Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

Abstract

BackgroundSolventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications.ResultsWe present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed.ConclusionsMicrobial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.

Development of a Gene Knockout System Using Mobile Group II Introns (Targetron) and Genetic Disruption of Acid Production Pathways in Clostridium beijerinckii

“…Model iCM925 simulations. Our C. beijerinckii genome-scale model was employed to explore the metabolic network of each mutant strain and provide explanations for alternative network routes (29). To aid in defining the metabolic network state of the observed phenotype, uptake and secretion rates were calculated for glucose, acetate, butyrate, acetone, butanol, and ethanol.…”

Optimizing genome-scale network reconstructions

“…This can be estimated by computing the number of blocked reactions (those reactions that cannot be used owing to missing connections in the network) 32 . The number of blocked reactions varies among reconstructions-for example, from 12% in the latest E. coli model (iJO1366) 23 to > 45% in the model of C. beijerinckii (iCM925) 53 . Some blocked reactions probably reflect gaps in biological knowledge.…”

Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum—evidence from a mathematical model

“…(1999) analysed the contribution of acid formation pathways in the metabolism of C. acetobutylicum ATCC 824 by using metabolic flux analysis. Genome-scaled models were applied to investigate the overall flux through the whole cell by several authors (McAnulty et al 2012;Milne et al 2011). However, these models lack regulatory and dynamic information.…”