荷兰成人中产超广谱β-内酰胺酶的大肠埃希菌的肠道定植及其与肠道微生物组和代谢组的关系：一项配对的病例对照研究。
荷兰成人中产超广谱β-内酰胺酶的大肠埃希菌的肠道定植及其与肠道微生物组和代谢组的关系：一项配对的病例对照研究。
Gut colonisation by extended-spectrum β-lactamase-producing Escherichia coli and its association with the gut microbiome and metabolome in Dutch adults: a matched case-control study.
发布日期 : 2022-06-06
文献类型 :论著
PMID :35659906
DOI :10.1016/S2666-5247(22)00037-4
作者 :
Ducarmon Quinten R , Zwittink Romy D , Willems Roel P J , Verhoeven Aswin , Nooij Sam , van der Klis Fiona R M , Franz Eelco ,
Kool Jolanda , Giera Martin , VandenbrouckeGrauls Christina M J E , Fuentes Susana , Kuijper Ed J
期刊 :LANCET (柳叶刀) IF: 79.323中科院 1 区
中文摘要
背景：产超广谱β-内酰胺酶(ESBL)的大肠埃希菌肠道定植是发生显性感染的危险因素。肠道微生物群可以对肠道病原体提供定植抗性，但尚不清楚它是否对产生ESBL的大肠杆菌产生耐药性。我们的目标是确定微生物组在控制这种耐药细菌定植方面的潜在作用。
方法：在这项配对病例对照研究中，我们使用荷兰横断面人群研究(PIENTER-3)中2751人的粪便来培养产生ESBL的细菌。在这些样本中，我们选择了49个样本，这些样本对产生ESBL的大肠杆菌呈阳性(ESBL阳性)，而对几个已知影响微生物组组成的变量呈阴性。这些样本与ESBL阴性样本根据个人的年龄、性别、过去6个月是否出国以及种族进行了1：1的匹配。进行了鸟枪式元基因组测序，并测定了分类种组成和功能注释(即微生物代谢和碳水化合物活性酶)。用靶向定量代谢谱(质子核磁共振波谱)研究代谢谱，并使用单变量(t检验和Wilcoxon检验)、多变量(主坐标分析、排列多变量方差分析和偏最小二乘判别分析)和机器学习方法(最小绝对收缩和选择算子和随机森林)的组合来分析所有分子数据。
结果：ESBL阳性组和ESBL阴性组在细菌物种水平组成的多样性参数或相对丰度方面没有差异。当使用微生物群组成或任何功能谱时，使用微生物群组成的机器学习方法不能准确地预测ESBL状态(接收器操作特征曲线下的面积[AUROC]=0.41)。ESBL组之间的代谢组也没有差异，通过包括随机森林(AUROC=0.61)在内的各种方法进行评估。
解释：通过结合多重组学和机器学习方法，我们得出结论，产超广谱β-内酰胺酶的大肠杆菌的无症状肠道携带与微生物群组成或功能的改变无关。这一发现可能表明，微生物群介导的对产超广谱β-内酰胺酶的大肠杆菌的定植耐药性并不像对其他肠道病原体和耐药细菌那样相关。
英文摘要
Background: 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.
Methods: 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.
Findings: 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).
Interpretation: 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.
