Department of Medical Microbiology, Leiden University Medical Centre, Leiden, Netherlands; Centre for Microbiome Analyses and Therapeutics, Leiden University Medical Centre, Leiden, Netherlands.
Department of Medical Microbiology, Leiden University Medical Centre, Leiden, Netherlands; Centre for Microbiome Analyses and Therapeutics, Leiden University Medical Centre, Leiden, Netherlands.
Lancet Microbe. 2022 Jun;3(6):e443-e451. doi: 10.1016/S2666-5247(22)00037-4. Epub 2022 Apr 20.
Gut colonisation by extended-spectrum β-lactamase (ESBL)-producing Escherichia coli is a risk factor for developing overt infection. The gut microbiome can provide colonisation resistance against enteropathogens, but it remains unclear whether it confers resistance against ESBL-producing E coli. We aimed to identify a potential role of the microbiome in controlling colonisation by this antibiotic-resistant bacterium.
For this matched case-control study, we used faeces from 2751 individuals in a Dutch cross-sectional population study (PIENTER-3) to culture ESBL-producing bacteria. Of these, we selected 49 samples that were positive for an ESBL-producing E coli (ESBL-positive) and negative for several variables known to affect microbiome composition. These samples were matched 1:1 to ESBL-negative samples on the basis of individuals' age, sex, having been abroad or not in the past 6 months, and ethnicity. Shotgun metagenomic sequencing was done and taxonomic species composition and functional annotations (ie, microbial metabolism and carbohydrate-active enzymes) were determined. Targeted quantitative metabolic profiling (proton nuclear magnetic resonance spectroscopy) was done to investigate metabolomic profiles and combinations of univariate (t test and Wilcoxon test), multivariate (principal coordinates analysis, permutational multivariate analysis of variance, and partial least-squares discriminant analysis) and machine-learning approaches (least absolute shrinkage and selection operator and random forests) were used to analyse all the molecular data.
No differences in diversity parameters or in relative abundance were observed between ESBL-positive and ESBL-negative groups based on bacterial species-level composition. Machine-learning approaches using microbiota composition did not accurately predict ESBL status (area under the receiver operating characteristic curve [AUROC]=0·41) when using either microbiota composition or any of the functional profiles. The metabolome also did not differ between ESBL groups, as assessed by various methods including random forest (AUROC=0·61).
By combining multiomics and machine-learning approaches, we conclude that asymptomatic gut carriage of ESBL-producing E coli is not associated with an altered microbiome composition or function. This finding might suggest that microbiome-mediated colonisation resistance against ESBL-producing E coli is not as relevant as it is against other enteropathogens and antibiotic-resistant bacteria.
None.
产超广谱β-内酰胺酶(ESBL)的大肠杆菌定植于肠道是发生显性感染的一个风险因素。肠道微生物组可以提供针对肠道病原体的定植抵抗,但目前尚不清楚它是否能抵抗产 ESBL 的大肠杆菌。我们旨在确定微生物组在控制这种抗生素耐药菌定植方面的潜在作用。
在一项荷兰横断面人群研究(PIENTER-3)中,我们使用 2751 名个体的粪便来培养产 ESBL 的细菌。在这些细菌中,我们选择了 49 个样本,这些样本对 ESBL 阳性且对几种已知影响微生物组组成的变量呈阴性。这些样本基于个体的年龄、性别、过去 6 个月是否出过国以及种族与 ESBL 阴性样本进行 1:1 匹配。我们进行了 shotgun 宏基因组测序,并确定了分类物种组成和功能注释(即微生物代谢和碳水化合物活性酶)。我们进行了靶向定量代谢谱分析(质子核磁共振光谱),以研究代谢组学图谱和单变量(t 检验和 Wilcoxon 检验)、多变量(主坐标分析、置换多元方差分析和偏最小二乘判别分析)以及机器学习方法(最小绝对收缩和选择算子和随机森林)的组合,用于分析所有分子数据。
基于细菌物种组成,ESBL 阳性和 ESBL 阴性组之间的多样性参数或相对丰度没有差异。使用微生物组组成的机器学习方法不能准确预测 ESBL 状态(接受者操作特征曲线下面积[AUROC]=0.41),无论是使用微生物组组成还是任何功能谱。通过各种方法(包括随机森林[AUROC=0.61]),ESBL 组之间的代谢组也没有差异。
通过结合多组学和机器学习方法,我们得出的结论是,无症状肠道产 ESBL 的大肠杆菌定植与微生物组组成或功能的改变无关。这一发现可能表明,微生物组介导的针对产 ESBL 的大肠杆菌定植抵抗并不像针对其他肠道病原体和抗生素耐药菌那样重要。
无。