Systematic prioritization of candidate genes in camptothecin biosynthesis using multi-omics and deep learning

Area of Science:

• Plant biochemistry and molecular biology
• Drug discovery and development

Background:

• Camptothecin (CPT) is a crucial plant-derived alkaloid precursor for cancer chemotherapy.
• The complete set of genes involved in CPT biosynthesis is currently unknown.
• This knowledge gap impedes pathway elucidation and biotechnological production of CPT.

Purpose of the Study:

• To identify candidate genes responsible for camptothecin (CPT) biosynthesis.
• To establish an experimental system for inducible CPT production.
• To leverage multi-omics and deep learning for pathway discovery.

Main Methods:

• Engineered an inducible callus system for CPT production in *Camptotheca acuminata*.
• Generated improved *C. acuminata* genome assembly and gene annotation.
• Performed transcriptomic analysis across various tissues and callus types.
• Utilized deep learning for protein-ligand complex structure prediction to prioritize candidate enzymes.

Main Results:

• Shortlisted candidate enzymes involved in CPT biosynthesis through transcriptomic analysis.
• Prioritized 117 candidate enzymes using deep learning-based structure prediction.
• Integrated experimental, genomic, transcriptomic, and deep learning data.

Conclusions:

• This study provides a foundational dataset and methodology for elucidating the complete CPT biosynthetic pathway.
• The findings pave the way for future research into CPT's biochemical reactions and potential heterologous production.