Natural and engineered precision antibiotics in the context of resistance.

Antibiotics are essential weapons in our fight against infectious disease, yet the consequences of broad-spectrum antibiotic use on microbiome stability and pathogen resistance are prompting investigations into more selective alternatives. Echoing the advent of precision medicine in oncology, precision antibiotics with focused activities are emerging as a means of addressing infections without damaging microbiomes or incentivizing resistance. Historically, antibiotic design principles have been gleaned from Nature, and reinvestigation of overlooked antibacterials is now providing scaffolds and targets for the design of pathogen-specific drugs. In this perspective, we summarize the biosynthetic and antibacterial mechanisms used to access these activities, and discuss how such strategies may be co-opted through engineering approaches to afford precision antibiotics.

Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences.

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com .

Continuous bioactivity-dependent evolution of an antibiotic biosynthetic pathway.

Antibiotic biosynthetic gene clusters (BGCs) produce bioactive metabolites that impart a fitness advantage to their producer, providing a mechanism for natural selection. This selection drives antibiotic evolution and adapts BGCs for expression in different organisms, potentially providing clues to improve heterologous expression of antibiotics. Here, we use phage-assisted continuous evolution (PACE) to achieve bioactivity-dependent adaptation of the BGC for the antibiotic bicyclomycin (BCM), facilitating improved production in a heterologous host. This proof-of-principle study demonstrates that features of natural bioactivity-dependent evolution can be engineered to access unforeseen routes of improving metabolic pathways and product yields.

Global analysis of prokaryotic tRNA-derived cyclodipeptide biosynthesis.

BACKGROUND: Among naturally occurring small molecules, tRNA-derived cyclodipeptides are a class that have attracted attention for their diverse and desirable biological activities. However, no tools are available to link cyclodipeptide synthases identified within prokaryotic genome sequences to their chemical products. Consequently, it is unclear how many genetically encoded cyclodipeptides represent novel products, and which producing organisms should be targeted for discovery. RESULTS: We developed a pipeline for identification and classification of cyclodipeptide biosynthetic gene clusters and prediction of aminoacyl-tRNA substrates and complete chemical structures. We leveraged this tool to conduct a global analysis of tRNA-derived cyclodipeptide biosynthesis in 93,107 prokaryotic genomes, and compared predicted cyclodipeptides to known cyclodipeptide synthase products and all known chemically characterized cyclodipeptides. By integrating predicted chemical structures and gene cluster architectures, we created a unified map of known and unknown genetically encoded cyclodipeptides. CONCLUSIONS: Our analysis suggests that sizeable regions of the chemical space encoded within sequenced prokaryotic genomes remain unexplored. Our map of the landscape of genetically encoded cyclodipeptides provides candidates for targeted discovery of novel compounds. The integration of our pipeline into a user-friendly web application provides a resource for further discovery of cyclodipeptides in newly sequenced prokaryotic genomes.

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes.

Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/.

Genomic charting of ribosomally synthesized natural product chemical space facilitates targeted mining.

Microbial natural products are an evolved resource of bioactive small molecules, which form the foundation of many modern therapeutic regimes. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) represent a class of natural products which have attracted extensive interest for their diverse chemical structures and potent biological activities. Genome sequencing has revealed that the vast majority of genetically encoded natural products remain unknown. Many bioinformatic resources have therefore been developed to predict the chemical structures of natural products, particularly nonribosomal peptides and polyketides, from sequence data. However, the diversity and complexity of RiPPs have challenged systematic investigation of RiPP diversity, and consequently the vast majority of genetically encoded RiPPs remain chemical "dark matter." Here, we introduce an algorithm to catalog RiPP biosynthetic gene clusters and chart genetically encoded RiPP chemical space. A global analysis of 65,421 prokaryotic genomes revealed 30,261 RiPP clusters, encoding 2,231 unique products. We further leverage the structure predictions generated by our algorithm to facilitate the genome-guided discovery of a molecule from a rare family of RiPPs. Our results provide the systematic investigation of RiPP genetic and chemical space, revealing the widespread distribution of RiPP biosynthesis throughout the prokaryotic tree of life, and provide a platform for the targeted discovery of RiPPs based on genome sequencing.

Polyketide and nonribosomal peptide retro-biosynthesis and global gene cluster matching.

Polyketides (PKs) and nonribosomal peptides (NRPs) are profoundly important natural products, forming the foundations of many therapeutic regimes. Decades of research have revealed over 11,000 PK and NRP structures, and genome sequencing is uncovering new PK and NRP gene clusters at an unprecedented rate. However, only ∼10% of PK and NRPs are currently associated with gene clusters, and it is unclear how many of these orphan gene clusters encode previously isolated molecules. Therefore, to efficiently guide the discovery of new molecules, we must first systematically de-orphan emergent gene clusters from genomes. Here we provide to our knowledge the first comprehensive retro-biosynthetic program, generalized retro-biosynthetic assembly prediction engine (GRAPE), for PK and NRP families and introduce a computational pipeline, global alignment for natural products cheminformatics (GARLIC), to uncover how observed biosynthetic gene clusters relate to known molecules, leading to the identification of gene clusters that encode new molecules.

Duodenal Bacteria From Patients With Celiac Disease and Healthy Subjects Distinctly Affect Gluten Breakdown and Immunogenicity.

BACKGROUND & AIMS: Partially degraded gluten peptides from cereals trigger celiac disease (CD), an autoimmune enteropathy occurring in genetically susceptible persons. Susceptibility genes are necessary but not sufficient to induce CD, and additional environmental factors related to unfavorable alterations in the microbiota have been proposed. We investigated gluten metabolism by opportunistic pathogens and commensal duodenal bacteria and characterized the capacity of the produced peptides to activate gluten-specific T-cells from CD patients. METHODS: We colonized germ-free C57BL/6 mice with bacteria isolated from the small intestine of CD patients or healthy controls, selected for their in vitro gluten-degrading capacity. After gluten gavage, gliadin amount and proteolytic activities were measured in intestinal contents. Peptides produced by bacteria used in mouse colonizations from the immunogenic 33-mer gluten peptide were characterized by liquid chromatography tandem mass spectrometry and their immunogenic potential was evaluated using peripheral blood mononuclear cells from celiac patients after receiving a 3-day gluten challenge. RESULTS: Bacterial colonizations produced distinct gluten-degradation patterns in the mouse small intestine. Pseudomonas aeruginosa, an opportunistic pathogen from CD patients, exhibited elastase activity and produced peptides that better translocated the mouse intestinal barrier. P aeruginosa-modified gluten peptides activated gluten-specific T-cells from CD patients. In contrast, Lactobacillus spp. from the duodenum of non-CD controls degraded gluten peptides produced by human and P aeruginosa proteases, reducing their immunogenicity. CONCLUSIONS: Small intestinal bacteria exhibit distinct gluten metabolic patterns in vivo, increasing or reducing gluten peptide immunogenicity. This microbe-gluten-host interaction may modulate autoimmune risk in genetically susceptible persons and may underlie the reported association of dysbiosis and CD.

Draft Genome Sequence of Streptomyces silvensis ATCC 53525, a Producer of Novel Hormone Antagonists.

Streptomyces silvensis produces nonribosomal peptides that act as antagonists of the human oxytocin and vasopressin receptors. Here, we present the genome sequence of S. silvensis ATCC 53525 and demonstrate that this organism possesses a number of additional biosynthetic gene clusters and might be a promising source for genome-guided drug discovery efforts.

Assembly and clustering of natural antibiotics guides target identification.

Antibiotics are essential for numerous medical procedures, including the treatment of bacterial infections, but their widespread use has led to the accumulation of resistance, prompting calls for the discovery of antibacterial agents with new targets. A majority of clinically approved antibacterial scaffolds are derived from microbial natural products, but these valuable molecules are not well annotated or organized, limiting the efficacy of modern informatic analyses. Here, we provide a comprehensive resource defining the targets, chemical origins and families of the natural antibacterial collective through a retrobiosynthetic algorithm. From this we also detail the directed mining of biosynthetic scaffolds and resistance determinants to reveal structures with a high likelihood of having previously unknown modes of action. Implementing this pipeline led to investigations of the telomycin family of natural products from Streptomyces canus, revealing that these bactericidal molecules possess a new antibacterial mode of action dependent on the bacterial phospholipid cardiolipin.

Informatic analysis reveals Legionella as a source of novel natural products.

Microbial natural products are a crucial source of bioactive molecules and unique chemical scaffolds. Despite their importance, rediscovery of known natural products from established productive microbes has led to declining interest, even while emergent genomic data suggest that the majority of microbial natural products remain to be discovered. Now, new sources of microbial natural products must be defined in order to provide chemical scaffolds for the next generation of small molecules for therapeutic, agricultural, and industrial purposes. In this work, we use specialized bioinformatic programs, genetic knockouts, and comparative metabolomics to define the genus Legionella as a new source of novel natural products. We show that Legionella spp. hold a diverse collection of biosynthetic gene clusters for the production of polyketide and nonribosomal peptide natural products. To confirm this bioinformatic survey, we create targeted mutants of L. pneumophila and use comparative metabolomics to identify a novel polyketide surfactant. Using spectroscopic techniques, we show that this polyketide possesses a new chemical scaffold, and firmly demonstrate that this unexplored genus is a source for novel natural products.

Informatic search strategies to discover analogues and variants of natural product archetypes.

Natural products are a crucial source of antimicrobial agents, but reliance on low-resolution bioactivity-guided approaches has led to diminishing interest in discovery programmes. Here, we demonstrate that two in-house automated informatic platforms can be used to target classes of biologically active natural products, specifically, peptaibols. We demonstrate that mass spectrometry-based informatic approaches can be used to detect natural products with high sensitivity, identifying desired agents present in complex microbial extracts. Using our specialised software packages, we could elaborate specific branches of chemical space, uncovering new variants of trichopolyn and demonstrating a way forward in mining natural products as a valuable source of potential pharmaceutical agents.

Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM).

Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/.

Exploration of Nonribosomal Peptide Families with an Automated Informatic Search Algorithm.

Microbial natural products are some of the most important pharmaceutical agents and possess unparalleled chemical diversity. Here we present an untargeted metabolomics algorithm that builds on our validated iSNAP platform to rapidly identify families of peptide natural products. By utilizing known or in silico-dereplicated seed structures, this algorithm screens tandem mass spectrometry data to elaborate extensive molecular families within crude microbial culture extracts with high confidence and statistical significance. Analysis of peptide natural product producers revealed an abundance of unreported congeners, revealing one of the largest families of natural products described to date, as well as a novel variant with greater potency. These findings demonstrate the effectiveness of the iSNAP platform as an accurate tool for rapidly profiling large families of nonribosomal peptides.

An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products.

Bacterial natural products are a diverse and valuable group of small molecules, and genome sequencing indicates that the vast majority remain undiscovered. The prediction of natural product structures from biosynthetic assembly lines can facilitate their discovery, but highly automated, accurate, and integrated systems are required to mine the broad spectrum of sequenced bacterial genomes. Here we present a genome-guided natural products discovery tool to automatically predict, combinatorialize and identify polyketides and nonribosomal peptides from biosynthetic assembly lines using LC-MS/MS data of crude extracts in a high-throughput manner. We detail the directed identification and isolation of six genetically predicted polyketides and nonribosomal peptides using our Genome-to-Natural Products platform. This highly automated, user-friendly programme provides a means of realizing the potential of genetically encoded natural products.

Automated identification of depsipeptide natural products by an informatic search algorithm.

Nonribosomal depsipeptides are a class of potent microbial natural products, which include several clinically approved pharmaceutical agents. Genome sequencing has revealed a large number of uninvestigated natural-product biosynthetic gene clusters. However, while novel informatic search methods to access these gene clusters have been developed to identify peptide natural products, depsipeptide detection has proven challenging. Herein, we present an improved version of our informatic search algorithm for natural products (iSNAP), which facilitates the detection of known and genetically predicted depsipeptides in complex microbial culture extracts. We validated this technology by identifying several depsipeptides from novel producers, and located a large number of novel depsipeptide gene clusters for future study. This approach highlights the value of chemoinformatic search methods for the discovery of genetically encoded metabolites by targeting specific areas of chemical space.

Gold biomineralization by a metallophore from a gold-associated microbe.

Microorganisms produce and secrete secondary metabolites to assist in their survival. We report that the gold resident bacterium Delftia acidovorans produces a secondary metabolite that protects from soluble gold through the generation of solid gold forms. This finding is the first demonstration that a secreted metabolite can protect against toxic gold and cause gold biomineralization.

Nonribosomal assembly of natural lipocyclocarbamate lipoprotein-associated phospholipase inhibitors.

EXPANDING OUR KNOWLEDGE: Natural lipocyclocarbamate natural products have provided the inspiration for the first-in-class synthetic phospholipase inhibitor darapladib, currently in phase III clinical trials for the treatment of atherosclerosis. Here, we discuss their biosynthesis by a nonribosomal peptide synthetase.