Development of a Genome-Informed Trait-Based Model for Microbial Biogeochemistry within Terrestrial and Aquatic Ecosystems
Eric King1, Sergi Molins
2, Ulas Karaoz
3, Jeffrey N Johnson
3, Nicholas Bouskill
4, Laura A Hug
5, Brian C Thomas
5, Cindy J Castelle
5, Harry R Beller
3, Jillian F Banfield
3,6, Carl I Steefel
7 and Eoin Brodie
8,9, (1)Lawrence Berkeley National Laboratory, Climate & Ecosystem Sciences Division, Berkeley, United States, (2)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences, Berkeley, CA, United States, (3)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (4)Lawrence Berkeley National Laboratory, Ecology, Berkeley, CA, United States, (5)University of California Berkeley, Berkeley, CA, United States, (6)University of California, Berkeley, Earth and Planetary Science, Berkeley, United States, (7)Lawrence Berkeley National Laboratory, Berkeley, United States, (8)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, CA, United States, (9)University of California Berkeley, Environmental Science, Policy, and Management, Berkeley, United States
Contact First Author: Eric King; eking@lbl.gov
Previously Published Material: As this is a work in progress, portions of this abstract were presented at the AGU conference in December 2014 in San Francisco. This abstract focusses on the advancements since then as well as on how this technique can be applied to specific situations.
English Abstract:
In extreme environments, microorganisms persist in systems characterized by low energy production, nutrient fluxes, and microbial growth. Understanding and predicting the biogeochemical dynamics within these systems necessitates an understanding of the metabolism and physiology of organisms that are often uncultured or studied
in situ under conditions that vary greatly from those seen in low energy environments. Cultivation independent approaches are therefore important and have greatly enhanced our ability to characterize functional microbial diversity. With the capability to reconstruct thousands of genomes from microbial populations using metagenomic techniques, one needs to develop an understanding of how these metabolic blueprints influence the fitness of organisms and how populations emerge and impact the physical and chemical properties of their environment.
Here, we discuss the development of a trait-based model of microbial activity that simulates coupled guilds of microorganisms, parameterized including traits extracted from large-scale metagenomic data. Each group within a functional guild is parameterized with a unique combination of traits governing organism fitness under dynamic environmental conditions. Using a reactive transport framework, we simulate the thermodynamics of coupled electron donor and acceptor reactions to predict the energy available for cellular maintenance, respiration, biomass development, and exo-enzyme production. This presentation will address our latest developments in the estimation of trait values related to growth and the use of metagenomic data to identify and link key fitness traits associated with respiratory and fermentative pathways, macromolecule depolymerization enzymes, and nitrogen fixation. Simulations explore abiotic controls on community emergence including identification of the processes regulating aquifer oxygen concentrations during seasonally fluctuating water table regimes at the Rifle floodplain.
