Location: Physiology and Pathology of Tree Fruits Research
Project Number: 2094-21220-003-017-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Dec 1, 2024
End Date: Nov 30, 2027
Objective:
Reduced productivity over time due to diminished soil fertility and increased pest and disease incidence are typical outcomes of conventional agricultural practices in highly manipulated or managed ecosystems. Disruption in the required functioning of the root-associated microbiome is considered to be one of the deleterious effects of continuous monocropping, a phenomenon which occurs in both perennial and annual cropping systems. Although alternative tactics (e.g., crop rotation, use of organic inputs) have been utilized to remediate the composition and function of disturbed soil microbial communities, the complexity of interactions among the diversity of organisms that reside in a soil ecosystem has hindered the ability to successfully direct the trajectory of microbial succession in a manner that leads to the desired function(s). Recent metagenomic studies have begun to develop a more comprehensive view of the functional potential of rhizosphere bacterial communities associated with apple replant disease-suppressive/conducive soil systems. As part of a previous BARD project, we provided a pioneering demonstration of how bacterial metagenomics data can be used in the network-based analysis of rhizosphere communities. The ultimate goal of this research is to obtain information that can be used to further the development of sustainable strategies for engineering indigenous microbial communities in apple. This study will include proof-of-concept experiments designed to test how specific environmental resources (such as those introduced by soil amendment treatments) control community shifts and/or metabolic activities occurring in the apple rhizobiome. In addition, community-level simulations which include actual and/or ecologically-relevant metabolomic data will be conducted. We hope to garner an understanding of the processes shaping the causal microbiology of replant diseases and the mechanisms behind the function of successful treatments by integrating metabolomic data with systems biology-based platforms. Finally, we hope that our experimental approach for deciphering microbe-microbe and host-microbe interactions paves the way for future research designed to engineer indigenous microbiomes in other crop plants.
Approach:
Previously, we developed a pipeline for engineering native rhizosphere microbial communities in crop plants. This framework is based on the generation of taxa-function predictions from metagenomic data and is publicly available (https://github.com/ot483/NetCom2). Here, we will test hypotheses (which were formulated based on this initial simulation system) for strategically engineering the rhizosphere microbiome towards alleviating apple replant disease (ARD). This will be accomplished by collecting rhizosphere soil from apples cultivated in orchard soil expressing replant disease (-BjSa seed meal) and in an effective soil amendment treatment (+BjSa seed meal). We will then isolate/culture select bacterial groups from rhizosphere soil and assess their ability to utilize and/or produce particular compounds. The ability of select microbial groups and/or compounds to contribute to replant disease progression or suppression will also be tested in microcosm experiments with apple seedlings. In parallel, the apple rhizosphere soil from the main experiment (+/- BjSa seed meal) will be profiled using non-targeted metabolomics. Targeted metabolomics will also be used to verify the presence of specific compounds in the rhizosphere. In addition, rhizosphere soil will be collected for metagenomic sequencing. Illumina technology combined with long-read (PacBio) sequencing is expected to provide us with an extensive collection of high-quality metagenomics assembled genomes (MAGs) of bacteria native to the apple rhizosphere of each system (+/- BjSa seed meal). The collection of MAGs will be used to construct high-quality genome-scale metabolic models (GSSM). GSMMs will then be incorporated into a silico simulation system for healthy/recovered and replant-diseased rhizosphere microbiomes and combined with treatment-specific metabolomics data from the same experiment. We expect that the results will provide testable predictions about condition-dependent effects on the GSSM-based bacterial community and lead to additional targeted manipulation experiments designed to validate in silico predictions.