Understanding factors influencing microbial interactions, and designing methods to identify key taxa that are candidates for synthetic communities, or SynComs, are complex challenges for achieving microbiome-based agriculture. Here, we study how grafting and the choice of rootstock influences root-associated fungal communities in a grafted tomato system. We studied three tomato rootstocks (BHN589, RST-04-106, and Maxifort) grafted to a BHN589 scion and profiled the fungal communities in the endosphere and rhizosphere by sequencing the internal transcribed spacer (ITS2). The data provided evidence for a rootstock effect (explaining ~2% of the total captured variation, P < 0.01) on the fungal community. Moreover, the most productive rootstock, Maxifort, supported greater fungal species richness than the other rootstocks or controls. We then constructed a phenotype-operational taxonomic unit (OTU) network analysis (PhONA) using an integrated machine learning and network analysis approach based on fungal OTUs and associated tomato yield as the phenotype. PhONA provides a graphical framework to select a testable and manageable number of OTUs to support microbiome-enhanced agriculture. We identified differentially abundant OTUs specific to each rootstock in both endosphere and rhizosphere compartments. Subsequent analyses using PhONA identified OTUs that were directly associated with tomato fruit yield and others that were indirectly linked to yield through their links to these OTUs. Fungal OTUs that are directly or indirectly linked with tomato yield may represent candidates for synthetic communities to be explored in agricultural systems. IMPORTANCE The realized benefits of microbiome analyses for plant health and disease management are often limited by the lack of methods to select manageable and testable synthetic microbiomes. We evaluated the composition and diversity of root-associated fungal communities from grafted tomatoes. We then constructed a phenotype-OTU network analysis (PhONA) using these linear and network models. By incorporating yield data in the network, PhONA identified OTUs that were directly predictive of tomato yield and others that were indirectly linked to yield through their links to these OTUs. Follow-up functional studies of taxa associated with effective rootstocks, identified using approaches such as PhONA, could support the design of synthetic fungal communities for microbiome-based crop production and disease management. The PhONA framework is flexible for incorporation of other phenotypic data, and the underlying models can readily be generalized to accommodate other microbiome or 'omics data.
Keywords: fungi; grafting; microbiome networks; model integration; phenotypes; tomato; variable phenotypes.