Investigating Moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model.

Abstract

Moorella thermoacetica is a strictly anaerobic, endospore-forming, and metabolically versatile acetogenic bacterium capable of conserving energy by both autotrophic (acetogenesis) and heterotrophic (homoacetogenesis) modes of metabolism. Its metabolic diversity and the ability to efficiently convert a wide range of compounds, including syngas (CO + H2) into acetyl-CoA have made this thermophilic bacterium a promising host for industrial biotechnology applications. However, lack of detailed information on M. thermoacetica's metabolism is a major impediment to its use as a microbial cell factory. In order to overcome this issue, a genome-scale constraint-based metabolic model of Moorella thermoacetica, iAI558, has been developed using its genome sequence and physiological data from published literature. The reconstructed metabolic network of M. thermoacetica comprises 558 metabolic genes, 705 biochemical reactions, and 698 metabolites. Of the total 705 model reactions, 680 are gene-associated while the rest are non-gene associated reactions. The model, in addition to simulating both autotrophic and heterotrophic growth of M. thermoacetica, revealed degeneracy in its TCA-cycle, a common characteristic of anaerobic metabolism. Furthermore, the model helped elucidate the poorly understood energy conservation mechanism of M. thermoacetica during autotrophy. Thus, in addition to generating experimentally testable hypotheses regarding its physiology, such a detailed model will facilitate rapid strain designing and metabolic engineering of M. thermoacetica for industrial applications.

DOI: 10.1039/c5ib00095e

Cite this paper

@article{Islam2015InvestigatingMT, title={Investigating Moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model.}, author={M. Ahsanul Islam and Karsten Zengler and Elizabeth A. Edwards and Radhakrishnan Mahadevan and Gregory Stephanopoulos}, journal={Integrative biology : quantitative biosciences from nano to macro}, year={2015}, volume={7 8}, pages={869-82} }