Distribution of tetracyclines and sulfonamides resistance genes around a smallholder pig farm: Modeling and attribution.

IF 8.2 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Science of the Total Environment Pub Date : 2024-12-01 Epub Date: 2024-09-29 DOI:10.1016/j.scitotenv.2024.176618
Haofei Li, Zishu Liu, Baolan Hu, Lizhong Zhu
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Abstract

Livestock farm is a major source of antibiotics and antibiotic resistance genes (ARGs) pollution. ARGs can directly enter the environment through runoff and air deposition. The impact extent and the driving factors require further investigation to inform effective policies and actions to mitigate their spread. This study investigated a smallholder pig farm and its surrounding areas to understand the spread of ARGs. Topsoil samples were collected from 56 different sites within one kilometer of the farm, and a comprehensive analysis was conducted to reveal effects of soil properties, antibiotic residues, microbiome, mobilome on the variation of typical ARGs. The results confirmed that the ARGs reduced exponentially with increasing distance from the farm, with a goodness of fit (R2) of 0.7 for total ARGs. For tetracyclines (TC) and sulfonamides (SA) resistance genes, the fitting R2 exceeded 0.9. Model estimates allowed for quantitative comparisons of in-farm increments, out-farm background levels, and spread abilities of ARGs with distinct resistance mechanisms. SA-specific resistance genes (SRGs, 0.097 copies/16S rRNA gene) and TC-specific resistance genes (TRGs, 0.036 copies/16S rRNA gene) showed higher within-farm increases compared to multidrug resistance genes (MDRGs, 0.020 copies/16S rRNA gene). MDRGs, however, had a higher background level and a greater impact distance (0.18 km, 4.4 times the farm radius). Additionally spread abilities of TRGs varied by resistance mechanism, with ribosome protection proteins showing greater spread than TC inactivating enzymes and TC efflux pumps, likely due to different fitness costs. Correlation analysis and structural equation modeling indicated that changes in bacterial community composition and mobilome are primary factors influencing ARGs variation during their spread. Abiotic factors like soil nutrients and antibiotics also selectively enriched ARGs within the farm. These findings provide insights into the ARGs dissemination and could inform strategies to prevent their spread from smallholder livestock farms.

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四环素类和磺胺类药物耐药性基因在小农养猪场的分布:建模与归因。
畜牧场是抗生素和抗生素耐药基因(ARGs)污染的主要来源。ARGs 可通过径流和空气沉降直接进入环境。需要进一步调查其影响范围和驱动因素,以便制定有效的政策和行动来减缓其传播。本研究调查了一个小农养猪场及其周边地区,以了解 ARGs 的传播情况。研究人员从猪场一公里范围内的 56 个不同地点采集了表土样本,并进行了综合分析,以揭示土壤特性、抗生素残留、微生物组、移动组对典型 ARGs 变化的影响。结果证实,随着与农场距离的增加,ARGs呈指数下降,总ARGs的拟合优度(R2)为0.7。四环素(TC)和磺胺(SA)抗性基因的拟合 R2 超过 0.9。通过模型估算,可对具有不同抗性机制的 ARGs 的农场内增量、农场外背景水平和传播能力进行定量比较。与多药抗性基因(MDRGs,0.020 个拷贝/16S rRNA 基因)相比,SA 特异性抗性基因(SRGs,0.097 个拷贝/16S rRNA 基因)和 TC 特异性抗性基因(TRGs,0.036 个拷贝/16S rRNA 基因)显示出更高的农场内增量。然而,MDRGs 的背景水平更高,影响距离更远(0.18 千米,是农场半径的 4.4 倍)。此外,TRGs的扩散能力因抗性机制而异,核糖体保护蛋白比TC灭活酶和TC外排泵的扩散能力更强,这可能是由于不同的适应成本造成的。相关分析和结构方程建模表明,细菌群落组成和动员组的变化是影响 ARGs 在扩散过程中变化的主要因素。土壤养分和抗生素等非生物因素也会选择性地富集农场内的 ARGs。这些发现有助于深入了解 ARGs 的传播情况,并为防止 ARGs 从小农畜牧场传播的策略提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Science of the Total Environment
Science of the Total Environment 环境科学-环境科学
CiteScore
17.60
自引率
10.20%
发文量
8726
审稿时长
2.4 months
期刊介绍: The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.
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