Environmental Microbiome Engineering and Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, 999077, Hong Kong.
Environmental Microbiome Engineering and Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, 999077, Hong Kong.
Water Res. 2020 Oct 15;185:116127. doi: 10.1016/j.watres.2020.116127. Epub 2020 Aug 2.
Antibiotic resistance has become a global public health concern, rendering common infections untreatable. Given the widespread occurrence, increasing attention is being turned toward environmental pathways that potentially contribute to antibiotic resistance gene (ARG) dissemination outside the clinical realm. Studies during the past decade have clearly proved the increased ARG pollution trend along with gradient of anthropogenic interference, mainly through marker-ARG detection by PCR-based approaches. However, accurate source-tracking has been always confounded by various factors in previous studies, such as autochthonous ARG level, spatiotemporal variability and environmental resistome complexity, as well as inherent method limitation. The rapidly developed metagenomics profiles ARG occurrence within the sample-wide genomic context, opening a new avenue for source tracking of environmental ARG pollution. Coupling with machine-learning classification, it has been demonstrated the potential of metagenomic ARG profiles in unambiguously assigning source contribution. Through identifying indicator ARG and recovering ARG-host genomes, metagenomics-based analysis will further increase the resolution and accuracy of source tracking. In this review, challenges and progresses in source-tracking studies on environmental ARG pollution will be discussed, with specific focus on recent metagenomics-guide approaches. We propose an integrative metagenomics-based framework, in which coordinated efforts on experimental design and metagenomic analysis will assist in realizing the ultimate goal of robust source-tracking in environmental ARG pollution.
抗生素耐药性已成为全球公共卫生关注的焦点，使常见感染变得难以治疗。鉴于其广泛发生，人们越来越关注可能导致临床以外抗生素耐药基因（ARG）传播的环境途径。过去十年的研究清楚地证明了随着人为干扰程度的增加，ARG 污染呈上升趋势，主要通过基于 PCR 的方法检测标记-ARG。然而，在以前的研究中，由于各种因素的存在，如本地 ARG 水平、时空变异性和环境抗性组的复杂性以及固有方法的限制，准确的溯源一直存在混淆。快速发展的宏基因组学在全样本基因组范围内描绘了 ARG 的发生情况，为环境 ARG 污染的溯源开辟了新途径。与机器学习分类相结合，宏基因组 ARG 图谱已被证明具有明确分配源贡献的潜力。通过识别指示性 ARG 和恢复 ARG 宿主基因组，基于宏基因组学的分析将进一步提高溯源的分辨率和准确性。在这篇综述中，我们将讨论环境 ARG 污染溯源研究中的挑战和进展，重点关注最近的宏基因组学指导方法。我们提出了一个综合的基于宏基因组学的框架，其中实验设计和宏基因组分析的协调努力将有助于实现环境 ARG 污染中强大溯源的最终目标。
