Multiplexed mobilization and expression of biosynthetic gene clusters

Sophisticated natural products as antibiotics

In this Review, we explore natural product antibiotics that do more than simply inhibit an active site of an essential enzyme. We review these compounds to provide inspiration for the design of much-needed new antibacterial agents, and examine the complex mechanisms that have evolved to effectively target bacteria, including covalent binders, inhibitors of resistance, compounds that utilize self-promoted entry, those that evade resistance, prodrugs, target corrupters, inhibitors of 'undruggable' targets, compounds that form supramolecular complexes, and selective membrane-acting agents. These are exemplified by β-lactams that bind covalently to inhibit transpeptidases and β-lactamases, siderophore chimeras that hijack import mechanisms to smuggle antibiotics into the cell, compounds that are activated by bacterial enzymes to produce reactive molecules, and antibiotics such as aminoglycosides that corrupt, rather than merely inhibit, their targets. Some of these mechanisms are highly sophisticated, such as the preformed β-strands of darobactins that target the undruggable β-barrel chaperone BamA, or teixobactin, which binds to a precursor of peptidoglycan and then forms a supramolecular structure that damages the membrane, impeding the emergence of resistance. Many of the compounds exhibit more than one notable feature, such as resistance evasion and target corruption. Understanding the surprising complexity of the best antimicrobial compounds provides a roadmap for developing novel compounds to address the antimicrobial resistance crisis by mining for new natural products and inspiring us to design similarly sophisticated antibiotics.

Perturbation-Specific Transcriptional Mapping for unbiased target elucidation of antibiotics

The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the post-genomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. We developed an unbiased strategy for MOA prediction, called Perturbation-Specific Transcriptional Mapping (PerSpecTM), in which large-throughput expression profiling of wildtype or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether chemical or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three new molecules with activity against Pseudomonas aeruginosa by comparing their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small molecule inhibition resemble those resulting from genetic depletion of essential targets by CRISPRi by PerSpecTM, demonstrating proof-of-concept that correlations between expression profiles of small molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.

A Versatile Thioesterase Involved in Dimerization during Cinnamoyl Lipid Biosynthesis

The cinnamoyl lipid compound youssoufene A1 (1), featuring a unique dearomatic carbon-bridged dimeric skeleton, exhibits increased inhibition against multidrug resistant Enterococcus faecalis as compared to monomeric youssoufenes. However, the formation process of this intriguing dearomatization/dimerization remains unknown. In this study, an unusual "gene-within-gene" thioesterase (TE) gene ysfF was functionally characterized. The gene was found to naturally encodes two proteins, an entire YsfF with α/β-hydrolase and 4-hydroxybenzoyl-CoA thioesterase (4-HBT)-like enzyme domains, and a nested YsfFHBT (4-HBT-like enzyme). Using an intracellular tagged carrier-protein tracking (ITCT) strategy, in vitro reconstitution and in vivo experiments, we found that: i) both domains of YsfF displayed thioesterase activities; ii) YsfF/YsfFHBT could accomplish the 6π-electrocyclic ring closure for benzene ring formation; and iii) YsfF and cyclase YsfX together were responsible for the ACP-tethered dearomatization/dimerization process, possibly through an unprecedented Michael-type addition reaction. Moreover, site-directed mutagenesis experiments demonstrated that N301, E483 and H566 of YsfF are critical residues for both the 6π-electrocyclization and dimerization processes. This study enhances our understanding of the multifunctionality of the TE protein family.

Employing synthetic biology to expand antibiotic discovery

Antimicrobial-resistant (AMR) bacterial pathogens are a continually growing threat as our methods for combating these infections continue to be overcome by the evolution of resistance mechanisms. Recent therapeutic methods have not staved off the concern of AMR infections, so continued research focuses on new ways of identifying small molecules to treat AMR pathogens. While chemical modification of existing antibiotics is possible, there has been rapid development of resistance by pathogens that were initially susceptible to these compounds. Synthetic biology is becoming a key strategy in trying to predict and induce novel, natural antibiotics. Advances in cloning and mutagenesis techniques applied through a synthetic biology lens can help characterize the native regulation of antibiotic biosynthetic gene clusters (BGCs) to identify potential modifications leading to more potent antibiotic activity. Additionally, many cryptic antibiotic BGCs are derived from non-ribosomal peptide synthase (NRPS) and polyketide synthase (PKS) biosynthetic pathways; complex, clustered genetic sequences that give rise to amino acid-derived natural products. Synthetic biology can be applied to modify and metabolically engineer these enzyme-based systems to promote rapid and sustainable production of natural products and their variants. This review will focus on recent advances related to synthetic biology as applied to genetic pathway characterization and identification of antibiotics from naturally occurring BGCs. Specifically, we will summarize recent efforts to characterize BGCs via general genomic mutagenesis, endogenous gene expression, and heterologous gene expression.

Controlling Substrate- and Stereospecificity of Condensation Domains in Nonribosomal Peptide Synthetases

Nonribosomal peptide synthetases (NRPSs) are sophisticated molecular machines that biosynthesize peptide drugs. In attempts to generate new bioactive compounds, some parts of NRPSs have been successfully manipulated, but especially the influence of condensation (C-)domains on substrate specificity remains enigmatic and poorly controlled. To understand the influence of C-domains on substrate preference, we extensively evaluated the peptide formation of C-domain mutants in a bimodular NRPS system. Thus, we identified three key mutations that govern the preference for stereoconfiguration and side-chain identity. These mutations show similar effects in three different C-domains (GrsB1, TycB1, and SrfAC) when di- or pentapeptides are synthesized in vitro or in vivo. Strikingly, mutation E386L allows the stereopreference to be switched from d- to l-configured donor substrates. Our findings provide valuable insights into how cryptic specificity filters in C-domains can be re-engineered to clear roadblocks for NRPS engineering and enable the production of novel bioactive compounds.

Exploring a general multi-pronged activation strategy for natural product discovery in Actinomycetes

Natural products possess significant therapeutic potential but remain underutilized despite advances in genomics and bioinformatics. While there are approaches to activate and upregulate natural product biosynthesis in both native and heterologous microbial strains, a comprehensive strategy to elicit production of natural products as well as a generalizable and efficient method to interrogate diverse native strains collection, remains lacking. Here, we explore a flexible and robust integrase-mediated multi-pronged activation approach to reliably perturb and globally trigger antibiotics production in actinobacteria. Across 54 actinobacterial strains, our approach yielded 124 distinct activator-strain combinations which consistently outperform wild type. Our approach expands accessible metabolite space by nearly two-fold and increases selected metabolite yields by up to >200-fold, enabling discovery of Gram-negative bioactivity in tetramic acid analogs. We envision these findings as a gateway towards a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature's chemical repertoire.

Highlights of bioinformatic tools and methods for validating bioinformatics derived hypotheses for microbial natural products research

Historically, bacterial natural products have served as an excellent source of drug leads, however, in recent decades the rate of discovery has slowed due to multiple challenges. Rapid advances in genome sequencing science in recent years have revealed the vast untapped encoded potential of bacteria to make natural products. To access these molecules, researchers can employ the ever-growing array of bioinformatic tools at their disposal and leverage newly developed experimental approaches to validate these bioinformatic-driven hypotheses. When used together effectively, bioinformatic and experimental tools enable researchers to deeply examine the full diversity of bacterial natural products. This review briefly outlines recent bioinformatic tools that can facilitate natural product research in bacteria including the use of CRISPR, co-occurrence network analysis, and combinatorial generation of microbial natural products to test bioinformatic hypotheses in the lab.

Role of Microbes in the degradation of organic semivolatile compounds in polar ecosystems: A review

The Arctic and the Antarctic Continent correspond to two eco-regions with extreme climatic conditions. These regions are exposed to the presence of contaminants resulting from human activity (local and global), which, in turn, represent a challenge for life forms in these environments. Anthropogenic pollution by semi-volatile organic compounds (SVOCs) in polar ecosystems has been documented since the 1960s. Currently, various studies have shown the presence of SVOCs and their bioaccumulation and biomagnification in the polar regions with negative effects on biodiversity and the ecosystem. Although the production and use of these compounds has been regulated, their persistence continues to threaten biodiversity and the ecosystem. Here, we summarize the current literature regarding microbes and SVOCs in polar regions and pose that bioremediation by native microorganisms is a feasible strategy to mitigate the presence of SVOCs. Our systematic review revealed that microbial communities in polar environments represent a wide reservoir of biodiversity adapted to extreme conditions, found both in terrestrial and aquatic environments, freely or in association with vegetation. Microorganisms adapted to these environments have the potential for biodegradation of SVOCs through a variety of genes encoding enzymes with the capacity to metabolize SVOCs. We suggest that a comprehensive approach at the molecular and ecological level is required to mitigate SVOCs presence in these regions. This is especially patent when considering that SVOCs degrade at slow rates and possess the ability to accumulate in polar ecosystems. The implications of SVOC degradation are relevant for the preservation of polar ecosystems with consequences at a global level.

Bacterial natural product discovery by heterologous expression

Natural products have found important applications in the pharmaceutical and agricultural sectors. In bacteria, the genes that encode the biosynthesis of natural products are often colocalized in the genome, forming biosynthetic gene clusters. It has been predicted that only 3% of natural products encoded in bacterial genomes have been discovered thus far, in part because gene clusters may be poorly expressed under laboratory conditions. Heterologous expression can help convert bioinformatics predictions into products. However, challenges remain, such as gene cluster prioritization, cloning of the complete gene cluster, high level expression, product identification, and isolation of products in practical yields. Here we reviewed the literature from the past 5 years (January 2018 to June 2023) to identify studies that discovered natural products by heterologous expression. From the 50 studies identified, we present analyses of the rationale for gene cluster prioritization, cloning methods, biosynthetic class, source taxa, and host choice. Combined, the 50 studies led to the discovery of 63 new families of natural products, supporting heterologous expression as a promising way to access novel chemistry. However, the success rate of natural product detection varied from 11% to 32% based on four large-scale studies that were part of the reviewed literature. The low success rate makes it apparent that much remains to be improved. The potential reasons for failure and points to be considered to improve the chances of success are discussed.

ONE-SENTENCE SUMMARY

At least 63 new families of bacterial natural products were discovered using heterologous expression in the last 5 years, supporting heterologous expression as a promising way to access novel chemistry; however, the success rate is low (11-32%) making it apparent that much remains to be improved-we discuss the potential reasons for failure and points to be considered to improve the chances of success. BioRender was used to generate the graphical abstract figure.