Divergent Relationships between Fecal Microbiota and Metabolome following Distinct Antibiotic-Induced Disruptions.
Divergent Relationships between Fecal Microbiota and Metabolome following Distinct Antibiotic-Induced Disruptions.
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DOI：
10.1128/msphere.00005-17
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发表时间：
2017-01
期刊：
影响因子：
4.8
通讯作者：
Rogers GB
中科院分区：
文献类型：
--
作者：
Choo JM;Kanno T;Zain NM;Leong LE;Abell GC;Keeble JE;Bruce KD;Mason AJ;Rogers GB
尽管抗生素治疗对人类健康至关重要，但它们对肠道微生物群的功能影响及其随后的恢复能力知之甚少。尽管微生物群落的许多成员具有相互依赖和重叠的功能，但该领域的许多研究都集中在微生物群组成的变化上。这些关系使得预测微生物群水平变化的功能影响变得困难，而仅基于代谢组的分析对支持代谢物水平变化的分类群水平变化提供的见解相对较少。在这里，我们使用联合微生物群和代谢组分析来表征与临床重要的抗生素组合相关的变化，这些抗生素组合对肠道有不同的影响。代谢组和微生物群变化的相关性分析表明，综合方法对于了解不同抗生素类别的功能影响的机制至关重要。肠道微生物群在调节宿主生理的许多方面起着至关重要的作用，抗生素暴露对肠道微生物群的破坏与一系列严重疾病的发展有关。肠道微生物群成员之间存在复杂的代谢关系以及功能途径的潜在冗余意味着需要对结构和功能变化进行综合分析，以了解抗生素暴露的影响。我们使用新一代测序和核磁共振（NMR）代谢组学的组合来表征两种临床上重要的抗生素治疗环丙沙星和万古霉素-亚胺培南对雌性C57BL/6小鼠肠道微生物组的影响。该评估是纵向进行的，包括抗生素挑战和随后的微生物组重建。两种抗生素治疗在挑战和恢复期间显著改变了粪便微球的微生物群和代谢物组成。Spearman对微生物群和NMR数据的相关性分析显示，虽然一些代谢物可能与单个操作分类单位（otu）相关，但通常多个otu与给定代谢物的显着变化相关。此外，一种代谢物精氨酸可能与不同条件下不同组OTUs的增加/减少有关。综上所述，这些发现表明，仅依赖于一个数据集的变化将产生抗生素干预功能效应的不完整图景。一个完整的机制理解将需要了解基本的微生物群组成，结合微生物群、代谢组学和表型数据的比较和整合。尽管抗生素治疗对人类健康至关重要，但它们对肠道微生物群的功能影响及其随后的恢复能力知之甚少。尽管微生物群落的许多成员具有相互依赖和重叠的功能，但该领域的许多研究都集中在微生物群组成的变化上。这些关系使得预测微生物群水平变化的功能影响变得困难，而仅基于代谢组的分析对支持代谢物水平变化的分类群水平变化提供的见解相对较少。在这里，我们使用联合微生物群和代谢组分析来表征与临床重要的抗生素组合相关的变化，这些抗生素组合对肠道有不同的影响。代谢组和微生物群变化的相关性分析表明，综合方法对于了解不同抗生素类别的功能影响的机制至关重要。
Despite the fundamental importance of antibiotic therapies to human health, their functional impact on the intestinal microbiome and its subsequent ability to recover are poorly understood. Much research in this area has focused on changes in microbiota composition, despite the interdependency and overlapping functions of many members of the microbial community. These relationships make prediction of the functional impact of microbiota-level changes difficult, while analyses based on the metabolome alone provide relatively little insight into the taxon-level changes that underpin changes in metabolite levels. Here, we used combined microbiota and metabolome profiling to characterize changes associated with clinically important antibiotic combinations with distinct effects on the gut. Correlation analysis of changes in the metabolome and microbiota indicate that a combined approach will be essential for a mechanistic understanding of the functional impact of distinct antibiotic classes. The intestinal microbiome plays an essential role in regulating many aspects of host physiology, and its disruption through antibiotic exposure has been implicated in the development of a range of serious pathologies. The complex metabolic relationships that exist between members of the intestinal microbiota and the potential redundancy in functional pathways mean that an integrative analysis of changes in both structure and function are needed to understand the impact of antibiotic exposure. We used a combination of next-generation sequencing and nuclear magnetic resonance (NMR) metabolomics to characterize the effects of two clinically important antibiotic treatments, ciprofloxacin and vancomycin-imipenem, on the intestinal microbiomes of female C57BL/6 mice. This assessment was performed longitudinally and encompassed both antibiotic challenge and subsequent microbiome reestablishment. Both antibiotic treatments significantly altered the microbiota and metabolite compositions of fecal pellets during challenge and recovery. Spearman’s correlation analysis of microbiota and NMR data revealed that, while some metabolites could be correlated with individual operational taxonomic units (OTUs), frequently multiple OTUs were associated with a significant change in a given metabolite. Furthermore, one metabolite, arginine, can be associated with increases/decreases in different sets of OTUs under differing conditions. Taken together, these findings indicate that reliance on shifts in one data set alone will generate an incomplete picture of the functional effect of antibiotic intervention. A full mechanistic understanding will require knowledge of the baseline microbiota composition, combined with both a comparison and an integration of microbiota, metabolomics, and phenotypic data. IMPORTANCE Despite the fundamental importance of antibiotic therapies to human health, their functional impact on the intestinal microbiome and its subsequent ability to recover are poorly understood. Much research in this area has focused on changes in microbiota composition, despite the interdependency and overlapping functions of many members of the microbial community. These relationships make prediction of the functional impact of microbiota-level changes difficult, while analyses based on the metabolome alone provide relatively little insight into the taxon-level changes that underpin changes in metabolite levels. Here, we used combined microbiota and metabolome profiling to characterize changes associated with clinically important antibiotic combinations with distinct effects on the gut. Correlation analysis of changes in the metabolome and microbiota indicate that a combined approach will be essential for a mechanistic understanding of the functional impact of distinct antibiotic classes.