Abstract
BackgroundMicroorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous.ResultsHere we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production.ConclusionsBy incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum and highlight remaining gaps in the existing genome annotations.
Highlights
Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels
We present a genome-scale model of C. thermocellum (ATCC 27405) consisting of 577 reactions involving 525 distinct metabolites, 73 membrane transport reactions, and 432 genes (19.1% of all C. thermocellum genes with function prediction)
The main result of this work was the development of a genome-scale metabolic model of C. thermocellum that includes a unique model representation of a cellulosome, a major functional unit in cellulose hydrolysis that accounts for a high percentage of the total protein content in C. thermocellum
Summary
Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Models for a range of different organisms with varying metabolic capabilities have been published over the last ten years [1,2,3,4] Each of these models is fundamentally defined by a list of mass-balanced, and possibly, charge-balanced reactions. By means of the quasi-steady state assumption, i.e., that metabolite concentrations are constant over short time scales, the reaction list can be used to define a space of possible steady state behaviors for the metabolic network. This solution space can be probed by a growing number of methods to obtain specific predictions of the organism’s behavior [4,7,8,9]. Of the commonly used objectives, evidence suggests that biomass optimization is most consistent with experimentally observed flux distributions in carbon-limited cells grown in batch culture [10,11,12]
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