Dataset Information

Antibiotic discovery throughout the Small World Initiative: A molecular strategy to identify biosynthetic gene clusters involved in antagonistic activity.

ABSTRACT: The emergence of bacterial pathogens resistant to all known antibiotics is a global health crisis. Adding to this problem is that major pharmaceutical companies have shifted away from antibiotic discovery due to low profitability. As a result, the pipeline of new antibiotics is essentially dry and many bacteria now resist the effects of most commonly used drugs. To address this global health concern, citizen science through the Small World Initiative (SWI) was formed in 2012. As part of SWI, students isolate bacteria from their local environments, characterize the strains, and assay for antibiotic production. During the 2015 fall semester at Bowling Green State University, students isolated 77 soil-derived bacteria and genetically characterized strains using the 16S rRNA gene, identified strains exhibiting antagonistic activity, and performed an expanded SWI workflow using transposon mutagenesis to identify a biosynthetic gene cluster involved in toxigenic compound production. We identified one mutant with loss of antagonistic activity and through subsequent whole-genome sequencing and linker-mediated PCR identified a 24.9 kb biosynthetic gene locus likely involved in inhibitory activity in that mutant. Further assessment against human pathogens demonstrated the inhibition of Bacillus cereus, Listeria monocytogenes, and methicillin-resistant Staphylococcus aureus in the presence of this compound, thus supporting our molecular strategy as an effective research pipeline for SWI antibiotic discovery and genetic characterization.

SUBMITTER: Davis E

PROVIDER: S-EPMC5458470 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Antibiotic discovery throughout the Small World Initiative: A molecular strategy to identify biosynthetic gene clusters involved in antagonistic activity.

Davis Elizabeth E Sloan Tyler T Aurelius Krista K Barbour Angela A Bodey Elijah E Clark Brigette B Dennis Celeste C Drown Rachel R Fleming Megan M Humbert Allison A Glasgo Elizabeth E Kerns Trent T Lingro Kelly K McMillin MacKenzie M Meyer Aaron A Pope Breanna B Stalevicz April A Steffen Brittney B Steindl Austin A Williams Carolyn C Wimberley Carmen C Zenas Robert R Butela Kristen K Wildschutte Hans H

MicrobiologyOpen 20170122 3

The emergence of bacterial pathogens resistant to all known antibiotics is a global health crisis. Adding to this problem is that major pharmaceutical companies have shifted away from antibiotic discovery due to low profitability. As a result, the pipeline of new antibiotics is essentially dry and many bacteria now resist the effects of most commonly used drugs. To address this global health concern, citizen science through the Small World Initiative (SWI) was formed in 2012. As part of SWI, stu ...[more]

PMID: 28110506

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Project description:Culture-independent metagenomic approaches offer a promising solution to the discovery of therapeutically relevant compounds such as antibiotics by enabling access to the hidden biosynthetic potential of microorganisms. These strategies, however, often entail laborious, multi-step, and time-consuming procedures to recover the biosynthetic gene clusters (BGCs) from soil metagenomes for subsequent heterologous expression. Here, we developed an efficient method we called single Nanopore read cluster mining (SNRCM), which enables the fast recovery of complete BGCs from a soil metagenome using long- and short-read sequencing. A metagenomic fosmid library of 83,700 clones was generated and sequenced using Nanopore as well as Illumina technologies. Hybrid assembled contigs of the sequenced fosmid library were subsequently analyzed to identify BGCs encoding secondary metabolites. Using SNRCM, we aligned the identified BGCs directly to Nanopore long-reads and were able to detect complete BGCs on single fosmids. This enabled us to select for and recover BGCs of interest for subsequent heterologous expression attempts. Additionally, the sequencing data of the fosmid library and its corresponding metagenomic DNA enabled us to assemble and recover a large nonribosomal peptide synthetase (NRPS) BGC from three different fosmids of our library and to directly amplify and recover a complete lasso peptide BGC from the high-quality metagenomic DNA. Overall, the strategies presented here provide a useful tool for accelerating and facilitating the identification and production of potentially interesting bioactive compounds from soil metagenomes. KEY POINTS: • An efficient approach for the recovery of BGCs from soil metagenomes was developed to facilitate natural product discovery. • A fosmid library was constructed from soil metagenomic HMW DNA and sequenced via Illumina and Nanopore. • Nanopore long-reads enabled the direct identification and recovery of complete BGCs on single fosmids.

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

Antibiotic discovery throughout the Small World Initiative: A molecular strategy to identify biosynthetic gene clusters involved in antagonistic activity.

Publications

Antibiotic discovery throughout the Small World Initiative: A molecular strategy to identify biosynthetic gene clusters involved in antagonistic activity.

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