Genome-scale metabolic model of caldicellulosiruptor bescii reveals optimal metabolic engineering strategies for bio-based chemical production
Date of Original Version
Metabolic modeling was used to examine potential bottlenecks that could be encountered for metabolic engineering of the cellulolytic extreme thermophile Caldicellulosiruptor bescii to produce bio-based chemicals from plant biomass. The model utilizes subsystems-based genome annotation, targeted reconstruction of carbohydrate utilization pathways, and biochemical and physiological experimental validations. Specifically, carbohydrate transport and utilization pathways involving 160 genes and their corresponding functions were incorporated, representing the utilization of C5/C6 monosaccharides, disaccharides, and polysaccharides such as cellulose and xylan. To illustrate its utility, the model predicted that optimal production from biomass-based sugars of the model product, ethanol, was driven by ATP production, redox balancing, and proton translocation, mediated through the interplay of an ATP synthase, a membrane-bound hydrogenase, a bifurcating hydrogenase, and a bifurcating NAD- and NADP-dependent oxidoreductase. These mechanistic insights guided the design and optimization of new engineering strategies for product optimization, which were subsequently tested in the C. bescii model, showing a nearly 2-fold increase in ethanol yields. The C. bescii model provides a useful platform for investigating the potential redox controls that mediate the carbon and energy flows in metabolism and sets the stage for future design of engineering strategies aiming at optimizing the production of ethanol and other bio-based chemicals.
Zhang, Ke, Weishu Zhao, Dmitry A. Rodionov, Gabriel M. Rubinstein, Diep N. Nguyen, Tania N.N. Tanwee, James Crosby, Ryan G. Bing, Robert M. Kelly, Michael W.W. Adams, and Ying Zhang. "Genome-scale metabolic model of caldicellulosiruptor bescii reveals optimal metabolic engineering strategies for bio-based chemical production." mSystems 6, 3 (2021). doi:10.1128/mSystems.01351-20.