利用大规模的Mycobacterium结核病全基因组序列数据来利用机器学习和统计方法来表征耐药突变
Siddharth Sanjay Pruthi1,2, Nina Billows1, Joseph Thorpe1
1Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Scientific reports
|November 7, 2024
概括
机器学习使用全基因组测序数据准确预测结核病 (TB) 的耐药性. 这种方法有助于识别新的耐药性标志物,并改善诊断以更好地控制感染.
科学领域:
- 基因组学和生物信息学
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 由Mycobacterium tuberculosis (Mtb) 引起的结核病 (TB) 仍然是一个关键的全球卫生问题,每年夺走100多万人的生命.
- 耐药性 (DR),特别是多耐药性结核病 (MDR-TB),显著复杂化了疾病控制工作.
- 全基因组测序 (WGS) 为通过基因型-表型关联分析识别新型耐药变异提供了强大的工具.
研究的目的:
- 将机器学习,特别是基于树的组合方法,应用于Mtb WGS和药物敏感性测试数据的大数据集.
- 评估使用药物特异性突变与全基因组变异预测耐药性的模型的预测性能.
- 识别新型药物耐药性标记物,了解基因突变与最小抑制度 (MIC) 现型之间的关系.
主要方法:
- 利用了35777万亿WGS的数据集和相关的表型药物敏感性测试结果.
- 采用基于树的集体机器学习模型来预测耐药性.
- 按功能影响聚合低频变体以提高预测准确度,并对MIC数据分析单核酸多态 (SNP).
主要成果:
- 在一线药物 (AUC: 88.3-96.5) 和二线药物 (AUC: 84.1-95.4) 实现了高预测准确度.
- 按功能影响组合突变显著提高了预测准确性 (例如,pyrazinamide灵敏度增加了25%).
- 确定了假定新型耐药性标志物,包括功能丧失突变 (例如,ndh 293dupG,Rv3861 78delC) 和与高水平耐药性相关的突变 (例如,inHA突变).
结论:
- 应用于大规模WGS数据的机器学习有效地预测了Mycobacterium结核病耐药性和MIC表型.
- 这种方法可以为诊断耐药性和指导治疗决策提供宝贵的见解.
- 这些发现有助于通过识别新的耐药性标志物和改善诊断能力来加强结核病感染控制策略.
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