高通量分析人类粪便、皮肤和水环境中共享抗生素抗性基因的分布

doi:10.13560/j.cnki.biotech.bull.1985.2022-1430

摘要/Abstract

摘要：

抗生素抗性基因（antibiotic resistance genes, ARGs）是一种新污染物，其在环境中的传播加剧了抗生素耐药问题。但是，关于人体和水环境中共享ARGs的分布及潜在宿主知之甚少。为此，本文使用高通量荧光定量PCR和16S rRNA基因测序，分析来自中国城郊地区的人体和水环境中的ARGs结构和细菌群落特征。在粪便、皮肤和水样中共确定了人体和水环境之间的70个共享ARGs、7个共享移动遗传元件（mobile genetic elements, MGEs）和58个共享细菌。粪便中占主导地位的共享ARGs为四环素和MLSB抗性基因。此外，通过LEfSe（linear discriminant analysis effect size）确定了20种细菌生物标志物。通过网络分析揭示了共享ARGs、MGEs与共享细菌具有显著相关（P < 0.05），表明这些细菌可能是ARGs的潜在宿主并且在人体和环境之间转移。本研究揭示了饮用水、粪便、皮肤、污水和地表水样本中共享的ARGs。研究结果更好地理解了共享ARGs和细菌在环境中的共同发生和传播，揭示了城郊地区人体与环境中ARGs的潜在宿主。

关键词: 高通量, 抗生素抗性基因, 水环境, 人体, 城郊

Abstract:

Antibiotic resistance genes（ARGs）are emerging contaminants, whose spread in the environment exacerbates antibiotic resistance issues. Little is known about the critical and shared antibiotic resistance genes（ARGs）and their potential hosts in human and water environments. Here, we characterized the ARG profile and bacterial community in human and water environments from a Chinese rural village using high-throughput quantitative polymerase chain reaction and 16S rRNA gene sequencing. A total of 70 shared ARGs, 7 shared mobile genetic elements（MGEs）, and 58 shared bacteria between human and water environments were identified. The dominant shared ARGs in feces were both tetracycline and MLSB resistance genes. Further, we identified 20 bacterial biomarkers by linear discriminant analysis effect size（LEfSe）. The network revealed a significant association among shared ARGs, MGEs, and bacteria（P < 0.05）, suggesting the potential transfer of ARGs between humans and environments via these bacteria. This study revealed the shared ARGs in drinking water, feces, skin, wastewater, and river samples. Our results may provide a better understanding of the co-occurrence of these genetic elements and identify the potential hosts of ARGs between humans and the environment in peri-urban areas. In conclusion, this study demonstrated the co-occurrence and transmission of shared ARGs and bacteria in humans and water environments.

Key words: high-throughput, antibiotic resistance gene, water environment, human, rural area

周振超, 郑吉, 帅馨怡, 林泽俊, 陈红. 高通量分析人类粪便、皮肤和水环境中共享抗生素抗性基因的分布[J]. 生物技术通报, 2023, 39(7): 288-297.

ZHOU Zhen-chao, ZHENG Ji, SHUAI Xin-yi, LIN Ze-jun, CHEN Hong. High-throughput Profiling and Analysis of Shared Antibiotic Resistance Genes in Human Feces, Skin and Water Environments[J]. Biotechnology Bulletin, 2023, 39(7): 288-297.

图/表 6

图1 人体和水环境样品中抗生素抗性基因相对丰度红色圆点代表了70个共享抗生素抗性基因。误差棒代表了最大最小值，中线代表了中位值

Fig. 1 Relative abundances of ARGs in humans and water samples Red points indicate commonly shared ARGs in human feces, skin and water（n =70）. Error bars indicate min/max values, and centre bars indicate median

图2 共享抗生素抗性基因丰度热图

Fig. 2 Heatmap of shared ARGs abundances

图3 不同环境中共享 ARGs 的相对丰度（A）和比例（B）

Fig. 3 Relative abundances（A）and proportions（B）of shared ARGs in different environments

图4 基于LEfSe区分人类粪便、皮肤和水环境中差异特征微生物分类群 A：在系统发育树中显示了门到科类别的分类群（Kruskal-Wallis 和成对 Wilcoxon 秩和检验的 alpha 值 < 0.05，LDA 效应大小≥ 4.0），圆圈大小与分类群丰度成正比。B，C：人类粪便、皮肤和水微生物群中区分微生物门（B）和科（C）的相对丰度。仅显示了一个类别中平均相对丰度≥ 1% 的分类群。误差条代表标准偏差，中线代表中位值，黑点代表异常值。

Fig. 4 Taxa discriminates among human feces, skin and water microbiota as determined by LEfSe A: Kingdom- to family-level taxa were shown in the phylogenetic tree. Colored taxa are discriminative between categories（alpha value of < 0.05 for both the Kruskal-Wallis and pairwise Wilcoxon rank-sum tests, and LDA effect size of ≥ 4.0）, circle size is proportional to the taxa abundance. B, C: Relative abundances of discriminative microbial phyla（B）and families（C）in human feces, skin and water microbiota. Only taxa with a mean relative abundance of ≥ 1% in one category are shown. Error bars indicate standard deviation（s. d.）, centre bars indicate median, and black points indicate outliers

图5 共享细菌丰度热图

Fig. 5 Heatmap of shared bacteria abundances

图6 所有检测到的 ARGs、MGEs和细菌的网络分析（A）和共享 ARGs、MGEs和细菌的网络分析（B）节点根据模块化等级着色。连接表示强（Spearman 相关系数（ρ）>0.7）和显著（P 值<0.01）相关性。根据连接数（即度）加权的节点大小和根据相关系数加权的边

Fig. 6 Network analysis of all detected ARGs, MGEs and bacteria（A）and shared ARGs, MGEs and bacteria（B） The nodes were colored according to modularity class. A connection represents a strong（Spearman's correlation coefficient（ρ）>0.7）and significant（P-value<0.01）correlation. Node size weighted according to the number of connections（that is the degree）and edges weighted according to the correlation coefficient

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