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

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.