Dataset Information

Biosynthetic potential-based strain prioritization for natural product discovery: a showcase for diterpenoid-producing actinomycetes.

ABSTRACT: Natural products remain the best sources of drugs and drug leads and serve as outstanding small-molecule probes to dissect fundamental biological processes. A great challenge for the natural product community is to discover novel natural products efficiently and cost effectively. Here we report the development of a practical method to survey biosynthetic potential in microorganisms, thereby identifying the most promising strains and prioritizing them for natural product discovery. Central to our approach is the innovative preparation, by a two-tiered PCR method, of a pool of pathway-specific probes, thereby allowing the survey of all variants of the biosynthetic machineries for the targeted class of natural products. The utility of the method was demonstrated by surveying 100 strains, randomly selected from our actinomycete collection, for their biosynthetic potential of four classes of natural products, aromatic polyketides, reduced polyketides, nonribosomal peptides, and diterpenoids, identifying 16 talented strains. One of the talented strains, Streptomyces griseus CB00830, was finally chosen to showcase the discovery of the targeted classes of natural products, resulting in the isolation of three diterpenoids, six nonribosomal peptides and related metabolites, and three polyketides. Variations of this method should be applicable to the discovery of other classes of natural products.

SUBMITTER: Xie P

PROVIDER: S-EPMC3963700 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Biosynthetic potential-based strain prioritization for natural product discovery: a showcase for diterpenoid-producing actinomycetes.

Xie Pengfei P Ma Ming M Rateb Mostafa E ME Shaaban Khaled A KA Yu Zhiguo Z Huang Sheng-Xiong SX Zhao Li-Xing LX Zhu Xiangcheng X Yan Yijun Y Peterson Ryan M RM Lohman Jeremy R JR Yang Dong D Yin Min M Rudolf Jeffrey D JD Jiang Yi Y Duan Yanwen Y Shen Ben B

Journal of natural products 20140131 2

Natural products remain the best sources of drugs and drug leads and serve as outstanding small-molecule probes to dissect fundamental biological processes. A great challenge for the natural product community is to discover novel natural products efficiently and cost effectively. Here we report the development of a practical method to survey biosynthetic potential in microorganisms, thereby identifying the most promising strains and prioritizing them for natural product discovery. Central to our ...[more]

PMID: 24484381

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Project description:Actinomycetes are prolific producers of industrially valuable and medically important compounds. Historically, the most efficient method of obtaining compounds has been bioactivity-guided isolation and characterization of drug-like molecules from culturable soil actinomycetes. Unfortunately, this pipeline has been met with an increasing number of rediscoveries, to the point where it is no longer considered an attractive approach for drug discovery. To address this challenge and to continue finding new compounds, researchers have increasingly focused on alternative environmental niches and screening methods. Here, we report the genetic investigation of actinomycetes from an underexplored source, New Zealand lichens. In this work, we obtain draft genome sequences for 322 lichen-associated actinomycetes. We then explore this genetic resource with an emphasis on biosynthetic potential. By enumerating biosynthetic gene clusters (BGCs) in our data sets and comparing these to various reference collections, we demonstrate that actinomycetes sourced from New Zealand lichens have the genetic capacity to produce large numbers of natural products, many of which are expected to be broadly different from those identified in previous efforts predominantly based on soil samples. Our data shed light on the actinomycete assemblage in New Zealand lichens and demonstrate that lichen-sourced actinobacteria could serve as reservoirs for discovering new secondary metabolites. IMPORTANCE Lichens are home to complex and distinctive microbial cohorts that have not been extensively explored for the ability to produce novel secondary metabolites. Here, we isolate and obtain genome sequence data for 322 actinomycetes from New Zealand lichens. In doing so, we delineate at least 85 potentially undescribed species, and show that lichen associated actinomycetes have the potential to yield many new secondary metabolites, and as such, might serve as a productive starting point for drug discovery efforts.

Project description:Microbial specialised metabolism is full of valuable natural products that are applied clinically, agriculturally, and industrially. The genes that encode their biosynthesis are often physically clustered on the genome in biosynthetic gene clusters (BGCs). Many BGCs consist of multiple groups of co-evolving genes called sub-clusters that are responsible for the biosynthesis of a specific chemical moiety in a natural product. Sub-clusters therefore provide an important link between the structures of a natural product and its BGC, which can be leveraged for predicting natural product structures from sequence, as well as for linking chemical structures and metabolomics-derived mass features to BGCs. While some initial computational methodologies have been devised for sub-cluster detection, current approaches are not scalable, have only been run on small and outdated datasets, or produce an impractically large number of possible sub-clusters to mine through. Here, we constructed a scalable method for unsupervised sub-cluster detection, called iPRESTO, based on topic modelling and statistical analysis of co-occurrence patterns of enzyme-coding protein families. iPRESTO was used to mine sub-clusters across 150,000 prokaryotic BGCs from antiSMASH-DB. After annotating a fraction of the resulting sub-cluster families, we could predict a substructure for 16% of the antiSMASH-DB BGCs. Additionally, our method was able to confirm 83% of the experimentally characterised sub-clusters in MIBiG reference BGCs. Based on iPRESTO-detected sub-clusters, we could correctly identify the BGCs for xenorhabdin and salbostatin biosynthesis (which had not yet been annotated in BGC databases), as well as propose a candidate BGC for akashin biosynthesis. Additionally, we show for a collection of 145 actinobacteria how substructures can aid in linking BGCs to molecules by correlating iPRESTO-detected sub-clusters to MS/MS-derived Mass2Motifs substructure patterns. This work paves the way for deeper functional and structural annotation of microbial BGCs by improved linking of orphan molecules to their cognate gene clusters, thus facilitating accelerated natural product discovery.

Dataset Information

Biosynthetic potential-based strain prioritization for natural product discovery: a showcase for diterpenoid-producing actinomycetes.

Publications

Biosynthetic potential-based strain prioritization for natural product discovery: a showcase for diterpenoid-producing actinomycetes.

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