Department of Chemical Engineering and Applied Chemistry, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
ISME J. 2011 Feb;5(2):305-16. doi: 10.1038/ismej.2010.117. Epub 2010 Jul 29.
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.
快速完成全基因组测序的出现,以及在基因组规模代谢模型中捕获这些信息的潜力,为全面模拟微生物群落相互作用提供了可能。例如,红杆菌属和地杆菌属是在缺氧地下环境中竞争的乙酸氧化的 Fe(III)还原剂,这种竞争可能会影响铀污染地下水的原位生物修复。因此,使用基因组规模的 Geobacter sulfurreducens 和 Rhodoferax ferrireducens 模型来评估在科罗拉多州里弗尔铀污染含水层中发现的不同条件下,Geobacter 和 Rhodoferax 物种如何竞争。该模型预测,在现场条件下预计的低乙酸通量速率下,只要有足够的铵可用,红杆菌属将比地杆菌属更具竞争力。该模型还预测,在原位生物修复过程中添加高浓度乙酸时,地杆菌属将占主导地位,这与现场规模的观察结果一致。这可以归因于 Rhodoferax 较高的预期生长产率和地杆菌属固定氮的能力。该模型预测了 Rifle 现场具有不同地球化学特征的区域中 Geobacter 和 Rhodoferax 的相对比例,与以前用分子技术记录的比例相当。该模型还预测,在固氮作用下,较高的碳和电子通量将被转移到呼吸作用中,而不是在地杆菌属中用于生物量形成,这为低铵区域中增强的原位 U(VI)还原提供了潜在的解释。这些结果表明,基因组规模的建模可以成为预测地下环境中微生物相互作用的有用工具,并为设计生物修复策略提供了希望。