Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.

Root-associated microbes are critical to plant health and performance, although understanding of the factors that structure these microbial communities and the theory to predict microbial assemblages are still limited. Here, we use a grafted tomato system to study the effects of rootstock genotypes and grafting in endosphere and rhizosphere microbiomes that were evaluated by sequencing 16S rRNA. We compared the microbiomes of nongrafted tomato cultivar BHN589, selfgrafted BHN589, and BHN589 grafted to Maxifort or RST-04-106 hybrid rootstocks. Operational taxonomic unit (OTU)-based bacterial diversity was greater in Maxifort compared to the nongrafted control, whereas bacterial diversity in the controls (selfgrafted and nongrafted) and the other rootstock (RST-04-106) was similar. Grafting itself did not affect bacterial diversity; diversity in the self-graft was similar to that of the nongraft. Bacterial diversity was higher in the rhizosphere than in the endosphere for all treatments. However, despite the lower overall diversity, there was a greater number of differentially abundant OTUs (DAOTUs) in the endosphere, with the greatest number of DAOTUs associated with Maxifort. In a permutational multivariate analysis of variance (PERMANOVA), there was evidence for an effect of rootstock genotype on bacterial communities. The endosphere-rhizosphere compartment and study site explained a high percentage of the differences among bacterial communities. Further analyses identified OTUs responsive to rootstock genotypes in both the endosphere and rhizosphere. Our findings highlight the effects of rootstocks on bacterial diversity and composition. The influence of rootstock and plant compartment on microbial communities indicates opportunities for the development of designer communities and microbiome-based breeding to improve future crop production. IMPORTANCE Understanding factors that control microbial communities is essential for designing and supporting microbiome-based agriculture. In this study, we used a grafted tomato system to study the effect of rootstock genotypes and grafting on bacterial communities colonizing the endosphere and rhizosphere. To compare the bacterial communities in control treatments (nongrafted and self-grafted plants) with the hybrid rootstocks used by farmers, we evaluated the effect of rootstocks on overall bacterial diversity and composition. These findings indicate the potential for using plant genotype to indirectly select bacterial taxa. In addition, we identify taxa responsive to each rootstock treatment, which may represent candidate taxa useful for biocontrol and in biofertilizers.Citation Poudel R, Jumpponen A, Kennelly MM, Rivard CL, Gomez-Montano L, Garrett KA. 2019. Rootstocks shape the rhizobiome: rhizosphere and endosphere bacterial communities in the grafted tomato system. Appl Environ Microbiol 85:e01765-18. https://

Root-associated fungal endophytes are vital component of root microbiome as some mitigate their host’s abiotic and biotic stress. We characterized root-associated fungal endophytes in cereal grains and their progenitors grown on two different soil-types. We aimed at determining how clay and desert soil affects the colonization of root fungal community. Both culture-dependent and culture-independent methods were employed to identify endophytes that successfully colonized greenhouse-grown host plants. The Internal Transcriber Spacer region of fungal ribosomal DNA was utilized for identification purposes. This study revealed soil as a prominent factor influencing the composition of microfungal communities inhabiting the roots of maize (Zea mays subsp. mays) and its conspecific progenitor, teosinte (Zea mays subsp. parviglumis). Similar results were found in wheat (Triticum aestivum subsp. aestivum) and its progenitor (Triticum monococcum subsp. monococcum). The multidimensional comparisons of Morisita-Horn similarity values of fungal colonists of various host plant taxa indicated that soil plays a primary role in shaping the root fungal community; a secondary effect was plant host identity, even when the plant host is a conspecific. Future studies focused on characterizing root endophytes in other cereal grains, and studying the effect of edaphic factors on fungal colonization, can ultimately contribute to crop productivity.