猪肠道微生物组抗菌素耐药性的多组学监测。

IF 4.9 Q1 MICROBIOLOGY
Judith Guitart-Matas, Arturo Vera-Ponce de León, Phillip B Pope, Torgeir R Hvidsten, Lorenzo Fraile, Maria Ballester, Yuliaxis Ramayo-Caldas, Lourdes Migura-Garcia
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引用次数: 0

摘要

背景:高通量测序技术在监测重大全球卫生挑战(如抗菌素耐药性的出现)方面发挥着越来越积极的作用。断奶后时期对养猪业至关重要,如果在此期间发生感染,仍然需要使用抗菌剂。在这里,两种测序方法,散弹枪宏基因组学和亚转录组学,已经被应用于破译断奶后腹泻中使用的不同处理对猪肠道微生物组转录组和抵抗组的影响。为此,我们建立了宏基因组组装基因组(MAG)目录,作为参考数据库,通过横断面和纵向设计,从140个猪粪便样本中获得转录本定位,研究差异基因表达。不同的治疗方法包括抗菌剂甲氧苄啶/磺胺甲恶唑、粘菌素、庆大霉素和阿莫西林,以及一种口服商业疫苗、一种水酸化对照和一种未经治疗的对照。对于转转录组学,在断奶前、治疗后3天和4周选择猪的粪便样本。结果:最终的非冗余MAGs收集包括从每个治疗组和元基因组学数据的采样时间中获得的单个组装和共组装的1396个基因组。与在reads水平发现的ARGs相比,在该组装水平分析抗菌耐药基因(ARGs)大大减少了鉴定出的ARGs总数。此外,从亚转录组学数据来看,在所有治疗组中,有一半的ARGs被检测到转录活性。处理后不同采样时间的差异基因表达发现,与连续阿莫西林处理组相比,差异表达基因(DEGs)数量较多,且与耐药性相关。此外,在治疗后3天,在庆大霉素组中检测到大量显著下调的基因。在这个采样时间,该组显示核糖体相关基因的表达改变,证明庆大霉素对抑制细菌蛋白质合成的快速作用。结论:不同的抗菌药物处理对微生物群落的转录组和抗性组的影响不同,这突出了新型测序方法监测抗性组的相关性,并有助于更有效的抗菌药物管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-omics surveillance of antimicrobial resistance in the pig gut microbiome.

Background: High-throughput sequencing technologies play an increasingly active role in the surveillance of major global health challenges, such as the emergence of antimicrobial resistance. The post-weaning period is of critical importance for the swine industry and antimicrobials are still required when infection occurs during this period. Here, two sequencing approaches, shotgun metagenomics and metatranscriptomics, have been applied to decipher the effect of different treatments used in post-weaning diarrhea on the transcriptome and resistome of pig gut microbiome. With this objective, a metagenome-assembled genome (MAG) catalogue was generated to use as a reference database for transcript mapping obtained from a total of 140 pig fecal samples in a cross-sectional and longitudinal design to study differential gene expression. The different treatments included antimicrobials trimethoprim/sulfamethoxazole, colistin, gentamicin, and amoxicillin, and an oral commercial vaccine, a control with water acidification, and an untreated control. For metatranscriptomics, fecal samples from pigs were selected before weaning, three days and four weeks post-treatment.

Results: The final non-redundant MAGs collection comprised a total of 1396 genomes obtained from single assemblies and co-assemblies per treatment group and sampling time from the metagenomics data. Analysis of antimicrobial resistance genes (ARGs) at this assembly level considerably reduced the total number of ARGs identified in comparison to those found at the reads level. Besides, from the metatranscriptomics data, half of those ARGs were detected transcriptionally active in all treatment groups. Differential gene expression between sampling times after treatment found major number of differential expressed genes (DEGs) against the group treated continuously with amoxicillin, with DEGs being correlated with antimicrobial resistance. Moreover, at three days post-treatment, a high number of significantly downregulated genes was detected in the group treated with gentamicin. At this sampling time, this group showed an altered expression of ribosomal-related genes, demonstrating the rapid effect of gentamicin to inhibit bacterial protein synthesis.

Conclusions: Different antimicrobial treatments can impact differently the transcriptome and resistome of microbial communities, highlighting the relevance of novel sequencing approaches to monitor the resistome and contribute to a more efficient antimicrobial stewardship.

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CiteScore
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