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 .

DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products.

Microbial natural products represent a rich resource of evolved chemistry that forms the basis for the majority of pharmacotherapeutics. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) are a particularly interesting class of natural products noted for their unique mode of biosynthesis and biological activities. Analyses of sequenced microbial genomes have revealed an enormous number of biosynthetic loci encoding RiPPs but whose products remain cryptic. In parallel, analyses of bacterial metabolomes typically assign chemical structures to only a minority of detected metabolites. Aligning these 2 disparate sources of data could provide a comprehensive strategy for natural product discovery. Here we present DeepRiPP, an integrated genomic and metabolomic platform that employs machine learning to automate the selective discovery and isolation of novel RiPPs. DeepRiPP includes 3 modules. The first, NLPPrecursor, identifies RiPPs independent of genomic context and neighboring biosynthetic genes. The second module, BARLEY, prioritizes loci that encode novel compounds, while the third, CLAMS, automates the isolation of their corresponding products from complex bacterial extracts. DeepRiPP pinpoints target metabolites using large-scale comparative metabolomics analysis across a database of 10,498 extracts generated from 463 strains. We apply the DeepRiPP platform to expand the landscape of novel RiPPs encoded within sequenced genomes and to discover 3 novel RiPPs, whose structures are exactly as predicted by our platform. By building on advances in machine learning technologies, DeepRiPP integrates genomic and metabolomic data to guide the isolation of novel RiPPs in an automated manner.

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.

Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm.

Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics.

Antibiotics and specialized metabolites from the human microbiota.

Covering: 2000 to 2017Decades of research on human microbiota have revealed much of their taxonomic diversity and established their direct link to health and disease. However, the breadth of bioactive natural products secreted by our microbial partners remains unknown. Of particular interest are antibiotics produced by our microbiota to ward off invasive pathogens. Members of the human microbiota exclusively produce evolved small molecules with selective antimicrobial activity against human pathogens. Herein, we expand upon the current knowledge concerning antibiotics derived from human microbiota and their distribution across body sites. We analyze, using our in-house chem-bioinformatic tools and natural products database, the encoded antibiotic potential of the human microbiome. This compilation of information may create a foundation for the continued exploration of this intriguing resource of chemical diversity and expose challenges and future perspectives to accelerate the discovery rate of small molecules from the human microbiota.

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/.

Pseudomonas aeruginosa-Derived Rhamnolipids and Other Detergents Modulate Colony Morphotype and Motility in the Burkholderia cepacia Complex.

Competitive interactions mediated by released chemicals (e.g., toxins) are prominent in multispecies communities, but the effects of these chemicals at subinhibitory concentrations on susceptible bacteria are poorly understood. Although Pseudomonas aeruginosa and species of the Burkholderia cepacia complex (Bcc) can exist together as a coinfection in cystic fibrosis airways, P. aeruginosa toxins can kill Bcc species in vitro Consequently, these bacteria become an ideal in vitro model system to study the impact of sublethal levels of toxins on the biology of typical susceptible bacteria, such as the Bcc, when exposed to P. aeruginosa toxins. Using P. aeruginosa spent medium as a source of toxins, we showed that a small window of subinhibitory concentrations modulated the colony morphotype and swarming motility of some but not all tested Bcc strains, for which rhamnolipids were identified as the active molecule. Using a random transposon mutagenesis approach, we identified several genes required by the Bcc to respond to low concentrations of rhamnolipids and consequently affect the ability of this microbe to change its morphotype and swarm over surfaces. Among those genes identified were those coding for type IVb-Tad pili, which are often required for virulence in various bacterial pathogens. Our study demonstrates that manipulating chemical gradients in vitro can lead to the identification of bacterial behaviors relevant to polymicrobial infections.IMPORTANCE Interspecies interactions can have profound effects on the development and outcomes of polymicrobial infections. Consequently, improving the molecular understanding of these interactions could provide us with new insights on the possible long-term consequences of these chronic infections. In this study, we show that P. aeruginosa-derived rhamnolipids, which participate in Bcc killing at high concentrations, can also trigger biological responses in Burkholderia spp. at low concentrations. The modulation of potential virulence phenotypes in the Bcc by P. aeruginosa suggests that these interactions contribute to pathogenesis and disease severity in the context of polymicrobial infections.

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.

Cyanide Toxicity to Burkholderia cenocepacia Is Modulated by Polymicrobial Communities and Environmental Factors.

Microbes within polymicrobial communities can establish positive and negative interactions that have the potential to influence the overall behavior of the community. Pseudomonas aeruginosa and species of the Burkholderia cepacia complex (Bcc) can co-exist in the lower airways, however several studies have shown that P. aeruginosa can effectively kill the Bcc in vitro, for which hydrogen cyanide (HCN) was recently proposed to play a critical role. Here we show that modification of the environment (i.e., culture medium), long-term genetic adaptation of P. aeruginosa to the cystic fibrosis (CF) lung, or the addition of another bacterial species to the community can alter the sensitivity of Burkholderia cenocepacia to P. aeruginosa toxins. We specifically demonstrate that undefined rich media leads to higher susceptibility of B. cenocepacia to P. aeruginosa toxins like cyanide as compared to a synthetic medium (SCFM), that mimics the CF lung nutritional content. Overall, our study shows that the polymicrobial environment can have profound effects on negative interactions mediated by P. aeruginosa against B. cenocepacia. In fact, evolved P. aeruginosa or the presence of other species such as Staphylococcus aureus can directly abolish the direct competition mediated by cyanide and consequently maintaining a higher level of species diversity within the community.

Draft Genome Sequence of Streptomyces canus ATCC 12647, a Producer of Telomycin.

We present here the genome sequence ofStreptomyces canusATCC 12647, a producer of the antibiotic telomycin, noted for its unique antibacterial activity against cardiolipin. Genomic analysis using the bioinformatics tool PRISM revealed the presence of multiple biosynthetic gene clusters, including those for telomycin and other natural products of potential pharmacological interest.

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 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.

Molecular mechanisms of membrane targeting antibiotics.

The bacterial membrane provides a target for antimicrobial peptides. There are two groups of bacteria that have characteristically different surface membranes. One is the Gram-negative bacteria that have an outer membrane rich in lipopolysaccharide. Several antimicrobials have been found to inhibit the synthesis of this lipid, and it is expected that more will be developed. In addition, antimicrobial peptides can bind to the outer membrane of Gram-negative bacteria and block passage of solutes between the periplasm and the cell exterior, resulting in bacterial toxicity. In Gram-positive bacteria, the major bacterial lipid component, phosphatidylglycerol can be chemically modified by bacterial enzymes to convert the lipid from anionic to cationic or zwitterionic form. This process leads to increased levels of resistance of the bacteria against polycationic antimicrobial agents. Inhibitors of this enzyme would provide protection against the development of bacterial resistance. There are antimicrobial agents that directly target a component of bacterial cytoplasmic membranes that can act on both Gram-negative as well as Gram-positive bacteria. Many of these are cyclic peptides with a rigid binding site capable of binding a lipid component. This binding targets antimicrobial agents to bacteria, rather than being toxic to host cells. This article is part of a Special Issue entitled: Antimicrobial peptides edited by Karl Lohner and Kai Hilpert.

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.

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/.

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.