Genome-scale metabolic reconstruction of Botrytis cinerea for enhanced abscisic acid biosynthesis

Abstract

Abscisic acid (ABA), as one of the five major natural plant growth regulators, plays an important role in agricultural production. Botrytis cinerea is a natural strain for ABA production. However, the insufficient understanding of its metabolic network makes it difficult to further increase the yield, which limits its application in the fermentation industry. In this research, a genome-scale metabolic network model (GSMM, iDK916) of B. cinerea was constructed, which includes 916 genes, 2318 reactions and 1770 metabolites. Firstly, the model's accuracy was validated by analyzing its growth on 35 carbon sources and 25 nitrogen sources, along with the identification of 79 essential genes consistent with those in the DEG database. Subsequently, dynamic flux balance analysis (dynamic FBA) revealed that the carbon source plays a more critical role in ABA yield: when fructose was used as the sole carbon source, the simulated ABA yield reached 0.95 mmol·gDW⁻¹·h⁻¹, while the yield obtained from wet lab experiments was 0.41 g·L⁻¹. Finally, using flux scanning based on enforced objective flux (FSEOF), 64 upregulated genes were identified. To validate the predictions, BCIN_12g04570 (Phosphomevalonate kinase, EC: 2.7.4.2) was selected for targeted overexpression in the strain, ultimately increasing ABA production to 1.35 g·L⁻¹, representing a 49.8% improvement compared to the original strain. This provides a reliable reference for subsequent research aimed at enhancing ABA production.