Related Experiment Video
Updated: Aug 7, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Systematic prioritization of candidate genes in camptothecin biosynthesis using multi-omics and deep learning
Shenqi Wang1,2,3, Xing Wu1,3, Maria Moreno1
1Department of Molecular, Cellular & Developmental Biology, Yale University, New Haven, Connecticut, USA.
Abstract:
Camptothecin (CPT), a plant-derived monoterpene indole alkaloid first identified in Camptotheca acuminata, is a drug precursor widely used for cancer chemotherapeutics. However, the full set of genes responsible for CPT biosynthesis remains unclear, hindering efforts to elucidate the complete pathway or establish biosynthetic production of CPT in heterologous hosts. In this study, we engineered an experimental callus system for inducible production of CPT, which enabled multi-omics and deep learning analyses to identify candidate genes in CPT biosynthesis. We first generated an improved genome assembly and gene annotation for C. acuminata. We then leveraged the natural variation of CPT levels in C. acuminata tissues and performed transcriptomic analysis of multiple callus and tissue types to shortlist candidate enzymes responsible for CPT biosynthesis. Finally, we conducted large-scale deep learning-enabled protein-ligand complex structure prediction to prioritize 117 candidate enzymes for studies that map their roles in CPT biochemical reactions. By integrating experimental, genomic, transcriptomic, and deep learning approaches, this study provides a valuable foundation for the complete elucidation of the CPT biosynthetic pathway.
Insights
Researchers identified key genes for camptothecin (CPT) biosynthesis using an engineered plant system. This breakthrough aids in understanding CPT production for cancer therapeutics.
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.
