An enhanced genome-scale metabolic reconstruction of Streptomyces clavuligerus identifies novel strain improvement strategies

Abstract

In this work, we expanded and updated a genome-scale metabolic model of Streptomyces clavuligerus. The model includes 1021 genes and 1494 biochemical reactions; genome-reaction information was curated and new features related to clavam metabolism and to the biomass synthesis equation were incorporated. The model was validated using experimental data from the literature and simulations were performed to predict cellular growth and clavulanic acid biosynthesis. Flux balance analysis (FBA) showed that limiting concentrations of phosphate and an excess of ammonia accumulation are unfavorable for growth and clavulanic acid biosynthesis. The evaluation of different objective functions for FBA showed that maximization of ATP yields the best predictions for cellular behavior in continuous cultures, while the maximization of growth rate provides better predictions for batch cultures. Through gene essentiality analysis, 130 essential genes were found using a limited in silico media, while 100 essential genes were identified in amino acid-supplemented media. Finally, a strain design was carried out to identify candidate genes to be overexpressed or knocked out so as to maximize antibiotic biosynthesis. Interestingly, potential metabolic engineering targets, identified in this study, have not been tested experimentally.

Genome‐Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production

“…Among all the GEMs of twelve actinomycetes reconstructed so far, GEMs of nine species have been released since 2017, which stresses the increasing industrial or medical importance of actinomycetes (Table ). In particular, GEMs of several representative species have been reconstructed multiple times over the years, including Streptomyces coelicolor , Streptomyces clavuligerus , Saccharopolyspora erythraea , Streptomyces lividans , and Streptomyces tsukubaensis . Recently, two different versions of GEMs have been released for S. coelicolor , namely Sco4 and iAA1259 .…”

“…iAA1259 covered an additional biosynthetic pathway of coelimycin P1 in comparison with the iMK1208 . In general, some of the commonly observed upgrades in actinomycetes’ GEMs include incorporating information from updated metabolic databases, improving gene‐protein‐reaction associations, resolution of metabolic gaps, updates in biomass compositions, and more extensive description of biosynthetic pathways of secondary metabolites (Figure ) …”

“…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.…”

“…Another approach was to use flux variability analysis to examine correlations between the flux span of each reaction and a target secondary metabolite production rate so as to identify precursors critical for the biosynthesis of a target secondary metabolite . More recently, a study evaluated two linear objective functions (i.e., maximization of biomass or ATP yields) and another two non‐linear objective functions (i.e., maximization of ATP or biomass yield per unit flux) for the simulation of S. clavuligerus GEM . Maximization of ATP yield appeared to be the best objective function to capture the secondary metabolites biosynthesis in S. clavuligerus , especially during chemostat cultivations .…”

“…More recently, a study evaluated two linear objective functions (i.e., maximization of biomass or ATP yields) and another two non‐linear objective functions (i.e., maximization of ATP or biomass yield per unit flux) for the simulation of S. clavuligerus GEM . Maximization of ATP yield appeared to be the best objective function to capture the secondary metabolites biosynthesis in S. clavuligerus , especially during chemostat cultivations . Another approach implemented to overcome the limitation of the pseudo‐steady state assumption was dynamic flux balance analysis (DFBA), which is an extension of FBA that allows analyzing dynamic metabolic responses to perturbations .…”