Choo Jocelyn M, Kanno Tokuwa, Zain Nur Masirah Mohd, Leong Lex E X, Abell Guy C J, Keeble Julie E, Bruce Kenneth D, Mason A James, Rogers Geraint B
Infection and Immunity Theme, South Australia Health and Medical Research Institute, North Terrace, Adelaide, SA, Australia; School of Medicine, Flinders University, Bedford Park, Adelaide, SA, Australia.
King's College London, Institute of Pharmaceutical Science, London, United Kingdom.
mSphere. 2017 Feb 8;2(1). doi: 10.1128/mSphere.00005-17. eCollection 2017 Jan-Feb.
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. 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.
肠道微生物群落在调节宿主生理学的许多方面发挥着重要作用，通过接触抗生素对其造成的破坏与一系列严重病理状况的发展有关。肠道微生物群成员之间存在复杂的代谢关系，且功能途径存在潜在冗余，这意味着需要对结构和功能的变化进行综合分析，以了解抗生素暴露的影响。我们结合使用下一代测序和核磁共振（NMR）代谢组学，来表征两种具有临床重要性的抗生素治疗（环丙沙星和万古霉素-亚胺培南）对雌性C57BL/6小鼠肠道微生物群的影响。该评估是纵向进行的，涵盖了抗生素挑战和随后的微生物群重建。两种抗生素治疗在挑战和恢复期间均显著改变了粪便颗粒的微生物群和代谢物组成。对微生物群和NMR数据进行的斯皮尔曼相关性分析表明，虽然一些代谢物可能与单个操作分类单元（OTU）相关，但通常多个OTU与给定代谢物的显著变化有关。此外，一种代谢物精氨酸在不同条件下可与不同OTU集的增加/减少相关。综上所述，这些发现表明，仅依赖一个数据集的变化将无法完整呈现抗生素干预的功能效应。要全面理解其作用机制，需要了解基线微生物群组成，并结合微生物群、代谢组学和表型数据进行比较和整合。尽管抗生素疗法对人类健康至关重要，但其对肠道微生物群的功能影响及其随后的恢复能力仍知之甚少。该领域的许多研究都集中在微生物群组成的变化上，尽管微生物群落的许多成员之间存在相互依存和重叠的功能。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撑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，我们使用微生物群和代谢组联合分析来表征与对肠道有不同影响的具有临床重要性的抗生素组合相关的变化。代谢组和微生物群变化的相关性分析表明，联合方法对于从机制上理解不同抗生素类别的功能影响至关重要。
