Reconstruction of a resource balance analysis model of Clostridium thermocellum examines the metabolic cost of glycolytic and cellulosome enzymes

  • Thomas C. Willis
  • , Wheaton L. Schroeder
  • , Daven B. Khana
  • , Xuejun Qian
  • , Sanjeev Dahal
  • , Daniel Amador-Noguez
  • , Costas D. Maranas

Research output: Contribution to journalArticlepeer-review

Abstract

Clostridium thermocellum is an increasingly well-studied organism with considerable advantages for consolidated bioprocessing towards ethanol production. Here, a genome-scale resource balance analysis (RBA) model of C. thermocellum, ctRBA, is reconstructed based on a recently published stoichiometric model (iCTH669), global proteomics, and 13C MFA datasets to analyze proteome allocation and the burden imposed on metabolism with regard to ethanol yield and titer. Glycolytic and fermentation enzyme concentrations were accurately quantified by the model, with glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), and acetaldehyde-alcohol dehydrogenase (AdhE) having predicted and measured higher concentrations relative to other enzymes in glycolysis and fermentation. The metabolic burden associated with the formation of the cellulosome, the enzyme complex responsible for carbon source degradation and solubilization, was assessed and found to be consequential in constraining ethanol yield and titer, but not biomass formation. Putative enzyme substitution strains were modeled, with each strain replacing a single enzyme in C. thermocellum with a variant that uses more favorable cofactors. Strains substituting GAPDH and phosphofructokinase (PFK) predicted 30 % and 86 % increases in maximum theoretical ethanol yield and titer, respectively, a result unavailable to typical stoichiometric modeling. Model ctRBA acts as a predictive tool for assessing the effect of genetic perturbations on proteome allocation and ethanol yield and titer.

Original languageEnglish (US)
Pages (from-to)14-23
Number of pages10
JournalMetabolic engineering
Volume93
DOIs
StatePublished - Jan 2026

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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