在中国的One Health部门中,区域性抗微生物耐药性基因流动
Yuqing Feng1, Xin Lu2, Jiayong Zhao3
1State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Microbiome
|January 6, 2025
概括
抗微生物耐药性基因 (ARG) 通过人类,食物和环境来源传播. 这项研究揭示了ARG如何在这些部门流动,确定了关键的传播路线和载体,以改善全球卫生监测.
科学领域:
- 微生物学 微生物学
- 基因组学就是基因组学.
- 环境科学 环境科学
- 公共卫生 公共卫生
背景情况:
- 抗微生物耐药性 (AMR) 是一个全球性的健康危机,在人类,动物和环境系统中相互连接.
- 了解这些部门之间的抗菌素耐药性基因 (ARG) 流动对于有效的抗菌素耐药性控制策略至关重要.
研究的目的:
- 在中国某个特定地区,在"一个健康"框架内调查ARG的传播动态.
- 确定主要载体和驱动ARG在人类,食品和环境样本中传播的机制.
- 探索生活方式和环境因素对ARG丰富性和传播的影响.
主要方法:
- 592个人类,食品和环境样本的大规模元基因组测序.
- 培养耐药细菌分离物以确定ARG类型和亚型.
- 分析与ARG相关的移动遗传元素 (MGE) 和微生物组成.
- 机器学习模型的应用用于预测耐卡巴胺菌株.
主要成果:
- 检测到多重耐药基因的高患病率,食品和环境样本显示的ARG负载明显高于人类样本.
- 鉴定出 Pseudomonadota,尤其是 Enterobacteriaceae,是主要的 ARG 载体. 饮食习惯和职业暴露显著影响了ARG丰度.
- 横向基因转移 (HGT) 通过质粒和菌体,以及菌株传播,是ARG流动的关键驱动因素. 和食物成为人类ARG的重要来源.
- 人体肠道中的移动碳烯酶基因OXA-347值得注意. 机器学习有效地预测了耐卡巴胺菌株.
结论:
- 本研究提供了一个全面的视图,在中国的一个健康背景下的区域ARG传播.
- 这些发现提高了对复杂的ARG传播路线的理解,并确定了AMR监测和控制的关键点.
- 这项研究强调了基于微生物组的机器学习模型在抗菌素耐药性监测中的潜力.
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