OOS 21-5
Modeling of bacterial induced changes in the root environment

Wednesday, August 13, 2014: 9:20 AM
203, Sacramento Convention Center
Collin Timm, Biosciences Division, Oak Ridge National Laboratory
Jeremiah A. Henning, Rocky Mountain Biological Laboratory, Crested Butte, CO
Sara Jawdy, Plant Systems Biology Group, Oak Ridge National Laboratory, Oak Ridge, TN
Dale A. Pelletier, Biological & Nanoscale Systems group, Oak Ridge National Laboratory
David J. Weston, Biosciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN
Background/Question/Methods

Plant associated bacteria can modify root traits through direct or indirect interactions with the plant. The plant in turn produces chemicals that can aid or inhibit the growth of bacteria. An example of such interactions occurs between the roots of poplar and phosphate solubilizing bacteria in the system. The plant sustains the growth of the phosphate solubilizing bacteria, while the bacteria produce acids and exo-enzymes to mobilize phosphorous. In this work we are developing genome-based models to predict carbon source utilization and production of secondary metabolites in the endosphere and rhizosphere that can affect plant growth and morphology, including plant root functional traits.

Results/Conclusions

We are developing genome-based models that can be used to predict rates of carbon source utilization and the transformation of specific carbon sources to compounds that affect plant growth and development. Models of nitrogen-fixing bacteria have predicted bacterial abundance on plants under nitrogen limiting conditions. The models also predict differential growth rates on carbon sources present in the exudate, suggesting a mechanism by which plants may control bacterial growth. Our current models are applied to a single bacterial species interacting with the plant, but experiments show that even simple communities of even 2 or 3 species can affect the plant biomass allocation and functional traits. We aim to develop predictive models of interacting bacterial species to understand how the plant and its corresponding microbiome act as a functional community.