使用机械到机器学习框架预测基因水平对JAK-STAT信号干扰的敏感性
Neha Cheemalavagu1,2,3, Karsen E Shoger2,3, Yuqi M Cao2,3
1University of Pittsburgh, Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA.
bioRxiv : the preprint server for biology
|June 9, 2023
概括
这项研究使用计算模型将STAT信号动态与基因表达联系起来. 它预测了细胞因子信号如何影响巨细胞基因活性,有助于未来对STAT驱动疾病的研究.
科学领域:
- 细胞信号传递和基因调节
- 计算生物学和系统免疫学
背景情况:
- 该JAK-STAT途径集成细胞因子信号,但STAT转录因子的功能多样性仍然不清楚.
- 了解STAT特异性对于破译对IL-6和IL-10等细胞因子的复杂细胞反应至关重要.
研究的目的:
- 从STAT酸化动态开发一个计算工作流来预测细胞因子诱导的基因表达.
- 模拟巨细胞对IL-6和IL-10的反应,它们利用具有独特信号模式的常见STAT.
- 将STAT信号动态与特定的基因表达输出联系起来.
主要方法:
- 开发了一个集成机械学和机器学习模型的计算工作流.
- 模拟了对IL-6和IL-10的巨细胞反应,分析了STAT酸化动态.
- 预测和验证了JAK2抑制对基因表达的影响.
主要成果:
- 鉴定了与晚期pSTAT3时间框架相关的细胞因子诱导的基因组.
- 在JAK2抑制下观察到偏好的pSTAT1降低.
- 发现了对JAK2变异敏感或不敏感的动态调节基因.
结论:
- 成功将STAT信号动态与特定的基因表达模式联系起来.
- 为信号系统输出的多级预测模型提供基础.
- 支持未来的努力,在病理学中针对STAT驱动的基因组.
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