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Response of microbial communities of karst river water to antibiotics and microbial source tracking for antibiotics. | LitMetric

Response of microbial communities of karst river water to antibiotics and microbial source tracking for antibiotics.

Sci Total Environ

School of Ocean Sciences, China University of Geosciences (Beijing), Beijing 100083, China; Beijing Key Laboratory of Water Resources and Environmental Engineering, China University of Geosciences (Beijing), Beijing 100083, China. Electronic address:

Published: March 2020


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Article Abstract

In southwestern China, karst river water is the main source of water for humans. As emerging pollutants, antibiotics have contaminated karst river water in some areas for a long time. Microbiota is highly susceptible to environmental changes, and can be used in tracing the source of antibiotics in complex systems such as karst water. Ten karst river water samples were collected along the river flow. The diversity and structure of the microbial community were analyzed together with environmental factors through correlation analysis, the random forest algorithm and co-occurrence network analysis. At genus level, Arcobacter was significantly positively correlated with the antibiotics, indicating that Arcobacter and antibiotics probably came from the same source. Based on co-occurrence network analysis between microbes, the microbial community was divided into eight modules, and the relative abundance of three modules was significantly correlated with antibiotics. The co-occurrence networks between bacteria and antibiotic resistance genes (ARGs) showed that pathogenic bacteria potentially carried multiple ARGs. This could increase the disease risk to humans and disease transmission in the study area. When river water flowed underground, the concentration of antibiotics decreased for the two underground river outlet sites, but abundance of bacteria and ARGs increased. Microbial source tracking studies showed that contamination was derived from humans rather than livestock. The ranking importance of prediction for antibiotics in this study area from random forest follows: specific bacteria Arcobacter > ARGs > ecological clusters. This study will be helpful in identifying the effect of antibiotics discharge on the microbial community, improving evaluation of antibiotics' risks and contaminants source tracking.

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http://dx.doi.org/10.1016/j.scitotenv.2019.135730DOI Listing

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