Modeling Human Gut Microbiome Community Structure in Healthy and Diseased States

 描述（由申请人提供）：历史上，细菌如果引起疾病则被视为致病性，如果与宿主共生则被视为良性。然而，在许多情况下，尚未鉴定出与疾病一致相关的单一生物体，而是认为健康个体和患病个体之间的细菌群落结构和细菌-细菌相互作用不同，也称为生态失调。自然，使用系统生物学方法研究健康和患病个体的微生物组可以深入了解生态失调的过程。为此，已经开发了从微生物组数据中识别共现网络的方法。然而，到目前为止，这种方法主要应用于健康的微生物组数据集，我们仍然缺乏对生态失调的共同特性的了解。为了解决这些差距，我建议使用来自英国成人双胞胎登记处（TwinsUK）的约2，500名个体为34种免疫疾病和133种定量表型建立肠道微生物组共现网络，此前已收集了微生物组和表型数据。首先，我将使用考虑到个体之间相关性的广义和线性混合模型来识别疾病相关细菌。接下来，我将使用模拟框架来比较六个微生物组共现网络软件包，以量化i）患有和不患有疾病的个体或ii）来自定量表型分布的相反尾部的个体的共现网络的一致性。使用这些网络，我将确定在疾病中差异丰富的共同发生的细菌的模块，检查一般的网络统计数据（如模块性和多样性），并检查目标1中确定的疾病相关节点的属性（如度，介数和接近中心性）。此外，我将利用我们数据中的孪生关系来识别可遗传的网络特征，并检查细菌对的协同遗传性。最后，这将是第一个比较 这些网络统计数据跨越一系列疾病，以开发微生物组网络结构如何在健康和疾病中不同的模型。此外，我将比较不同表型的潜在网络结构，以确定具有相似肠道微生物组改变的疾病。了解这一水平的肠道微生物组将有助于设计针对微生物群的疗法，并最终改善人类健康。

 DESCRIPTION (provided by applicant): Historically, bacteria were either seen as pathogenic if they caused disease or benign if they lived commensally with the host. In many cases, however, single organisms have not been identified that are consistently associated with disease, but rather it is thought that bacterial community structure and bacteria- bacteria interactions differ between healthy and diseased individuals, otherwise known as dysbiosis. Naturally, investigating the microbiomes of healthy and diseased individuals using systems biology methodology could lead to insight into the processes that underlie dysbiosis. To that end, methodology has been developed to identify co-occurrence networks from microbiome data. However, to date this methodology has primarily been applied to healthy microbiome datasets and we still lack an understanding of the common properties of dysbiosis. To address these gaps, I propose to build gut microbiome co-occurrence networks for 34 immune diseases and 133 quantitative phenotypes using ~2,500 individuals from the United Kingdom Adult Twins Registry (TwinsUK), for which microbiome and phenotype data has previously been collected. First, I will identify disease-associated bacteria using generalized and linear mixed models that take into account relatedness between individuals. Next, I will use a simulation framework to compare six microbiome co- occurrence network software packages to quantify the consistency of co-occurrence networks for individuals either i) with and without disease or ii) from opposite tails of quantitative phenotype distributions. Using these networks, I will identify modules of co-occurring bacteria that are differentially abundant in disease, examine general network statistics (such as modularity and diversity), and examine the properties of disease-associated nodes as determined in Aim 1 (such as degree, betweenness, and closeness centrality). Additionally, I will take advantage of the twin relationships in our data to identify heritable network characteristics and examine co- heritability of pairs of bacteria. Finally, this will be the first study to compare these network statistics across a range of diseases in order to develop a model of how microbiome network structure differs in health and disease. Additionally, I will compare underlying network structure across phenotypes to identify diseases that share similar gut microbiome alterations. Understanding the gut microbiome at this level will be beneficial for designing therapies to target the microbiota and ultimately improve human health.