全基因组测序和机器学习揭示了对抗利芬素结核病的唾液转化延迟的主要驱动因素
Qing Fang1, Xiangchen Li2,3, Yewei Lu3
1Departments of Pulmonary Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Frontiers in cellular and infection microbiology
|August 25, 2025
概括
延迟的唾液培养转化与耐利芬结核病 (RR-TB) 相关,结果较差. 这项研究使用全基因组测序和机器学习来确定RR-TB患者治疗失败的关键预测因素.
科学领域:
- 微生物学
- 基因组学
- 计算生物学
背景情况:
- 抗利芬素结核病 (RR-TB) 构成了全球严重的健康威胁.
- 在RR-TB患者中延迟的菌培养转化 (SCC) 与不良治疗结果有关.
- 确定SCC故障的决定因素对于优化RR-TB管理至关重要.
研究的目的:
- 整合全基因组测序 (WGS) 和机器学习 (ML) 以确定与RR-TB患者的SCC衰竭相关的临床和基因组因素.
- 使用WGS和临床数据开发SCC故障的预测模型.
- 引导针对性治疗RR-TB的策略.
主要方法:
- 150名RR-TB患者的全基因组测序和表型/基因型分析 (2019-2023年).
- 机器学习 (随机森林) 用于确定2个月和6个月后SCC失败的预测因素.
- 单变量和多变量逻辑回归分析以确定独立的预测因素.
主要成果:
- 观察到高比例的异化物耐药性 (74. 0%) 和rpoB突变 (97. 3%),其中90%属于2系 (北京家族).
- 64. 7% 的患者在 2 个月后获得SCC,而 18. 0% 的患者在 6 个月后仍然呈阳性培养.
- 2个月SCC失败的独立预测因素包括涂抹阳性和抗素耐药性. ML模型显示出强大的预测 (AUC:0. 86在2个月;0. 76在6个月),突出了特定的突变 (例如,embB_p.Met306Ile,katG_p.Ser315Thr,gyrA_p.Asp94Gly) 和临床因素.
结论:
- 涂抹阳性,抗素耐药性和特定的基因突变是RR-TB中SCC衰竭的关键驱动因素.
- 结合WGS和临床数据的ML模型可以有效预测SCC故障.
- 这些发现支持开发个性化和有针对性的RR-TB治疗策略.
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