废水微生物组的等离子体宽容性可以通过机器学习从16SrRNA序列中预测
Danesh Moradigaravand1,2, Liguan Li3,4, Arnaud Dechesne3
1Laboratory of Infectious Disease Epidemiology, KAUST Smart-Health Initiative and Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Bioinformatics (Oxford, England)
|June 22, 2023
概括
废水中的微生物群体可以预测等离子体转移的容易程度,有助于评估抗菌素耐药性 (AMR) 的传播. 这项研究开发了一种预测工具,以了解水系统中的水平基因转移 (HGT) 风险.
科学领域:
- 环境微生物学环境微生物学
- 遗传学 是一个遗传学.
- 计算生物学是一种计算生物学.
背景情况:
- 污水处理厂 (WWTP) 拥有多种微生物,是抗菌素耐药性 (AMR) 基因转移的热点.
- 抗抗药性决定因素的水平基因转移 (HGT) 是一个重要的公共卫生问题,但缺乏预测工具.
研究的目的:
- 调查水循环中的微生物社区组成是否可以预测等离子体对结合的允许性.
- 开发一个预测框架来评估废水系统中AMR污染风险.
主要方法:
- 利用部分16S rRNA基因序列推断微生物社区组成.
- 采用机器学习模型,包括随机森林,来预测等离子体宽容性.
- 根据使用光生物报告器等离子体和IncP等离子体 (pKJK5,pB10,RP4) 的实验性过器配对试验的验证预测.
主要成果:
- 随机森林模型显示了预测和实验等离子体宽容性之间的中度至强度的相关性 (例如,RP4的0.53).
- 预测性系遗传信号甚至在广泛的宿主范围等离子体中也被观察到.
- 建立了一个评估废水中AMR污染风险的框架.
结论:
- 微生物社区的组成是等离子体可转移性的有价值预测指标.
- 开发的预测工具提供了一种新的方法来评估WWTP中的HGT风险.
- 这项研究有助于了解和减轻水生环境中抗药性传播.
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