算工具,RIDDEN (受体行为数据驱动推断),用于预测受体活动.
- 从受体调节的基因表达特征直接推断受体活性.
- 为了使细胞和疾病特异性受体活性变化的系统级分析.
主要方法:
- 在229个受体中使用14463个扰乱基因表达特征训练了RIDDEN模型.
- RIDDEN推断受体活性来自下游基因表达,而不是连接体或受体基因表达.
- 在独立的体外和体外受体扰动数据集上验证了模型.
主要成果:
- RIDDEN有效地预测了批量和单细胞转录组学数据中的受体活性.
- 模型重量与已知的受体-转录因子调节相互作用保持一致.
- 预测的受体活动与体内数据中的受体和连接体表达相关.
- 在接受免疫检查点封锁治疗的癌症患者队列中,RIDDEN确定了机械生物标志物.
结论:
- 到目前为止,RIDDEN是迄今为止最大的基于转录组学的受体活动推断模型.
- 该工具可以识别具有受体活性改变的细胞群.
- 瑞登 (RIDDEN) 通过转录组学数据促进了细胞与细胞通信的研究.
- 这种方法促进了对生理和疾病状态中的受体功能的理解.
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