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

Abstract

In this study we have applied an integrated system biology approach to characterize the metabolic landscape of Streptomyces ambofaciens and to identify a list of potential metabolic engineering targets for the overproduction of the secondary metabolites in this microorganism. We focused on an often overlooked growth period (i.e., post-first rapid growth phase) and, by integrating constraint-based metabolic modeling with time resolved RNA-seq data, we depicted the main effects of changes in gene expression on the overall metabolic reprogramming occurring in S. ambofaciens. Moreover, through metabolic modeling, we unraveled a set of candidate overexpression gene targets hypothetically leading to spiramycin overproduction. Model predictions were experimentally validated by genetic manipulation of the recently described ethylmalonyl-CoA metabolic node, providing evidence that spiramycin productivity may be increased by enhancing the carbon flow through this pathway. The goal was achieved by over-expressing the ccr paralog srm4 in an ad hoc engineered plasmid. This work embeds the first metabolic reconstruction of S. ambofaciens and the successful experimental validation of model predictions and demonstrates the validity and the importance of in silico modeling tools for the overproduction of molecules with a biotechnological interest. Finally, the proposed metabolic reconstruction, which includes manually refined pathways for several secondary metabolites with antimicrobial activity, represents a solid platform for the future exploitation of S. ambofaciens biotechnological potential.

Subtilisin-Involved Morphology Engineering for Improved Antibiotic Production in Actinomycetes

“…Indeed, the deletion of APASM_4178 led to an alleviated fragmentation, 26.99% improved biomass and 43.65% increased AP-3 yield ( Figure 2 A–C). Our work displays the accuracy and efficiency of comparative transcriptome analysis in the identification of targets for genetic engineering, as also shown in the titer improvements of actinorhodin in S. coelicolor and of spiramycin in Streptomyces ambofaciens [ 53 , 54 ].…”

Genome‐Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production

“…In this regard, computational resources dedicated to studies on secondary metabolites should additionally be considered to more comprehensively describe secondary metabolism in the GEMs of actinomycetes, including genome mining tools for the BGC detection, cheminformatic tools for compound identification and dereplication, and databases for BGCs and secondary metabolites . As recently released high‐quality GEMs started to be supported with automatic GEM reconstruction technologies, such as ModelSEED for GEMs of Streptomyces ambofaciens and S. clavuligerus , and RAVEN Toolbox 2 for S. coelicolor , additional use of the abovementioned secondary metabolite‐relevant computational resources will allow more streamlined GEM reconstructions that cover both primary and secondary metabolism systematically and comprehensively.…”

“…tSOT was experimentally validated by applying to the enhanced production of actinorhodin using S. coelicolor . iMAT was also used to integrate transcriptome data obtained from four different time points of the cultivation profile with the GEM of S. ambofaciens in order to understand changes in overall metabolic flux distributions during mycelial growth and spiramycin production along the time . In another study, metabolome data together with the GEM simulation were useful in narrowing a list of candidate gene manipulation targets .…”

“…Apart from modeling unique features of secondary metabolism, constraint‐based algorithms for predicting gene manipulation targets developed for metabolic engineering purpose have also actively been applied to engineering actinomycetes for the enhanced secondary metabolites production. Examples include “Flux Scanning based on Enforced Objective Flux” (FSEOF) method for the optimal production of a group of secondary metabolites (e.g., actinorhodin, undecylprodigiosin, calcium‐dependent antibiotic, and geosmin) using Sco4, the latest version of S. coelicolor GEM, as well as spiramycin using the S. ambofaciens GEM; “Robust, Overexpression, Knockout, and Dampening” (RobOKoD) method for the production of CA using S. clavuligerus GEM; FBA, minimization of metabolic adjustment (MOMA), and OptGene for the balhimycin production using the A. balhimycina GEM; and finally simple FBA for the production of spinosad and erythromycin using the GEMs of S. spinosa and S. erythraea , respectively, as well as for the genome reduction of S. lividans , an attractive host for heterologous protein secretion . Despite increasing examples of applying gene targeting algorithms to actinomycetes’ GEM for the secondary metabolites production, more experimental validations await as future challenges.…”

Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism

“…Genome-scale metabolic models (GSMM) have been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization [ 10 – 12 ], and several generations of models of Streptomyces metabolism have been developed for this purpose [ 13 – 17 ]. The use of constraint-based modelling, in particular with flux balance analysis (FBA), enables the reconstruction and analysis of large metabolic networks from the genome sequence as well as predictions of growth associated phenotypes (metabolic fluxes, growth rates, metabolic gene essentiality) [ 18 ].…”