Genome-Scale Comparison and Constraint-Based Metabolic Reconstruction of the Facilitative Anaerobic Fe(III)-Reducer Rhodoferax Ferrireducens

Derek Lovley, University of Massachusetts - Amherst
Carla Risso
Jun Sun
Kai Zhuang
Radhakrishnan Mahadevan
Robert DeBoy
Wael Ismail
Susmita Shrivastava
Heather Huot
Sagar kothari
Sean Daughtry
Olivia Bui
Christophe H. Schilling
Barbara A. Methѐ

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This article was harvested from BioMed Central doi:10.1186/1471-2164-10-447 Attribution-ShareAlike CC BY-SA

Abstract

Background Rhodoferax ferrireducens is a metabolically versatile, Fe(III)-reducing, subsurface microorganism that is likely to play an important role in the carbon and metal cycles in the subsurface. It also has the unique ability to convert sugars to electricity, oxidizing the sugars to carbon dioxide with quantitative electron transfer to graphite electrodes in microbial fuel cells. In order to expand our limited knowledge about R. ferrireducens, the complete genome sequence of this organism was further annotated and then the physiology of R. ferrireducens was investigated with a constraint-based, genome-scale in silico metabolic model and laboratory studies. Results The iterative modeling and experimental approach unveiled exciting, previously unknown physiological features, including an expanded range of substrates that support growth, such as cellobiose and citrate, and provided additional insights into important features such as the stoichiometry of the electron transport chain and the ability to grow via fumarate dismutation. Further analysis explained why R. ferrireducens is unable to grow via photosynthesis or fermentation of sugars like other members of this genus and uncovered novel genes for benzoate metabolism. The genome also revealed that R. ferrireducens is well-adapted for growth in the subsurface because it appears to be capable of dealing with a number of environmental insults, including heavy metals, aromatic compounds, nutrient limitation and oxidative stress. Conclusion This study demonstrates that combining genome-scale modeling with the annotation of a new genome sequence can guide experimental studies and accelerate the understanding of the physiology of under-studied yet environmentally relevant microorganisms.