Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome
Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome
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DOI：
10.7554/elife.39733
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发表时间：
2019-06-13
期刊：
影响因子：
7.7
通讯作者：
Segre, Daniel
中科院分区：
文献类型：
--
作者：
Bernstein, David B.;Dewhirst, Floyd E.;Segre, Daniel
微生物的生物合成能力是其生长和相互作用的基础，在微生物群落结构中起着重要作用。对于大型、多样的微生物群落，由于代谢功能和环境条件的不确定性，对这些能力的预测受到限制。为了解决这一挑战，我们提出了一种受渗透理论启发的概率方法，以计算量化基因组衍生的代谢网络在可变环境集合下产生给定代谢物集的稳稳性。我们使用这种方法编制了456种人类口腔微生物中97种代谢物的预测生物合成能力图谱。该图谱捕获了生物量组成的分类相关趋势，并使估计与微生物共生相关的微生物间代谢距离成为可能。我们还发现了一个独特的挑剔/未培养分类群，包括几个糖菌（TM7）物种，其特征是它们具有丰富的代谢缺陷。通过拥抱不确定性，我们的方法可以广泛应用于理解复杂微生物生态系统中的代谢相互作用。
The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.