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

Summary

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.

Abstract

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.

Introduction

Microbiome research has important implications for understanding and manipulating ecosystem processes^1,2. Microbial populations are interconnected by interacting ecological networks, whose characteristics can affect the response of microorganisms to environmental changes^3,4. 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....

Protocol

1. Data Download

1. 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.
   ​NOTE: The rhizosphere, rhizoplane, and endosphere microbiome data were presented by the OTUs table in accession PRJNA386367.

2. Optimal power value determination

NOTE: The WGCNA package contains all of the following functional parameters. WGCNA is an R package for weighted correlation network a....

Results

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.......

Discussion

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 WGCNA^13,14,15. The package includes functions for network construction, module detection, calculations of topological properties, data simulation, vi.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
R	The University of Auckland	version 4.0.2	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.
RStdio	JJ Allaire	version 1.4.1103	The RStudio IDE is a set of integrated tools designed to help you be more productive with R and Python.
Cytoscape		version 3.7.1	Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data.
NCBI database			The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information.