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Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management.

R Poudel1, A Jumpponen1, D C Schlatter1

  • 1First and seventh authors: Plant Pathology Department, Institute for Sustainable Food Systems, and Emerging Pathogens Institute, University of Florida, Gainesville 32611-0680; second author: Division of Biology and Ecological Genomics Institute, Kansas State University, Manhattan 66506; third and fourth authors: U.S. Department of Agriculture-Agriculture Research Service, Wheat Health, Genetics, and Quality Research Unit, Washington State University, Pullman, WA 99164; fifth author: Department of Plant Pathology, The Ohio State University-OARDC, Wooster 44691; and sixth author: Department of Plant Pathology, University of Minnesota, St. Paul 55108.

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This study introduces a framework for interpreting plant microbiome networks to identify beneficial microbes for disease management. It helps generate testable hypotheses about microbes influencing plant health and disease suppression.

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Area of Science:

  • Microbial ecology
  • Plant pathology
  • Bioinformatics

Background:

  • Microbiome network models offer potential for disease management but pose interpretation challenges.
  • Understanding microbial community interactions is crucial for plant health and agricultural applications.

Purpose of the Study:

  • To present a framework for interpreting plant microbiome network structures.
  • To generate testable hypotheses about candidate microbes impacting plant health and disease.

Main Methods:

  • Developed a framework with four network analysis types: general, host-focused, pathogen-focused, and disease-focused.
  • Applied the framework to analyze oak phyllosphere and wheat rhizosphere/soil microbiomes.
  • Identified candidate taxa based on direct/indirect associations with plant health outcomes or pathogens.

Main Results:

  • The framework characterizes microbes directly and indirectly associated with disease suppression, biofertilization, and plant resistance.
  • Demonstrated the framework's utility in identifying key microbial players in plant-microbe interactions.
  • Revealed associations between specific taxa and disease presence/absence in wheat.

Conclusions:

  • Network analysis provides a robust method for interpreting complex microbiome data.
  • The framework facilitates the discovery of microbes that can enhance plant health and manage diseases.
  • This approach aids in understanding the ecological roles of microbes in plant health.