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Gut Microbiome中Metabolic Dependence Network的构建

已有 1278 次阅读 2020-3-24 10:36 |个人分类:科研文章|系统分类:科研笔记

文章:Metabolic Dependencies Underlie Interaction Patterns of Gut Microbiota During Enteropathogenesis

杂志:Froniters in Microbiology

年份:2019


研究目的:unsing metabolic interaction network analysis to predict majority of the changes in terms of the changed directions in the gut microbiota during enteropathogenesis.


网络构建与分析的方法:

       1)基于growth rates,计算2种菌之间的权重值,根据权重值的正负去判定方向,因为每一对pair有2个菌,所以构建的网络是个双向的网络,也就是2个菌之间是有2个权重值存在的。Based on these growth rates, we calculated the “weight” of the interaction between bacteria using the following equation, w=Log2(P/S), where P stands for growth rate of the species of interest when co-cultivated with another bacterium (paired growth rate) and S stands for growth rate when cultivated alone. A “w” value of 0 indicates the growth rate of a bacterium is not changed by the other co-cultivated bacterium; a positive (negative) value of “w” indicates the growth rate can be promoted (inhibited) by the co-cultivated bacterium. The interactions between two bacteria are thus bi-directional. 此策略中S和P,也就是单菌和配对的生长速率是最为关键的基础信息,这个决定着此方法的实现可能性。这篇文章用到的生长速率来自于另一篇文章的结果,Genome-wide metabolic models for 773 human gut microbes were obtained from Stefanía et al. (Magnusdottir et al., 2017). Pairwise interactions, i.e., changes in silico growth rates of two co-culturing microbes as compared with that of cultured alone were calculated using the methods described in the literature (Magnusdottir et al., 2017).

      2)此文下载了3份公共数据进行了分析处理和网络的构建,具体操作流程如下:

    3)For each network, the nodes were microbial species selected from the union of the top 50 most abundance species in patients and the respective healthy controls, whose combined account for more than 90% of the total abundances of all species, while the edges were pairwise interactions (“weights”) between two connected species. To account for the impact of diets [Western and High fiber diet, as described in the literature (Magnusdottir et al., 2017)], two networks were constructed for each of the patient and control groups. At the end, four networks were obtained for each dataset. An open-source tool, Gephi (Bastian et al., 2009), was used for network visualization and analysis. 利用Metaphlan2鉴定的菌物种,选择了case和control并集中top 50的菌进行彼此间两两的权重分析,因为生产速率的知识库中考虑了饮食的影响,如此则可以构建不同饮食条件下的case和control网络。网络的可视化用工具Gephi实现。

     4)网络的关键模块分析,To identify subclusters in which nodes are more densely connected than to the rest of the network, we used a modularity algorithm (a “community” detection technique) implemented in Gephi (Bastian et al., 2009) and identified two main subclusters (Figure 1); 用工具Gephi中提供的modularity算法。

     5)网络中重要节点的分析,We then checked the top important nodes in the metabolic dependency network. We used the Gephi’s PageRank algorithm (Chen et al., 2007) to rank the nodes.利用Gephi中提供的PageRank算法。


此外,文章结合3份数据的实际情况对此方法得到的网络结果进一步讨论以证明此方法的实用之处。



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