呼伦贝尔草原不同环境介质中特定抗生素耐药性基因的分布与演变特征（英文）

doi:10.7523/j.ucas.2025.007

摘要/Abstract

摘要：

采用荧光定量PCR、16S rRNA测序和网络分析等方法，研究2019—2021年夏季3种环境介质样品（雨水、表层土壤和PM_2.5）中特定的抗生素耐药基因（ARGs）。结果显示：呼伦贝尔草原3种环境介质均存在ARGs污染，并且土壤中丰度最高；与ARGs显著相关的细菌菌属有助于相关抗性基因在多种环境介质中分布传播并增强ARGs的潜在暴露风险。

关键词: 抗生素耐药性基因（ARGs）, 分布, 丰度, 演变, 多种环境介质, 呼伦贝尔草原

Abstract:

We measured the relative abundance of antibiotic resistance genes （ARGs） related to the treatment of brucellosis by quantitative real-time polymerase chain reaction （PCR）， characterized the bacterial composition by 16S rRNA gene sequencing， and analysed the relationships between ARGs and bacteria by network analyses in multiple environmental media samples （rainwater， topsoil and PM_2.5） collected in the summers from 2019 to 2021. The three environmental media of Hulunbuir Grassland were contaminated with some degree of ARGs with the highest ARG abundance in the topsoil. Significant relationships were observed between ARGs and bacteria， thereby promoting the spread of antibiotic resistance. Extensive distribution of ARG carrying bacteria in atmospheric particles， rainwater， and soil may also increase potential ARG exposure risks. These findings are valuable in understanding the ARG pollution levels and distribution characteristics in three environmental media， which can aid to design and implement policies to curb ARGs in local areas.

Key words: ARGs, distribution, abundance, evolution, multiple environmental media, Hulunbuir Grassland

中图分类号:

TK124

李圆圆, 郭立, 杜睿, 赵华, 贾泽宇. 呼伦贝尔草原不同环境介质中特定抗生素耐药性基因的分布与演变特征（英文）[J]. 中国科学院大学学报, 2026, 43(3): 336-349.

Yuanyuan LI, Li GUO, Rui DU, Hua ZHAO, Zeyu JIA. Distribution and evolution of certain antibiotic resistance genes in different environmental media in Hulunbuir Grassland[J]. Journal of University of Chinese Academy of Sciences, 2026, 43(3): 336-349.

图/表 7

Table 1 Sample information for detection of ARGs

sample ID	sample type	date	AQI
2019R1	rainwater	2019/07/19	47
2019R2	rainwater	2019/07/20	26
2020R1	rainwater	2020/07/30	23
2020R2	rainwater	2020/07/30	23
2020R3	rainwater	2020/08/01	22
2020R4	rainwater	2020/08/02	30
2021R1	rainwater	2021/07/30	30
2021R2	rainwater	2021/07/30	29
2021R3	rainwater	2021/07/31	24
2019S1	soil	2019/06/25	28
2019S2	soil	2019/07/14	43
2019S3	soil	2019/08/19	31
2020S1	soil	2020/07/30	23
2020S2	soil	2020/08/01(after rainwater)	22
2021S1	soil	2021/07/29	30
2021S2	soil	2021/08/01(after rainwater)	26
2020A1	air	2020/07/28	34
2020A2	air	2020/07/30	23
2021A1	air	2021/07/28	29

Fig.1 Heatmap （a） and relative abundance （b， c， d） of target genes detected in each sample

Fig.2 Distribution of target genes in multiple environment samples during the three years

Fig.3 Community structures of environmental samples at phylum （a） and genus （b） level

Fig.4 Community heatmap analysis at genus level

Fig.5 Co-occurrence network analysis of target genesNodes and edges are colored according to the homology of the ARGs. The size of each node is proportional to the number of connections， and the edges represent interactions between nodes. The thickness of each edge connecting two nodes is proportional to the value of the correlation coefficient， with the red representing positive and light green negative correlation.

Fig.6 Network analysis of target ARG genes and microbiota at the genus levelThe bacterial genera and ARGs categories are marked by various colors， with gray， blue， orange， and green representing sulfonamide， tetracycline， quinolone， and aminoglycoside resistance genes， respectively.

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