Analysis of Biostimulated Microbial Communities from Two Field Experiments Reveals Temporal and Spatial Differences in Proteome Profiles

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Simple and inexpensive methods for assessing the metabolic status and bioremediation activities of subsurface microorganisms are required before bioremediation practitioners will adopt molecular diagnosis of the bioremediation community as a routine practice for guiding the development of bioremediation strategies. Quantifying gene transcripts can diagnose important aspects of microbial physiology during bioremediation but is technically challenging and does not account for the impact of translational modifications on protein abundance. An alternative strategy is to directly quantify the abundance of key proteins that might be diagnostic of physiological state. To evaluate this strategy, an antibody-based quantification approach was developed to investigate subsurface Geobacter communities. The abundance of citrate synthase corresponded with rates of metabolism of Geobacter bemidjiensis in chemostat cultures. During in situ bioremediation of uranium-contaminated groundwater the quantity of Geobacter citrate synthase increased with the addition of acetate to the groundwater and decreased when acetate amendments stopped. The abundance of the nitrogenfixation protein, NifD, increased as ammonium became less available in the groundwater and then declined when ammonium concentrations increased. In a petroleum-contaminated aquifer, the abundance of BamB, an enzyme subunit involved in the anaerobic degradation of mono-aromatic compounds by Geobacter species, increased in zones in which Geobacter were expected to play an important role in aromatic hydrocarbon degradation. These results suggest that antibody-based detection of key metabolic proteins, which should be readily adaptable to standardized kits, may be a feasible method for diagnosing the metabolic state of microbial communities responsible for bioremediation, aiding in the rational design of bioremediation strategies.

Section: Resultsmentioning

confidence: 96%

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confidence: 99%

Monitoring the Metabolic Status of Geobacter Species in Contaminated Groundwater by Quantifying Key Metabolic Proteins with Geobacter-Specific Antibodies

Yun

Ueki

Miletto

et al. 2011

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“…All available databases can be searched for the MS data sets; however, this is likely to boost the chances of peptides being matched to multiple proteins from different organisms (including proteins with low sequence identity), leading to false protein identifications and erroneous conclusions. Use of a reference database has been reported in many studies in which the actual genome or metagenome for the sample was unavailable (37,38). The reference database was chosen on the basis of some functional similarities between organisms whose genomes were chosen for protein matching.…”

Section: Discussionmentioning

confidence: 99%

Insights into the Structure and Metabolic Function of Microbes That Shape Pelagic Iron-Rich Aggregates (“Iron Snow”)

Chourey

Reiche

et al. 2013

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dMicrobial ferrous iron [Fe(II)] oxidation leads to the formation of iron-rich macroscopic aggregates ("iron snow") at the redoxcline in a stratified lignite mine lake in east-central Germany. We aimed to identify the abundant Fe-oxidizing and Fe-reducing microorganisms likely to be involved in the formation and transformation of iron snow present in the redoxcline in two basins of the lake that differ in their pH values. Nucleic acid-and lipid-stained microbial cells of various morphologies detected by confocal laser scanning microscopy were homogeneously distributed in all iron snow samples. The dominant iron mineral appeared to be schwertmannite, with shorter needles in the northern than in the central basin samples. Total bacterial 16S rRNA gene copies ranged from 5.0 ؋ 10 8 copies g (dry weight) ؊1 in the acidic central lake basin (pH 3.3) to 4.0 ؋ 10 10 copies g (dry weight) ؊1 in the less acidic (pH 5.9) northern basin. Total RNA-based quantitative PCR assigned up to 61% of metabolically active microbial communities to Fe-oxidizing-and Fe-reducing-related bacteria, indicating that iron metabolism was an important metabolic strategy. Molecular identification of abundant groups suggested that iron snow surfaces were formed by chemoautotrophic iron oxidizers, such as Acidimicrobium, Ferrovum, Acidithiobacillus, Thiobacillus, and Chlorobium, in the redoxcline and were rapidly colonized by heterotrophic iron reducers, such as Acidiphilium, Albidiferax-like, and Geobacter-like groups. Metaproteomics yielded 283 different proteins from northern basin iron snow samples, and protein identification provided a glimpse into some of their in situ metabolic processes, such as primary production (CO 2 fixation), respiration, motility, and survival strategies.

“…Although the initial goal of the in silico model development was to fundamentally account for intracellular and environmental exchange reactions to more accurately predict TEAP conversion rates, the advent of mass spectrometry-based protein identification technology now provides an experimental way to assess the detailed intracellular reactions. Further advancements in the measurement of subsurface in situ processes via environmental proteomics now offers the potential to track metabolic functions to specific bacterial species (9,61).…”

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confidence: 99%

“…In this study, the metabolic functions of the genome-scale metabolic model of Geobacter under dynamic field conditions was evaluated using shotgun global proteomic analyses of planktonic biomass in groundwater samples from the Integrated Field Research Challenge (IFRC) site near Rifle, CO (9,61). At the Rifle IFRC site, acetate amendment was used to stimulate microbially mediated immobilization of uranium in the unconfined aquifer (2,12,59,62).…”

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confidence: 99%

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Fang

Wilkins

Yabusaki

et al. 2012

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bAccurately 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 enzymecoding 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. Microbial environments have been engineered for enhanced oil recovery (7,23,29,33,42) and remediation of soil and groundwater contaminated by chlorinated hydrocarbons, metals, and radionuclides (4,24,30,40,47,51,63) in the past 30 years and have recently been studied to manage carbon dioxide (46). Mathematical models of microbial processes have been used for experimental interpretation, design, and prediction in recent decades (3,10,20,22,39,48,49,53,56). These processes were very complex to model because there was no way to measure the detailed metabolisms inside the system. They were usually represented by specific terminal electron acceptor process (TEAP) reactions with fixed stoichiometry throughout the simulation and were traditionally modeled by Monod kinetics. These models are sufficient under nonlimiting conditions of nutrients. However, the kinetic parameters of these models, which were calibrated but not directly measurable, cannot reflect the sophisticated mechanisms developed by microorganisms to adapt to the changing environment through the regulation of metabolic pathways, such as their ability to respond to nutrient gradients and environmental stress (5, 6).With the ad...