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. 2025 Jul 18;26(14):6906.
doi: 10.3390/ijms26146906.

Reconstruction of a Genome-Scale Metabolic Model for Aspergillus oryzae Engineered Strain: A Potent Computational Tool for Enhancing Cordycepin Production

Affiliations

Reconstruction of a Genome-Scale Metabolic Model for Aspergillus oryzae Engineered Strain: A Potent Computational Tool for Enhancing Cordycepin Production

Nachon Raethong et al. Int J Mol Sci. .

Abstract

Cordycepin, a bioactive adenosine analog, holds promise in pharmaceutical and health product development. However, large-scale production remains constrained by the limitations of natural producers, Cordyceps spp. Herein, we report the reconstruction of the first genome-scale metabolic model (GSMM) for a cordycepin-producing strain of recombinant Aspergillus oryzae. The model, iNR1684, incorporated 1684 genes and 1947 reactions with 93% gene-protein-reaction coverage, which was validated by the experimental biomass composition and growth rate. In silico analyses identified key gene amplification targets in the pentose phosphate and one-carbon metabolism pathways, indicating that folate metabolism is crucial for enhancing cordycepin production. Nutrient optimization simulations revealed that chitosan, D-glucosamine, and L-aspartate preferentially supported cordycepin biosynthesis. Additionally, a carbon-to-nitrogen ratio of 11.6:1 was identified and experimentally validated to maximize production, higher than that reported for Cordyceps militaris. These findings correspond to a faster growth rate, enhanced carbon assimilation, and broader substrate utilization by A. oryzae. This study demonstrates the significant role of GSMM in uncovering rational engineering strategies and provides a quantitative framework for precision fermentation, offering scalable and sustainable solutions for industrial cordycepin production.

Keywords: Aspergillus oryzae; cordycepin; metabolic modeling; nutrient optimization; precision fermentation.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic workflow for reconstructing a GSMM of an engineered A. oryzae strain. GSMM, genome-scale metabolic model.
Figure 2
Figure 2
Biomass composition (% w/w DCW) of A. oryzae BCC7051. DCW, dry cell weight.
Figure 3
Figure 3
Highlighted fluxes relevant to cordycepin biosynthesis in Aspergillus oryzae. Red circles indicate gene amplification targets for the improvement of cordycepin production. Abbreviated metabolite names are as follows: G6P, alpha-d-glucose 6-phosphate; F6P, beta-d-fructofuranose 6-phosphate; FBP, D-fructose 1,6-bisphosphate; G3P, glyceraldehyde 3-phosphate; DHAP, glycerone phosphate; α-KG, α-ketoglutarate; D6PGL, 6-o-phosphono-d-glucono-1,5-lactone; D6PGC, 6-phospho-d-gluconate; R5P, ribose 5-phosphate; Ru5P, ribulose 5-phosphate; Xu5P, d-xylulose 5-phosphate; PRPP, 5-phospho-alpha-d-ribose 1-diphosphate; PRAM, 5-phospho-ribosylamine; GAR, n(1)-(5-phospho-d-ribosyl)glycinamide; FGAR, n(2)-formyl-n(1)-(5-phospho-d-ribosyl)glycinamide; FGAM, 2-formamido-n(1)-(5-phospho-d-ribosyl)acetamidine; AIR, 5-amino-1-(5-phospho-ribosyl) imidazole; CAIR, 1-(5-phospho-d-ribosyl)-5-amino-4-imidazolecarboxylate; SAICAR, (2S)-2-[5-amino-1-(5-phospho-β-D-ribosyl)imidazole-4-carboxamido]succinic acid; AICAR, 5-amino-1-(5-phospho-d-ribosyl)imidazole-4-carboxamide; PRFICA, 5-formamido-1-(5-phospho-d-ribosyl)imidazole-4-carboxamide; IMP, inosine monophosphate; ASUC, adenylosuccinate; AMP, adenosine-50-monophosphate; ADE, adenosine; 3AMP, adenosine-30-monophosphate; GL3P, glycerol 3-phosphate; AGP, acylglycerol 3-phosphate; PA, phosphatidate; CDP-DAG, CDP-diacylglycerol; PS, phosphatidylserine; PE, phosphatidylethanolamines; LPE, lysophosphatidylethanolamine; GPE, glycerol phosphatidylethanolamine; EA, Ethanolamine; GA, glycolaldehyde; FALD, formaldehyde; FOR, formate; FTHF, 10-formyltetrahydrofolate and THF, tetrahydrofolate.
Figure 4
Figure 4
In silico identification of nutrients and carbon-to-nitrogen (C:N) ratios influencing cordycepin production in Aspergillus oryzae. (A) Cordycepin production fluxes are predicted below 57 single-nutrient supplementation scenarios. Simulations were performed using the iNR1684 model with growth fixed at 0.5 h−1. Each nutrient was added individually (1000 mmol) under three conditions: CN-limited (no glucose or ammonia), N-limited (1 mmol glucose), and C-limited (1 mmol ammonia). Only essential nutrients (oxygen, phosphate, sulfate) were constantly provided; (B) Effect of the C:N ratio on growth and cordycepin production. The optimal C:N ratio for balanced growth and cordycepin production was marked with a star.

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