Transcriptomics‐based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor

Abstract

In silico model-driven analysis using genome-scale model of metabolism (GEM) has been recognized as a promising method for microbial strain improvement. However, most of the current GEM-based strain design algorithms based on flux balance analysis (FBA) heavily rely on the steady-state and optimality assumptions without considering any regulatory information. Thus, their practical usage is quite limited, especially in its application to secondary metabolites overproduction. In this study, we developed a transcriptomics-based strain optimization tool (tSOT) in order to overcome such limitations by integrating transcriptomic data into GEM. Initially, we evaluated existing algorithms for integrating transcriptomic data into GEM using Streptomyces coelicolor dataset, and identified iMAT algorithm as the only and the best algorithm for characterizing the secondary metabolism of S. coelicolor. Subsequently, we developed tSOT platform where iMAT is adopted to predict the reaction states, and successfully demonstrated its applicability to secondary metabolites overproduction by designing actinorhodin (ACT), a polyketide antibiotic, overproducing strain of S. coelicolor. Mutants overexpressing tSOT targets such as ribulose 5-phosphate 3-epimerase and NADP-dependent malic enzyme showed 2 and 1.8-fold increase in ACT production, thereby validating the tSOT prediction. It is expected that tSOT can be used for solving other metabolic engineering problems which could not be addressed by current strain design algorithms, especially for the secondary metabolite overproductions.

Principles of proteome allocation are revealed using proteomic data and genome-scale models

“…These methods have been useful for the systems biology community. For example, tissue-specific models could be generated for health applications6 or computational strain design could be improved for metabolic engineering7. Despite many methods for omics integration in COBRA, the general problem of relating gene expression to metabolic flux and cell physiology remains challenging.…”

Time-Resolved Transcriptomics and Constraint-Based Modeling Identify System-Level Metabolic Features and Overexpression Targets to Increase Spiramycin Production in Streptomyces ambofaciens

“…The achieved increase was rather limited when compared to that obtained with similar metabolic engineering strategy that resulted in about 2- and 1.8-fold increase in actinorhodin production by S. coelicolor A3(2) strains overexpressing, respectively, the ribulose 5-phosphate 3-epimerase and the NADP-dependent malic enzyme (Kim et al, 2016). However, the increase was comparable to those obtained in other streptomycetes by genetic modification of single targets (Huang et al, 2012, 2013).…”

“…In general, many methods to achieve this task have been developed to date (Machado and Herrgard, 2014). However, a recent work (Kim et al, 2016) showed that, among them, iMAT (Shlomi et al, 2008) led to more accurate predictions in the analysis of Streptomyces coelicolor metabolic model. Briefly, iMAT uses gene expression values to divide reactions into two groups: highly and lowly expressed.…”

“…Furthermore, a comprehensive and system-level understanding of the metabolic landscape of S. lividans and S. coelicolor has been depicted using this methodology, to predict flux changes that occur when the cell switches from biomass to antibiotic production (Alam et al, 2010). Interestingly, the potential of integrating gene expression data and metabolic modeling for exploring Streptomyces biology (and biotechnological potential) has been assessed in a recent work focused on S. coelicolor (Kim et al, 2016). Besides benchmarking currently available tools for transcriptomic data integration into metabolic reconstructions and identifying the outperforming tool among them (iMAT, Shlomi et al, 2008), this study has identified a list of potential metabolic engineering targets for the overproduction of the actinorhodin secondary metabolite in this and other streptomycetes.…”

Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites

“…Genome-scale metabolic models of S. erythraea and S. spinosa were used to identify the effects of supplementing amino acids in media on production yield, 44,58 while those of A. balhimycina, S. coelicolor and S. tsukubaensis were used to identify gene manipulation targets to enhance target production. 45,60,61 In these metabolic models, only experimentally known secondary metabolite biosynthetic pathways were considered. For example, separate biosynthetic pathways for actinorhodin, undecylprodigiosin, calcium-dependent antibiotic, ectoine, and germicidin were included in the latest version of the S. coelicolor metabolic model, 45 while the S. erythraea metabolic model describes biosynthetic pathways for erythromycin, 2-methylisoborneol, rhamnosylaviolin, and erythrochelin.…”