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使用多层链图研究猪中氟喹诺酮和大环内酯耐药性的危险因素。

Risk factors for fluoroquinolone- and macrolide-resistance among swine using multi-layered chain graphs.

作者信息

Wang C Annie, Love William J, Jara Manuel, van Vliet Arnoud H M, Thakur Siddhartha, Lanzas Cristina

机构信息

Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA.

School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.

出版信息

bioRxiv. 2025 Jan 16:2025.01.16.633345. doi: 10.1101/2025.01.16.633345.

Abstract

resistant to fluoroquinolones and macrolides are serious public health threats. Studies aiming to identify risk factors for drug-resistant have narrowly focused on antimicrobial use at the farm level. Using chain graphs, we quantified risk factors for fluoroquinolones- and macrolide-resistance in isolated from two distinctive swine production systems, conventional and antibiotic-free (ABF). The chain graphs were learned using genotypic and phenotypic resistance data from 1082 isolates and host exposures obtained through surveys for 18 cohorts of pigs. The gyrA T86I point mutation alone explained at least 58 % of the variance in ciprofloxacin minimum inhibitory concentration (MIC) for ABF and 79 % in conventional farms. For macrolides, genotype and host exposures explained similar variance in azithromycin and erythromycin MIC. Among host exposures, heavy metal exposures were identified as risk factors in both conventional and ABF. Chain graph models can generate insights into the complex epidemiology of antimicrobial resistance by characterizing context-specific risk factors and facilitating causal discovery.

摘要

对氟喹诺酮类和大环内酯类药物耐药是严重的公共卫生威胁。旨在确定耐药风险因素的研究一直狭隘地聚焦于养殖场层面的抗菌药物使用情况。我们使用链式图,对从传统养殖和无抗生素(ABF)这两种不同的生猪生产系统中分离出的菌株,量化了氟喹诺酮类和大环内酯类药物耐药的风险因素。链式图是利用来自1082株菌株的基因型和表型耐药数据以及通过对18个猪群进行调查获得的宿主暴露情况构建的。仅gyrA T86I点突变就至少解释了ABF养殖场中 ciprofloxacin最低抑菌浓度(MIC)变异的58%以及传统养殖场中79%的变异。对于大环内酯类药物,基因型和宿主暴露情况在阿奇霉素和红霉素MIC方面解释了相似的变异。在宿主暴露因素中,重金属暴露被确定为传统养殖和ABF养殖中的风险因素。链式图模型通过刻画特定背景下的风险因素并促进因果关系发现,能够深入了解抗菌药物耐药性的复杂流行病学情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5904/11761704/909bda0558cb/nihpp-2025.01.16.633345v1-f0001.jpg

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