B078-0005
The Relative Importance of a Trait in the Rhizosphere: Linking Genome Inference, Biophysical Modeling and Experiments.

Monday, 14 December 2020
Poster
Gianna Marschmann1, Jinyun Tang1, Kateryna Zhalnina2, Heejung Cho1,3, Ulas Karaoz4, Erin E Nuccio5, Jennifer Pett-Ridge6 and Eoin Brodie7,8, (1)Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States, (3)University of California, Berkeley, Department of Plant and Microbial Biology, Berkeley, United States, (4)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences, Berkeley, CA, United States, (5)Lawrence Livermore National Laboratory, Physical and Life Sciences Directorate, Livermore, CA, United States, (6)Lawrence Livermore National Laboratory, Livermore, CA, United States, (7)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (8)University of California Berkeley, Environmental Science, Policy, and Management, Berkeley, CA, United States
Abstract:
Predictive trait-based modeling in microbial ecology requires model-data integration and experimental systems in which ecological theories can be tested. Data from the rhizosphere of an annual grass show that microbial community assembly is mechanistically linked to plant exudation of low molecular weight compounds and microbial substrate uptake traits that are predictable from genome sequences. At the same time, hierarchical clustering of upregulated carbohydrate depolymerization genes suggest specialization for substrates provided by live or decaying root biomass. In order to disentangle ecological interactions underlying microbial assembly in the rhizosphere, we coupled a dynamic energy budget model for the development and growth of plants with a trait-based representation of microbial metabolism, substrate uptake and depolymerization rates. We find that substrate-explicit (genomic allocation to transporter genes, thermodynamic efficiency and stoichiometry of plant metabolites) and allometric traits (cellular carbon demand) expressed as network parameters of equilibrium chemistry approximation kinetics (predicted max. uptake rates, affinity constants) describe the observed preference of rhizosphere bacteria for aromatic organic acids exuded by plants. Ongoing work explores different strategies for the optimal deployment of extracellular enzymes in the rhizosphere. We anticipate our model will provide insight into the intrinsic dimensionality of microbial functional traits that explain observed community dynamics, i.e. to provide a rank-ordering of biophysical, life-history, and metabolic traits that distinguish microbial strategies. Altogether, this modeling concept provides a platform to connect genome-level properties of organisms and phenotypic traits relevant to ecological fitness in the rhizosphere, and ultimately connect microbial ecological processes to soil biogeochemical function.

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