Harnessing Rare Actinomycete Interactions and Intrinsic Antimicrobial Resistance Enables Discovery of an Unusual Metabolic Inhibitor

Antibiotics from rare actinomycetes, beyond the genus Streptomyces

Throughout the golden age of antibiotic discovery, Streptomyces have been unsurpassed for their ability to produce bioactive metabolites. Yet, this success has been hampered by rediscovery. As we enter a new stage of biodiscovery, omics data and existing scientific repositories can enable informed choices on the biodiversity that may yield novel antibiotics. Here, we focus on the chemical potential of rare actinomycetes, defined as bacteria within the order Actinomycetales, but not belonging to the genus Streptomyces. They are named as such due to their less-frequent isolation under standard laboratory practices, yet there is increasing evidence to suggest these biologically diverse genera harbour considerable biosynthetic and chemical diversity. In this review, we focus on examples of successful isolation and genera that have been the focus of more concentrated biodiscovery efforts, we survey the representation of rare actinomycete taxa, compared with Streptomyces, across natural product data repositories in addition to its biosynthetic potential. This is followed by an overview of clinically useful drugs produced by rare actinomycetes and considerations for future biodiscovery efforts. There is much to learn about these underexplored taxa, and mounting evidence suggests that they are a fruitful avenue for the discovery of novel antimicrobials.

Interplay of emerging and established technologies drives innovation in natural product antibiotic discovery

A continued rise of antibiotic resistance and shortages of effective antibiotics necessitate the discovery and development of new antibiotics with novel modes of action (MoAs) against resistant pathogens. While natural products remain the best resource for antibiotic discovery, their exploration faces many challenges, including (i) unknown MoAs, (ii) high rediscovery rates, (iii) tedious isolation and structure elucidation, and (iv) insufficient production for further development. We have identified recent innovations in screening methods, microbiology, bioinformatics, and metabolomics technologies, as well as natural product-inspired synthesis and synthetic biology, that have contributed to new natural product antibiotics in the past two years. We highlight their interplay as the key element for successful applications, driving future opportunities to increase the pool of natural product-based antibacterial antibiotics.

Streptomyces cell-free systems for natural product discovery and engineering

Streptomyces bacteria are a major microbial source of natural products, which are encoded within so-called biosynthetic gene clusters (BGCs). This highlight discusses the emergence of native Streptomyces cell-free systems as a new tool to accelerate the study of the fundamental chemistry and biology of natural product biosynthesis from these bacteria. Cell-free systems provide a prototyping platform to study plug-and-play reactions in microscale reactions. So far, Streptomyces cell-free systems have been used to rapidly characterise gene expression regulation, access secondary metabolite biosynthetic enzymes, and catalyse cell-free transcription, translation, and biosynthesis of example natural products. With further progress, we anticipate the development of more complex systems to complement existing experimental tools for the discovery and engineering of natural product biosynthesis from Streptomyces and related high G + C (%) bacteria.

Diversity and taxonomic distribution of bacterial biosynthetic gene clusters predicted to produce compounds with therapeutically relevant bioactivities

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 SUMMARY

This 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.