Abstract
BackgroundGenome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications. With the continuously increasing number of genomes sequenced, we are confronted with an overwhelming number of predicted clusters. In order to guide the effective prioritization of biosynthetic gene clusters towards finding the most promising compounds, knowledge about diversity, phylogenetic relationships and distribution patterns of biosynthetic gene clusters is necessary.ResultsHere, we provide a comprehensive analysis of the model actinobacterial genus Amycolatopsis and its potential for the production of secondary metabolites. A phylogenetic characterization, together with a pan-genome analysis showed that within this highly diverse genus, four major lineages could be distinguished which differed in their potential to produce secondary metabolites. Furthermore, we were able to distinguish gene cluster families whose distribution correlated with phylogeny, indicating that vertical gene transfer plays a major role in the evolution of secondary metabolite gene clusters. Still, the vast majority of the diverse biosynthetic gene clusters were derived from clusters unique to the genus, and also unique in comparison to a database of known compounds. Our study on the locations of biosynthetic gene clusters in the genomes of Amycolatopsis’ strains showed that clusters acquired by horizontal gene transfer tend to be incorporated into non-conserved regions of the genome thereby allowing us to distinguish core and hypervariable regions in Amycolatopsis genomes.ConclusionsUsing a comparative genomics approach, it was possible to determine the potential of the genus Amycolatopsis to produce a huge diversity of secondary metabolites. Furthermore, the analysis demonstrates that horizontal and vertical gene transfer play an important role in the acquisition and maintenance of valuable secondary metabolites. Our results cast light on the interconnections between secondary metabolite gene clusters and provide a way to prioritize biosynthetic pathways in the search and discovery of novel compounds.
Highlights
Genome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications
Biosynthetic gene cluster (BGC) encoding for closely related biosynthetic pathways that produce highly similar chemical compounds are summarized under the term gene cluster families (GCFs)
The version described in this paper is version NMUL01000000 for Amycolatopsis sp
Summary
Genome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications. To facilitate the discovery of novel compounds, bacterial genome sequences are screened for genome regions that are likely to code for the production of secondary metabolites. This bioinformatics approach is the first important step in the genome mining pipeline that is necessary to guide the discovery of novel compounds [4, 5]. BGCs encoding for closely related biosynthetic pathways that produce highly similar chemical compounds are summarized under the term gene cluster families (GCFs). Other notable classes include ribosomally synthesized and post-translationally modified peptides (RiPPs) and terpenes [7, 8]
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