Acidithiobacillus ferrooxidans's comprehensive model driven analysis of the electron transfer metabolism and synthetic strain design for biomining applications

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Abstract

Acidithiobacillus ferrooxidans is a gram-negative chemolithoautotrophic γ-proteobacterium. It typically grows at an external pH of 2 using the oxidation of ferrous ions by oxygen, producing ferric ions and water, while fixing carbon dioxide from the environment. A. ferrooxidans is of great interest for biomining and environmental applications, as it can process mineral ores and alleviate the negative environmental consequences derived from the mining processes. In this study, the first genome-scale metabolic reconstruction of A. ferrooxidans ATCC 23270 was generated (iMC507). A total of 587 metabolic and transport/exchange reactions, 507 genes and 573 metabolites organized in over 42 subsystems were incorporated into the model. Based on a new genetic algorithm approach, that integrates flux balance analysis, chemiosmotic theory, and physiological data, the proton translocation stoichiometry for a number of enzymes and maintenance parameters under aerobic chemolithoautotrophic conditions using three different electron donors were estimated. Furthermore, a detailed electron transfer and carbon flux distributions during chemolithoautotrophic growth using ferrous ion, tetrathionate and thiosulfate were determined and reported. Finally, 134 growth-coupled designs were calculated that enables Extracellular Polysaccharide production. iMC507 serves as a knowledgebase for summarizing and categorizing the information currently available for A. ferrooxidans and enables the understanding and engineering of Acidithiobacillus and similar species from a comprehensive model-driven perspective for biomining applications.

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Campodonico, M. A., Vaisman, D., Castro, J. F., Razmilic, V., Mercado, F., Andrews, B. A., … Asenjo, J. A. (2016). Acidithiobacillus ferrooxidans’s comprehensive model driven analysis of the electron transfer metabolism and synthetic strain design for biomining applications. Metabolic Engineering Communications, 3, 84–96. https://doi.org/10.1016/j.meteno.2016.03.003

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