Enhancement of rapamycin production by metabolic engineering in Streptomyces hygroscopicus based on genome-scale metabolic model

Abstract

Rapamycin, as a macrocyclic polyketide with immunosuppressive, antifungal, and anti-tumor activity produced by Streptomyces hygroscopicus, is receiving considerable attention for its significant contribution in medical field. However, the production capacity of the wild strain is very low. Hereby, a computational guided engineering approach was proposed to improve the capability of rapamycin production. First, a genome-scale metabolic model of Streptomyces hygroscopicus ATCC 29253 was constructed based on its annotated genome and biochemical information. The model consists of 1003 reactions, 711 metabolites after manual refinement. Subsequently, several potential genetic targets that likely guaranteed an improved yield of rapamycin were identified by flux balance analysis and minimization of metabolic adjustment algorithm. Furthermore, according to the results of model prediction, target gene pfk (encoding 6-phosphofructokinase) was knocked out, and target genes dahP (encoding 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase) and rapK (encoding chorismatase) were overexpressed in the parent strain ATCC 29253. The yield of rapamycin increased by 30.8% by knocking out gene pfk and increased by 36.2 and 44.8% by overexpression of rapK and dahP, respectively, compared with parent strain. Finally, the combined effect of the genetic modifications was evaluated. The titer of rapamycin reached 250.8 mg/l by knockout of pfk and co-expression of genes dahP and rapK, corresponding to a 142.3% increase relative to that of the parent strain. The relationship between model prediction and experimental results demonstrates the validity and rationality of this approach for target identification and rapamycin production improvement.

Developing an endogenous quorum-sensing based CRISPRi circuit for autonomous and tunable dynamic regulation of multiple targets in Streptomyces

“…In another study, based on a genome-scale metabolic model, pfk (encoding 6-phosphofructokinase) was deleted, and two target genes including dahp (encoding a DAHP synthase) and rapK (encoding a chorismatase) were overexpressed, resulting in approximately 30.8%, 36.2% and 44.8% increases in rapamycin titers, respectively. Combinatorial metabolic engineering by pfk knockout and co-overexpression of dahP and rapK , led to further enhanced rapamycin production by 142.3%, achieving a 250.8 mg/l rapamycin titer ( 49 ). Thus, compared to previous studies utilizing static metabolic engineering approaches, our EQCi-based regulation of multiple essential pathways is much more effective for improving rapamycin titers.…”

Rational engineering of Streptomyces albus J1074 for the overexpression of secondary metabolite gene clusters

“…Avermectin production is correlated with increased activity of pentose phosphate pathway in S. avermetilis [ 48 ]. S. hygroscopicus Δ pfk mutant increases rapamycin production by 30.8% [ 21 ]. Here we showed that S. albus Δ pfk strain is more resistant to diamide than the wild type.…”

Genome‐Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production

“…Sco4 includes biosynthetic pathways of eleven secondary metabolites, but it is still far from fully covering the information on 27 BGCs predicted using a genome mining tool antiSMASH (Table ). Such dearth of information on secondary metabolism is observed even more in GEMs of other actinomycetes, most of which cover biosynthetic pathway of only one target antibiotics, for example rapamycin in Streptomyces hygroscopicus , tacrolimus (FK506) in S. tsukubaensis , spinosad in Saccharopolyspora spinosa and balhimycin in Amycolatopsis balhimycina . 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 .…”