QMRA of beach water by Nanopore sequencing-based viability-metagenomics absolute quantification

单叠氮丙二钠 基因组 工作流程 生物 大肠杆菌 水质 计算生物学 环境科学 生态学 计算机科学 遗传学 实时聚合酶链反应 基因 数据库
作者
Yu Yang,Yu Deng,Xianghui Shi,Lei Liu,Xiaole Yin,Wanwan Zhao,Shuxian Li,Chao Yang,Tong Zhang
出处
期刊:Water Research [Elsevier BV]
卷期号:235: 119858-119858 被引量:14
标识
DOI:10.1016/j.watres.2023.119858
摘要

The majority of the current regulatory practices for routine monitoring of beach water quality rely on the culture-based enumeration of faecal indicator bacteria (FIB) to develop criteria for promoting the general public's health. To address the limitations of culture methods and the arguable reliability of FIB in indicating health risks, we developed a Nanopore metagenomic sequencing-based viable cell absolute quantification workflow to rapidly and accurately estimate a broad range of microbes in beach waters by a combination of propidium monoazide (PMA) and cellular spike-ins. Using the simple synthetic bacterial communities mixed with viable and heat-killed cells, we observed near-complete relic DNA removal by PMA with minimal disturbance to the composition of viable cells, demonstrating the feasibility of PMA treatment in profiling viable cells by Nanopore sequencing. On a simple mock community comprised of 15 prokaryotic species, our results showed high accordance between the expected and estimated concentrations, suggesting the accuracy of our method in absolute quantification. We then further assessed the accuracy of our method for counting viable Escherichia coli and Vibrio spp. in beach waters by comparing to culture-based method, which were also in high agreement. Furthermore, we demonstrated that 1 Gb sequences obtained within 2 h would be sufficient to quantify a species having a concentration of ≥ 10 cells/mL in beach waters. Using our viability-resolved quantification workflow to assess the microbial risk of the beach water, we conducted (1) screening-level quantitative microbial risk assessment (QMRA) to investigate human illness risk and site-specific risk patterns that might guide risk management efforts and (2) metagenomics-based resistome risk assessment to evaluate another layer of risk caused by difficult illness treatment due to antimicrobial resistance (AMR). In summary, our metagenomic workflow for the rapid absolute quantification of viable bacteria demonstrated its great potential in paving new avenues toward holistic microbial risk assessment.
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