Dataset Information

A new genome-mining tool redefines the lasso peptide biosynthetic landscape.

ABSTRACT: Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining of RiPP data difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden-Markov-model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physicochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping to date of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including one with an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides and, more broadly, provide a framework for future genome-mining efforts.

SUBMITTER: Tietz JI

PROVIDER: S-EPMC5391289 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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A new genome-mining tool redefines the lasso peptide biosynthetic landscape.

Tietz Jonathan I JI Schwalen Christopher J CJ Patel Parth S PS Maxson Tucker T Blair Patricia M PM Tai Hua-Chia HC Zakai Uzma I UI Mitchell Douglas A DA

Nature chemical biology 20170228 5

Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining of RiPP data difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden-Markov-model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precur ...[more]

PMID: 28244986

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Project description:Natural products (NPs) and their derivatives are a major contributor to modern medicine. Historically, microorganisms such as bacteria and fungi have been instrumental in generating drugs and lead compounds because of the ease of culturing and genetically manipulating them. However, the ever-increasing demand for novel drugs highlights the need to bioprospect previously unexplored taxa for their biosynthetic potential. Next-generation sequencing technologies have expanded the range of organisms that can be explored for their biosynthetic content, as these technologies can provide a glimpse of an organism's entire biosynthetic landscape, without the need for cultivation. The entirety of biosynthetic genes can be compared to the genes of known function to identify the gene clusters potentially coding for novel products. In this study, we mine the genomes of nine lichen-forming fungal species of the genus Umbilicaria for biosynthetic genes, and categorize the biosynthetic gene clusters (BGCs) as "associated product structurally known" or "associated product putatively novel". Although lichen-forming fungi have been suggested to be a rich source of NPs, it is not known how their biosynthetic diversity compares to that of bacteria and non-lichenized fungi. We found that 25%-30% of biosynthetic genes are divergent as compared to the global database of BGCs, which comprises 1,200,000 characterized biosynthetic genes from plants, bacteria, and fungi. Out of 217 BGCs, 43 were highly divergant suggesting that they potentially encode structurally and functionally novel NPs. Clusters encoding the putatively novel metabolic diversity comprise polyketide synthases (30), non-ribosomal peptide synthetases (12), and terpenes (1). Our study emphasizes the utility of genomic data in bioprospecting microorganisms for their biosynthetic potential and in advancing the industrial application of unexplored taxa. We highlight the untapped structural metabolic diversity encoded in the lichenized fungal genomes. To the best of our knowledge, this is the first investigation identifying genes coding for NPs with potentially novel properties in lichenized fungi.

Dataset Information

A new genome-mining tool redefines the lasso peptide biosynthetic landscape.

Publications

A new genome-mining tool redefines the lasso peptide biosynthetic landscape.

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