Globally distributed mining-impacted environments are underexplored hotspots of multidrug resistance genes.
Xinzhu YiJie-Liang LiangJian-Qiang SuPu JiaJing-Li LuJin ZhengZhang WangShi-Wei FengZhen-Hao LuoHong-Xia AiBin LiaoWen-Sheng ShuWen-Sheng ShuYong-Guan ZhuPublished in: The ISME journal (2022)
Mining is among the human activities with widest environmental impacts, and mining-impacted environments are characterized by high levels of metals that can co-select for antibiotic resistance genes (ARGs) in microorganisms. However, ARGs in mining-impacted environments are still poorly understood. Here, we conducted a comprehensive study of ARGs in such environments worldwide, taking advantage of 272 metagenomes generated from a global-scale data collection and two national sampling efforts in China. The average total abundance of the ARGs in globally distributed studied mine sites was 1572 times per gigabase, being rivaling that of urban sewage but much higher than that of freshwater sediments. Multidrug resistance genes accounted for 40% of the total ARG abundance, tended to co-occur with multimetal resistance genes, and were highly mobile (e.g. on average 16% occurring on plasmids). Among the 1848 high-quality metagenome-assembled genomes (MAGs), 85% carried at least one multidrug resistance gene plus one multimetal resistance gene. These high-quality ARG-carrying MAGs considerably expanded the phylogenetic diversity of ARG hosts, providing the first representatives of ARG-carrying MAGs for the Archaea domain and three bacterial phyla. Moreover, 54 high-quality ARG-carrying MAGs were identified as potential pathogens. Our findings suggest that mining-impacted environments worldwide are underexplored hotspots of multidrug resistance genes.
Keyphrases
- antibiotic resistance genes
- genome wide
- genome wide identification
- microbial community
- wastewater treatment
- anaerobic digestion
- genome wide analysis
- bioinformatics analysis
- human health
- escherichia coli
- transcription factor
- copy number
- endothelial cells
- quality improvement
- heavy metals
- risk assessment
- gene expression
- multidrug resistant
- big data
- deep learning
- neural network
- health risk assessment
- solid state