基于AlphaGenome的非编码调控变异的分析,以湿实验室验证为基础的RHD表达
bioRxiv : the preprint server for biology
|February 12, 2026
概括
人工智能 (AI) 和基编辑有效地识别了调节RHD抗原表达的功能非编码变体. 这种人工智能引导的方法为了解血型基因调节和改进输血医学提供了一个可扩展,具有成本效益的替代方案.
科学领域:
- 人类遗传学 人类遗传学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 识别功能性非编码调节变异在人类遗传学中至关重要但具有挑战性.
- 像CRISPR查和GWAS这样的传统方法昂贵且耗时.
- 仅仅编码变异无法完全解释RHD抗原的表达,因此需要对非编码区域进行研究.
研究的目的:
- 开发和验证一个集成的AI引导和实验框架,用于识别影响RHD表达的功能非编码监管变体.
- 为了证明AI的实用性,特别是AlphaGenome,在RHD位置内优先考虑监管变体.
- 为非编码变异分析提供可扩展和具有成本效益的替代传统选方法.
主要方法:
- 应用AlphaGenome,一个深度学习模型,使用多omics数据系统地分析RHD位置.
- 用于删除和SNP扰动分析来预测RHD表达的变异效应.
- 在K562细胞中优先考虑的非编码SNP位点进行CRISPR介导的基编辑,然后进行qPCR和流细胞计验证.
主要成果:
- 阿尔法基因组优先考虑了促进体,5'UTR中的调节区域,以及RHD位点的内基区域.
- 在 silico 分析中预测了变异在促进者和内基区域的抑制作用.
- 实验验证显示,人工智能预测与观察到的RHD转录和表型结果之间有很强的一致性,对高优先级变体进行了有针对性的基准编辑,证明了显著的功能效应.
结论:
- 人工智能引导的变体优先级和基础编辑的结合是一种强大,可扩展和具有成本效益的策略,用于识别功能性非编码变体.
- 这一框架对基因组学为基础的RHD类型和推进输血医学有直接影响.
- 这项研究代表了使用湿实验室实验对AlphaGenome预测的第一个表型验证.
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