Prototyping Carbon-Conserving Networks for Diacid Production

Authors:

Ray Westenberg^1,2*, Garrett Helwig², James Carothers^4,5, Pamela Peralta-Yahya^1,2,3

Institutions:

¹Bioengineering Program, Georgia Institute of Technology; ²School of Chemical and Biomolecular Engineering, Georgia Institute of Technology; ³School of Chemistry and Biochemistry, Georgia Institute of Technology; ⁴Molecular Engineering and Sciences Institute and Center for Synthetic Biology, University of Washington; ⁵Department of Chemical Engineering, University of Washington

Abstract

Decarboxylation, the loss of carbon dioxide (CO₂) from a compound, is used in metabolism to commit carbon flux to a specific pathway. However, decarboxylation also limits the product carbon yield, with acetyl-CoA (two C₂) achieving only 66% carbon recovery when routing glucose (C₆) via glycolysis and oxidative decarboxylation. Carbon-conservation networks (CCNs) that circumvent CO₂ release can theoretically lead to carbon and product yields beyond those seen with endogenous metabolism (Westenberg and Peralta-Yahya 2023). Engineering metabolic pathways and developing technologies to improve carbon yield has the potential to increase the economic viability of large-volume low-cost chemicals. Toward this goal, the effects of overlaying CCNs have been mathematically modeled onto the endogenous metabolism of non-model organisms, such as Pseudomonas putida and Rhodobacter sphaeroides. The model predictions and prototyping combinations of existing and de novo CCNs predicted to improve carbon and product yields are now being implemented. As a proof-of-concept, the research team is measuring the effects of CCNs on the production of industrially relevant diacids: malic and itaconic. Going forward, the generality of CCNs will enable their implementation toward production of other large-scale chemicals that suffer from metabolic carbon loss.

References

Westenberg, R., and P. Peralta-Yahya. 2023. “Toward Implementation of Carbon-Conservation Networks in Nonmodel Organisms,” Current Opinion in Biotechnology 81, 102949. DOI:10.1016/j.copbio.2023.102949.