In silico-guided metabolic engineering of Bacillus subtilis for efficient biosynthesis of purine nucleosides by blocking the key backflow nodes

Purine nucleosides play essential roles in cellular physiological processes and have a wide range of applications in the fields of antitumor/antiviral drugs and food. However, microbial overproduction of purine nucleosides by de novo metabolic engineering remains a great challenge due to their strict and complex regulatory machinery involved in biosynthetic pathways.

Our study reveals that in silico-guided metabolic engineering successfully optimized the purine synthesis pathway by exploring efficient targets, which could be applied as a superior strategy for efficient biosynthesis of biotechnological products.

In this study, we designed an in silico-guided strategy for overproducing purine nucleosides based on a genome-scale metabolic network model in Bacillus subtilis. The metabolic flux was analyzed to predict two key backflow nodes, Drm (purine nucleotides toward PPP) and YwjH (PPP-EMP), to resolve the competitive relationship between biomass and purine nucleotide synthesis. In terms of the purine synthesis pathway, the first backflow node Drm was inactivated to block the degradation of purine nucleotides, which greatly increased the inosine production to 13.98-14.47 g/L without affecting cell growth. Furthermore, releasing feedback inhibition of the purine operon by promoter replacement enhanced the accumulation of purine nucleotides. In terms of the central carbon metabolic pathways, the deletion of the second backflow node YwjH and overexpression of Zwf were combined to increase inosine production to 22.01 ± 1.18 g/L by enhancing the metabolic flow of PPP. By switching on the flux node of the glucose-6-phosphate to PPP or EMP, the final inosine engineered strain produced up to 25.81 ± 1.23 g/L inosine by a pgi-based metabolic switch with a yield of 0.126 mol/mol glucose, a productivity of 0.358 g/L/h and a synthesis rate of 0.088 mmol/gDW/h, representing the highest yield in de novo engineered inosine bacteria. Under the guidance of this in silico-designed strategy, a general chassis bacterium was generated, for the first time, to efficiently synthesize inosine, adenosine, guanosine, IMP and GMP, which provides sufficient precursors for the synthesis of various purine intermediates. Conclusions: Our study reveals that in silico-guided metabolic engineering successfully optimized the purine synthesis pathway by exploring efficient targets, which could be applied as a superior strategy for efficient biosynthesis of biotechnological products.