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Source tracking of antibiotic resistance genes in the environment -Challenges, progress, and prospects

机译:环境中抗生素抗性基因的源跟踪 - 挑战,进展和前景

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摘要

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. (C) 2020 Elsevier Ltd. All rights reserved.
机译:抗生素抗性已成为全球性的公共卫生问题,使常见感染无法治疗。鉴于广泛的发生,增加关注的环境途径,可能导致临床领域外抗生素抗性基因(Arg)散发。过去十年中的研究已经明确证明了arg污染趋势以及人为干扰的梯度,主要是通过基于PCR的方法的标志物检测。然而,先前研究中的各种因素总是被围绕的源跟踪,例如自加密的Arg水平,时尚变异性和环境抵抗力复杂性,以及固有的方法限制。迅速发展的偏心神经谱在样本范围内的基因组背景下发生,开设了一个新的环境arg污染来源跟踪。耦合与机器学习分类,已经证明了在明确分配源贡献中的Metagenomic arg概况的潜力。通过识别指标arg和恢复arg-宿主基因组,基于偏见的分析将进一步提高源跟踪的分辨率和准确性。在本次审查中,将讨论对环境arg污染的来源跟踪研究中的挑战和进展,并特别关注最近的近期宫颈导向方法。我们提出了一种基于综合的肉质组合的框架,其中对实验设计和偏见分析的协调努力将有助于实现环境arg污染的强大源跟踪的最终目标。 (c)2020 elestvier有限公司保留所有权利。

著录项

  • 来源
    《Water Research》 |2020年第15期|116127.1-116127.12|共12页
  • 作者单位

    Univ Hong Kong Dept Civil Engn Environm Microbiome Engn & Biotechnol Lab Pokfulam Rd Hong Kong 999077 Peoples R China;

    Univ Hong Kong Dept Civil Engn Environm Microbiome Engn & Biotechnol Lab Pokfulam Rd Hong Kong 999077 Peoples R China;

    Univ Hong Kong Dept Civil Engn Environm Microbiome Engn & Biotechnol Lab Pokfulam Rd Hong Kong 999077 Peoples R China;

    Univ Hong Kong Dept Civil Engn Environm Microbiome Engn & Biotechnol Lab Pokfulam Rd Hong Kong 999077 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Antibiotic resistance gene; Environmental pollution; Source tracking; PCR; Metagenomics; Machine-learning classification;

    机译:抗生素抗性基因;环境污染;源跟踪;PCR;偏心神经;机器学习分类;

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