使用病原体基因组学和机器学习估计特定国家的结核病耐药性抗生素图谱
Avika Dixit1,2, Luca Freschi2, Roger Vargas2,3
1Division of Infectious Diseases, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.
BMJ global health
|March 28, 2024
概括
全基因组测序数据可以估计全球结核病耐药性,但公开数据超标了耐药菌株. 这凸显了南亚的诺基诺使用和在多药耐药结核病治疗中埃西奥纳米德未充分利用的担忧.
科学领域:
- 基因组学就是基因组学.
- 微生物学 微生物学
- 流行病学 流行病学
背景情况:
- 全球结核病 (TB) 耐药性 (DR) 监测主要侧重于利芬辛耐药性.
- 关于对多种结核病药物耐药性的国家一级流行情况的数据有限.
研究的目的:
- 评估公共全基因组测序 (WGS) 数据对预测Mycobacterium结核病 (Mtb) 耐药性的有用性.
- 使用in silico耐药性预测生成扩展的国家级抗生素图.
主要方法:
- 来自29个国家的精选和质量控制的Mtb WGS数据.
- 采用随机森林模型来预测表型对12种药物的耐药性.
- 针对模型性能,采样和过量采样的应用偏差校正,以及对利芬辛耐药性的过量采样.
主要成果:
- 分析了来自19149个MTB分离的公共WGS数据.
- 在 silico 预测显示与南非 DR 调查数据重叠,其中一些药物低估或高估.
- 观察到高比例的异化物耐药性 (10.9%) 和异化物耐药性和埃西奥纳米德敏感性 (74.4%) 之间的不一致.
结论:
- 这项研究提供了使用公共WGS数据的第一个全球Mtb抗生素估计.
- 公开的WGS数据可以可靠地估计DR患病率,但可能会过量采样多药耐药分离物.
- 调查结果引发了对南亚的诺基诺使用和在MDR-TB中埃西奥纳米德未充分利用的担忧.
关键词:
结核病是一种疾病.
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