Mining raw plant transcriptomic data for new cyclopeptide alkaloids.

Area of Science:

• Natural Product Discovery
• Bioinformatics
• Plant Sciences

Background:

• Genome and transcriptome mining accelerate natural product discovery in microbes.
• Plant natural product discovery is hindered by limited genomic data and inconsistent gene clustering.
• Burpitides, a class of ribosomally synthesized and post-translationally modified peptides (RiPPs), present an opportunity for bioinformatics-guided plant discovery due to short biosynthetic pathways.

Purpose of the Study:

• To investigate the potential distribution of split burpitide precursor peptides in Streptophyta using a bioinformatics approach.
• To identify novel cyclopeptide alkaloids in plants.

Main Methods:

• High-throughput assembly and analysis of 700 public transcriptomic datasets.
• Bioinformatic prediction of burpitide precursor peptide distribution.
• Metabolomic analysis of selected plant species.

Main Results:

• Uncovered the potential distribution of split burpitide precursor peptides across Streptophyta.
• Confirmed bioinformatic predictions through metabolomic analysis.
• Identified new cyclopeptide alkaloids from both known and previously uncharacterized plant sources.

Conclusions:

• Bioinformatics-guided discovery is effective for identifying RiPPs, like burpitides, in plants.
• This study expands the known sources and diversity of plant-derived cyclopeptide alkaloids.
• The findings pave the way for further exploration of plant natural products.