监督机器学习识别了β细胞中线粒体质量控制受损的情况,从而导致2型糖尿病的发展
Mirza Muhammad Fahd Qadir1, Charles Dana2, Paul Mauvais-Jarvis2
1Tulane Center of Excellence in Sex-based Precision Medicine, New Orleans, LA, USA.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
机器学习模型通过分析单细胞RNA测序数据,准确预测2型糖尿病 (T2D). 这项研究确定PINK1是β细胞线粒体健康的关键调节者,对T2D进展至关重要.
科学领域:
- 内分泌学和新陈代谢学
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
背景情况:
- 2型糖尿病 (T2D) 的发病包括导致β细胞衰竭的复杂分子途径,使用标准单细胞分析难以阐明.
- 了解T2D中β细胞功能障碍的具体机制对于开发向疗法至关重要.
研究的目的:
- 开发一个可解释的,监督的机器学习框架来分析单细胞RNA测序 (scRNA-seq) 数据,以解决T2D相关的β细胞衰竭.
- 确定关键的分子调节器和参与β细胞功能障碍和T2D代谢健康的途径.
主要方法:
- 结合稀疏的基于规则的分类 (SnakeClassifier),受路径约束的建模 (BlackSwanClassifier) 和β细胞线粒体健康分层 (Kolmogorov-Arnold神经网络 - KANN) 使用52个人类捐赠者的scRNA-seq数据.
- 开发了捐赠者级糖尿病评分和线粒体适应性指数 (MFI) 用于综合疾病机制分析.
- 利用了基因表达模式,线粒体细胞分裂,线粒体蛋白质稳定,生物发生和氧化酸化指标.
主要成果:
- SnakeClassifier准确地预测了单细胞水平的T2D,并产生了与高血糖相关的糖尿病得分.
- 确定了不同的β细胞亚型 (β1-4),其中β1具有弹性,而β2-4在T2D中表现出压力和功能受损.
- 由PINK1,BNIP3和FUNDC1调节的线粒成为T2D相关的主导程序;随着疾病的严重程度而降低PINK1水平.
- 由KANN衍生的MFI (R2=0.934) 突出了PINK1,SQSTM1,PRKN和BNIP3作为T2D进展的主要贡献者.
结论:
- 开发的透明机器学习模型有效地将预测与T2D疾病机制集成在一起.
- 线粒细胞受体PINK1被确定为β细胞代谢适应性的中心决定因素,也是T2D的潜在治疗标.
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