元基因组学对坎帕拉废水中微生物抗性组和病毒组组成的深入研究

Stephen Kanyerezi, Patricia Nabisubi, Grace Kebirungi, Ivan Sserwadda, Benson R. Kidenya, Daudi Jjingo, Gerald Mboowa
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引用次数: 0

摘要

背景抗微生物药物(AMR)感染是全球健康的一大威胁,每年全球约有 70 万人直接死于 AMR 相关问题。在非洲,42.6% 的国家缺乏足够的 AMR 数据,这凸显了我们报告中的一个重要缺口。因此,迫切需要全面的 AMR 监测数据。城市污水中的微生物种类繁多,它们来自数量可观且大多健康的人群,这为我们提供了一个绝佳的采样机会。本研究旨在识别和评估坎帕拉城市污水中的微生物,同时分析与城市污水相关的微生物抗药性组和病毒组。方法 从两个污水处理设施中采集样本,同时采集雨季和旱季的数据,以反映不同季节的种群行为。从这些样本中提取 DNA,并进行射枪元基因组测序。使用定制的元基因组学方法对产生的 FastQ 文件进行分析,以确定微生物特征、抗生素耐药基因和毒力因子。结果 在所研究的病原生物群中,精神假单胞菌(一种鱼类病原体)的感染率最高,而肺炎克雷伯菌的感染率最低。分析发现了 23 个抗性基因,主要是对四环素类药物的抗性。此外,还发现了 29 个毒力因子,主要与细菌的运动能力有关。值得注意的是,所有这些毒力因子都是在铜绿假单胞菌菌株 PAO1 中发现的。结论 在污水分析中利用枪式元基因组学对持续监测微生物多样性和抗菌药耐药性至关重要。这种方法能揭示复杂的细节,而通过 PCR 和基于培养的技术等传统方法获得这些细节具有挑战性或成本高昂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metagenomics insights into the microbial resistome and virulome composition of Kampala’s wastewater
Background Antimicrobial-resistant (AMR) infections represent a major global health threat, causing approximately 700,000 deaths each year directly due to AMR-related issues worldwide. In Africa, 42.6% of countries lack sufficient data on AMR, highlighting a crucial gap in our reports. Consequently, there's a pressing need for thorough AMR surveillance data. Urban sewage, harboring a diverse array of microbes from sizable and mostly healthy populations, offers an excellent sampling opportunity. This study set out to identify and assess the microbes present in urban sewage in Kampala, while also analyzing the microbial resistome and virulome associated with urban sewage. Methods Samples were gathered from two wastewater treatment facilities, capturing data from both wet and dry seasons to reflect population behavior across seasons. DNA was extracted from these samples and underwent shotgun metagenomics sequencing. The resulting FastQ files were analyzed using a tailored metagenomics approach to identify microbial profiles, antibiotic-resistant genes, and virulence factors. Results In the pathobiome examined, Pseudomonas psychrophila, a fish pathogen, was the most prevalent, while Klebsiella pneumoniae was the least prevalent. Analysis identified 23 resistant genes, primarily conferring resistance to tetracyclines. Additionally, 29 virulence factors were identified, with a predominant association with bacterial motility. Notably, all of these virulence factors were found within Pseudomonas aeruginosa strain PAO1. Conclusion The utilization of shotgun metagenomics in sewage analysis is crucial for ongoing monitoring of microbial diversity and antimicrobial resistance. This approach uncovers intricate details that would be challenging or costly to obtain through conventional methods like PCR and culture-based techniques.
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