Karimi-Fard Abbas, Saidi Abbas, Tohidfar Masoud, Emami Seyedeh Noushin
Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.
Department of Molecular Biosciences, Wenner-Gren Institute, Stockholm University, SE 106 91, Stockholm, Sweden.
Genetica. 2025 Aug 20;153(1):28. doi: 10.1007/s10709-025-00243-6.
Bacteria are constantly exposed to diverse environmental stresses, necessitating complex adaptive mechanisms for survival. Thermus thermophilus, a thermophilic extremophile, serves as an excellent model for investigating these responses due to its remarkable resilience to harsh conditions. Recent advances in artificial intelligence, particularly in machine learning, have transformed the identification of novel stress-responsive biomarkers. In this study, we analyzed transcriptomic data from 65 T. thermophilus HB8 samples subjected to various abiotic stresses to identify key genes involved in stress adaptation. We applied a suite of supervised machine learning algorithms to classify samples and prioritize informative features. Among the tested models, Extreme Gradient Boosting (XGBoost) and Random Forest (RF) achieved the highest classification performance, with XGBoost attaining perfect discrimination between stressed and control samples (AUC = 1.00) and RF closely following (AUC = 0.99). Feature importance analysis consistently identified three candidate genes: TTHA0029, TTHA1720, and TTHA1359. Functional validation using RT-qPCR confirmed the significant upregulation of TTHA0029 and TTHA1720 under salt and hydrogen peroxide stress, suggesting roles in redox regulation and ionic homeostasis. Phylogenetic analysis further revealed the specificity of these genes to the Thermus genus. Overall, our findings highlight central molecular players in stress tolerance in T. thermophilus and demonstrate the utility of machine learning in biomarker discovery. The identified genes, TTHA0029 and TTHA1720, may serve as promising targets for genetic engineering to improve stress resilience in both crops and industrially relevant microorganisms.
细菌不断面临各种环境压力，因此需要复杂的适应机制来生存。嗜热栖热菌是一种嗜热极端微生物，由于其对恶劣条件具有显著的复原力，是研究这些反应的极佳模型。人工智能的最新进展，特别是机器学习，已经改变了新型应激反应生物标志物的识别。在本研究中，我们分析了65个嗜热栖热菌HB8样本在各种非生物胁迫下的转录组数据，以确定参与胁迫适应的关键基因。我们应用了一套监督机器学习算法对样本进行分类，并对信息特征进行排序。在测试的模型中，极端梯度提升（XGBoost）和随机森林（RF）实现了最高的分类性能，XGBoost在应激样本和对照样本之间实现了完美区分（AUC = 1.00），RF紧随其后（AUC = 0.99）。特征重要性分析一致确定了三个候选基因：TTHA0029、TTHA1720和TTHA1359。使用RT-qPCR进行的功能验证证实，在盐和过氧化氢胁迫下，TTHA0029和TTHA1720显著上调，表明它们在氧化还原调节和离子稳态中发挥作用。系统发育分析进一步揭示了这些基因对栖热菌属的特异性。总体而言，我们的研究结果突出了嗜热栖热菌中胁迫耐受性的核心分子参与者，并证明了机器学习在生物标志物发现中的实用性。鉴定出的基因TTHA0029和TTHA1720可能是基因工程的有希望的靶点，以提高作物和工业相关微生物的胁迫复原力。
