Uncovering Novel Pathways for Enhancing Hyaluronan Synthesis in Recombinant Lactococcus lactis: Genome-Scale Metabolic Modeling and Experimental Validation

Abstract

Hyaluronan (HA), a glycosaminoglycan with important medical applications, is commercially produced from pathogenic microbial sources. The metabolism of HA-producing recombinant generally regarded as safe (GRAS) systems needs to be more strategically engineered to achieve yields higher than native producers. Here, we use a genome-scale model (GEM) to account for the entire metabolic network of the cell while predicting strategies to improve HA production. We analyze the metabolic network of Lactococcus lactis adapted to produce HA and identify non-conventional strategies to enhance HA flux. We also show experimental verification of one of the predicted strategies. We thus identified an alternate route for enhancement of HA synthesis, originating from the nucleoside inosine, that can function in parallel with the traditionally known route from glucose. Adopting this strategy resulted in a 2.8-fold increase in HA yield. The strategies identified and the experimental results show that the cell is capable of involving a larger subset of metabolic pathways in HA production. Apart from being the first report to use a nucleoside to improve HA production, we demonstrate the role of experimental validation in model refinement and strategy improvisation. Overall, we point out that well-constructed GEMs could be used to derive efficient strategies to improve the biosynthesis of high-value products.

A Computational Framework to Identify Metabolic Engineering Strategies for the Co-Production of Metabolites

“…In this study, we present co-FSEOF, by adapting the effective FSEOF algorithm to study the co-optimization of a set of metabolites. FSEOF is a well-established constraint-based modelling algorithm, which has been used to reliably predict metabolic engineering strategies for a variety of systems (Choi et al, 2010;Boghigian et al, 2012;Badri et al, 2019;Srinivasan et al, 2019). It has a simple and efficient framework and can identify both deletion and amplification targets.…”

A computational framework to identify metabolic engineering strategies for the co-production of metabolites

“…In this study, we present XFSEOF, by adapting the effective FSEOF algorithm to study the cooptimization of a set of metabolites. FSEOF is a well-established constraint-based modelling algorithm, which has been used to reliably predict metabolic engineering strategies for a variety of systems (21,(44)(45)(46). Extending FSEOF, we examined the co-production of multiple pairs of metabolites, and both deletion and amplification targets were obtained in E. coli and S. cerevisiae under both aerobic and anaerobic conditions.…”

The Effect of Recombinant Protein Production in Lactococcus lactis Transcriptome and Proteome

“…The aim is to increase the productivity of L. lactis for compounds such as alanine (used as a food sweetener and for pharmaceutical applications) [17] or diacetyl (used in many dairy products as well as in the wine industry) [18]. Additionally, metabolic pathways based on enzymes that contribute on a smaller scale to the L. lactis core proteome could be engineered to increase the production of pharmaceutically valuable compounds, such as folate (vitamin B11) [19][20][21], riboflavin (vitamin B2) [21] and hyaluronic acid (polysaccharide with medical applications) [22]. Recently, Zhu et al [23] successfully reduced the genome of L. lactis NZ9000 strain by 2.8%, via the deletion of nonessential DNA regions using the Cre-loxP deletion system, turning it into a faster growing strain, with a higher biomass yield, an increased ATP content and less maintenance demands.…”