A Rational Way of Designing Metabolic Engineering Strategies to Enhance Camptothecin Production in Plant Cells

Camptothecin (CPT) is an important monoterpene indole alkaloid that is currently being used in anti-cancer therapeutics. The primary commercial source of CPT are the plants, Camptotheca acuminata and Nothapodytes nimmoniana (native to South-east Asia). Understanding the complex metabolic network including CPT biosynthesis is a prerequisite for rational design of metabolic engineering strategies to optimize CPT production. A robust metabolic model predicted strategy can maximize CPT productivity with minimum time and resources spent, unlike a hit-and-trial experimental approach. To this effect, we have reconstructed a C. acuminata Genome-scale Metabolic model (CamGEM) using the whole-genome sequence of C. acuminata and simulated it using constraint-based modelling methods. CamGEM is a comprehensive metabolic reconstruction that includes 1809 genes, 1227 metabolites, and 1233 reactions compartmentalized into 11 compartments such as stroma, mitochondria, cell wall, etc. A draft reconstruction was generated from ModelSEED database and further manually curated. The model represents both primary and secondary metabolism of the plant. This model was used to predict and rank suitable reaction or gene targets for overexpression and knockouts using in silico approaches, which can be experimentally implemented for enhanced CPT production. Our model predicted strictosidine synthase and geraniol hydroxylase as overexpression targets, which can lead to enhanced CPT production. This concurs with literature evidence where up to 56% improvement in CPT was reported^[1]. The model also predicts over-expression of Deoxy-xylulose-5-phosphate reductoisomerase(DXR) from Methyl-erythritol phosphate (MEP) pathway. This is also in accordance with literature, where knock-out of DXR by the addition of its inhibitor Fosmidomycin resulted in CPT reduction by 64%^[2]. Our model also predicts uncommon targets like S-adenosyl-methionine, homocysteine and methyltetrahydrofolate which need further validation. We thus demonstrate the utility of model-based metabolic engineering for multi-fold enhancement of important plant secondary metabolites.

[1] Cui,L. et al. Sci Rep 8227(2015).

[2] Rather,G.A. et al. BMC Plant Biol 301(2019).