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Pan-Genome of the Genus Streptomyces and Prioritization of Biosynthetic Gene Clusters With Potential to Produce Antibiotic Compounds

ABSTRACT: Species of the genus Streptomyces are known for their ability to produce multiple secondary metabolites; their genomes have been extensively explored to discover new bioactive compounds. The richness of genomic data currently available allows filtering for high quality genomes, which in turn permits reliable comparative genomics studies and an improved prediction of biosynthetic gene clusters (BGCs) through genome mining approaches. In this work, we used 121 genome sequences of the genus Streptomyces in a comparative genomics study with the aim of estimating the genomic diversity by protein domains content, sequence similarity of proteins and conservation of Intergenic Regions (IGRs). We also searched for BGCs but prioritizing those with potential antibiotic activity. Our analysis revealed that the pan-genome of the genus Streptomyces is clearly open, with a high quantity of unique gene families across the different species and that the IGRs are rarely conserved. We also described the phylogenetic relationships of the analyzed genomes using multiple markers, obtaining a trustworthy tree whose relationships were further validated by Average Nucleotide Identity (ANI) calculations. Finally, 33 biosynthetic gene clusters were detected to have potential antibiotic activity and a predicted mode of action, which might serve up as a guide to formulation of related experimental studies.

SUBMITTER: Caicedo-Montoya C

PROVIDER: S-EPMC8510958 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Bacteria have long been a source of natural products with diverse bioactivities that have been developed into therapeutics to treat human disease. Historically, researchers have focused on a few taxa of bacteria, mainly Streptomyces and other actinomycetes. This strategy was initially highly successful and resulted in the golden era of antibiotic discovery. The golden era ended when the most common antibiotics from Streptomyces had been discovered. Rediscovery of known compounds has plagued natural product discovery ever since. Recently, there has been increasing interest in identifying other taxa that produce bioactive natural products. Several bioinformatics studies have identified promising taxa with high biosynthetic capacity. However, these studies do not address the question of whether any of the products produced by these taxa are likely to have activities that will make them useful as human therapeutics. We address this gap by applying a recently developed machine learning tool that predicts natural product activity from biosynthetic gene cluster (BGC) sequences to determine which taxa are likely to produce compounds that are not only novel but also bioactive. This machine learning tool is trained on a dataset of BGC-natural product activity pairs and relies on counts of different protein domains and resistance genes in the BGC to make its predictions. We find that rare and understudied actinomycetes are the most promising sources for novel active compounds. There are also several taxa outside of actinomycetes that are likely to produce novel active compounds. We also find that most strains of Streptomyces likely produce both characterized and uncharacterized bioactive natural products. The results of this study provide guidelines to increase the efficiency of future bioprospecting efforts.One-sentence summaryThis paper combines several bioinformatics workflows to identify which genera of bacteria are most likely to produce novel natural products with useful bioactivities such as antibacterial, antitumor, or antifungal activity.

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Pan-Genome of the Genus Streptomyces and Prioritization of Biosynthetic Gene Clusters With Potential to Produce Antibiotic Compounds

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