Conurbation size drives antibiotic resistance along the river

Shu-Yi-Dan Zhou, Fu-Yi Huang (Lead / Corresponding author), Xin-Yuan Zhou, Chenshuo Lin, Ming-Kang Jin, Roy Neilson, Hu Li, Jian-Qiang Su

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    Abstract

    With growing concerns about antibiotic resistance, the tracking of antibiotic resistance genes (ARGs) in urban waterways will facilitate our increased understanding of the impact of urbanization on ARGs dissemination. In the current study, we assessed the ARGs profiles and antibiotic resistome in water samples along the Jiulong River basin, a distance of 250 km, to better understand the impact of anthropogenic activities. A total of 244 ARGs and 12 MGEs were detected from 21 sampling sites. Both relative and absolute abundance of the observed resistome decreased with increasing distance from urban areas. Ordinary least-squares (OLS) regression revealed that both the relative and absolute resistome abundance were positively correlated with city size. The resistome had several inputs and outputs and Fast Expectation Maximization Microbial Source Tracking (FEAST), suggested that the majority of the antibiotic resistome originated from anthropogenic activities. A total of 8 ARGs and 20 microbial OTUs were considered as biomarkers that differentiated the location of sampling sites. Bacterial communities were significantly correlated with ARGs according to Procrustes analysis and Mantel test, which was also supported by a co-occurrence network. Variation partitioning analysis revealed that ARG profiles were driven by multiple factors. Although antibiotic resistome abundance significantly increased near urban conurbations, overall resistome abundance decreased as the river flowed downstream. Our study highlights the effect of conurbation size on antibiotic resistance profiles within the river basin and the potential resilience of rivers to recover from ARGs contamination.

    Original languageEnglish
    Article number153822
    Number of pages7
    JournalScience of the Total Environment
    Volume823
    Early online date11 Feb 2022
    DOIs
    Publication statusPublished - 1 Jun 2022

    Keywords

    • ARGs
    • Urbanization
    • Source tracking
    • Machine learning
    • Microbiome

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