In silico genome mining of potential novel biosynthetic gene clusters for drug discovery from Burkholderia bacteria

As an emerging resource, Gram-negative Burkholderia bacteria were able to produce a wide range of bioactive secondary metabolites with potential therapeutic and biotechnological applications. Genome mining has emerged as an influential platform for screening and pinpointing natural product diversity with the increasing number of Burkholderia genome sequences. Here, for genome mining of potential biosynthetic gene clusters (BGCs) and prioritizing prolific producing Burkholderia strains, we investigated the relationship between species evolution and distribution of main BGC groups using computational analysis of complete genome sequences of 248 Burkholderia species publicly available. We uncovered significantly differential distribution patterns of BGCs in the Burkholderia phyla, even among strains that are genetically very similar. We found various types of BGCs in Burkholderia, including some representative and most common BGCs for biosynthesis of encrypted or known terpenes, non-ribosomal peptides (NRPs) and some hybrid BGCs for cryptic products. We also observed that Burkholderia contain a lot of unspecified BGCs, representing high potentials to produce novel compounds. Analysis of BGCs for RiPPs (Ribosomally synthesized and posttranslationally modified peptides) and a texobactin-like BGC as examples showed wide classification and diversity of RiPP BGCs in Burkholderia at species level and metabolite predication. In conclusion, as the biggest investigation in silico by far on BGCs of the particular genus Burkholderia, our data implied a great diversity of natural products in Burkholderia and BGC distributions closely related to phylogenetic variation, and suggested different or concurrent strategies used to identify new drug molecules from these microorganisms will be important for the selection of potential BGCs and prolific producing strains for drug discovery.