纳米毒理学中的TRIumph:将转录组学简化为单一的预测变量
Viacheslav Muratov1, Karolina Jagiello1,2, Tomasz Puzyn1,2
1University of Gdansk, Faculty of Chemistry, Laboratory of Environmental Chemoinformatics, Wita Stwosza 63, 80-308 Gdansk, Poland. karolina.jagiello@ug.edu.pl.
Nanoscale horizons
|September 3, 2025
概括
一个新的转录基因反应指数 (TRI) 将复杂的基因表达数据简化为单个变量. 这种TRI与多壁碳纳米管特性相连,使得准确的预测和减少计算需求.
科学领域:
- 毒基因组学
- 计算生物学
- 纳米毒理学
背景情况:
- 由于高维度,转录组数据分析存在挑战.
- 现有的方法需要大量的计算资源.
- 需要采用新方法来减少动物试验.
研究的目的:
- 引入一种新的转录学反应指数 (TRI),以简化转录学数据.
- 将TRI与吸入的多壁碳纳米管 (MWCNT) 的物理化学特性联系起来.
- 根据MWCNT特性开发基因表达变化的预测模型.
主要方法:
- 开发了一种转录物质反应指数 (TRI),将转录物质空间压缩成一个变量.
- 使用定量结构-活性关系 (QSAR) 和纳米-QSAR模型.
- 训练成千上万个差异表达基因 (DEG) 的折叠变化模型.
主要成果:
- TRI成功将5167个DEG压缩成一个变量,解释了99.9%的转录空间.
- 连接TRI和MWCNT属性的纳米-QSAR模型获得了高统计意义 (R2=0.83,Q_CV2=0.8,Q2=0.78).
- 使用单个变量预测基因表达变化的能力.
结论:
- TRI提供了一个强大的方法来管理转录组数据的复杂性.
- 这种方法通过减少动物试验和计算负载来支持NAM.
- 开发了ChemBioML平台,这是监管科学中机器学习模型开发的用户友好工具.
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