Reconstruction of the Genome-Scale Metabolic Model of Saccharopolyspora erythraea and Its Application in the Overproduction of Erythromycin

Abstract

Saccharopolyspora erythraea is considered to be an effective host for erythromycin. However, little is known about the regulation in terms of its metabolism. To develop an accurate model-driven strategy for the efficient production of erythromycin, a genome-scale metabolic model (iJL1426) was reconstructed for the industrial strain. The final model included 1426 genes, 1858 reactions, and 1687 metabolites. The accurate rates of the growth predictions for the 27 carbon and 31 nitrogen sources available were 92.6% and 100%, respectively. Moreover, the simulation results were consistent with the physiological observation and 13C metabolic flux analysis obtained from the experimental data. Furthermore, by comparing the single knockout targets with earlier published results, four genes coincided within the range of successful knockouts. Finally, iJL1426 was used to guide the optimal addition strategy of n-propanol during industrial erythromycin fermentation to demonstrate its ability. The experimental results showed that the highest erythromycin titer was 1442.8 μg/mL at an n-propanol supplementation rate of 0.05 g/L/h, which was 45.0% higher than that without n-propanol supplementation, and the erythromycin-specific synthesis rate was also increased by 30.3%. Therefore, iJL1426 will lead to a better understanding of the metabolic capabilities and, thus, is helpful in a systematic metabolic engineering approach.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

“…Metabolic fluxes possess a fundamental mechanistic interpretation owing to its robust association with underlying biochemistry. Notably, 13 C-Metabolic Flux Analysis ( 13 C-MFA) provides a more accurate understanding of intracellular flux distributions when comparing with GSMM [ 44 ]. Likewise, 13 C-MFA often assumes that the system is in a quasi-steady state during the labeling experiment.…”

“…The GSMM encompasses all the documented biochemical reactions and transmembrane transporters that occur within an organism. Mathematically, the reaction network is represented as a stoichiometric matrix S , capturing the precise ratios of reactants and products involved in each biochemical conversion [ 20 ]. Under the assumption of metabolic steady-state, reaction rates (fluxes) are governed by mass and energy balance principles and can be described by a vector v , which represents the fluxes across the metabolic network.…”

“…Additionally, the development of biofuels serves as a crucial contributor to energy diversification and a reduction in dependence on fossil fuels [ 18 ]. Recognizing the substantial commercial value of bioethanol, prior reviews have outlined commonly employed strategies to enhance production [ 19 , 20 ].…”

Enhancing erythromycin production in Saccharopolyspora erythraea through rational engineering and fermentation refinement: A Design‐Build‐Test‐Learn approach

“…Metabolic Flux Analysis (MFA) stands as a well-established methodology rooted in the tenets of metabolic balance, biochemical constraints, and quasi-steady-state concentrations of intracellular metabolites. [30][31][32] The metabolic network model utilized in this study is a streamlined iteration derived from previous published report (Table S3). [30] The model involved 25 biochemical reactions, for instance central carbon metabolism, the uptake reaction of oxygen and substrate, and the formation reaction of carbon dioxide and erythromycin.…”

“…[30][31][32] The metabolic network model utilized in this study is a streamlined iteration derived from previous published report (Table S3). [30] The model involved 25 biochemical reactions, for instance central carbon metabolism, the uptake reaction of oxygen and substrate, and the formation reaction of carbon dioxide and erythromycin. In addition, the consideration extended to the rates of glucose and oxygen consumption (q glu and q O2 ), as well as the rates of erythromycin and carbon dioxide production (q ery and q CO2 ).…”