(491f) A Computational Modeling to Integrate Multi-Omics in Clostridium cellulovorans to Guide Metabolic Engineering

The cellulolytic Clostridium cellulovorans has been engineered to produce n-butanol from low-value lignocellulosic biomass in consolidated bioprocessing (CBP). The objective of this study was to integrate fermentation data and the multi-Omics experimental data by developing a mathematic model, targeting to predict the key regulators for metabolic engineering. In the process engineering, the corn corb cellulosic fermentation produced butanol with >3 g/L, yield >0.14 g/g, and selectivity >3 g/g by C. cellulovorans-adhE2. Our metabolomics study identified a total of 474 intracellular metabolites, including amino acid, carbohydrate, lipid, cofactor, nucleotide, peptide, and secondary metabolism. Meanwhile, about 1000 proteins were identified in the proteomics study. The primary static flux analysis indicated that cellulose favored butyrate production and the metabolic flux distribution from C2 to C4. The first-generation enzyme catalyzed reactions-based dynamic model was built by integrating the fermentation results, metabolomics data and proteomics data that involved in the metabolic pathways from pyruvate to butanol formation. This dynamic model simulated the intracellular metabolite fluctuation, included the effect of enzyme expression, and predicted the limiting steps at a systematic level. For example, we found that the carbon flux distribution from C2 to C4 was reduced in the mutant using cellulose as substrate, which was caused by the low expression of thiolase (thl) that catalyzes the reaction of acetyl-CoA to acetoacetyl-CoA and crotonase (crt) that catalyzes the reaction of 3-hydroxybutyryl-CoA to crotonyl-CoA. It was predicted that the butanol production can be improved to titer of >5 g/L and selectivity of >0.6 g/g from cellulose under optimized conditions.