Abstract
BackgroundStreptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose.ResultsHere, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.ConclusionsThe updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.
Highlights
IntroductionIntroduction to methodology and encoding rulesJ Chem Inf Model. 1988;28:31–6. 44. Scheltema RA, Jankevics A, Jansen RC, Swertz MA, Breitling R
Introduction to methodology and encoding rulesJ Chem Inf Model. 1988;28:31–6. 44
2) The biosynthetic pathway for the secondary metabolite yCPK [26,27,28] was added to the model. This cryptic Biosynthetic Gene Clusters (BGC) is awakened under phosphate-limited condition, in nitrogen and carbon rich media [26, 29], such as in the minimal media used for systems biology studies of S. coelicolor [30]
Summary
Introduction to methodology and encoding rulesJ Chem Inf Model. 1988;28:31–6. 44. Scheltema RA, Jankevics A, Jansen RC, Swertz MA, Breitling R. Thomas L, Hodgson DA, Wentzel A, Nieselt K, Ellingsen TE, Moore J, et al Metabolic switches and adaptations deduced from the proteomes of Streptomyces coelicolor wild type and phoP mutant grown in batch culture. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Streptomyces coelicolor A3(2) is a well-studied model organism for the production of antibiotics in this genus The genome of this soil-dwelling bacterium encodes more than 20 secondary metabolite biosynthetic gene clusters (BGCs) [2], and the species is known to produce multiple antibiotics such as Actinorhodin (Act), Undecylprodigiosin (Red), Calcium-Dependant Antibiotic (CDA) and the yellow Coelicolor Polyketide, Coelimycin P1 (yCPK) [3]. A major issue faced in strain design is the ability to integrate test data (e.g. metabolomics) to improve the design [6], and many of the issues encountered are related to metabolic optimization, such as metabolic bottlenecks to increase production [7], heterologous biosynthetic pathway
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