River Ganges water as reservoir of microbes with antibiotic and metal ion resistance genes: High throughput metagenomic approach

被引:101
|
作者
Reddy, Bhaskar [1 ]
Dubey, Suresh Kumar [1 ]
机构
[1] Banaras Hindu Univ, Inst Sci, Ctr Adv Study Bot, Mol Ecol Lab, Varanasi 221005, Uttar Pradesh, India
关键词
Antibiotics; Metagenomics; Antibiotic resistance genes (ARG); Metal ion resistance genes (MRGs); Heavy metals; ESCHERICHIA-COLI; TREATMENT PLANTS; ANALYSIS REVEALS; HEAVY-METALS; VARANASI; BACTERIA; ENVIRONMENT; MECHANISMS; PROTEIN; SLUDGE;
D O I
10.1016/j.envpol.2018.12.022
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The large scale usage of antibiotics and trace elements leads to their progressive release in the environment, and ultimately the spread of antibiotic resistance genes (ARGs) and metal ion resistance genes (MRGs) in bacteria. A high-throughput metagenomic sequencing of the microbial community in water and sediments in the river Ganges harboring resistance genes was performed. The results revealed that the river harbors a broad spectrum of resistance genes with high abundance in sediments. The highly dominant ARGs type was beta-lactam, multidrug/efflux and elfamycin. The ARGs such as (tuf, part, ileS, mfd) were highly abundant in water and sediments. The MRGs subtype acn was the most abundant metal resistance gene in water and sediments. Majority of ARGs types showed significant (p <= 0.05) positive correlation with the MRGs types in the river environment suggesting their distribution and transfer to be possibly linked. Taxonomic classification revealed that Proteobacteria and Actinobacteria were the two most abundant phyla in water and sediments. Arcobacter, Terrimicrobium, Acidibacter and Pseudomonas were the most abundant genera. This study suggests that antibiotics and metals are the driving force for the emergence of resistance genes, and their subsequent propagation and accumulation in the environmental bacteria. The present metagenomic investigation highlights significance of such study, and attracts attention for the mitigation of pollutants associated with the propagation of ARGs and MRGs in the river environment. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:443 / 451
页数:9
相关论文
共 50 条
  • [21] Metagenomic assembly insight into the antibiotic resistance genes and antibiotic resistant bacteria in packaged drinking water system
    Xia, Xiyang
    Gu, Qihui
    Chen, Ling
    Zhang, Jumei
    Guo, Weipeng
    Liu, Zhenjie
    Li, Aimei
    Jiang, Xinhui
    Deng, Meiqing
    Zeng, Jiahui
    Lin, Xiuhua
    Peng, Feiting
    Chen, Wei
    Ye, Yingwang
    Wu, Qingping
    JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING, 2025, 13 (02):
  • [22] The Sources and Potential Hosts Identification of Antibiotic Resistance Genes in the Yellow River, Revealed by Metagenomic Analysis
    Zhang, Kai
    Li, Kuangjia
    Liu, Ziyi
    Li, Qidi
    Li, Wenpeng
    Chen, Qi
    Xia, Yangchun
    Hu, Feiyue
    Yang, Fengxia
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (16)
  • [23] Metagenomic analysis reveals the composition and sources of antibiotic resistance genes in coastal water ecosystems of the Yellow Sea and Yangtze River Delta
    Wang, Xin
    Lin, Yude
    Li, Shaoxuan
    Wang, Jiahui
    Li, Xiaohui
    Zhang, Demeng
    Duan, Delin
    Shao, Zhanru
    ENVIRONMENTAL POLLUTION, 2025, 371
  • [24] Persistence of antibiotic resistance genes from river water to tap water in the Yangtze River Delta
    Yang, Juan
    Wang, Hong
    Roberts, Dustin James
    Du, Hao-Nan
    Yu, Xin-Feng
    Zhu, Ning-Zheng
    Meng, Xiang-Zhou
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 742 (742)
  • [25] DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
    Gustavo Arango-Argoty
    Emily Garner
    Amy Pruden
    Lenwood S. Heath
    Peter Vikesland
    Liqing Zhang
    Microbiome, 6
  • [26] DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
    Arango-Argoty, Gustavo
    Garner, Emily
    Prudent, Amy
    Heath, Lenwood S.
    Vikesland, Peter
    Zhang, Liqing
    MICROBIOME, 2018, 6
  • [27] High-throughput profiling of antibiotic resistance genes in drinking water treatment plants and distribution systems
    Xu, Like
    Ouyang, Weiying
    Qian, Yanyun
    Su, Chao
    Su, Jianqiang
    Chen, Hong
    ENVIRONMENTAL POLLUTION, 2016, 213 : 119 - 126
  • [28] Metagenomic analysis manifesting intrinsic relatedness between antibiotic resistance genes and sulfate- and iron-reducing microbes in sediment cores of the Pearl River Estuary
    Li, Zhaohong
    Lin, Lan
    Xie, Xiuqin
    Ming, Lili
    Li, Songzhang
    Liu, Lan
    Yuan, Ke
    Lin, Li
    Hu, Ligang
    Luan, Tiangang
    Chen, Baowei
    ENVIRONMENTAL POLLUTION, 2024, 363
  • [29] Characteristics of antibiotics and antibiotic resistance genes in Qingcaosha Reservoir in Yangtze River Delta, China
    Ting Xu
    Wanting Zhao
    Xueping Guo
    Hongchang Zhang
    Shuangqing Hu
    Zhifeng Huang
    Daqiang Yin
    Environmental Sciences Europe, 2020, 32
  • [30] Characteristics of antibiotics and antibiotic resistance genes in Qingcaosha Reservoir in Yangtze River Delta, China
    Xu, Ting
    Zhao, Wanting
    Guo, Xueping
    Zhang, Hongchang
    Hu, Shuangqing
    Huang, Zhifeng
    Yin, Daqiang
    ENVIRONMENTAL SCIENCES EUROPE, 2020, 32 (01)