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
NOTE: The WGCNA package contains all of the following functional parameters. WGCNA is an R package for weighted correlation network analysis. The key command lines refer to the Supplement S1.
<ol>
	<li>In the R language environment, open the Rstudio software and install the WGCNA package.</li>
	<li>Load the data and use the goodSamplesGenes function to check the correctness of the data. Execute the command lines: 
	&#34;gsg = goodSamplesGenes(datExpr0, verbose = 3) 
	gsg$allOK &#34; 
	Click Run.</li>
	<li>Check for outliers and store samples that meet the requirements. When the check result is TRUE, continue to the next step. Save the result.</li>
	<li>Use the PickSoftThreshold function to calculate the scale-free index R2 of the two groups of the data under different power values. Execute the command line: 
	&#34;sft = pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5)&#34; 
	Click Run.</li>
	<li>Visualize the results (Figure 1). Execute the command line: 
	&#34;plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2], 
	&#160; &#160;xlab=&#34;Soft Threshold (power)&#34;,ylab=&#34;Scale Free Topology Model Fit,signed R^2&#34;,type=&#34;n&#34;, 
	&#160; &#160;main = paste(&#34;ES_Scale independence&#34;)); 
	text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2], 
	&#160; &#160;labels=powers,cex=cex1,col=&#34;red&#34;); 
	abline(h=0.9,col=&#34;red&#34;) 
	plot(sft$fitIndices[,1], sft$fitIndices[,5], 
	&#160; &#160;xlab=&#34;Soft Threshold (power)&#34;,ylab=&#34;Mean Connectivity&#34;, type=&#34;n&#34;, 
	&#160; &#160;main = paste(&#34;ES_Mean connectivity&#34;)) 
	text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col=&#34;red&#34;)&#34; 
	Click Run. 
	NOTE: The premise of the weighted correlation network algorithm is that the established co-expression network structure conforms to the standards of the scale-free topology criterion, increasing its robustness. A scale-free index closer to 1 indicates a network structure that is closer to the scale-free network.</li>
	<li>Select the power value when the scale-free index R2 squared greater than 0.9 and proceed to the next step of analysis. 
	&#8203;NOTE: When the scale-free index is close to 1, the network structure is closer to the scale-free network. When analyzing two or more networks, it is necessary to choose to make each network close to the power value of the scale-free network to satisfy the comparability between the co-expressed networks.</li>
</ol>
3. Construction of a co-expression network and module identification
NOTE: Based on the above calculated power value, the co-occurrence network is constructed. The key command lines refer to the Supplement S2.
<ol>
	<li>Use the adjacency function in the WGCNA package to add signed parameters for the construction of a symbolic co-occurrence network. Execute the command line: 
	&#34;adjacency = adjacency(datExpr0, power = softPower)&#34; 
	Click Run.</li>
	<li>Apply the TOM-similarity function to develop a topological overlapping network and calculate the dissimilarity network. Execute the command line: 
	&#34;TOM = TOMsimilarity(adjacency); 
	dissTOM = 1-TOM&#34; 
	Click Run. 
	NOTE: The signed parameter was added to set the topology overlap network type.</li>
	<li>Use the hclust function to select the average linkage hierarchical clustering method for hierarchical clustering. Execute the command line: 
	&#34;geneTree = hclust(as.dist(dissTOM), method = &#34;average&#34;);&#34; 
	Click Run.</li>
	<li>Use the cutreeDynamic function to perform dynamic branch cutting and set the minClusterSize parameter to 30. Obtain the module recognition result. Execute the command line: 
	&#34;dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM, deepSplit = 2, pamRespectsDendro = FALSE, minClusterSize = minModuleSize);&#34; 
	Click Run. 
	NOTE: The minimum module size could not be lower than 30.</li>
	<li>Calculate the module eigen of each OTUs module by the moduleEigengenes function. Execute the command line: 
	&#34;MEList = moduleEigengenes(datExpr0, colors = dynamicColors) 
	MEs = MEList$eigengenes&#34; 
	Click Run. 
	NOTE: The module eigen represented the overall OTU expression level in the module. It was not a specific OTU, but the first principal component of each cluster obtained by singular network value decomposition.</li>
	<li>Perform the cluster function based on the correlation coefficient of module eigen. Use the mergeCloseModules function to merge the modules with a value lower than 0.25. Execute the command line: 
	&#34;merge = mergeCloseModules(datExpr0, dynamicColors, cutHeight = MEDissThres, verbose = 3)&#34; 
	Click Run.</li>
	<li>Finally, use the plotDendroAndColors function for visualization to obtain the module assignment display diagram of each co-expression network (Figure 2). Use the table function to extract the module attribution corresponding of each OTin the module assignment table. Execute the command line: 
	&#34;plotDendroAndColors(geneTree, mergedColors, &#34;Merged dynamic&#34;,dendroLabels = FALSE, 
	&#160; &#160;hang = 0.03,addGuide = TRUE, guideHang = 0.05, 
	&#160; &#160;main = &#34;ES_Gene dendrogram and module colors&#34;)&#34; 
	Click Run. 
	&#8203;NOTE: In the module assignment diagram of the co-expressing network, different colors represent different modules, and gray represents OTUs that cannot be classified into any module. A greater number of OTUs in the gray module indicates that the early-stage preprocessing quality of the expression matrix is poor.</li>
</ol>
4. Module comparison
NOTE: This method can be used to compare the network modules of two ecological microbial communities. In this article, compare the differences of microbial network modules between endosphere and rhizoplane, endosphere and rhizosphere, rhizosphere and rhizoplane.
<ol>
	<li>Preservation test
	<ol>
		<li>Load the parameters and results of the two data sets saved in the previous steps.</li>
		<li>Set the network module assignment result of a group of microbial data as the reference group, whereas the other group as the test group.</li>
		<li>Use the modulePreservation function to calculate the values of conservativeness statistical parameters Z_summary and medianRank. Execute the command line: 
		&#34;system.time({mp=modulePreservation(multiExpr, 
		multiColor,referenceNetworks=1, 
		nPermutation=100, randomSeed=1,quickCor=0,verbose=3)})&#34; 
		Click Run. 
		NOTE: This result can quantified the conservativeness between modules. Z_summary&#62;10 indicates that two modules are highly preserved, whereas Z_summary&#60;2 denotes non- preserved modules. medianRank expresses the relative preservation of the module assessed by ranking. Higher medianRank values denote non- preserved modules. (The key command lines refer to the Supplement S3.)</li>
		<li>Use the plot function to visualize the results (Figure 3). Get the parameters Z_summary and medianRank (Table 1). 
		NOTE: The network modules that satisfy both the Z_summary value less than 2 and the median Rank value at the top, is the most highly non-preserved module in the two ecological microbial communities.</li>
		<li>Based on the results of the aforementioned two statistical parameters to identify the module with most highly non-preserved module of the two networks.</li>
	</ol>
	</li>
	<li>Correlation analysis of the module membership
	<ol>
		<li>Set the module assignment results of the two networks were set as the reference and the test group, respectively. 
		NOTE: The settings need to be the same as Preservation test.</li>
		<li>Use the corPvalueStudent function to extract the kME (module membership) value of each OTU in several candidate modules. 
		Execute the command line: 
		&#34;Pvalue = as.data.frame(corPvalueStudent(as.matrix 
		(ModuleMembership), Samples))&#34; 
		Click Run. 
		NOTE: kME stands for the degree of module membership. ME stands for module eigen, which represents the overall level of OTU expression in the module. kME is the correlation coefficient between each OTU and the ME. Quantify the importance of OTU in the network by the kME value of OTU. (The key command lines refer to the Supplement S4.)</li>
		<li>Then, use the verboseScatterplot function to calculate the correlation coefficient of the kME value of the corresponding OTUs in the two networks and draw the correlation analysis diagram (Figure 4). 
		Execute the command line: 
		&#34;verboseScatterplot(abs(TModuleMembership 
		[TmoduleGenes, Tcolumn]), 
		&#160; &#160;abs(NModuleMembership[NmoduleGenes, Ncolumn]), 
		&#160; &#160;xlab = paste(&#34;kME in&#34;, &#34;ES&#34;), 
		&#160; &#160;ylab = paste(&#34;kME in&#34;, &#34;RP&#34;), 
		&#160; &#160;main = paste(&#34;lightyellow&#34;), 
		&#160; &#160;cex.main = 1.7, cex.lab = 1.6, cex.axis = 1.6, col =&#160; &#160; &#160; &#160; &#160; modulecolor)&#34; 
		Click Run.</li>
		<li>Select the module with the smallest correlation coefficient of the kME value of the OTU of the two networks. Consider this module to have the largest difference of the two networks.</li>
	</ol>
	</li>
</ol>
5. Analysis of the microbial differential network module
<ol>
	<li>Obtain data of the dominant bacteria phyla through statistical analysis of the OTU sequence set of the module with the largest difference. 
	NOTE: The OTU sequence set of the module with the largest difference is summed by the taxonomy of phyla. The dominant bacteria phyla accounted for more than 10%.</li>
	<li>Then, use the exportNetworkToCytoscape function to obtain the file containing the interaction relationship information of the OTU in the largest differential module. 
	Execute the command line: 
	&#34;cyt = exportNetworkToCytoscape(modTOM, 
	edgeFile = paste(&#34;NEW-ES_CytoscapeInput-edges-&#34;, modules , &#34;.txt&#34;, sep=&#34;&#34;), 
	nodeFile = paste(&#34;NEW-ES_CytoscapeInput-nodes-&#34;, modules, &#34;.txt&#34;, sep=&#34;&#34;), 
	weighted = TRUE,threshold = 0.5, nodeNames = modProbes, 
	altNodeNames = modGenes, nodeAttr = moduleColors[inModule])&#34; 
	Click Run.</li>
	<li>Import the file into Cytoscape. Set the threshold to 0.5 and adjust other parameters as needed.</li>
	<li>Construct a co-occurrence network of differential microorganisms (Figure 5).</li>
	<li>Obtained the information of the core genus that has the most important regulatory role in the network. 
	NOTE: According to the the kME value of OUT, the core genus can be defined.</li>
	<li>Finally, the functions of the core genus were assessed and its influence on the entire difference network was analyzed.</li>
</ol>

<ol>
	<li>Philippot, L., Raaijmakers, J. M., Lemanceau, P., vander Putten, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Going+back+to+the+roots:+the+microbial+ecology+of+the+rhizosphere.">Going back to the roots: the microbial ecology of the rhizosphere.</a> Nature Reviews Microbiology. 11, 789-799 (2013).</li><li>Fierer, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Embracing+the+unknown:+disentangling+the+complexities+of+the+soil+microbiome.">Embracing the unknown: disentangling the complexities of the soil microbiome.</a> Nature Review Microbiology. 15 (10), 579-590 (2017).</li><li>Jin, J., Wang, G. H., Liu, X. B., Liu, J. D., Chen, X. L., Herbert, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+and+spatial+dynamics+of+bacterial+community+in+the+rhizosphere+of+soybean+genotypes+grown+in+a+black+soil.">Temporal and spatial dynamics of bacterial community in the rhizosphere of soybean genotypes grown in a black soil.</a> Pedosphere. 19 (6), 808-816 (2009).</li><li>Ma, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+correlation+network+prediction+of+forest+soil+microbial+functional+organization.">Genetic correlation network prediction of forest soil microbial functional organization.</a> ISME J. 12 (10), 2492-2505 (2018).</li><li>de Vries, F. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Soil+bacterial+networks+are+less+stable+under+drought+than+fungal+networks.">Soil bacterial networks are less stable under drought than fungal networks.</a> Nature Communications. 9 (1), 3033 (2018).</li><li>Colin, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correlating+transcriptional+networks+to+breast+cancer+survival:+a+large-scale+coexpression+analysis.">Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis.</a> Carcinogenesis. (10), 2300-2308 (2013).</li><li>Ma, B., Zhao, K., Lv, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+correlation+network+prediction+of+forest+soil+microbial+functional+organization.">Genetic correlation network prediction of forest soil microbial functional organization.</a> ISME J. 12, 2492-2505 (2018).</li><li>Zhang, B., Horvath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+general+framework+for+weighted+gene+co-expression+network+analysis.">A general framework for weighted gene co-expression network analysis.</a> Statistical applications in genetics and molecular biology. 4 (1), (2005).</li><li>Edwards, J. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Compositional+shifts+in+root-associated+bacterial+and+archaeal+microbiota+track+the+plant+life+cycle+in+field-grown+rice.">Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice.</a> PLoS Biology. 16 (2), 2003862 (2018).</li><li>Bashan, Y., De-Bashan, L. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+the+Plant+Growth-Promoting+Bacterium+Azospirillum+Promotes+Plant+Growth-A+Critical+Assessment.">How the Plant Growth-Promoting Bacterium Azospirillum Promotes Plant Growth-A Critical Assessment.</a> Advances in Agronomy. 108, 77-136 (2010).</li><li>Lovley, D. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Geobacter:+The+Microbe+Electric's+Physiology,+Ecology,+and+Practical+Applications.">Geobacter: The Microbe Electric's Physiology, Ecology, and Practical Applications.</a> Advances in Microbial Physiology. 59, 1 (2011).</li><li>Langfelder, P., Horvath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WGCNA:+an+R+package+for+weighted+correlation+network+analysis.">WGCNA: an R package for weighted correlation network analysis.</a> BMC Bioinformatics. 9, 559 (2008).</li><li>Zhang, B., Horvath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+General+Framework+for+Weighted+Gene+Co-expression+Network+Analysis.">A General Framework for Weighted Gene Co-expression Network Analysis.</a> Statistical Applications in Genetics and Molecular Biology. 4 (1), 17 (2005).</li><li>Horvath, S., Dong, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Geometric+interpretation+of+Gene+Co-expression+Network+Analysis.">Geometric interpretation of Gene Co-expression Network Analysis.</a> PLoS Computational Biology. 4 (8), 1000117 (2008).</li><li>Langfelder, P., Horvath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eigengene+networks+for+studying+the+relationships+between+co-expression+modules.">Eigengene networks for studying the relationships between co-expression modules.</a> BMC Systems Biology. 1, 54 (2007).</li><li>Horvath, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Oncogenic+Signaling+Networks+in+Glioblastoma+Identifies+ASPM+as+a+Novel+Molecular+Target.">Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target.</a> Proceedings of the National Academy of Sciences of the United States of America. 103 (46), 17402-17407 (2006).</li><li>Carlson, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=and+Sequence+Conservation:+Predictions+from+Modular+Yeast+Co-expression+Networks.">and Sequence Conservation: Predictions from Modular Yeast Co-expression Networks.</a> BMC Genomics. 7 (1), 40 (2006).</li><li>Fuller, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Weighted+Gene+Co-expression+Network+Analysis+Strategies+Applied+to+Mouse+Weight.">Weighted Gene Co-expression Network Analysis Strategies Applied to Mouse Weight.</a> Mammalian Genome. 6 (18), 463-472 (2007).</li><li>Yin, L., Wang, Y., Lin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Explorative+analysis+of+the+gene+expression+profile+during+liver+regeneration+of+mouse:+a+microarray-based+study[J].">Explorative analysis of the gene expression profile during liver regeneration of mouse: a microarray-based study[J].</a> Artificial Cells Nanomedicine &amp; Biotechnology. 47 (1), 1113-1121 (2019).</li><li>Oldham, M., Horvath, S., Geschwind, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Conservation+and+Evolution+of+Gene+Co-expression+Networks+in+Human+and+Chimpanzee+Brains.">Conservation and Evolution of Gene Co-expression Networks in Human and Chimpanzee Brains.</a> Proceedings of the National Academy of Sciences of the United States of America. 103 (47), 17973-17978 (2006).</li><li>Oldham, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+organization+of+the+transcriptome+in+human+brain.">Functional organization of the transcriptome in human brain.</a> Nature Neuroscience. 11 (11), 1271-1282 (2008).</li><li>Keller, M. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+gene+expression+network+model+of+type+2+diabetes+links+cell+cycle+regulation+in+islets+with+diabetes+susceptibility.">A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility.</a> Genome Research. 18 (5), 706-716 (2008).</li><li>Weston, D., Gunter, L., Rogers, A., Wullschleger, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Connecting+genes,+coexpression+modules,+and+molecular+signatures+to+environmental+stress+phenotypes+in+plants.">Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants.</a> BMC Systems Biology. 2 (1), 16 (2008).</li><li>Jorda&#180;n, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Keystone+species+and+food+webs.">Keystone species and food webs.</a> Biological Sciences. 364, 1733-1741 (2009).</li><li>Backhed, F., Ley, R. E., Sonnenburg, J. L., Peterson, D. A., Gordon, J. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Host-Bacterial+Mutualism+in+the+Human+Intestine.">Host-Bacterial Mutualism in the Human Intestine.</a> Science. 307, 1915-1920 (2009).</li></ol>

The authors have nothing to disclose.

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, visualization, and capability for interfacing with external software. WGCNA has been extensively employed to analyze gene expression data from brain cancer16, yeast cell cycle17, mouse genetics18,19, primate brain tissue20,21, diabetes22, and plants23. Use the weighted gene correlation network analysis to construct the network must include at least 8 sample. In this article, we focused on gene co-expression networks that describe the interactions between microbial populations in different environments. We obtained differential networks between microbial populations in divergent environments and identified the key species in each network. The notion that key species are important to community has been widely employed in food-web research24. Some of the species in a complex microbial community can be essential to maintain the stability and functionality of the community, such as Bacteroides in the intestinal flora25. The analysis of microbial communities can be substantially simplified by targeting specific species of potential importance.
Our representative results highlight the differences in microbial communities, which can be identified using the above-described method. Here, microorganisms in different niches of the rice root system were subjected to WGCNA. The differential among the three niches were identified using conservative and module membership analyses. We determined the key species in the difference modules and obtained information on the differences in the composition of the microbial communities in the three niches. Meanwhile, the co-occurrence network revealed the presence of a significant interaction between the changing microorganisms in the roots of rice. Our findings provide direct evidence for the significance and feasibility of WGCNA in the evaluation of microbial community differences in various environments.

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 networks (Figure 2). In the endosphere, rhizoplane, and rhizosphere soil microbial co-expression network, 23, 22, and 21 modules were identified, respectively. These results indicate that the numbers of microbial interaction networks in the three niches were basically equal.
We further compared the differences in the microbial network modules within the endosphere, rhizoplane, and rhizosphere soil. The following preservation test results of the modules of the three niches groups were obtained. Three extremely non-preserved modules existed between the rhizosphere soil and the rhizoplane (Figure 3a, Table 1). Additionally, nine extremely non-preserved modules were present between the rhizosphere soil and endosphere (Figure 3b, Table 2) and six extremely non-preserved modules between the rhizoplane and endosphere (Figure 3c, Table 3). Furthermore, extremely non-preserved modules among the three niches were found, indicating the presence of large differences in the composition of microorganisms among the three niches. The results of the module membership correlation analysis of the obtained non-conservative modules are illustrated in Figure 4. From the figure, a significantly different module with the least correlation between each two niches is visible among the three niches, which represents the most significant difference between them.
In the rhizosphere-rhizoplane difference network (Figure 5a), the dominant phylum was Proteobacteria (72.97%). In the rhizosphere-endosphere difference network (Figure 5b), the dominant phyla were Proteobacteria (66.36%), Actinobacteria (10.1%), and Bacteroidetes (10.9%). In the rhizoplane-endosphere difference network (Figure 5c), the dominant phyla were Proteobacteria (41.41%), Bacteroidetes (10.10%), Firmicutes (12.12%), and Verrucomicrobia.
Three core genera (Figure 5a), including Rhodobacter and Novosphingobium, six core genera (Figure 5b), including Blvii28 and Dechloromonas, and five core genera (Figure 5c), including Cellvibrio and Geobacter, exerted important regulatory functions in the three differential co-occurrence networks. All core genera, except for Dechloromonas, had influence on only one network, indicating the availability of considerable differences in the relative abundance of microbial populations and species among the three niches of rice roots, which critically affected the abundance and diversity of the existing root microbial communities.
The core genus Azospirillum, present in the rhizosphere-endosphere difference network of rice, participated in nitrogen fixation and promoted plant growth10. Additionally, the Geobacter genus, which was significantly enriched in the rhizoplane-endosphere difference network, may be the main factor inducing the reduction of insoluble Fe and Mn oxides in many soils and sediments11. These microorganisms interact with a range of microbial communities in the root niches and actively participate in the regulation of microbial networks, which might be critically important for the growth and development of rice roots.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62205/62205fig01.jpg" /> 
Figure 1. Evaluation of power&#946; in the datasets. (a) Evaluation of power &#946; in the ES dataset; (b) Evaluation of power &#946; in the RS dataset; (c) Evaluation of power &#946; in the RP dataset, distribution of the scale-free index R2 (left), distribution of the mean connectivity (right) along different soft power indices. The value of the best power was achieved when R2 tended to saturation and was not lower than 0.8. The three co-expression networks had to be set to the same power value to ensure their comparability. (RS: rhizosphere soil, RP: rhizoplane, and ES: endosphere). <a href="https://www.jove.com/files/ftp_upload/62205/62205fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62205/62205fig02.jpg" /> 
Figure 2. OTU dendrogram obtained by average linkage hierarchical clustering. (a) OTU dendrogram from ES network; (b) OTU dendrogram from RS network; (c) OTU dendrogram from RP network. The color row underneath the dendrogram indicates the module assignment determined by the Dynamic Tree Cut algorithm. (RS: rhizosphere soil, RP: rhizoplane, and ES: endosphere) <a href="https://www.jove.com/files/ftp_upload/62205/62205fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62205/62205fig03.jpg" /> 
Figure 3. Results of the preservation test. (a) The analysis results are based on the RS co-expression network module allocation as the reference group and the RP co-expression network module allocation results as the test group; (b) The analysis results are based on the ES co-expression network module allocation as the reference group and the RS co-expression network module allocation results as the test group; (c) The analysis results are based on the ES co-expression network module allocation as the reference group and the RP co-expression network module allocation results as the test group. Z_summary &#62; 10 indicates that two modules are highly preserved, whereas Z_summary &#60; 2 denotes non- preserved modules. medianRank expresses the relative preservation of the module assessed by ranking. Higher medianRank values denote non- preserved modules. (RS: rhizosphere soil, RP: rhizoplane, and ES: endosphere) <a href="https://www.jove.com/files/ftp_upload/62205/62205fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62205/62205fig04.jpg" /> 
Figure 4. Correlation analysis of the module memberships. (a) Module correlation of the kME value between the RS and RP network; (b) Module correlation of the kME value between RS and RP network; (c) Module correlation of the kME value between RP and ES network (RS: rhizosphere soil, RP: rhizoplane, ES: endosphere, and KME: module membership). <a href="https://www.jove.com/files/ftp_upload/62205/62205fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62205/62205fig05.jpg" /> 
Figure 5. Co-occurrence network of differential microbialpopulation in the root of rice. (a) Co-occurrence network of differential microbial populations in RS-RP; (b) Co-occurrence network of differential microbial populations in ES-RS; (c) Co-occurrence network of differential microbial populations in ES-RP. The analysis was performed using Cytoscape software. Different colors represent different gates in the figure. (RS: Rhizosphere soil, RP: rhizoplane, and ES: endosphere). <a href="https://www.jove.com/files/ftp_upload/62205/62205fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>module</td>
			<td>medianRank</td>
			<td>Zsummary</td>
			<td>module</td>
			<td>medianRank</td>
			<td>Zsummary</td>
		</tr>
		<tr>
			<td>greenyellow</td>
			<td>2</td>
			<td>14</td>
			<td>yellow</td>
			<td>13</td>
			<td>5.2</td>
		</tr>
		<tr>
			<td>pink</td>
			<td>2</td>
			<td>17</td>
			<td>grey60</td>
			<td>14</td>
			<td>3.1</td>
		</tr>
		<tr>
			<td>midnightblue</td>
			<td>3</td>
			<td>10</td>
			<td>royalblue</td>
			<td>15</td>
			<td>2.1</td>
		</tr>
		<tr>
			<td>brown</td>
			<td>4</td>
			<td>18</td>
			<td>black</td>
			<td>16</td>
			<td>2.7</td>
		</tr>
		<tr>
			<td>lightcyan</td>
			<td>6</td>
			<td>8.5</td>
			<td>tan</td>
			<td>17</td>
			<td>3.2</td>
		</tr>
		<tr>
			<td>purple</td>
			<td>7</td>
			<td>10</td>
			<td>salmon</td>
			<td>18</td>
			<td>1.3</td>
		</tr>
		<tr>
			<td>blue</td>
			<td>7</td>
			<td>22</td>
			<td>magenta</td>
			<td>18</td>
			<td>2.2</td>
		</tr>
		<tr>
			<td>green</td>
			<td>8</td>
			<td>12</td>
			<td>darkred</td>
			<td>20</td>
			<td>-0.24</td>
		</tr>
		<tr>
			<td>cyan</td>
			<td>10</td>
			<td>4.8</td>
			<td>gold</td>
			<td>20</td>
			<td>14</td>
		</tr>
		<tr>
			<td>lightgreen</td>
			<td>11</td>
			<td>5.1</td>
			<td>darkgreen</td>
			<td>22</td>
			<td>-1.1</td>
		</tr>
		<tr>
			<td>lightyellow</td>
			<td>12</td>
			<td>5.3</td>
			<td>grey</td>
			<td>22</td>
			<td>0.21</td>
		</tr>
		<tr>
			<td>red</td>
			<td>12</td>
			<td>6.1</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1. Result of Zsummary and medianRank between the rhizosphere soil and the rhizoplane.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>module</td>
			<td>medianRank</td>
			<td>Zsummary</td>
			<td>module</td>
			<td>medianRank</td>
			<td>Zsummary</td>
		</tr>
		<tr>
			<td>salmon</td>
			<td>1</td>
			<td>19</td>
			<td>lightcyan</td>
			<td>13</td>
			<td>1.1</td>
		</tr>
		<tr>
			<td>black</td>
			<td>3</td>
			<td>5.3</td>
			<td>red</td>
			<td>15</td>
			<td>0.95</td>
		</tr>
		<tr>
			<td>yellow</td>
			<td>4</td>
			<td>5.8</td>
			<td>midnightblue</td>
			<td>15</td>
			<td>-0.0016</td>
		</tr>
		<tr>
			<td>lightgreen</td>
			<td>5</td>
			<td>0.27</td>
			<td>royalblue</td>
			<td>16</td>
			<td>0.83</td>
		</tr>
		<tr>
			<td>greenyellow</td>
			<td>7</td>
			<td>3</td>
			<td>magenta</td>
			<td>16</td>
			<td>0.52</td>
		</tr>
		<tr>
			<td>darkturquoise</td>
			<td>7</td>
			<td>1.2</td>
			<td>darkgreen</td>
			<td>17</td>
			<td>0.16</td>
		</tr>
		<tr>
			<td>grey60</td>
			<td>9</td>
			<td>1.1</td>
			<td>tan</td>
			<td>18</td>
			<td>0.64</td>
		</tr>
		<tr>
			<td>blue</td>
			<td>10</td>
			<td>3.9</td>
			<td>lightyellow</td>
			<td>19</td>
			<td>0.52</td>
		</tr>
		<tr>
			<td>purple</td>
			<td>10</td>
			<td>2.3</td>
			<td>darkred</td>
			<td>19</td>
			<td>-0.18</td>
		</tr>
		<tr>
			<td>brown</td>
			<td>12</td>
			<td>2.3</td>
			<td>pink</td>
			<td>19</td>
			<td>-0.71</td>
		</tr>
		<tr>
			<td>cyan</td>
			<td>12</td>
			<td>0.78</td>
			<td>gold</td>
			<td>21</td>
			<td>11</td>
		</tr>
		<tr>
			<td>green</td>
			<td>13</td>
			<td>1.7</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2. Result of Zsummary and medianRank between the rhizosphere soil and endosphere.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>module</td>
			<td>medianRank</td>
			<td>Zsummary</td>
			<td>module</td>
			<td>medianRank</td>
			<td>Zsummary</td>
		</tr>
		<tr>
			<td>black</td>
			<td>1</td>
			<td>15</td>
			<td>darkturquoise</td>
			<td>13</td>
			<td>1.7</td>
		</tr>
		<tr>
			<td>salmon</td>
			<td>2</td>
			<td>27</td>
			<td>midnightblue</td>
			<td>13</td>
			<td>1.6</td>
		</tr>
		<tr>
			<td>yellow</td>
			<td>3</td>
			<td>13</td>
			<td>lightgreen</td>
			<td>13</td>
			<td>0.64</td>
		</tr>
		<tr>
			<td>cyan</td>
			<td>4</td>
			<td>5.4</td>
			<td>darkgreen</td>
			<td>14</td>
			<td>1.5</td>
		</tr>
		<tr>
			<td>blue</td>
			<td>8</td>
			<td>3.9</td>
			<td>darkred</td>
			<td>16</td>
			<td>1.5</td>
		</tr>
		<tr>
			<td>lightcyan</td>
			<td>9</td>
			<td>2.6</td>
			<td>purple</td>
			<td>17</td>
			<td>2.3</td>
		</tr>
		<tr>
			<td>pink</td>
			<td>10</td>
			<td>3.6</td>
			<td>greenyellow</td>
			<td>18</td>
			<td>0.8</td>
		</tr>
		<tr>
			<td>royalblue</td>
			<td>10</td>
			<td>1.5</td>
			<td>lightyellow</td>
			<td>18</td>
			<td>0.42</td>
		</tr>
		<tr>
			<td>brown</td>
			<td>12</td>
			<td>2.9</td>
			<td>magenta</td>
			<td>19</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>green</td>
			<td>12</td>
			<td>1.9</td>
			<td>gold</td>
			<td>21</td>
			<td>18</td>
		</tr>
		<tr>
			<td>red</td>
			<td>12</td>
			<td>1.9</td>
			<td>grey60</td>
			<td>21</td>
			<td>-0.21</td>
		</tr>
		<tr>
			<td>tan</td>
			<td>13</td>
			<td>2.5</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3. Result of Zsummary and medianRank between the rhizoplane and endosphere.
Supplement S1: <a href="https://www.jove.com/files/ftp_upload/62205/Supplement S1.txt" target="_blank">Please click here to download this File.</a>
Supplement S2: <a href="https://www.jove.com/files/ftp_upload/62205/Supplement S2.txt" target="_blank">Please click here to download this File.</a>
Supplement S3: <a href="https://www.jove.com/files/ftp_upload/62205/Supplement S3.txt" target="_blank">Please click here to download this File.</a>
Supplement S4: <a href="https://www.jove.com/files/ftp_upload/62205/Supplement S4.txt" target="_blank">Please click here to download this File.</a>

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, ...

divergence-root-microbiota-different-habitats-based-on-weighted

Nanyang Technological University

Research

JoVE Journal

Biology

4.2K Views.  Guizhou Normal University. 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.

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

How to Culture, Record and Stimulate Neuronal Networks on Micro-electrode Arrays (MEAs)

For the last century, many neuroscientists around the world have dedicated their lives to understanding how neuronal networks work and why they stop working in various diseases. Studies have included neuropathological observation, fluorescent microscopy with genetic labeling, and intracellular recording in both dissociated neurons and slice preparations. This protocol discusses another technology, which involves growing dissociated neuronal cultures on micro-electrode arrays (also called multi-electrode arrays, MEAs).
There are multiple advantages to using this system over other technologies. Dissociated neuronal cultures on MEAs provide a simplified model in which network activity can be manipulated with electrical stimulation sequences through the array's multiple electrodes. Because the network is small, the impact of stimulation is limited to observable areas, which is not the case in intact preparations. The cells grow in a monolayer making changes in morphology easy to monitor with various imaging techniques. Finally, cultures on MEAs can survive for over a year in vitro which removes any clear time limitations inherent with other culturing techniques.1
Our lab and others around the globe are utilizing this technology to ask important questions about neuronal networks. The purpose of this protocol is to provide the necessary information for setting up, caring for, recording from and electrically stimulating cultures on MEAs. In vitro networks provide a means for asking physiologically relevant questions at the network and cellular levels leading to a better understanding of brain function and dysfunction.

How to Culture, Record and Stimulate Neuronal Networks on Micro-electrode Arrays MEAs

This protocol provides the necessary information for setting up, caring for, recording from and electrically stimulating cultures on MEAs. In vitro networks provide a means for asking physiologically relevant questions at the network and cellular levels leading to a better understanding of brain function and dysfunction.

For the last century, many neuroscientists around the world have dedicated their lives to understanding how neuronal networks work and why they stop ...

Bioengineering Human Microvascular Networks in Immunodeficient Mice

The future of tissue engineering and cell-based therapies for tissue regeneration will likely rely on our ability to generate functional vascular networks in vivo. In this regard, the search for experimental models to build blood vessel networks in vivo is of utmost importance 1. The feasibility of bioengineering microvascular networks in vivo was first shown using human tissue-derived mature endothelial cells (ECs) 2-4; however, such autologous endothelial cells present problems for wide clinical use, because they are difficult to obtain in sufficient quantities and require harvesting from existing vasculature. These limitations have instigated the search for other sources of ECs. The identification of endothelial colony-forming cells (ECFCs) in blood presented an opportunity to non-invasively obtain ECs 5-7. We and other authors have shown that adult and cord blood-derived ECFCs have the capacity to form functional vascular networks in vivo 7-11. Importantly, these studies have also shown that to obtain stable and durable vascular networks, ECFCs require co-implantation with perivascular cells. The assay we describe here illustrates this concept: we show how human cord blood-derived ECFCs can be combined with bone marrow-derived mesenchymal stem cells (MSCs) as a single cell suspension in a collagen/fibronectin/fibrinogen gel to form a functional human vascular network within 7 days after implantation into an immunodeficient mouse. The presence of human ECFC-lined lumens containing host erythrocytes can be seen throughout the implants indicating not only the formation (de novo) of a vascular network, but also the development of functional anastomoses with the host circulatory system. This murine model of bioengineered human vascular network is ideally suited for studies on the cellular and molecular mechanisms of human vascular network formation and for the development of strategies to vascularize engineered tissues.

Here, we describe a methodology to deliver human cord blood-derived endothelial colony-forming cells (ECFCs) and bone marrow-derived mesenchymal stem cells (MSCs), embedded in a collagen/fibronectin gel, subcutaneously into immunodeficient mice. This cell/gel combination generates a human vascular network that connects with the mouse vasculature.

The future of tissue engineering and cell-based therapies for tissue regeneration will likely rely on our ability to generate functional vascular networks ...

Lateral Root Inducible System in Arabidopsis and Maize

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is however tedious, because it occurs only in a few cells inside the root and in an unpredictable manner. To circumvent this problem, a Lateral Root Inducible System (LRIS) has been developed. By treating seedlings consecutively with an auxin transport inhibitor and a synthetic auxin, highly controlled lateral root initiation occurs synchronously in the primary root, allowing abundant sampling of a desired developmental stage. The LRIS has first been developed for Arabidopsis thaliana, but can be applied to other plants as well. Accordingly, it has been adapted for use in maize (Zea mays). A detailed overview of the different steps of the LRIS in both plants is given. The combination of this system with comparative transcriptomics made it possible to identify functional homologs of Arabidopsis lateral root initiation genes in other species as illustrated here for the CYCLIN B1;1 (CYCB1;1) cell cycle gene in maize. Finally, the principles that need to be taken into account when an LRIS is developed for other plant species are discussed.

Lateral Root Inducible System in Arabidopsis and Maize

The Lateral Root Inducible System (LRIS) allows for synchronous induction of lateral roots and is presented for Arabidopsis thaliana and maize.

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

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

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

Very intriguing questions arise with our advancing knowledge on gut microbiota composition and the relationship with health, particularly relating to the factors that contribute to maintaining the population balance. However, there are limited available methodologies to evaluate these factors. Bacteriocins are antimicrobial peptides produced by many bacteria that may confer a competitive advantage for food acquisition and/or niche establishment. Many probiotic lactic acid bacteria (LAB) strains have great potential to promote human and animal health by preventing the growth of pathogens. They can also be used for immuno-modulation, as they produce bacteriocins. However, the antagonistic activity of bacteriocins is normally determined by laboratory bioassays under well-defined but over-simplified conditions compared to the complex gut environment in humans and animals, where bacteria face multifactorial influences from the host and hundreds of microbial species sharing the same niche. This work describes a complete and efficient procedure to assess the effect of a variety of bacteriocins with different target specificities in a murine system. Changes in the microbiota composition during the bacteriocin treatment are monitored using compositional 16S rDNA sequencing. Our approach uses both the bacteriocin producers and their isogenic non-bacteriocin-producing mutants, the latter giving the ability to distinguish bacteriocin-related from non-bacteriocin-related modifications of the microbiota. The fecal DNA extraction and 16S rDNA sequencing methods are consistent and, together with the bioinformatics, constitute a powerful procedure to find faint changes in the bacterial profiles and to establish correlations, in terms of cholesterol and triglyceride concentration, between bacterial populations and health markers. Our protocol is generic and can thus be used to study other compounds or nutrients with the potential to alter the host microbiota composition, either when studying toxicity or beneficial effects.

Bacteriocins are believed to play a key role in defining microbial diversity in different ecological niches. Here, we describe an efficient procedure to assess how bacteriocins affect gut microbiota composition in an animal model.

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

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

Plant biologists often need to observe the growth behavior of their chosen species. To this end, the plants need constant environmental and stable light conditions, which are preferably variable in quantity and quality so that studies under different setups can be conducted. These requirements are met by climatic chambers featuring light emitting diodes (LED) lights, which can &#8211; in contrast to fluorescent lights &#8211; be set to different wavelengths. LEDs are energy conserving and emit virtually no heat even at light intensities, which often constitutes a problem with other light sources. The presented protocol provides a step-by-step guidance of how to program a climatic chamber equipped with variable LED lights as well as describing several approaches for in depth analysis of growth phenotypes. Depending on the experimental set-up various characteristics of the growing plants can be observed and analyzed. Here we describe how to determine fresh weight, leaf area, photosynthetic activity, and stomatal density. We demonstrate that in order to obtain reliable data and draw valid conclusions it is mandatory to use a sufficient number of individuals for statistical evaluation. Taking too few plants for this kind of analysis results in high statistical errors and consequently in less clear interpretations of the data.

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

Here, we present a protocol for studying plant growth behavior and especially phenotypes in a reproducible manner. We show how to provide variable and at the same time stable light conditions. Proper analyses depend on sufficient sample numbers and valid statistical evaluations.

Plant biologists often need to observe the growth behavior of their chosen species. To this end, the plants need constant environmental and stable light ...

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

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 culture, termed senolytics, can reduce the senescent cell burden in vivo and extend healthspan. Multiple classes of senolytics have been identified to date including HSP90 inhibitors, Bcl-2 family inhibitors, piperlongumine, a FOXO4 inhibitory peptide and the combination of Dasatinib/Quercetin. Detection of SA-&#946;-Gal at an increased lysosomal pH is one of the best characterized markers for the detection of senescent cells. Live cell measurements of senescence-associated &#946;-galactosidase (SA-&#946;-Gal) activity using the fluorescent substrate C12FDG in combination with the determination of the total cell number using a DNA intercalating Hoechst dye opens the possibility to screen for senotherapeutic drugs that either reduce overall SA-&#946;-Gal activity by killing of senescent cells (senolytics) or by suppressing SA-&#946;-Gal and other phenotypes of senescent cells (senomorphics). Use of a high content fluorescent image acquisition and analysis platform allows for the rapid, high throughput screening of drug libraries for effects on SA-&#946;-Gal, cell morphology and cell number.

Cellular senescence is the key factor in the development of chronic age-related pathologies. Identification of therapeutics that target senescent cells show promise for extending healthy aging. Here, we present a novel assay to screen for the identification of senotherapeutics based on measurement of senescence associated &#946;-Galactosidase activity in single cells.

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

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a 'sample preparation protocol' for maize materials (i.e., stem, leaf, and root) suitable for ordinary microcomputed tomography (micro-CT) scanning. Based on high-resolution CT images of maize stem, leaf, and root, we describe two protocols for the phenotypic analysis of vascular bundles: (1) based on the CT image of maize stem and leaf, we developed a specific image analysis pipeline to automatically extract 31 and 33 phenotypic traits of vascular bundles; (2) based on the CT image series of maize root, we set up an image processing scheme for the three-dimensional (3-D) segmentation of metaxylem vessels, and extracted two-dimensional (2-D) and 3-D phenotypic traits, such as volume, surface area of metaxylem vessels, etc. Compared with traditional manual measurement of vascular bundles of maize materials, the proposed protocols significantly improve the efficiency and accuracy of micron-scale phenotypic quantification.

We provide a novel method to improve the X-ray absorption contrast of maize tissue suitable for ordinary microcomputed tomography scanning. Based on CT images, we introduce a set of image-processing workflows for different maize materials to effectively extract microscopic phenotypes of vascular bundles of maize.

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

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

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

In Vitro Analysis of E3 Ubiquitin Ligase Function

The covalent attachment of ubiquitin (Ub) to internal lysine residue(s) of a substrate protein, a process termed ubiquitylation, represents one of the most important post-translational modifications in eukaryotic organisms. Ubiquitylation is mediated by a sequential cascade of three enzyme classes including ubiquitin-activating enzymes (E1 enzymes), ubiquitin-conjugating enzymes (E2 enzymes), and ubiquitin ligases (E3 enzymes), and sometimes, ubiquitin-chain elongation factors (E4 enzymes). Here, in vitro protocols for ubiquitylation assays are provided, which allow the assessment of E3 ubiquitin ligase activity, the cooperation between E2-E3 pairs, and substrate selection. Cooperating E2-E3 pairs can be screened by monitoring the generation of free poly-ubiquitin chains and/or auto-ubiquitylation of the E3 ligase. Substrate ubiquitylation is defined by selective binding of the E3 ligase and can be detected by western blotting of the in vitro reaction. Furthermore, an E2~Ub discharge assay is described, which is a useful tool for the direct assessment of functional E2-E3 cooperation. Here, the E3-dependent transfer of ubiquitin is followed from the corresponding E2 enzyme onto free lysine amino acids (mimicking substrate ubiquitylation) or internal lysines of the E3 ligase itself (auto-ubiquitylation). In conclusion, three different in vitro protocols are provided that are fast and easy to perform to address E3 ligase catalytic functionality.

In Vitro Analysis of E3 Ubiquitin Ligase Function

The present study provides detailed in vitro ubiquitylation assay protocols for the analysis of E3 ubiquitin ligase catalytic activity. Recombinant proteins were expressed using prokaryotic systems such as Escherichia coli culture.

The covalent attachment of ubiquitin (Ub) to internal lysine residue(s) of a substrate protein, a process termed ubiquitylation, represents one of the ...