Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.
Microbiology and Immunology Graduate Program, Stanford University School of Medicine, Stanford, CA, USA.
Nature. 2021 Jul;595(7867):415-420. doi: 10.1038/s41586-021-03707-9. Epub 2021 Jul 14.
Gut microorganisms modulate host phenotypes and are associated with numerous health effects in humans, ranging from host responses to cancer immunotherapy to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microorganisms has hindered efforts to define mechanistic connections between individual microbial strains and host phenotypes. One key way in which the gut microbiome influences host physiology is through the production of small molecules, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect the products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites in diverse sample types. We report the metabolic profiles of 178 gut microorganism strains using our library of 833 metabolites. Using this metabolomics resource, we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover a previously undescribed type of metabolism in Bacteroides, and reveal candidate biochemical pathways using comparative genomics. Microbiota-dependent metabolites can be detected in diverse biological fluids from gnotobiotic and conventionally colonized mice and traced back to the corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microorganisms and interactions between microorganisms and their host.
肠道微生物群调节宿主表型,并与人类的许多健康影响相关,从宿主对癌症免疫治疗的反应到代谢疾病和肥胖。然而,由于难以对人类肠道微生物进行准确和高通量的功能分析,因此难以确定个体微生物菌株与宿主表型之间的机制联系。肠道微生物组影响宿主生理学的一个关键途径是通过产生小分子,然而,由于缺乏针对肠道厌氧生物化学产物的校准工具,阐明这种化学相互作用的进展受到了阻碍。在这里,我们构建了一个以微生物组为重点的集成质谱分析管道,以加速鉴定不同样本类型中依赖微生物组的代谢物。我们使用我们的 833 种代谢物文库报告了 178 种肠道微生物菌株的代谢特征。利用这个代谢组学资源,我们确定了系统发育和代谢之间关系的偏差,使用机器学习发现了拟杆菌中以前未描述的代谢类型,并通过比较基因组学揭示了候选生化途径。依赖于微生物组的代谢物可以在无菌和常规定植的小鼠的多种生物流体中检测到,并追溯到培养细菌的相应代谢组学特征。总的来说,我们的以微生物组为重点的代谢组学分析管道和交互式代谢组学图谱浏览器是一种强大的工具,可用于表征微生物及其与宿主之间的相互作用。