Centella asiatica is a medicinal plant containing various ursane-type saponins that serve as a model system for studying triterpenoid accumulation. We assembled a haplotype-resolved genome for C. asiatica, and it showed allelic imbalance between haplotypes. The genome underwent one whole-genome duplication event followed by chromosomal fusion resulting in the current karyotype. We also constructed the metabolic regulatory network of triterpenoid saponins and found that triterpenoid biosynthesis was accompanied by gene duplication. These findings may assist in mining genes for the metabolism of C. asiatica and improving the understanding of the genetic basis for the diversity of triterpenoid biosynthesis.

In light of the pressing global challenges of climate change, declining crop resilience, and hidden hunger, it is imperative to overcome the limitations of conventional crop breeding to enhance both the nutritional quality and stress tolerance of crops. Synthetic metabolic engineering presents innovative strategies for the precision modification and de novo design of metabolic pathways. This approach generally encompasses three essential steps: identifying key metabolites through metabolomics, integrating multi-omics technologies to investigate the synthesis and regulation of these metabolites, and utilizing gene editing or de novo design to modify crop metabolic pathways associated with desirable agronomic traits. This review underscores the vital role of plant metabolite diversity in enhancing crop nutritional quality and stress resilience. Integrated multi-omics analyses facilitate the metabolic engineering by identifying key genes, transporters, and transcription factors that regulate metabolite biosynthesis. Precision modification strategies employ genome editing tools to reprogram endogenous metabolic networks, while de novo design reconstructs metabolic pathways through the introduction of exogenous biological elements—thereby both approaches enable the targeted enhancement of desired traits. These strategies have been effectively implemented in major food crops. However, simultaneously enhancing nutritional quality and stress resilience remains challenging due to inherent trade-offs and resource competition in distinct metabolic pathways within plants. Future research should integrate AI-driven predictive models with multi-omics datasets to decipher dynamic metabolic homeostasis and engineer climate-smart crops that maximize yield while preserving quality and environmental adaptability.