肠道微生物菌群共存网络的构建与分析  

Construction and analysis of co-occurrence network in the gut microbiome

作　　者：马越 王军[1] 胡永飞[1] 陈亮[1] 李晶[1] 律娜[1,3] 刘飞[1,3] 王黎明[1] 封雨晴 朱宝利[1,2,3] Yue Ma;Jun Wang;Yongfei Hu;Liang Chen;Jing Li;Na Lu;Fei Liu;Liming Wang;Yuqing Feng;Baoli Zhu(CAS Key Laboratory of Pathogenic Microbiology and Immunology,Institute of Microbiology,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Microbial Genome Research Center,Institute of Microbiology,Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[1]中国科学院微生物研究所中国科学院病原微生物与免疫学重点实验室,北京100101 [2]中国科学院大学,北京100049 [3]中国科学院微生物研究所微生物基因组学联合研究中心,北京100101

出　　处：《微生物学报》2018年第11期2011-2019,共9页Acta Microbiologica Sinica

基　　金：国家自然科学基金(31471203;31601081)~~

摘　　要：【目的】将网络分析应用到肠道微生物的分析之中,探究肠道微生物共存网络拓扑结构等相关网络系数的分析,从而展现肠道微生物共存网络的特性。【方法】将之前研究中的肠道微生物数据根据雌马酚代谢能力划分成雌马酚产生者和非产生者两组,计算两组微生物相对丰度,得出菌种之间的相关系数,构建肠道微生物的共存网络,分析两组间共存网络参数的差异;运用随机网络检验现实网络拓扑结构的特异性,分析两组网络中菌种间的差异。【结果】共存网络中两组节点数分别为45个和47个,即分别有45个和47个不同菌种。比较两组网络结构的差异,发现雌马酚产生者组中的共存网络菌群具有更复杂的连接,且两组之间的其他网络参数存在一定的差异。通过将现实网络与随机网络对比可知,现实网络的拓扑结构具有一定的特异性。将具有代谢雌马酚相关物质能力的菌种在两组网络中标出,发现它们在雌马酚产生者组共存网络中更趋向与来自不同门的菌种产生相互联系。【结论】将网络分析应用于肠道微生物分析之中,可以发掘菌种之间的相互作用和网络拓扑结构的复杂性与差异性,展现肠道菌群结构中之前较少被认识到的一些特征。因而,网络分析的方法可以为未来肠道微生物的研究提供新的视角。[Objective] To explore the networked and the topological structure of gut microbiota, we applied network analysis in this study to characterize the gut microbiome co-occurrence networks. [Methods] Gut microbiome data were divided into two groups based on the equol-metaboliting ability of hosts. We constructed the co-occurrence network of gut microbiota with Spearman correlation coefficients with FDR judgment in each group and analyzed the difference between groups. At the same time, the topological structure of random network was used to compare with the real network to uncover the significant differences. Finally, the species taxonomy information was taken into the network and revealed different features. [Results] The networks of two groups retained 45 and 47 different species respectively and show different complexity. From our data, we found the structure of the real network topology is specific and more interaction within different phylum in equol producer group. [Conclusion] By network analysis, we can discover the complexity of the interactions among the different species of gut microbes, and demonstrate the feature of network topology that was rarely reported before. And the method will also provide a new perspective of gut microbiota research in the future.

关 键 词：肠道微生物 网络分析 随机网络 雌马酚 

分 类 号：R37[医药卫生—病原生物学]