摘要

       宏基因组研究有可能为微生物生态学提供前所未有的洞察，例如与抗菌素耐药性 (AMR) 相关的生态学。我们使用鸟枪宏基因组学对常规饲养牛 (CONV) 或无抗生素暴露 (RWA) 的畜牧业中的微生物抗性组进行了表征。从 CONV 和 RWA 饲养场和奶牛场收集粪便、集水盆地的废水和应用废水的土壤样本。在 DNA 提取和测序后，将鸟枪宏基因组读数与参考数据库进行比对，以识别细菌 (Kraken) 和抗生素抗性基因 (ARG) 种质 (MEGARes)。在具有不同生产实践（CONV 与 RWA）、牛的类型（牛肉与乳制品）和样品类型（粪便与废水与土壤）的农场之间发现微生物抗性组的差异。粪便中每个样本的 ARG 数量最多（CONV 和 RWA 中的平均值分别为 118 和 79），四环素外排泵、大环内酯磷酸转移酶和氨基糖苷类核苷酸转移酶在 CONV 中的耐药机制比 RWA 粪便中更丰富。四环素类和大环内酯类-林可酰胺类-链霉菌素类耐药性在饲养场牛中比在奶牛粪便中更丰富，而β-内酰胺类在奶牛粪便中更丰富。 ARGs 和微生物群落之间缺乏一致性（procrustes 分析）表明，除了使用抗生素外，其他因素（例如，农场的位置、牛源、管理实践、饮食、水平 ARGs 转移和耐药性的共同选择）可能已经影响了抵抗组谱。出于这个原因，我们无法建立抗菌素使用和 AMR 之间的因果关系，尽管粪便和流出物中的 ARGs 与根据农场记录（饲养场中的四环素和大环内酯类，饲养场中的 β-内酰胺类）用于治疗动物的药物类别有关。乳制品），而土壤中的 ARG 以多药耐药性为主。动物源性和环境样本的“抗性潜力”表征是将宏基因组方法纳入农业系统 AMR 监测的第一步。需要进一步研究来评估与不同微生物抗性组相关的公共卫生风险。

       Metagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide–lincosamide–streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the β-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle source, management practices, diet, horizontal ARGs transfer, and co-selection of resistance), in addition to antimicrobial use, could have impacted resistome profiles. For that reason, we could not establish a cause–effect relationship between antimicrobial use and AMR, although ARGs in feces and effluents were associated with drug classes used to treat animals according to farms’ records (tetracyclines and macrolides in feedlots, β-lactams in dairies), whereas ARGs in soil were dominated by multidrug resistance. Characterization of the “resistance potential” of animal-derived and environmental samples is the first step toward incorporating metagenomic approaches into AMR surveillance in agricultural systems. Further research is needed to assess the public-health risk associated with different microbial resistomes.

https://www.frontiersin.org/articles/10.3389/fmicb.2019.01980/full