Multi-omics Approach to Identifying the Molecular Mechanisms of the Pseudomonas Response to Exudates in the Rhizosphere
EMSL Project ID
49346
Abstract
Rhizosphere communities, the bacteria and fungi that live in symbiotic association with plant roots, have a significant effect on nutrient cycling and biomass accumulation in terrestrial environments. These symbiotic communities are highly dynamic, controlled by complex intercellular signaling pathways that respond to changes in the nutrient environment and the rhizosphere community composition that dictate how nutrients are shared and stored. The specific nature of these intercellular signaling compounds and the molecular mechanisms through which they are detected, however, remains largely unknown. We propose using a laboratory model of rhizosphere community interaction, comprised of the DOE model tree species aspen, the ectomycorrhizal fungi Laccaria bicolor, and rhizosphere-associated bacteria Pseudomonas fluorescens, to identify the specific root, fungal, and mycorrhizal signals received by bacterial sensors, and the bacterial responses that underlie mycorrhizal interactions. This laboratory system has been well developed in our laboratory and has formed the basis of much of our previous investigations into mycorrhizal interactions as well as a prior EMSL collaboration. In order to isolate the specific exudate-sensor interactions in Pseudomonas, we will collect exudates from aspen roots, Laccaria, and aspen-Laccaria mycorrhizal laboratory cultures under two levels of phosphorus nutrient concentrations. These exudates will be highly characterized at EMSL, using metabolomic and proteomic analysis methods. Cultures of P. fluorescens will be exposed to these well characterized exudates and bacterial responses will be analyzed by transcriptomics and exudate metabolomics characterization. Initial analysis will identify specific plant, fungal, and mycorrhizal exudates that are significantly associated with community composition and/or phosphorus nutrient concentrations. P. fluorescens transcriptomics will identify sets of genes whose expression levels are significantly associated with an exudate type. This will be a novel observation and lead to significant advances in understanding mycorrhizal interactions. In more advanced analyses, we will apply machine learning techniques to identify specific molecular mechanisms for P. fluorescens sensing and response networks. Specifically, the P. fluorescens gene regulatory network will be modeled as an Artificial Neural Network (ANN) whose inputs are plant/fungal/mycorrhizal exudate compound concentrations. This ANN will serve to highlight possible regulatory pathways, and identify specific exudate components predicted to drive particular changes in bacterial the transcriptome. Selected predictions will be confirmed by additional biological experiments. Also exudate compounds predicted to drive bacterial responses and that have ambiguous molecular identification will be subjected to additional NMR and mass spectrometry characterization at EMSL. This project directly addresses the Biosystems Dynamics and Design goals for "inter- and intracellular signaling influencing system-level processes" and "modeling and simulation of metabolic pathways to support synthetic biology, coupled with data-driven validation" and will significantly contribute to the understanding of intercellular signaling in rhizosphere communities. Novel signaling compounds and bacterial sensors validated here will be important tools for future synthetic community design for nutrient cycling in terrestrial plants.
Project Details
Project type
Large-Scale EMSL Research
Start Date
2016-10-01
End Date
2018-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
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