Dataset Information

Identification of Thiotetronic Acid Antibiotic Biosynthetic Pathways by Target-directed Genome Mining.

ABSTRACT: Recent genome sequencing efforts have led to the rapid accumulation of uncharacterized or "orphaned" secondary metabolic biosynthesis gene clusters (BGCs) in public databases. This increase in DNA-sequenced big data has given rise to significant challenges in the applied field of natural product genome mining, including (i) how to prioritize the characterization of orphan BGCs and (ii) how to rapidly connect genes to biosynthesized small molecules. Here, we show that by correlating putative antibiotic resistance genes that encode target-modified proteins with orphan BGCs, we predict the biological function of pathway specific small molecules before they have been revealed in a process we call target-directed genome mining. By querying the pan-genome of 86 Salinispora bacterial genomes for duplicated house-keeping genes colocalized with natural product BGCs, we prioritized an orphan polyketide synthase-nonribosomal peptide synthetase hybrid BGC (tlm) with a putative fatty acid synthase resistance gene. We employed a new synthetic double-stranded DNA-mediated cloning strategy based on transformation-associated recombination to efficiently capture tlm and the related ttm BGCs directly from genomic DNA and to heterologously express them in Streptomyces hosts. We show the production of a group of unusual thiotetronic acid natural products, including the well-known fatty acid synthase inhibitor thiolactomycin that was first described over 30 years ago, yet never at the genetic level in regards to biosynthesis and autoresistance. This finding not only validates the target-directed genome mining strategy for the discovery of antibiotic producing gene clusters without a priori knowledge of the molecule synthesized but also paves the way for the investigation of novel enzymology involved in thiotetronic acid natural product biosynthesis.

SUBMITTER: Tang X

PROVIDER: S-EPMC4758359 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Thiotetronic Acid Antibiotic Biosynthetic Pathways by Target-directed Genome Mining.

Tang Xiaoyu X Li Jie J Millán-Aguiñaga Natalie N Zhang Jia Jia JJ O'Neill Ellis C EC Ugalde Juan A JA Jensen Paul R PR Mantovani Simone M SM Moore Bradley S BS

ACS chemical biology 20151021 12

Recent genome sequencing efforts have led to the rapid accumulation of uncharacterized or "orphaned" secondary metabolic biosynthesis gene clusters (BGCs) in public databases. This increase in DNA-sequenced big data has given rise to significant challenges in the applied field of natural product genome mining, including (i) how to prioritize the characterization of orphan BGCs and (ii) how to rapidly connect genes to biosynthesized small molecules. Here, we show that by correlating putative anti ...[more]

PMID: 26458099

Similar Datasets

Project description:Fungal secondary metabolites are a rich source of valuable natural products, and genome sequencing has revealed a proliferation of predicted biosynthetic gene clusters in the genomes. However, it is currently an unfeasible task to characterize all biosynthetic gene clusters and to identify possible uses of the compounds. Therefore, a rational approach is needed to identify a short list of gene clusters responsible for producing valuable compounds. To this end, several bioactive clusters include a resistance gene, which is a paralog of the target gene inhibited by the compound. This mechanism can be used to identify these clusters. We have developed the FRIGG (fungal resistance gene-directed genome mining) pipeline for identifying this type of biosynthetic gene cluster based on homology patterns of the cluster genes. In this work, the FRIGG pipeline was run using 51 Aspergillus and Penicillium genomes, identifying 72 unique families of putative resistance genes. The pipeline also identified the previously characterized resistance gene inpE from the fellutamide B cluster, thereby validating the approach. We have successfully developed an approach to identify putative valuable bioactive 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 the right clusters producing novel valuable compounds will only become more crucial. IMPORTANCE Species belonging to the Aspergillus genus are known to produce a large number of secondary metabolites; some of these compounds are used as pharmaceuticals, such as penicillin, cyclosporine, and statin. With whole-genome sequencing, it became apparent that the genetic potential for secondary metabolite production is much larger than expected. As an increasing number of species are whole-genome sequenced, thousands 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 to predict genes involved in the production of bioactive compounds based on a resistance gene hypothesis approach.

Dataset Information

Identification of Thiotetronic Acid Antibiotic Biosynthetic Pathways by Target-directed Genome Mining.

Publications

Identification of Thiotetronic Acid Antibiotic Biosynthetic Pathways by Target-directed Genome Mining.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure