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复杂网络中的 P/N(正-负链接)比值——一种用于检测人类微生物组中发生变化的有前途的计算生物学生物标志物。

The P/N (Positive-to-Negative Links) Ratio in Complex Networks-A Promising In Silico Biomarker for Detecting Changes Occurring in the Human Microbiome.

机构信息

Computational Biology and Medical Ecology Lab, State Key Lab of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.

出版信息

Microb Ecol. 2018 May;75(4):1063-1073. doi: 10.1007/s00248-017-1079-7. Epub 2017 Oct 11.

Abstract

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.

摘要

在区分健康和患病微生物组的方法学方面,除了比较微生物群落多样性与传统的物种丰富度或香农指数外,几乎没有取得什么进展。越来越多的人呼吁使用网络分析来完成这项任务,但目前大多数微生物组数据集仅允许构建简单的物种关联网络(SCN)。SCN 分析的主要结果是一系列网络属性,如网络度和模块性,但这些网络属性的指标往往会产生不一致的证据。我们提出了一种简单的新网络属性,即 P/N 比,定义为微生物 SCN 中正关联的数量与负关联的数量之比。我们假设 P/N 比应该反映微生物种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变化之一。我们用代表五个主要人体微生物组部位的五个数据集来检验我们的假设,发现 P/N 比在健康和患病微生物组之间表现出明显的差异,并且可以作为一种计算生物标志物,用于检测人类微生物组中与疾病相关的变化,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。

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