Metabolic Engineering of Corynebacterium glutamicum for Production of UDP-N-Acetylglucosamine

Uridine diphosphate-N-acetylglucosamine (UDP-GlcNAc) is an acetylated amino sugar nucleotide that naturally serves as precursor in bacterial cell wall synthesis and is involved in prokaryotic and eukaryotic glycosylation reactions. UDP-GlcNAc finds application in various fields including the production of oligosaccharides and glycoproteins with therapeutic benefits. At present, nucleotide sugars are produced either chemically or in vitro by enzyme cascades. However, chemical synthesis is complex and non-economical, and in vitro synthesis requires costly substrates and often purified enzymes. A promising alternative is the microbial production of nucleotide sugars from cheap substrates. In this study, we aimed to engineer the non-pathogenic, Gram-positive soil bacterium Corynebacterium glutamicum as a host for UDP-GlcNAc production. The native glmS, glmU, and glmM genes and glmM of Escherichia coli, encoding the enzymes for UDP-GlcNAc synthesis from fructose-6-phosphate, were over-expressed in different combinations and from different plasmids in C. glutamicum GRS43, which lacks the glucosamine-6-phosphate deaminase gene (nagB) for glucosamine degradation. Over-expression of glmS, glmU and glmM, encoding glucosamine-6-phosphate synthase, the bifunctional glucosamine-1-phosphate acetyltransferase/N-acetyl glucosamine-1-phosphate uridyltransferase and phosphoglucosamine mutase, respectively, was confirmed using activity assays or immunoblot analysis. While the reference strain C. glutamicum GlcNCg1 with an empty plasmid in the exponential growth phase contained intracellularly only about 0.25 mM UDP-GlcNAc, the best engineered strain GlcNCg4 accumulated about 14 mM UDP-GlcNAc. The extracellular UDP-GlcNAc concentrations in the exponential growth phase did not exceed 2 mg/L. In the stationary phase, about 60 mg UDP-GlcNAc/L was observed extracellularly with strain GlcNCg4, indicating the potential of C. glutamicum to produce and to release the activated sugar into the culture medium. To our knowledge, the observed UDP-GlcNAc levels are the highest obtained with microbial hosts, emphasizing the potential of C. glutamicum as a suitable platform for activated sugar production

Revisiting Theoretical Tools and Approaches for the Valorization of Recalcitrant Lignocellulosic Biomass to Value-Added Chemicals

Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment; enzyme engineering for enhanced saccharification and delignification; and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals