|BERIHU, MARIA - Agricultural Research Organization Of Israel
|MALIK, A - University Of Haifa
|MEDINA, SHLOMIT - Agricultural Research Organization Of Israel
|PIOMBO, EDOARDO - Swedish University Of Agricultural Sciences
|TAL, OFIR - Agricultural Research Organization Of Israel
|COHEN, MATAN - Agricultural Research Organization Of Israel
|GINAT, ALON - Agricultural Research Organization Of Israel
|OFEK-LALZAR, MAYA - University Of Haifa
|DORON-FAIGENBOIM, ADI - Agricultural Research Organization - Volcani Center
|MAZZOLA, MARK - Stellenbosch University
|FREILICH, SHIRI - Agricultural Research Organization Of Israel
Submitted to: Microbiome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/28/2022
Publication Date: 1/12/2023
Citation: Berihu, M., Somera, T.S., Malik, A., Medina, S., Piombo, E., Tal, O., Cohen, M., Ginat, A., Ofek-Lalzar, M., Doron-Faigenboim, A., Mazzola, M., Freilich, S. 2023. A framework for the targeted recruitment of crop-beneficial soil taxa based on network analysis of metagenomics data. Microbiome. 11. Article 8. https://doi.org/10.1186/s40168-022-01438-1.
Interpretive Summary: Sustainable soil-borne disease control solutions are commonly based on harnessing the potential of specific elements of the soil microbial community by the addition of various soil amendments such as compost of Brassica seed meal formulations. Unfortunately, the consistent use of this soil amendment-based approaches to disease management suffers from a lack of understanding of the functional elements of the microbial community that operate in disease control and what metabolites from the amendment serve to increase populations of these functional microbes. In this study, we employed metagenomics to discern what shifts in potential metabolic pathways were associated with seed meal amendment and determination of which specific microbes were responsible for production of specific metabolites. Metagenomic data were converted into community level metabolic networks from a control and a seed meal amended soil to determine metabolites specific to the seed meal amended soil. Production of the metabolites was modeled to predict how elimination of metabolic pathways, and associated microorganisms, would potentially affect disease control outcomes. The generation of testable predictions based on the interpretation of genomic data is a first step towards untangling the intricate web of interactions in soil. In an era of ecosystem degradation and climate change, organizing plant–microorganism interactions by recruiting indigenous microorganisms with diverse functions and suppressing high pathogen loads will enable reducing the use of chemicals, offering one of the few untapped reservoirs of opportunities to confront sustainability issues in agriculture.
Technical Abstract: Intensive agricultural management practices are designed to maximize the growth of a single crop but come at high costs to the environment. Soilborne diseases are among the outcomes of monoculture farming where plant-induced modifications to the rhizosphere microbiome promote a community that diminishes productivity and yield. Sustainable disease control solutions are commonly based on harnessing the potential of the indigenous microbiome through the introduction of organic amendments. Here, we explored changes in the microbiome in response to amendment-based modifications which lead to recovery from soil-borne disease in apple orchards. Metagenomic data from 'sick' vs 'healthy/recovered' microbiomes was converted into community-level metabolic networks. Simulations explored the functional contribution of treatment-associated taxonomic groups, linked them with amendment-induced specific metabolites and guided formulation, testing and validation of predictions. Our research demonstrates how model-based predictions can serve the design of defined sustainable solutions for suppressing soilborne disease symptoms in crop production systems.