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人类微生物组功能转变的分类驱动因素的系统表征与分析

Systematic Characterization and Analysis of the Taxonomic Drivers of Functional Shifts in the Human Microbiome.

作者信息

Manor Ohad, Borenstein Elhanan

机构信息

Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.

Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA; Santa Fe Institute, Santa Fe, NM 87501, USA.

出版信息

Cell Host Microbe. 2017 Feb 8;21(2):254-267. doi: 10.1016/j.chom.2016.12.014. Epub 2017 Jan 19.

Abstract

Comparative analyses of the human microbiome have identified both taxonomic and functional shifts that are associated with numerous diseases. To date, however, microbiome taxonomy and function have mostly been studied independently and the taxonomic drivers of functional imbalances have not been systematically identified. Here, we present FishTaco, an analytical and computational framework that integrates taxonomic and functional comparative analyses to accurately quantify taxon-level contributions to disease-associated functional shifts. Applying FishTaco to several large-scale metagenomic cohorts, we show that shifts in the microbiome's functional capacity can be traced back to specific taxa. Furthermore, the set of taxa driving functional shifts and their contribution levels vary markedly between functions. We additionally find that similar functional imbalances in different diseases are driven by both disease-specific and shared taxa. Such integrated analysis of microbiome ecological and functional dynamics can inform future microbiome-based therapy, pinpointing putative intervention targets for manipulating the microbiome's functional capacity.

摘要

对人类微生物组的比较分析已经确定了与多种疾病相关的分类学和功能变化。然而,迄今为止,微生物组分类学和功能大多是独立研究的,功能失衡的分类学驱动因素尚未得到系统识别。在这里,我们展示了FishTaco,这是一个分析和计算框架,它整合了分类学和功能比较分析,以准确量化分类单元水平对疾病相关功能变化的贡献。将FishTaco应用于几个大规模宏基因组队列,我们表明微生物组功能能力的变化可以追溯到特定的分类单元。此外,驱动功能变化的分类单元集及其贡献水平在不同功能之间有显著差异。我们还发现,不同疾病中类似的功能失衡是由疾病特异性和共享的分类单元共同驱动的。这种对微生物组生态和功能动态的综合分析可以为未来基于微生物组的治疗提供信息,确定用于操纵微生物组功能能力的假定干预靶点。

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