ABSTRACT Clostridium beijerinckii is a well-known solvent-producing microorganism with great potential for biofuel and biochemical production. To better understand and improve the biochemical pathway to solvents, the development of genetic tools for engineering C. beijerinckii is highly desired. Based on mobile group II intron technology, a targetron gene knockout system was developed for C. beijerinckii in this study. This system was successfully employed to disrupt acid production pathways in C. beijerinckii, leading to pta (encoding phosphotransacetylase)- and buk (encoding butyrate kinase)-negative mutants. In addition to experimental characterization, the mutant phenotypes were analyzed in the context of our C. beijerinckii genome-scale model. Compared to those of the parental strain (C. beijerinckii 8052), acetate production in the pta mutant was substantially reduced and butyrate production was remarkably increased, while solvent production was dependent on the growth medium. The pta mutant also produced much higher levels of lactate, suggesting that disrupting pta influenced the energy generation and electron flow pathways. In contrast, acetate and butyrate production in the buk mutant was generally similar to that of the wild type, but solvent production was consistently 20 to 30% higher and glucose consumption was more rapid and complete. Our results suggest that the acid and solvent production of C. beijerinckii can be effectively altered by disrupting the acid production pathways. As the gene disruption method developed in this study does not leave any antibiotic marker in a disrupted allele, multiple and high-throughput gene disruption is feasible for elucidating genotype and phenotype relationships in C. beijerinckii.

Background Solventogenic 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. Results We present the first genome-scale metabolic model ( i CM925) 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 i CM925, 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. Conclusions Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. i CM925 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.

Anaerobic bacteria such as the solventogenic clostridia can ferment a wide range of carbon sources (e.g., glucose, galactose, cellobiose, mannose, xylose, and arabinose) to produce carboxylic acids (acetic and butyric) and solvents such as acetone, butanol, and ethanol (ABE). The fermentation process typically proceeds in two phases (acidogenic and solventogenic) in a batch mode. Poor solvent resistance by the solventogenic clostridia and other fermenting microorganisms is a major limiting factor in the profitability of ABE production by fermentation. The toxic effect of solvents, especially butanol, limits the concentration of these solvents in the fermentation broth, limiting solvent yields and adding to the cost of solvent recovery from dilute solutions. The accepted dogma is that toxicity in the ABE fermentation is due to chaotropic effects of butanol on the cell membranes of the fermenting microorganisms, which poses a challenge for the biotechnological whole-cell bio-production of butanol. This mini-review is focused on (1) the effects of solvents on inhibition of cell metabolism (nutrient transport, ion transport, and energy metabolism); (2) cell membrane fluidity, death, and solvent tolerance associated with the ability of cells to tolerate high concentrations of solvents without significant loss of cell function; and (3) strategies for overcoming poor solvent resistance in acetone and butanol-producing microorganisms.

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.