Elucidating central metabolic redox obstacles hindering ethanol production in Clostridium thermocellum

Abstract

Clostridium thermocellum is an anaerobic, Gram-positive, thermophilic bacterium that has generated great interest due to its ability to ferment lignocellulosic biomass to ethanol. However, ethanol production is low due to the complex and poorly understood branched metabolism of C. thermocellum, and in some cases overflow metabolism as well. In this work, we developed a predictive stoichiometric metabolic model for C. thermocellum which incorporates the current state of understanding, with particular attention to cofactor specificity in the atypical glycolytic enzymes and the complex energy, redox, and fermentative pathways with the goal of aiding metabolic engineering efforts. We validated the model's capability to encompass experimentally observed phenotypes for the parent strain and derived mutants designed for significant perturbation of redox and energy pathways. Metabolic flux distributions revealed significant alterations in key metabolic branch points (e.g., phosphoenol pyruvate, pyruvate, acetyl-CoA, and cofactor nodes) in engineered strains for channeling electron and carbon fluxes for enhanced ethanol synthesis, with the best performing strain doubling ethanol yield and titer compared to the parent strain. In silico predictions of a redox-imbalanced genotype incapable of growth were confirmed in vivo, and a mutant strain was used as a platform to probe redox bottlenecks in the central metabolism that hinder efficient ethanol production. The results highlight the robustness of the redox metabolism of C. thermocellum and the necessity of streamlined electron flux from reduced ferredoxin to NAD(P)H for high ethanol production. The model was further used to design a metabolic engineering strategy to phenotypically constrain C. thermocellum to achieve high ethanol yields while requiring minimal genetic manipulations. The model can be applied to design C. thermocellum as a platform microbe for consolidated bioprocessing to produce ethanol and other reduced metabolites.

Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome

“…2c). Further, with this GAM parameter the model was used to predict the growth of several mutants while constraining fermentation product fluxes to previously reported values [36], and the model predictions correlated very well with the experimentally observed growth rates (see Table S1 of Additional file 2). …”

“…In particular, we updated the cofactor specificity of glycolytic enzymes [29] based on in vitro protein characterization as well as performed Cofactory analysis [40] to resolve cofactor specificity when in vitro data was unavailable (see “Methods” section). We also manually curated the intricate carbon overflow and redox metabolisms with recently acquired knowledge [9, 36]. …”

“…After construction, we proceeded to train the model using pH-controlled batch fermentation data collected for the wild-type DSM1313 grown on MTC defined media with cellobiose as a carbon source [36, 44]. Table 2 presents the experimental fluxes used to constrain the model, and for all simulations, a non-growth-associated maintenance (NGAM) cost of 3.27 mmol ATP/g DCW/h was used [38].…”

“…Extensive compilation of fermentation data for cell growth in comparable conditions—equivalent defined media recipes with non-limited amounts of cellobiose or cellulose in both batch and continuous cultures under different growth (or dilution) rates—revealed several unique and interesting phenotypes regarding bioenergetics of C. thermocellum [9, 35, 36, 38, 44]. For instance, the ethanol to acetate (E:A) ratio is a commonly used indicator of bioenergetic balance in a given metabolic state of an anaerobic cell culture, where ethanol production is primarily tied to redox balance and acetate production is coupled with ATP synthesis.…”

“…In this work, we constructed a new GEM for DSM 1313 from the KEGG database, expanding upon our previously constructed central metabolic model [36], and manually curated the GEM with the most current knowledge of C. thermocellum metabolism. We next refined the GEM using several sets of high-quality batch fermentation data for cell growth on various carbon sources, i.e., cellobiose and cellulose.…”