Resistance Gene-Directed Genome Mining of 50 Aspergillus species

Abstract

AbstractFungal secondary metabolites are a rich source of valuable natural products. Genome sequencing have revealed an enormous potential from predicted biosynthetic gene clusters. It is however currently a time consuming task and an unfeasible task to characterize all biosynthetic gene cluster and to identify possible uses of the compounds. A rational approach is needed to identify promising gene clusters responsible for producing valuable compounds. Several valuable bioactive clusters have been shown to include a resistance gene which is a paralog of the target gene inhibited by the compound. This mechanism can be used to design a rational approach selecting those clusters.We have developed a pipeline FRIGG (Fungal Resistance Gene-directed Genome mining) identifying putative resistance genes found in biosynthetic gene clusters based on homology patterns of the cluster genes. The FRIGG pipeline has been run using 51 Aspergillus and Penicillium genomes, identifying 72 unique protein families with putative resistance genes using various settings in the pipeline. The pipeline was also able to identify the characterized resistance gene inpE from the Fellutamide B cluster thereby validating the approach.We have successfully developed an approach identifying putative valuable bio-active clusters based on a specific resistance mechanism. This approach will be highly useful as an ever increasing amount of genomic data becomes available — the art of identifying and selecting clusters producing novel valuable compounds will only become more crucial.ImportanceSpecies belonging to the Aspergillus genus are known to produce a large number of secondary metabolites, some of these compounds are bioactive and used as pharmaceuticals such as penicillin, cyclosporin and statin. With whole genome sequencing it became apparent that the genetic potential for secondary metabolite production is much bigger than expected. As an increasing number of species are whole genome sequenced an immense number of secondary metabolite genes are predicted and the question of how to selectively identify novel bioactive compounds from this information arises. To address this question, we have created a pipeline identifying genes likely involved in the production of bioactive compounds based on a resistance gene hypothesis approach.