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

The development of this manuscript was supported by funds from National Natural Science Foundation of China-Guizhou Provincial People&#39;s Government Karst Science Research Center Project (U1812401), Doctoral Research Project of Guizhou Normal University (GZNUD[2017]1), Science and Technology Support Project of Guizhou Province (QKHZC[2021]YB459) and the Science and Technology Project of the Guiyang([2019]2-8).
The authors would like to thank Edwards J.A et al for providing rice microbiome data in public databases and support from TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.

1. Data Download
<ol>
	<li>Download the data of the accession PRJNA386367 form the NCBI database. From the data of the accession PRJNA386367, select the rhizosphere, rhizoplane, and endosphere microbiome data from rice plants grown for 14 weeks in a submerged rice field in Arbuckle, California in 2014. 
	&#8203;NOTE: The rhizosphere, rhizoplane, and endosphere microbiome data were presented by the OTUs table in accession PRJNA386367.</li>
</ol>
2. Optimal power value determination
<p clas...

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Correlation networks have been increasingly used in bioinformatics applications. WGCNA is a systems biology method for descriptive analysis of the relationships between various elements of a biological system12. R software package was used in earlier work on WGCNA13,14,15. The package includes functions for network construction, module detection, calculations of topological properties, data simulation, vi...

Microbiome research has important implications for understanding and manipulating ecosystem processes1,2. Microbial populations are interconnected by interacting ecological networks, whose characteristics can affect the response of microorganisms to environmental changes3,4. Furthermore, the properties of these networks affect the stability of microbial communities, and are closely associated with soil function5. Weighted gene correlation network analysis has now been widely applied for research on the relationship between genes and microbial communities6. Previous studies have focused mainly on the associations between networks of different genes or populations and the outside world7. However, the differences in correlation networks formed by microbial populations under different environmental conditions have been scarcely investigated. The purpose of the research presented in this paper is to provide insights and details on the rapid implementation of the WGCNA algorithm to construct a co-occurrence network of microbiome samples collected under different environmental conditions. Based on the analysis results, we assessed the composition and differences of the population and further discussed the relationship between different microbial populations. The following basic flow of weighted correlation network algorithm8 was applied. First, a similarity matrix needed to be constructed by calculating the Pearson correlation coefficient between the Operational Taxonomic Units (OTU) expression profiles. Then, the parameters of the adjacency functions (the power or the sigmoid adjacency functions) were adopted with a scale-free topology criterion, the similarity matrix was transformed into an adjacency matrix, and each co-occurrence network corresponded to an adjacency matrix. We used average linkage hierarchical clustering coupled with the TOM-based dissimilarity to group OTUs with coherent expression profiles into modules. Further, we calculated the relationship between conservative statistics and the related parameter analysis modules, finally identifying the hub OTU in the module. These methods are particularly suitable for analysis of the differences in network structures among various microbial populations under divergent environmental conditions. In this manuscript, we have described in detail the method of co-expression network development, the analysis of the dissimilarities between the modules, and have provided a brief overview of the steps in the procedure applied to obtain the core species in different module networks.

<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

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, which can effectively explore the interaction relationship or co-occurrence model of different microbial species in different environments. The purpose of this manuscript is to provide details on how to use the weighted correlation network algorithm to analyze different co-occurrence networks that may occur in microbial communities due to different ecological environments. All analysis of the experiment is performed in the WGCNA package. WGCNA is an R package for weighted correlation network analysis. The experimental data used to demonstrate these methods were microbial community data from the NCBI (National Center for Biotechnology Information) database for three niches of the rice (Oryza sativa) root system. We used the weighted correlation network algorithm to construct co-abundance networks of microbial community in each of the three niches. Then, differential co-abundance networks among endosphere, rhizoplane and rhizosphere soil were identified. In addition, the core genera in network were obtained by the "WGCNA" package, which plays an important regulated role in network functions. These methods enable researchers to analyze the response of microbial network to environmental disturbance and verify different microbial ecological response theories. The results of these methods show that the significant differential microbial networks identified in the endosphere, rhizoplane and rhizosphere soil of rice.

The representative results in this article were downloaded from the 2014 California Abaker rice root microbiome data in the NCBI database (PRJNA386367)9. The data includes the rhizosphere, rhizoplane, and endosphere microbiome samples from rice plants grown for 14 weeks in a submerged rice field. We used the WGCNA algorithm to select the power value that satisfied the three networks that were close to the scale-free network (Figure 1) and developed three co-expression...

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

Network analysis was applied to evaluate the association of various ecological microbial communities, such as soil, water and rhizosphere. Presented here is a protocol on how to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.

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

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