整合机器学习和RT-qPCR分析确定了Thermus thermophilus HB8中关键的应激基因
Abbas Karimi-Fard1, Abbas Saidi2, Masoud Tohidfar3
1Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.
Genetica
|August 20, 2025
概括
机器学习在Thermus thermophilus中发现了应激适应的关键基因. 基因TTHA0029和TTHA1720有望增强微生物和作物抗压能力.
科学领域:
- 微生物学和分子生物学
- 生物信息学和计算生物学
背景情况:
- 像Thermus thermophilus这样的细菌拥有复杂的适应机制,可以在环境压力下生存.
- 极端爱好者为研究压力反应提供了有价值的模型.
- 人工智能,尤其是机器学习,
研究的目的:
- 使用转录基因数据识别Thermus thermophilus HB8中参与应激适应的关键基因.
- 应用机器学习算法进行样本分类和特征优先排序.
- 在功能上验证候选应激基因.
主要方法:
- 在各种非生物压力下分析65个T. thermophilus HB8样本的转录组数据.
- 应用监督机器学习算法,包括极端梯度提升 (XGBoost) 和随机森林 (RF),用于分类.
- 用于识别候选基因的特征重要性分析
- 用于功能验证的逆转录定量PCR (RT-qPCR).
- 基因特异性的基因分析.
主要成果:
- XGBoost和RF模型实现了高分类性能,XGBoost在应力样本和对照样本之间显示了完美的区别 (AUC=1.00).
- 特性重要性分析始终强调TTHA0029,TTHA1720和TTHA1359作为关键基因.
- 在盐和过氧化压力下,RT-qPCR证实了TTHA0029和TTHA1720的显著上调.
- 遗传学分析表明这些基因是Thermus属的特异性.
结论:
- TTHA0029和TTHA1720在应激耐受性中起着重要作用,可能参与T. thermophilus的氧化还原调节和离子稳定.
- 机器学习是一个强大的工具,用于发现极端友细菌中应激生物标志物.
- 这些已识别的基因代表了基因工程增强微生物和作物的抗压能力的潜在目标.
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