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人类肠道细菌分离株文库与纵向多组学数据配对,可实现基于机制的微生物组研究。

A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research.

机构信息

Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA.

出版信息

Nat Med. 2019 Sep;25(9):1442-1452. doi: 10.1038/s41591-019-0559-3. Epub 2019 Sep 2.

Abstract

Our understanding of how the gut microbiome interacts with its human host has been restrained by limited access to longitudinal datasets to examine stability and dynamics, and by having only a few isolates to test mechanistic hypotheses. Here, we present the Broad Institute-OpenBiome Microbiome Library (BIO-ML), a comprehensive collection of 7,758 gut bacterial isolates paired with 3,632 genome sequences and longitudinal multi-omics data. We show that microbial species maintain stable population sizes within and across humans and that commonly used 'omics' survey methods are more reliable when using averages over multiple days of sampling. Variation of gut metabolites within people over time is associated with amino acid levels, and differences across people are associated with differences in bile acids. Finally, we show that genomic diversification can be used to infer eco-evolutionary dynamics and in vivo selection pressures for strains within individuals. The BIO-ML is a unique resource designed to enable hypothesis-driven microbiome research.

摘要

我们对肠道微生物组与其人类宿主相互作用的理解受到限制,因为我们只能有限地获得纵向数据集来检查稳定性和动态,并且只能使用少数几个分离株来测试机制假设。在这里,我们展示了 Broad Institute-OpenBiome 微生物组文库(BIO-ML),这是一个包含 7758 个肠道细菌分离株的综合集合,与 3632 个基因组序列和纵向多组学数据配对。我们表明,微生物物种在个体内和个体间保持稳定的种群数量,并且当使用多天采样平均值时,常用的“组学”调查方法更可靠。随着时间的推移,人体内肠道代谢物的变化与氨基酸水平有关,人与人之间的差异与胆汁酸的差异有关。最后,我们表明基因组多样化可用于推断个体内菌株的生态进化动态和体内选择压力。BIO-ML 是一个独特的资源,旨在支持基于假设的微生物组研究。

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