2021
DOI: 10.3390/bioengineering8080103
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TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus

Abstract: Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce clavulanic acid (CA), a potent inhibitor of β-lactamase enzymes. In this study, S. clavuligerus cultivated in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using a bottom-up approach and validated using experimental data. The reduced model was implemented for in silico studies… Show more

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Cited by 4 publications
(4 citation statements)
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“…In this study, we metabolically engineered S. venezuelae ATCC 15439 for the enhanced pikromycin production on the basis of gene manipulation targets predicted using its genome‐scale metabolic model (GEM). GEMs are a computational model that contains metabolic gene–protein‐reaction (GPR) associations at a genome‐scale, and can be simulated to predict metabolic flux distributions under a specific condition, which has demonstrated to be useful for systems metabolic engineering (Gu et al, 2019), including the production of secondary metabolites (Kim et al, 2016; Kittikunapong et al, 2021; Ramirez‐Malule et al, 2021). A GEM of S. venezuelae was newly reconstructed in this study, and used to predict overexpression and downregulation targets to improve the pikromycin production using S. venezuelae .…”
Section: Introductionmentioning
confidence: 99%
“…In this study, we metabolically engineered S. venezuelae ATCC 15439 for the enhanced pikromycin production on the basis of gene manipulation targets predicted using its genome‐scale metabolic model (GEM). GEMs are a computational model that contains metabolic gene–protein‐reaction (GPR) associations at a genome‐scale, and can be simulated to predict metabolic flux distributions under a specific condition, which has demonstrated to be useful for systems metabolic engineering (Gu et al, 2019), including the production of secondary metabolites (Kim et al, 2016; Kittikunapong et al, 2021; Ramirez‐Malule et al, 2021). A GEM of S. venezuelae was newly reconstructed in this study, and used to predict overexpression and downregulation targets to improve the pikromycin production using S. venezuelae .…”
Section: Introductionmentioning
confidence: 99%
“…Metabolic engineering of host strains using genome-scale metabolic model (GEM) has been suggested as a promising strategy to improve the secondary metabolite production [ 25 , 26 ]. The GEM is a computational model that can predict metabolic flux distributions associated with the precursors of secondary metabolites under certain conditions, rendering it a valuable tool in the systems metabolic engineering [ 27 29 ]. Using GEM, it is possible to identify specific target genes for deletion and/or overexpression to enhance the production of desired secondary metabolites.…”
Section: Introductionmentioning
confidence: 99%
“…The low CA productivity in the submerged cultivation of S. clavuligerus and the complexity of the downstream process have a direct impact on the production cost of this pharmaceutical compound, which is commonly coformulated with wide-spectrum β-lactam antibiotics. Strategies like nutritional and environmental perturbation appear as feasible alternatives to enhance the strain's productivity Ramirez-Malule, 2018). Metabolic engineering emerges as an alternative O n l i n e F i r s t for the rational enhancement of CA in S. clavuligerus cultures through the perturbation of metabolic and regulatory features (Liras and Martín, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…In this sense, the systems biology approaches have contributed to the understanding of S. clavuligerus metabolism under different nutritional and environmental conditions (Gómez-Rios et al, 2022;Ramirez-Malule et al, 2021). Nevertheless, the classical steady-state approaches like the flux balance analysis (FBA) present limitations like the prediction of cellular growth and product secretion rates only for fixed values of substrate uptake rates.…”
Section: Introductionmentioning
confidence: 99%