|POUDEL, A. - University Of Florida|
|JUMPPONEN, A. - Kansas State University|
|MCSPADDEN GARDENER, B - The Ohio State University|
|KINKEL, L. - University Of Minnesota|
|GARRETT, K. - University Of Florida|
Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/4/2016
Publication Date: 10/4/2016
Citation: Poudel, A., Jumpponen, A., Schlatter, D.C., Paulitz, T.C., Mcspadden Gardener, B., Kinkel, L.L., Garrett, K.A. 2016. A systems framework for identifying candidate microbial assemblages for disease management. Phytopathology. 106:1083-1096.
Interpretive Summary: Network analysis is a technique of looking at complex microbial communities and the relationships between the taxa. This paper summarizes a number of types of network analysis, and gives two case study examples to demonstrate how these analyses can generate testable hypotheses. The two examples are on the phylloplane of oak leaves and bacteria associated with diseased Rhizoctonia patches in wheat and disease suppression.
Technical Abstract: Network models of soil and plant microbiomes present new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how the observed structure of networks 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 to maintain an existing microbial community. “Host-focused analysis” includes a node representing a plant response such as yield, identifying taxa with direct or indirect links to that node, interpreted as either beneficial or detrimental to plant health. “Pathogen-focused analysis” identifies taxa with direct or indirect links to taxon nodes known a priori to represent pathogens, which can be interpreted as agonists and antagonists, respectively. “Disease-focused analysis” identifies key nodes for both plant and pathogen responses. We illustrate the interpretation of network structure with analyses of two microbiomes: the oak phyllosphere and soil associated with the presence or absence of infection by Rhizoctonia solani. Such network analyses can be used to further characterize microbial communities and associated conditions involved in the suppression of plant pathogens, the biofertilization of crop plants, and/or the expression of host resistance in crop plants.