Journal Article
Applied and Environmental Microbiology, vol. 78, iss. 24, pp. 8735-8742, 2012
Authors
Yilin Fang, Michael J. Wilkins, Steven B. Yabusaki, Mary S. Lipton, Philip E. Long
Abstract
ABSTRACT
Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an
in silico
model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of
Geobacter metallireducens
—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based
in silico
model
of G. metallireducens
relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637
G. metallireducens
proteins detected during the 2008 experiment were associated with specific metabolic reactions in the
in silico
model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the
in silico
model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the
in silico
model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.
