The P/N (Positive-to-Negative Links) Ratio in Complex Networks-A Promising In Silico Biomarker for Detecting Changes Occurring in the Human Microbiome
The P/N (Positive-to-Negative Links) Ratio in Complex Networks-A Promising In Silico Biomarker for Detecting Changes Occurring in the Human Microbiome
复制标题
复杂网络中的 P/N（正负链接）比率——一种有望用于检测人类微生物组中发生的变化的计算机生物标记
DOI：
10.1007/s00248-017-1079-7
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发表时间：
2018-05-01
影响因子：
3.6
通讯作者：
Ma, Zhanshan (Sam)
中科院分区：
文献类型：
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作者：
Ma, Zhanshan (Sam)
除了将微生物群落多样性与传统物种丰富度或香农指数进行比较之外，区分健康和患病微生物组的方法学进展相对较小。这项任务越来越需要网络分析，但目前大多数可用的微生物组数据集仅允许构建简单的物种相关网络（SCN）。 SCN分析的主要结果是一系列网络属性，例如网络度和模块化，但这些网络属性的度量常常产生不一致的证据。我们提出了一个简单的新网络属性，即 P/N 比，定义为微生物 SCN 中正链接与负链接数量的比率。我们假设 P/N 比应反映微生物物种之间促进和抑制相互作用之间的平衡，这可能是患病微生物组中发生的最重要的变化之一。我们用代表五个主要人类微生物组位点的五个数据集测试了我们的假设，发现 P/N 比在健康和患病微生物组之间表现出对比差异，可以用作计算机生物标志物来检测人类微生物组中与疾病相关的变化，并且可能在人类微生物组相关疾病的个性化诊断中发挥重要作用。
Relatively little progress in the methodology for differentiating between the healthy and diseased microbiomes, beyond comparing microbial community diversities with traditional species richness or Shannon index, has been made. Network analysis has increasingly been called for the task, but most currently available microbiome datasets only allows for the construction of simple species correlation networks (SCNs). The main results from SCN analysis are a series of network properties such as network degree and modularity, but the metrics for these network properties often produce inconsistent evidence. We propose a simple new network property, the P/N ratio, defined as the ratio of positive links to the number of negative links in the microbial SCN. We postulate that the P/N ratio should reflect the balance between facilitative and inhibitive interactions among microbial species, possibly one of the most important changes occurring in diseased microbiome. We tested our hypothesis with five datasets representing five major human microbiome sites and discovered that the P/N ratio exhibits contrasting differences between healthy and diseased microbiomes and may be harnessed as an in silico biomarker for detecting disease-associated changes in the human microbiome, and may play an important role in personalized diagnosis of the human microbiome-associated diseases.