Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments

Abstract

The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies.

Figure 1

Conceptual model of uranium bioremediation. (a) Before acetate injection, acetate is generated in the subsurface primarily through fermentation. During bioremediation, acetate is injected to the subsurface at high concentrations through the injection galleries. The artificial flow of acetate is combined with natural acetate flow, and follows the direction of the ground water. This acetate stimulates the growth of multiple microbial species downstream of the injection galleries, including Geobacter and Rhodoferax species. Groundwater samples are collected periodically at the test wells downstream of the injection galleries, including wells D02, D05 and D08. Various tests were performed on these groundwater samples, including ammonium concentration measurements and 16S rRNA-based analysis of the relative microbial abundance (Mouser et al. 2009). (b) Both Geobacter and Rhodoferax oxidize the dissolved acetate, and carry out the reduction of Fe(III) by attaching to a Fe(III) surface. Geobacter is also capable of reducing U(VI) in its planktonic phase. Given that Fe(III) is the primary electron acceptor for Geobacter, this implies that the organisms compete for both electron donor and acceptor. The reduction of Fe(III) creates dissolved Fe(II), whereas the reduction of dissolved U(VI) creates U(IV), which precipitates. The reductive precipitation of uranium effectively removes uranium from the groundwater. Abbreviations: Rf, Rhodoferax; GsA, Geobacter (attached to sediment); GsP, Geobacter (planktonic). (c) Ammonium concentrations at wells D02, D05 and D08 have been measured periodically (Mouser et al., 2009) Here, the initial and average ammonium concentrations are shown. The Geobacter and Rhodoferax columns show the number of Geobacter and Rhodoferax 16S rRNA genes as a percentage of the total genes in the sample. The Geobacter fraction is calculated using equation (1). The relative abundance of these two organisms seems to be related to the concentration of ammonium.

Figure 2

DMMM of Geobacter and Rhodoferax community. The in silico representation of a minimal community whose growth depends on the oxidation of acetate coupled to Fe(III) reduction. Here, μ is the biomass growth rate, V_i^Gs is the flux of metabolite, i predicted by the G. sulfurreducens model (Mahadevan et al. 2006), V_i^Rf is the flux of metabolite, i predicted by the R. ferrireducens model (Risso et al. 2009), X is the biomass concentration, [S_i] is the concentration of i^th metabolite, K_sⁱ is the saturation constant for i^th metabolite. The simulations in this paper assume the field to be a spatial homogeneous chemostat to simplify the transport and geochemical process.

Figure 3

Comparison between predicted and measured Geobacter fractions under natural conditions. Predicted Geobacter fractions at D02, D05 and D08 before the acetate addition are generated using acetate turnover rates of 0.2, 0.25 and 0.38 μ h⁻¹. The predictions are compared with the Geobacter fraction values calculated from experimental 16S rRNA measurements at the respective wells, at day 0 (Mouser et al. 2009). This comparison suggests that the acetate turnover rates are close to 0.2 μ h⁻¹ at well D02, close to 0.38 μ h⁻¹ at well D05, and close to 0.25 μ h⁻¹ at well D08. All three inferred acetate turnover rates fall within the range measured in similar environments, which supports the predicted competition dynamics at natural conditions.

Figure 4

Relative composition of Geobacter and Rhodoferax in Fe(III) reducing community before acetate amendment. The steady-state community compositions at three different acetate turnover rate ranges are simulated. The Geobacter fraction is used to measure the relative success of Geobacter to Rhodoferax species. The competition with respect to the ammonium concentration and acetate turnover rate is viewed at three different scales with respect to the acetate turnover rate. (a) The acetate turnover rates range from 0 to 0.2 μ h⁻¹. At this scale, the nonlinearity of the competition is highlighted. (b) The acetate turnover rates range from 0 to 0.54 μ h⁻¹, corresponding to the range of subsurface acetate availability reported by Hansen et al. (2001). (c) The acetate turnover rates range from 0 to 0.1 μ h⁻¹, corresponding to the rates measured in an aquifer extremely poor in acetate (Chapelle and Lovley, 1990).

Figure 5

Comparison between predicted and measured Geobacter fractions during acetate addition. The predicted Geobacter fractions at wells D02, D05 and D08 are compared with the experimentally measured fractions at the respective wells. Simulations are initiated with the Geobacter fractions and ammonium concentrations measured at day 0 in wells D02 (58%, 40 μ), D05 (64%, 21 μ) and D08 (20%, 400 μ) (Mouser et al. 2009). The predicted Geobacter fractions (solid lines) are compared with the Geobacter fractions calculated from the experimental 16S rRNA measurements (▴).

Figure 6

Simulation of the competition dynamics in a Fe(III)-reducing community during acetate addition. Biomass concentrations of G. sulfurreducens and R. ferrireducens (a), Geobacter fractions (b), acetate concentration (c) and Fe(III) concentration (d) under both ammonium-limiting and ammonium-excess conditions are shown. Under both conditions, Geobacter outcompetes Rhodoferax soon after acetate addition begins. The blue lines are the ammonium-excess simulation results; the red lines are the ammonium-limiting simulation results. The black bar in (a) represents the experimentally measured range of the Geobacter cell density at day 19 (Holmes et al., 2007). Simulations are initialized with equal concentrations of Geobacter and Rhodoferax. The acetate injection rate of 3 m day⁻¹ (4.2 μ h⁻¹) is used for both ammonium-limiting ((ammonium)=0.005 m) and ammonium-excess conditions ((ammonium)=0.4 m). Note: the y axis of (a) is in log scale, whereas the y axis of (b, c and d) are in linear scale.

Figure 7

Comparison of the predicted flux distribution during growth with ammonium vs nitrogen-fixation dependent growth of G. sulfurreducens. The predicted flux distributions of G. sulfurreducens during growth with unlimited ammonium uptake and nitrogen-fixation dependent growth are compared. The biomass flux is measured in h⁻¹; all other fluxes are measured in mmol gDW⁻¹ h⁻¹. The red fluxes are increased during nitrogen fixation; the blue fluxes are decreased during nitrogen fixation. The first number represents the flux through the reaction when ammonium is acquired through environmental uptake; the second number represents the flux through the reaction when ammonium is provided through nitrogen fixation.