Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species.
Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species.
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DOI：
10.1371/journal.pcbi.1007951
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发表时间：
2020-10
影响因子：
4.3
通讯作者：
Ye Y
中科院分区：
文献类型：
--
作者：
Lam TJ;Stamboulian M;Han W;Ye Y
微生物群落成员表现出各种形式的相互作用。利用微生物组数据日益增加的可用性，已经开发了许多计算方法来从不同微生物群落中微生物的共存推断细菌相互作用。此外，基因组尺度代谢模型的引入也使得细菌物种之间的合作和竞争代谢相互作用得以推断。从本质上讲，系统发育相似的微生物物种由于其基因组相似性更有可能共享共同的功能概况或生物途径。如果没有适当地考虑到系统发育关系，任何基于功能/途径的物种间竞争与合作的估计都可能会影响下游应用。为了解决这些挑战，我们开发了一种新的方法来估计一对微生物物种的竞争和互补性指数，根据它们的系统发育距离进行调整。一个自动化管道，PhyloMint，实现了从微生物基因组衍生的基因组尺度代谢模型构建竞争和互补指数。对2815种人类肠道相关细菌的研究表明，系统发育距离与细菌之间的代谢竞争/合作指数高度相关。使用离散化方法，我们能够检测到具有合作得分的细菌对，其合作得分明显高于具有相似系统发育距离的细菌对的平均水平。高代谢合作低竞争的网络群落分析揭示了细菌相互作用的不同模块。我们的研究结果表明，生态位分化在微生物相互作用中起主导作用，而生境过滤在某些分支的细菌物种中也起作用。微生物群落，也被称为微生物组，是通过各种微生物物种的相互作用而形成的。利用基因组测序，可以推断群落的组成以及预测它们的代谢相互作用。然而，由于一些物种彼此之间的亲缘关系更为相似，而另一些物种的亲缘关系则更为遥远，因此，如果不首先考虑它们的系统发育相关性，就不能直接比较代谢关系。在这里，我们开发了一个计算管道来预测细菌物种之间的互补和竞争代谢关系，同时对它们的系统发育相关性进行规范化。我们的研究结果表明，系统发育距离与代谢相互作用相关，并且分解这种关系可以帮助更好地理解驱动群落形成的微生物相互作用。
Microbial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of cooperative and competitive metabolic interactions between bacterial species. By nature, phylogenetically similar microbial species are more likely to share common functional profiles or biological pathways due to their genomic similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation between species based on functional/pathway profiles may bias downstream applications. To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut associated bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species. Microbial communities, also known as microbiomes, are formed through the interactions of various microbial species. Utilizing genomic sequencing, it is possible to infer the compositional make-up of communities as well as predict their metabolic interactions. However, because some species are more similarly related to each other, while others are more distantly related, one cannot directly compare metabolic relationships without first accounting for their phylogenetic relatedness. Here we developed a computational pipeline which predicts complimentary and competitive metabolic relationships between bacterial species, while normalizing for their phylogenetic relatedness. Our results show that phylogenetic distances are correlated with metabolic interactions, and factoring out such relationships can help better understand microbial interactions which drive community formation.
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影响因子：
14.9
作者：
Finn RD;Clements J;Arndt W;Miller BL;Wheeler TJ;Schreiber F;Bateman A;Eddy SR
通讯作者：
Eddy SR
影响因子：
7
作者：
Chaffron, Samuel;Rehrauer, Hubert;von Mering, Christian
通讯作者：
von Mering, Christian
影响因子：
11
作者：
Dumbrell, Alex J.;Nelson, Michaela;Fitter, Alastair H.
通讯作者：
Fitter, Alastair H.
影响因子：
11
作者：
Lawson, Melissa A. E.;O'Neill, Ian J.;Hall, Lindsay J.
通讯作者：
Hall, Lindsay J.
影响因子：
14.9
作者：
Dias O;Rocha M;Ferreira EC;Rocha I
通讯作者：
Rocha I