Name: divergence of root microbiota in different habitats based on weighted correlation networks
Uploaded: 2021-09-25T13:30:00
Description: Watch this Scientific Journal Video about Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks at JoVE.com

该手稿的研制得到了中国贵州省人民政府喀斯特科学研究中心项目（U1812401）、贵州师范大学博士研究项目（GZNUD[201]）的资助 7[1），贵州省科技支撑项目（QKHZC[2021]YB459）和贵阳科技项目（[2019]2-8）。
作者感谢Ededs J.A等人在公共数据库中提供水稻微生物群数据，并感谢TopEdit（www.topeditsci.com）在编写这份手稿期间提供的语言援助。

1. 数据下载
<ol><li>从 NCBI 数据库下载加入 PRJNA386367 的数据。从加入PRJNA386367的数据中，从2014年加利福尼亚州阿巴克尔的水下稻田中生长了14周的水稻植物中选择日光圈、日生行星和内层微生物群数据。 注：在加入PRJNA386367的O图表中，介绍了日星圈、日光和内层微生物群数据。</li>
</ol>2. 最佳功率值确定
注：WGCNA 软件包包含以下所有功能参数。WGCNA 是用于?...

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相关网络越来越多地用于生物信息学应用。WGCNA是一种系统生物学方法，用于描述分析生物系统12的各种元素之间的关系。R软件包用于WGCNA13、14、15的早期工作。该软件包包括网络构建、模块检测、拓扑特性计算、数据模拟、可视化和与外部软件交互的能力等功能。WGCNA被广泛应用于分析来自脑癌...

微生物群研究对理解和操纵生态系统过程具有重要的意义。微生物种群通过相互作用的生态网络相互连接，其特征会影响微生物对环境变化的反应。此外，这些网络的特性影响微生物群落的稳定，并与土壤功能5密切相关。加权基因相关性网络分析现已被广泛应用于基因与微生物群落关系的研究。先前的研究主要集中于不同基因或种群网络与外界之间的关联。然而，微生物群在不同环境条件下形成的相关网络差异几乎没有被研究过。本文所介绍的研究旨在提供有关快速实施WGCNA算法的见解和细节，以构建在不同环境条件下收集的微生物群样本的共发生网络。根据分析结果，我们评估了种群的组成和差异，并进一步讨论了不同微生物种群之间的关系。应用了加权相关网络算法8的以下基本流。首先，需要通过计算运营分类单位 （OTU） 表达配置文件之间的 Pearson 相关系数来构建相似性矩阵。然后，采用了具有无尺度拓扑标准的邻接函数参数（功率或 sigmoid 邻接函数），将相似性矩阵转换为邻接矩阵，并且每个共发生网络对应于邻接矩阵。我们使用平均链接分层聚类，以及基于 TOM 的差异，将具有连贯表达配置文件的 OTUs 组合成模块。此外，我们计算了保守统计与相关参数分析模块之间的关系，最终确定了模块中的集线器 OTU。这些方法特别适合分析不同环境条件下不同微生物种群之间网络结构的差异。在这份手稿中，我们详细描述了共同表达网络开发的方法，分析了模块之间的差异，并简要概述了在不同模块网络中获取核心物种的程序中应用的步骤。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>R</td>
			<td>The University of Auckland</td>
			<td>version 4.0.2</td>
			<td>R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.</td>
		</tr>
		<tr>
			<td>RStdio</td>
			<td>JJ Allaire</td>
			<td>version 1.4.1103</td>
			<td>The RStudio IDE is a set of integrated tools designed to help you be more productive with R and Python.</td>
		</tr>
		<tr>
			<td>Cytoscape</td>
			<td></td>
			<td>version 3.7.1</td>
			<td>Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data.</td>
		</tr>
		<tr>
			<td>NCBI database</td>
			<td></td>
			<td></td>
			<td>The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information.</td>
		</tr>
	</tbody>
</table>

divergence of root microbiota in different habitats based on weighted correlation networks

根微生物群在植物生长和环境适应中起着重要作用。网络分析是研究社区的重要工具，可以有效探索不同环境中不同微生物物种的相互作用关系或共发生模式。本文稿的目的是提供如何使用加权相关网络算法分析因不同生态环境而可能发生在微生物群落中的不同共生网络的详细信息。实验的所有分析均在 WGCNA 包中执行。WGCNA 是用于加权相关网络分析的 R 包。用于证明这些方法的实验数据是来自NCBI（国家生物技术信息中心）数据库的微生物群落数据，用于水稻（Oryza水稻）根系的三个利基。我们使用加权相关网络算法在三个利基中每个利基中构建微生物群落的共同丰度网络。然后，确定了内层、地层和日光圈土壤之间的微分共丰度网络。此外，网络的核心属是通过"WGCNA"一揽子计划获得的，它在网络功能中起着重要的调节作用。这些方法使研究人员能够分析微生物网络对环境扰动的反应，并验证不同的微生物生态反应理论。这些方法的结果表明，水稻内层、地层和稻谷圈土壤中所识别的显著微分微生物网络。

本文的代表性结果从 NCBI 数据库 （PRJNA386367）9中的 2014 年加州阿巴克水稻根微生物群数据中下载。数据包括在水下稻田中生长了14周的稻田中的稻田、日光和内层微生物群样本。我们使用WGCNA算法来选择满足三个网络的功率值，这三个网络接近无规模网络（图1），并开发了三个共同表达网络（图2）。在内圈、日光和日光圈土壤微生物...

Watch this Scientific Journal Video about Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks at JoVE.com

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

应用网络分析来评估各种生态微生物群落（如土壤、水和岩石圈）的关联。这里介绍的是如何使用WGCNA算法来分析由于不同的生态环境而可能发生在微生物群落中的不同共生网络的协议。

基于加权相关网络的不同栖息地根微生物群的差异

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, ...

该方法可以有效探索不同环境中不同微生物物种的互动关系或共发生网络。它提供了如何使用WGCNA算法构建微生物群共发生网络的细节。此外，根据研究结果，该方法评估微生物关系的差异和微生物财团之间的组成。下面是方法的基本流程。微生物群的组成和丰度数据从 NCBI 数据库下载，或从样本序列数据中下载。首先，打开 RStudio 软件并安装 WGCNA 软件包。然后，加载数据并使用良好的样本基因功能来检查数据的正确性。检查符合我们要求的离群值和存储样品。当检查结果属实时，请继续下一步。保存结果。使用拾取阈值函数计算不同功率值下的数据的刻度自由索引（R 平方）。可视化结果。当无比例指数接近一个时，网络结构更接近于规模自由网络。当刻度自由指数（R 平方）大于 0.9 时，选择功率值。最后，使用相同的方法分析微生物群的其余数据。首先，利用邻存函数构建一个象征性的共发生网络。此外，使用 TOM 相似性功能开发拓扑、重叠网络。其次，使用 Hclust 功能执行分层聚类并绘制生成的聚类树。第三，使用 cutree 动态功能执行动态分支切割，并使用最小集群大小参数设置最小的模块大小。最小的模块大小通常设置在 30 以上。第四，计算每个模块的微生物特征。分层聚类根据相关系数执行，高度小于 0.25 的模块合并以获取每个模块的分布。第五，使用情节和颜色功能来可视化结果。获得共发生网络模块的分配显示图。然后，重命名模块颜色并构建与颜色对应的数字标签。保存它们以供后续部件使用。最后，重复上述过程以获得其他数据集。在此部分，比较和分析两组数据并进行保存测试。首先，加载前几个步骤中保存的两个数据集的参数和结果。然后，将一个数据集的模块结果设置为参考组，将另一个数据集设置为测试组并执行模块比较。其次，计算保守度、统计参数、Z 汇总值和中位数值，以量化模块之间的保守度。最后，可视化结果。确定满足 Z 汇总值（小于 2 个）和顶部中位数排名值的网络模块。该模块是两个微生物数据中最未保存的模块。对模块成员进行相关分析。分别将两个网络的模块分配结果设置为参考和测试组。设置必须与保存测试相同。首先，根据保存测试的结果，在几个候选模块中计算每个 OTU 的 KME 值。以黄色模块为例。计算两个黄色模块中 KME 值的相关系数，然后绘制模块中 KME 值的相关分析图。最后，根据数字中的相关系数，判断两个数据集中模块的保守性。选择相关系数最小的模块。首先，使用输出网络到 Cytoscape 功能将网络导出到 Cytoscape 可以读取的边缘和节点列表文件。然后，将文件导入 Cytoscape 中。将阈值设置为 0.5，并根据需要调整其他参数。最后，获得不同微生物的共同发生网络。在本文中，WGCNA 算法用于分析米根系统三个利基的差异。选择满足接近规模免费网络的三个网络的功率值。在内圈、日光和日生圈微生物发生网络中，分别识别23、22和21个模型。使用保存测试和相关分析来查找米根系统每个利基之间极不保守的模块。使用不同的颜色为这三个模块构建一个共发生网络，以表示不同的微生物。蛋白菌、活性细菌、细菌、肌瘤和维鲁科米罗比亚主导着三个不同的微生物网络。此外，17个核心属主要管理这些网络。观看此视频后，您应该很好地了解如何执行一系列步骤，使用 WGCNA 算法分析由于不同生态环境在微生物群落中可能发生的不同共生网络。

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Lateral Root Inducible System in Arabidopsis and Maize

侧根诱导系统<em&gt;拟南芥</em&gt;和玉米

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is ...

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

利用数字图像相关表征的血管组织标本本地菌株

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and ...

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

评估细菌素对小鼠肠道微生物的影响的方法

Very intriguing questions arise with our advancing knowledge on gut microbiota composition and the relationship with health, particularly relating to the ...

SA-&#946;-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

SA-ÃƒÆ’Ã…Â½Ãƒâ€šÃ‚Â²-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

SA-β-加拉基沙酶筛查检测,用于识别治疗药物

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell ...

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

基于 X 射线 Microcomputed 层析成像的玉米血管束微米尺度分型技术研究

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

使用两步PCR和下一代16S rRNA基因测序进行微生物群分析

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

Quantification of Skeletal Muscle Fatty Infiltration via Decellularization

Decellularization-Based Quantification of Skeletal Muscle Fatty Infiltration

Decellularization-Based Quantification of Skeletal Muscle Fatty Infiltration (Video) | JoVE 

基于脱细胞的骨骼肌脂肪浸润定量

Fatty infiltration is the accumulation of adipocytes between myofibers in skeletal muscle and is a prominent feature of many myopathies, metabolic ...

One-Step Hairy Root Transformation Method for Advancing Root-Related Biology

An Efficient and Reproducible Method for Producing Composite Plants by Agrobacterium rhizogenes-Based Hairy Root Transformation

Streamlining Composite Plant Production via Agrobacterium rhizogenes-Mediated Hairy Root Transformation (Video) | JoVE

一种基于根 茎农杆菌毛根转化生产复合植物的高效且可重复的方法

Producing composite plants with transgenic roots and nontransgenic stems and buds using Agrobacterium rhizogenes-mediated hairy root transformation is a ...