Genome‐Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production

Abstract

Systems biology approaches are increasingly applied to explore the potential of actinomycetes for the discovery and optimal production of antibiotics. In particular, genome-scale metabolic models (GEMs) of various actinomycetes are reconstructed at a faster rate in recent years, which has opened avenues to study interaction between primary and secondary metabolism at systems level, and to predict gene manipulation targets for overproduction of important antibiotics. Here, the status of actinomycetes' GEMs and their applications for designing antibiotics-overproducing strains are presented. Despite advances in the practice of GEM reconstruction, actinomycetes' GEMs still remain incomplete in describing a full set of biosynthetic pathways of secondary metabolites. As to the GEM-based strategies, various simulation methods are deployed to better describe secondary metabolism by introducing changes in constraints and/or objective function as well as by using omics data. Gene manipulation targeting algorithms developed for metabolic engineering of model organisms have also been actively applied to actinomycetes for the antibiotics production. Further consideration of computational resources dedicated to secondary metabolites in addition with automated GEM reconstruction tools will further upgrade GEMs of actinomycetes for antibiotics discovery and development.

Enzyme-Constrained Models and Omics Analysis of Streptomyces coelicolor Reveal Metabolic Changes that Enhance Heterologous Production

“…Since the first reconstruction in 2005 ( Borodina et al., 2005 ) five GEMs have been published ( Alam et al., 2010 ; Amara et al., 2018 ; Kim et al., 2014 ; Kumelj et al., 2019 ; Wang et al., 2018 ), including three in 2018: iKS1317 ( Kumelj et al., 2019 ), Sco4 ( Wang et al., 2018 ), and iAA1259 ( Amara et al., 2018 ). In addition, as a model organism for the Actinomycetes, the GEMs of S. coelicolor are frequently used as template for model development of closely related strains ( Mohite et al., 2019 ), such as Streptomyces clavuligerus ( Toro et al., 2018 ), Saccharopolyspora erythraea ( Licona-Cassani et al., 2012 ) and Streptomyces lividans ( Valverde et al., 2018 ). The recent updates of the S. coelicolor GEM were developed in parallel by different research groups: although all groups share the common interest of utilizing a high-quality model for predictions and data analysis, the prevailing approach of independent parallel development is inefficient.…”

Oceans as a Source of Immunotherapy

“…Using metabolic engineering, we can model organisms using existing metabolic reconstructions to discover gene knockouts which could improve the yield of products of interest. Genome-scale metabolic constraint-based flux models have been constructed for Streptomyces coelicolor A3 strains with the aim of improving and optimizing the production of antibiotics [301]. The deletion of the gonCP gene in marine actinobacterium Streptomyces coniferous resulted in improved antitumor activity of two derivatives, PM100117 and PM100118 [302].…”

Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production

“…The increased interest in using genome-scale models of S. coelicolor is conspicious: since the first reconstruction in 2005 (Borodina et al, 2005), five GEMs have been published Amara et al, 2018;Kim et al, 2014;Kumelj et al, 2018;Wang et al, 2018), and three of those within 2018: iKS1317 (Kumelj et al, 2018), Sco4 (Wang et al, 2018) and iAA1259 (Amara et al, 2018). Additionally, as a model organism for the Actinomycetes, the GEMs of S. coelicolor are frequently used as template for model development of closely related strains (Mohite et al, 2019), such as S. clavuligerus (Toro et al, 2018), Saccharopolyspora erythraea (Licona-Cassani et al, 2012) and S. lividans (Valverde et al, 2018). The recent updates of the S. coelicolor GEM were developed in parallel by different research groups: while all groups share the common interest of utilizing a high-quality model for predictions and data analysis, the prevailing approach of independent parallel development is inefficient.…”