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>

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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, 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><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...

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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

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JoVE Journal

Biology

4.3K Views.  Guizhou Normal University. Deze methode kan effectief de interactieve relatie of het co-occurrence netwerk van verschillende microbiële soorten in verschillende omgevingen verkennen. Het biedt details over het gebruik van het WGCNA-algoritme om een co-occurrence netwerk van microbiota te bouwen. Bovendien beoordeelt deze methode op basis van de resultaten de differentiatie van microbiële relaties en samenstelling tussen microbiële consortia.Hier is de basisstroom van de methode. De samenstellings- en overvloe...

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

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

Genetics

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses (Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture (A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula). Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

Excavation of plant roots from the field as well as processing of samples into endosphere, rhizosphere, and soil are described in detail, including DNA extraction and data analysis methods. This paper is designed to enable other laboratories to use these techniques for the study of soil, endosphere, and rhizosphere microbiomes.

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The ...

Environment

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Investigation of Plant Interactions Across Common Mycorrhizal Networks Using Rotated Cores

Arbuscular mycorrhizal (AM) fungi influence plant mineral nutrient uptake and growth, hence, they have the potential to influence plant interactions. The power of their influence is in extraradical mycelia that spread beyond nutrient depletion zones found near roots to ultimately interconnect individuals within a common mycorrhizal network (CMN). Most experiments, however, have investigated the role of AM fungi in plant interactions by growing plants with versus without mycorrhizal fungi, a method that fails to explicitly address the role of CMNs. Here, we propose a method that manipulates CMNs to investigate their role in plant interactions. Our method uses modified containers with conical bottoms with a nylon mesh and/or hydrophobic material covering slotted openings, 15N fertilizer, and a nutrient-poor interstitial sand. CMNs are left either intact between interacting individuals, severed by rotation of containers, or prevented from forming by a solid barrier. Our findings suggest that rotating containers is sufficient to disrupt CMNs and prevent their effects on plant interactions across CMNs. Our approach is advantageous because it mimics aspects of nature, such as seedlings tapping into already established CMNs and the use of a suite of AM fungi that may provide diverse benefits. Although our experiment is limited to investigating plants at the seedling stage, plant interactions across CMNs can be detected using our approach which therefore can be applied to investigate biological questions about the functioning of CMNs in ecosystems.

Most plants within communities likely are interconnected by arbuscular mycorrhizal (AM) fungi, but mediation of plant interactions by them has been investigated primarily by growing plants with versus without mycorrhizas. We present a method to manipulate common mycorrhizal networks among mycorrhizal plants to investigate their consequences for plant interactions.

Arbuscular mycorrhizal (AM) fungi influence plant mineral nutrient uptake and growth, hence, they have the potential to influence plant interactions. The ...

Applying Advanced In Vitro Culturing Technology to Study the Human Gut Microbiota

The human gut microbiota plays a vital role in both human health and disease. Studying the gut microbiota using an in vivo model, is difficult due to its complex nature, and its diverse association with mammalian components. The goal of this protocol is to culture the gut microbiota in vitro, which allows for the study of the gut microbiota dynamics, without having to consider the contribution of the mammalian milieu. Using in vitro culturing technology, the physiological conditions of the gastro intestinal tract are simulated, including parameters such as pH, temperature, anaerobiosis, and transit time. The intestinal surface of the colon is simulated by adding mucin-coated carriers, creating a mucosal phase, and adding further dimension. The gut microbiota is introduced by inoculating with the human fecal material. Upon inoculation with this complex mixture of bacteria, specific microbes are enriched in the different longitudinal (ascending, transverse and descending colons) and transversal (luminal and mucosal) environments of the in vitro model. It is crucial to allow the system to reach a steady state, in which the community and the metabolites produced remain stable. The experimental results in this manuscript demonstrate how the inoculated gut microbiota community develops into a stable community over time. Once steady state is achieved, the system can be used to analyze bacterial interactions and community functions or to test the effects of any additives on the gut microbiota, such as food, food components, or pharmaceuticals.

Here, we present a protocol for culturing the gut microbiota of the colon in vitro, using a series of bioreactors that simulate the physiological conditions of the gastro intestinal tract.

The human gut microbiota plays a vital role in both human health and disease. Studying the gut microbiota using an in vivo model, is difficult due to its ...

Bioengineering

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are needed to decipher the utilization of microbiome modulators for human health benefits. However, the large-scale screening and optimization of modulators to validate microbiome modulation and predict related health benefits may be practically difficult due to the need for large number of animals and/or human subjects. To this end, in vitro or ex vivo models can facilitate preliminary screening of microbiome modulators. Herein, it is optimized and demonstrated an ex vivo fecal microbiota culture system that can be used for examining the effects of various interventions of gut microbiome modulators including probiotics, prebiotics and other food ingredients, aside from nutraceuticals and drugs, on the diversity and composition of the human gut microbiota. Inulin, one of the most widely studied prebiotic compounds and microbiome modulators, is used as an example here to examine its effect on the healthy fecal microbiota composition and its metabolic activities, such as fecal pH and the fecal levels of organic acids including lactate and short-chain fatty acids (SCFAs). The protocol may be useful for studies aimed at estimating the effects of different interventions of modulators on fecal microbiota profiles and at predicting their health impacts.

This protocol describes an in vitro batch-culture fermentation system of human fecal microbiota, using inulin (a well-known prebiotic and one of the most widely studied microbiota modulators) to demonstrate the use of this system in estimating effects of specific interventions on fecal microbiota composition and metabolic activities.

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are ...

Immunology and Infection

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 ...

Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays

Arbuscular mycorrhizal fungi are symbionts in the roots of plants. Their role is to sustain host development and maintain the nutritional equilibrium in the ecosystems. The colonization process is dependent on several factors like soil ecology, the genetic diversity of the fungi and host, and agronomic practices. Their synchronized action leads to the development of a complex hyphal network and leads to the secondary development of vesicles and arbuscules in the root cells. The aim of this research was to analyze the efficiency of the mycorrhizal patterns (MycoPatt) method for the positioning of fungal structures in the roots of Festuca rubra and Zea mays. Another objective was to explore the fungal colonization strategy as revealed by mycorrhizal maps of each species. The acquisition and assemblage of multiple microscopic images allow mycorrhizal colonization assessment in both corn and red fescue plants to provide information on the realistic position of the developed structures. The observed mycorrhizal patterns highlight the variable efficiency of each plant in terms of developing connections with soil symbiotic fungi, caused by applied treatments and growth stage. Mycorrhizal detailed maps obtained through the MycoPatt method are useful for the early detection of plant efficiency in symbiotic acquisition from the soil.

Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays

The protocol here describes the methods for the assessment of the arbuscular mycorrhizal colonization patterns and strategy in roots for two species: Zea mays and Festuca rubra. The use of the MycoPatt method permits the calculation of parameters, the conversion of mycorrhizal structures into digital data, and the mapping of their real position in roots.

Arbuscular mycorrhizal fungi are symbionts in the roots of plants. Their role is to sustain host development and maintain the nutritional equilibrium in ...

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

The nematode Caenorhabditis elegans is a model system for host-microbe and host-microbiome interactions. Many studies to date use batch digests rather than individual worm samples to quantify bacterial load in this organism. Here it is argued that the large inter-individual variability seen in bacterial colonization of the C. elegans intestine is informative, and that batch digest methods discard information that is important for accurate comparison across conditions. As describing the variation inherent to these samples requires large numbers of individuals, a convenient 96-well plate protocol for disruption and colony plating of individual worms is established.

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

This protocol describes a 96-well disruption of individual bacterially colonized Caenorhabditis elegans following cold paralysis and surface bleaching&#160;to remove external bacteria. The resulting suspension is plated on agar plates to allow accurate, medium-throughput quantification of bacterial load in large numbers of individual worms.

The nematode Caenorhabditis elegans is a model system for host-microbe and host-microbiome interactions. Many studies to date use batch digests rather ...

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

The nematode Caenorhabditis elegans is emerging as a useful model for studying the molecular mechanisms underlying interactions between hosts and their gut microbiomes. While experiments with well-characterized bacteria or defined bacterial communities can facilitate the analysis of molecular mechanisms, studying nematodes in their natural microbial context is essential for exploring the diversity of such mechanisms. At the same time, the isolation of worms from the wild is not always feasible, and, even when possible, sampling from the wild restricts the use of the genetic toolkit otherwise available for C. elegans research. The following protocol describes a method for microbiome studies utilizing compost microcosms for the in-lab&#160;growth in microbially diverse and natural-like environments.
Locally sourced soil can be enriched with produce to diversify the microbial communities in which worms are raised and from which they are harvested, washed, and surface-sterilized for subsequent analyses. Representative experiments demonstrate the ability to modulate the microbial community in a common soil by enriching it with different produce and further demonstrate that worms raised in these distinct environments assemble similar gut microbiomes distinct from their respective environments, supporting the notion of a species-specific core gut microbiome. Overall, compost microcosms provide natural-like in-lab environments for microbiome research as an alternative to synthetic microbial communities or to the isolation of wild nematodes.

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

Compost microcosms bring the microbial diversity found in nature into the laboratory to facilitate microbiome research in Caenorhabditis elegans. Provided here are protocols for setting up microcosm experiments, with the experiments demonstrating the ability to modulate environmental microbial diversity to explore the relationships between environmental microbial diversity and worm gut microbiome composition.

The nematode Caenorhabditis elegans is emerging as a useful model for studying the molecular mechanisms underlying interactions between hosts and their ...

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

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