基于机器学习的结核分枝杆菌耐药突变分析
Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.
摘要(中文译文)
结核病是由结核分枝杆菌引起的致命性空气传播疾病。耐药结核病对疾病的治疗和控制构成重大挑战。结核分枝杆菌的耐药菌株源于细菌的特定突变。鉴定和表征耐药相关突变对于有效的治疗策略至关重要，因为该病的一线和二线药物靶向细菌中不同的基因并服务于不同的目的。本研究开发了一种机器学习预测模型，用于分析多种耐药类型中的突变。所提出的框架预测四种耐药类型的耐药突变，包括利福平耐药、异烟肼耐药、耐多药和准广泛耐药结核病。NIAID-NIH结核病门户是一个公开可用的结核病患者数据集，包含耐药信息。本研究分析了3，065例耐药结核病病例。研究实施了八种监督机器学习算法。采用10折交叉验证的随机森林分类器显示出优于其他七种算法的预测性能，因此被选用于进一步分析。使用SHapley加法解释特征重要性识别了显著的耐药突变。世界卫生组织突变目录被视为耐药突变的金标准，用于评估预测结果。在预测后比较分析阶段，识别出WHO目录中未报告的突变，因为它们可能代表值得进一步研究的潜在耐药标记，包括结构性和功能性验证或实验验证。这些突变为rpoB-I480T、rpoC-G332R、L527V、gyrA-D94V、KatG-G99E、A106V、W191R、W328C、T380I和M420T。研究进一步使用计算工具（包括I-Mutant 2.0和PredictSNP）检查了突变的稳定性和致病性。研究结果基于突变对耐药性的贡献，进一步阐明了其重要性并提供了更多证据。
Abstract
Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis presents significant challenges for treatment and control of the disease. Resistant strains of Mycobacterium tuberculosis arise from specific mutations in the bacterium. Identification and characterization of resistance-associated mutations are crucial for effective treatment strategies because the first- and second-line drugs for the disease target distinct genes in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid Resistance, Multidrug Resistance, and Pre-extensively Drug-Resistant tuberculosis. The NIAID-NIH TB portal is a publicly available dataset of tuberculosis patients, including drug-resistance information. Our study analyzed 3,065 cases of drug-resistant TB. Eight supervised ML algorithms were implemented for the study. A Random Forest classifier with 10-fold cross-validation shows higher predictive performance than the other seven algorithms considered for further analysis. Significant drug resistance mutations were identified using SHapley Additive exPlanations feature importance. The World Health Organisation mutation catalogues, considered the gold standard for drug-resistant mutations, were used to evaluate prediction results. Mutations not reported in the WHO catalogues were identified during the post-prediction comparative analysis stage, as they may represent potential resistance-conferring markers warranting further investigation, including structural and functional validation or experimental validation. The mutations are rpoB-I480T, rpoC-G332R, L527V, gyrA-D94V, KatG-G99E, A106V, W191R, W328C, T380I, and M420T. The study further checks the stability and pathogenicity of the mutations using computational tools, including I-Mutant 2.0 and PredictSNP. The findings added more clarity and further evidence for the significance of the mutation, based on its contribution to drug resistance.
如何引用
AMA
Athira Thankamani, Biji C L, George Priya Doss C. Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.. PloS one. 2026; doi:10.1371/journal.pone.0352863.
APA
Athira Thankamani, Biji C L, George Priya Doss C (2026). Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.. PloS one. https://doi.org/10.1371/journal.pone.0352863
GB/T 7714
Athira Thankamani, Biji C L, George Priya Doss C. Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.[J]. PloS one, 2026 doi:10.1371/journal.pone.0352863.
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