模拟农业食品样本中抗微生物耐药性基因检测极限:生物信息学工具的比较分析
Ashley L Cooper1,2, Andrew Low1, Alex Wong2
1Research and Development, Ottawa Laboratory (Carling), Canadian Food Inspection Agency, Ottawa, ON, Canada.
BMC microbiology
|January 20, 2024
概括
在农业食品元基因组中准确检测抗菌素耐药性基因 (ARG) 需要至少5X的隔离基因组覆盖. 降低检测值可以提高灵敏度,但可能会识别非目标ARG.
科学领域:
- 微生物学 微生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 通过食物传播的抗菌素耐药性 (AMR) 是一个重大问题.
- 有限的研究存在于农业食品中的AMR细菌的流行.
- 甲基因组学为分析农业食品微生物组和抗体组提供了潜力.
研究的目的:
- 评估样本类型和生物信息学如何影响农业食品元基因组中抗菌素耐药性基因 (ARG) 的检测极限.
- 评估测序覆盖范围对ARG检测准确性的影响.
主要方法:
- 模拟的甲基基因组与AMR病原体的不同比例被分析.
- 多种生物信息学工具 (克拉肯2/布拉肯,梅塔普兰3/4,KMA,CARD-RGI,SRST2) 用于分类学和ARG分析.
- 基于隔离基因组覆盖率和检测值来评估性能.
主要成果:
- 精确的ARG检测显著下降到5X隔离基因组覆盖范围以下.
- 降低ARG目标覆盖率切断值 (<80%) 提高了灵敏度,但确定了替代性等位基因.
- 背景微生物群的存在影响了ARG检测的准确性 (例如,在生菜和牛肉中检测mcr-1).
- SRST2错误地报告了与ARG有远距离关系的ARG,而KMA和CARD-RGI更具体.
结论:
- 在农业食品元基因组中准确检测ARG是可行的,如果有足够的隔离体覆盖 (约. 5X) 的意思.
- 调整检测值需要仔细考虑,以平衡灵敏度和特异性.
- 测序技术 (深度,读取长度) 的进步可以提高ARG检测,用于跟踪临床重要ARG.
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