PanBGC: a pangenome-inspired framework for comparative analysis of biosynthetic gene clusters

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Bacterial secondary metabolites are crucial therapeutics and ecological factors.
• Biosynthetic gene clusters (BGCs) encode these metabolites but lack population-scale diversity analysis frameworks.
• Gene cluster families (GCFs) group related BGCs, but internal diversity remains understudied.

Purpose of the Study:

• Introduce PanBGC, a novel framework for analyzing BGC internal diversity at a population scale.
• Classify genes within GCFs into core, accessory, and unique categories.
• Quantify compositional diversity using openness metrics.

Main Methods:

• Developed a pangenome-inspired framework (PanBGC) to treat GCFs as populations of BGCs.
• Applied PanBGC to over 250,000 BGCs from 35,000 genomes, analyzing over 80,000 GCFs.
• Created PanBGC-DB, an interactive web platform for comparative BGC analysis.

Main Results:

• Gene composition reshuffling is the primary driver of diversity within GCFs.
• Most GCFs exhibit closed gene repertoires but high compositional variability.
• Transporter domains are common core genes, highlighting the importance of compound export.

Conclusions:

• PanBGC provides a scalable framework for exploring BGCs at population resolution.
• PanBGC-DB facilitates exploration and contextualization of newly discovered BGCs.
• The study offers new insights into the drivers of secondary metabolite diversity in bacteria.