IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Recent developments and new directions in the use of natural products for the treatment of inflammatory bowel disease

BACKGROUND

Inflammatory bowel disease (IBD) represents a significant global health challenge, and there is an urgent need to explore novel therapeutic interventions. Natural products have demonstrated highly promising effectiveness in the treatment of IBD.

PURPOSE

This study systematically reviews the latest research advancements in leveraging natural products for IBD treatment.

METHODS

This manuscript strictly adheres to the PRISMA guidelines. Relevant literature on the effects of natural products on IBD was retrieved from the PubMed, Web of Science and Cochrane Library databases using the search terms "natural product," "inflammatory bowel disease," "colitis," "metagenomics", "target identification", "drug delivery systems", "polyphenols," "alkaloids," "terpenoids," and so on. The retrieved data were then systematically summarized and reviewed.

RESULTS

This review assessed the different effects of various natural products, such as polyphenols, alkaloids, terpenoids, quinones, and others, in the treatment of IBD. While these natural products offer promising avenues for IBD management, they also face challenges in terms of clinical translation and drug discovery. The advent of metagenomics, single-cell sequencing, target identification techniques, drug delivery systems, and other cutting-edge technologies heralds a new era in overcoming these challenges.

CONCLUSION

This paper provides an overview of current research progress in utilizing natural products for the treatment of IBD, exploring how contemporary technological innovations can aid in discovering and harnessing bioactive natural products for the treatment of IBD.

Multifactorial genetic control and magnesium levels govern the production of a Streptomyces antibiotic with unusual cell density dependence

Streptomyces bacteria are renowned both for their antibiotic production capabilities and for their cryptic metabolic potential. Their metabolic repertoire is subject to stringent genetic control, with many of the associated biosynthetic gene clusters being repressed by the conserved nucleoid-associated protein Lsr2. In an effort to stimulate new antibiotic production in wild Streptomyces isolates, we leveraged the activity of an Lsr2 knockdown construct and successfully enhanced antibiotic production in the wild Streptomyces isolate WAC07094. We determined that this new activity stemmed from increased levels of the angucycline-like family member saquayamycin. Saquayamycin has both antibiotic and anti-cancer activities, and intriguingly, beyond Lsr2-mediated repression, we found saquayamycin production was also suppressed at high density on solid or in liquid growth media; its levels were greatest in low-density cultures. This density-dependent control was exerted at the level of the cluster-situated regulatory gene sqnR and was mediated in part through the activity of the PhoRP two-component regulatory system, where deleting phoRP led to both constitutive antibiotic production and sqnR expression. This suggests that PhoP functions to repress the expression of sqnR at high cell density. We further discovered that magnesium supplementation could alleviate this density dependence, although its action was independent of PhoP. Finally, we revealed that the nitrogen-responsive regulators GlnR and AfsQ1 could relieve the repression exerted by Lsr2 and PhoP. Intriguingly, we found that this low density-dependent production of saquayamycin was not unique to WAC07094; saquayamycin production by another wild isolate also exhibited low-density activation, suggesting that this spatial control may serve an important ecological function in their native environments.IMPORTANCEStreptomyces specialized metabolic gene clusters are subject to complex regulation, and their products are frequently not observed under standard laboratory growth conditions. For the wild Streptomyces isolate WAC07094, production of the angucycline-family compound saquayamycin is subject to a unique constellation of control factors. Notably, it is produced primarily at low cell density, in contrast to the high cell density production typical of most antibiotics. This unusual density dependence is conserved in other saquayamycin producers and is driven by the pathway-specific regulator SqnR, whose expression is influenced by both nutritional and genetic elements. Collectively, this work provides new insights into an intricate regulatory system governing antibiotic production and indicates there may be benefits to including low-density cultures in antibiotic screening platforms.

Genetic toolbox for Photorhabdus and Xenorhabdus: pSEVA based heterologous expression systems and CRISPR/Cpf1 based genome editing for rapid natural product profiling

BACKGROUND

Bacteria of the genus Photorhabdus and Xenorhabdus are motile, Gram-negative bacteria that live in symbiosis with entomopathogenic nematodes. Due to their complex life cycle, they produce a large number of specialized metabolites (natural products) encoded in biosynthetic gene clusters (BGC). Genetic tools for Photorhabdus and Xenorhabdus have been rare and applicable to only a few strains. In the past, several tools have been developed for the activation of BGCs and the deletion of individual genes. However, these often have limited efficiency or are time consuming. Among the limitations, it is essential to have versatile expression systems and genome editing tools that could facilitate the practical work.

RESULTS

In the present study, we developed several expression vectors and a CRISPR-Cpf1 genome editing vector for genetic manipulations in Photorhabdus and Xenorhabdus using SEVA plasmids. The SEVA collection is based on modular vectors that allow exchangeability of different elements (e.g. origin of replication and antibiotic selection markers with the ability to insert desired sequences for different end applications). Initially, we tested different SEVA vectors containing the broad host range origins and three different resistance genes for kanamycin, gentamycin and chloramphenicol, respectively. We demonstrated that these vectors are replicative not only in well-known representatives, e.g. Photorhabdus laumondii TTO1, but also in other rarely described strains like Xenorhabdus sp. TS4. For our CRISPR/Cpf1-based system, we used the pSEVA231 backbone to delete not only small genes but also large parts of BGCs. Furthermore, we were able to activate and refactor BGCs to obtain high production titers of high value compounds such as safracin B, a semisynthetic precursor for the anti-cancer drug ET-743.

CONCLUSIONS

The results of this study provide new inducible expression vectors and a CRISPR/CPf1 encoding vector all based on the SEVA (Standard European Vector Architecture) collection, which can improve genetic manipulation and genome editing processes in Photorhabdus and Xenorhabdus.

ABC-HuMi: the Atlas of Biosynthetic Gene Clusters in the Human Microbiome

The human microbiome has emerged as a rich source of diverse and bioactive natural products, harboring immense potential for therapeutic applications. To facilitate systematic exploration and analysis of its biosynthetic landscape, we present ABC-HuMi: the Atlas of Biosynthetic Gene Clusters (BGCs) in the Human Microbiome. ABC-HuMi integrates data from major human microbiome sequence databases and provides an expansive repository of BGCs compared to the limited coverage offered by existing resources. Employing state-of-the-art BGC prediction and analysis tools, our database ensures accurate annotation and enhanced prediction capabilities. ABC-HuMi empowers researchers with advanced browsing, filtering, and search functionality, enabling efficient exploration of the resource. At present, ABC-HuMi boasts a catalog of 19 218 representative BGCs derived from the human gut, oral, skin, respiratory and urogenital systems. By capturing the intricate biosynthetic potential across diverse human body sites, our database fosters profound insights into the molecular repertoire encoded within the human microbiome and offers a comprehensive resource for the discovery and characterization of novel bioactive compounds. The database is freely accessible at https://www.ccb.uni-saarland.de/abc_humi/.

HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search

Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

CAGECAT: The CompArative GEne Cluster Analysis Toolbox for rapid search and visualisation of homologous gene clusters

BACKGROUND

Co-localized sets of genes that encode specialized functions are common across microbial genomes and occur in genomes of larger eukaryotes as well. Important examples include Biosynthetic Gene Clusters (BGCs) that produce specialized metabolites with medicinal, agricultural, and industrial value (e.g. antimicrobials). Comparative analysis of BGCs can aid in the discovery of novel metabolites by highlighting distribution and identifying variants in public genomes. Unfortunately, gene-cluster-level homology detection remains inaccessible, time-consuming and difficult to interpret.

RESULTS

The comparative gene cluster analysis toolbox (CAGECAT) is a rapid and user-friendly platform to mitigate difficulties in comparative analysis of whole gene clusters. The software provides homology searches and downstream analyses without the need for command-line or programming expertise. By leveraging remote BLAST databases, which always provide up-to-date results, CAGECAT can yield relevant matches that aid in the comparison, taxonomic distribution, or evolution of an unknown query. The service is extensible and interoperable and implements the cblaster and clinker pipelines to perform homology search, filtering, gene neighbourhood estimation, and dynamic visualisation of resulting variant BGCs. With the visualisation module, publication-quality figures can be customized directly from a web-browser, which greatly accelerates their interpretation via informative overlays to identify conserved genes in a BGC query.

CONCLUSION

Overall, CAGECAT is an extensible software that can be interfaced via a standard web-browser for whole region homology searches and comparison on continually updated genomes from NCBI. The public web server and installable docker image are open source and freely available without registration at: https://cagecat.bioinformatics.nl .

The IMG/M data management and analysis system v.7: content updates and new features

The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) at the Department of Energy (DOE) Joint Genome Institute (JGI) continues to provide support for users to perform comparative analysis of isolate and single cell genomes, metagenomes, and metatranscriptomes. In addition to datasets produced by the JGI, IMG v.7 also includes datasets imported from public sources such as NCBI Genbank, SRA, and the DOE National Microbiome Data Collaborative (NMDC), or submitted by external users. In the past couple years, we have continued our effort to help the user community by improving the annotation pipeline, upgrading the contents with new reference database versions, and adding new analysis functionalities such as advanced scaffold search, Average Nucleotide Identity (ANI) for high-quality metagenome bins, new cassette search, improved gene neighborhood display, and improvements to metatranscriptome data display and analysis. We also extended the collaboration and integration efforts with other DOE-funded projects such as NMDC and DOE Biology Knowledgebase (KBase).

Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery

Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.

Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics

Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy aging. Their molecular properties are validated through the use of recent high-throughput genomic, transcriptomic, and metabolomic tools and techniques. Together, these updated multi-omic tools have been used to study fungal metabolites structure and their mode of action on biological and cellular processes. Diverse groups of fungi produce different proteins and secondary metabolites, which possess tremendous biotechnological and pharmaceutical applications. Furthermore, its use and acceptability can be accelerated by adopting multi-omics, bioinformatics, and machine learning tools that generate a huge amount of molecular data. The integration of artificial intelligence and machine learning tools in the era of omics and big data has opened up a new outlook in both basic and applied researches in the area of nutraceuticals and functional food and nutrition. KEY POINTS: • Multi-omic tool helps in the identification of novel fungal metabolites • Intra-omic data from genomics to bioinformatics • Novel metabolites and application in human health.

Diversity and bioprospecting for industrial hydrolytic enzymes of microbial communities isolated from deserted areas of south-east Morocco

The current study aimed to analyze bacterial communities' diversity and abundance in three different deserted areas (Merzouga, Mhamid Elghizlane, and Erg lihoud) located in Moroccan Sahara, as well as to investigate osmotolerant microorganisms producing hydrolytic enzymes. The isolates were taxonomically affiliated using 16S rRNA gene sequencing. Four different hydrolase activities (amylase, lipase, cellulase, and protease) and osmotic stress tolerance were evaluated. The phylogenetic analysis of 364 screened isolates belonged to three phyla (Firmicutes 73%, Proteobacteria 26% and Actinobacteria 1%) and 18 different genera, from Bacillus, Ornithinibacillus, Paenibacillus, Geobacillus, Pseudomonas, Acinetobacter, Agrobacterium, Arthrobacter, Paenarthrobacter, Enterobacter, Staphylococcus, Erwinia, Herbasprillum, Ocuria, Massilia, Planomicrobium, Hodococcus, and Stenotrophomonas. The results detected a high proportion of osmotolerant and enzymes producing bacteria, many isolates can tolerate up to 55 °C (40%, 28%, and 30% in Merzouga, Mhamid Elghizlane, and Erg lihoudi, respectively). Meanwhile, the salinity tolerance reached 12% in some isolates with different proportions in each site, 29% in Merzouga, 24% in Mhamid Elghizlane, and 9% in Erg lihoudi. Furthermore, the enzymatic tests showed the presence of an amylolytic, lipolytic, cellulolytic, proteolytic activities in 20%, 31%, 63% and 72% of total strains, respectively.

As a result, the present study is thus a preliminary yet critical step towards identifying the best bacterial candidates for further biotechnological applications.

Genome mining for drug discovery: progress at the front end

Microbial genome mining for drug discovery and development has been accelerating in recent years, driven by technical advancements in genome sequencing, bioinformatics, metabolomics/metabologenomics, and synthetic biology. Microbial genome mining is a multistep process that starts with the sequencing of microbes that encode multiple secondary metabolites and identifying new and novel secondary metabolite biosynthetic gene clusters (BGCs) to pursue. The initial steps in the process are critical for the overall success, and they encompass the most innovative new technologies to revitalize natural product discovery. As microbial genome mining has matured in recent years, unvalidated conjectures about what microbes to pursue, how to identify legitimate secondary metabolite BGCs, and how to sequence DNA to satisfactory levels of completion have been identified. The solutions to correct the misconceptions around these topics are beginning to be implemented.

Phylogenetic analysis of the salinipostin γ-butyrolactone gene cluster uncovers new potential for bacterial signalling-molecule diversity

Bacteria communicate by small-molecule chemicals that facilitate intra- and inter-species interactions. These extracellular signalling molecules mediate diverse processes including virulence, bioluminescence, biofilm formation, motility and specialized metabolism. The signalling molecules produced by members of the phylum Actinobacteria generally comprise γ-butyrolactones, γ-butenolides and furans. The best-known actinomycete γ-butyrolactone is A-factor, which triggers specialized metabolism and morphological differentiation in the genus Streptomyces . Salinipostins A–K are unique γ-butyrolactone molecules with rare phosphotriester moieties that were recently characterized from the marine actinomycete genus Salinispora . The production of these compounds has been linked to the nine-gene biosynthetic gene cluster (BGC) spt . Critical to salinipostin assembly is the γ-butyrolactone synthase encoded by spt9 . Here, we report the surprising distribution of spt9 homologues across 12 bacterial phyla, the majority of which are not known to produce γ-butyrolactones. Further analyses uncovered a large group of spt -like gene clusters outside of the genus Salinispora , suggesting the production of new salinipostin-like diversity. These gene clusters show evidence of horizontal transfer and location-specific recombination among Salinispora strains. The results suggest that γ-butyrolactone production may be more widespread than previously recognized. The identification of new γ-butyrolactone BGCs is the first step towards understanding the regulatory roles of the encoded small molecules in Actinobacteria.

Recent Advances in Discovery of Lead Structures from Microbial Natural Products: Genomics- and Metabolomics-Guided Acceleration

Natural products (NPs) are evolutionarily optimized as drug-like molecules and remain the most consistently successful source of drugs and drug leads. They offer major opportunities for finding novel lead structures that are active against a broad spectrum of assay targets, particularly those from secondary metabolites of microbial origin. Due to traditional discovery approaches’ limitations relying on untargeted screening methods, there is a growing trend to employ unconventional secondary metabolomics techniques. Aided by the more in-depth understanding of different biosynthetic pathways and the technological advancement in analytical instrumentation, the development of new methodologies provides an alternative that can accelerate discoveries of new lead-structures of natural origin. This present mini-review briefly discusses selected examples regarding advancements in bioinformatics and genomics (focusing on genome mining and metagenomics approaches), as well as bioanalytics (mass-spectrometry) towards the microbial NPs-based drug discovery and development. The selected recent discoveries from 2015 to 2020 are featured herein.

Synthetic biology-inspired strategies and tools for engineering of microbial natural product biosynthetic pathways

Microbial-derived natural products (NPs) and their derivative products are of great importance and used widely in many fields, especially in pharmaceutical industries. However, there is an immediate need to establish innovative approaches, strategies, and techniques to discover new NPs with novel or enhanced biological properties, due to the less productivity and higher cost on traditional drug discovery pipelines from natural bioresources. Revealing of untapped microbial cryptic biosynthetic gene clusters (BGCs) using DNA sequencing technology and bioinformatics tools makes genome mining possible for NP discovery from microorganisms. Meanwhile, new approaches and strategies in the area of synthetic biology offer great potentials for generation of new NPs by engineering or creating synthetic systems with improved and desired functions. Development of approaches, strategies and tools in synthetic biology can facilitate not only exploration and enhancement in supply, and also in the structural diversification of NPs. Here, we discussed recent advances in synthetic biology-inspired strategies, including bioinformatics and genetic engineering tools and approaches for identification, cloning, editing/refactoring of candidate biosynthetic pathways, construction of heterologous expression hosts, fitness optimization between target pathways and hosts and detection of NP production.

Deep learning approaches for natural product discovery from plant endophytic microbiomes

Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich 'in planta' community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40793-021-00375-0.

Antibiotic resistance: Global health crisis and metagenomics

Antibiotic resistance is a global problem which affects human health. The imprudent use of antibiotics (medicine, agriculture, aquaculture, and food industry) has resulted in the broader dissemination of resistance. Urban wastewater & sewage treatment plants act as the hotspot for the widespread of antimicrobial resistance. Natural environment also plays an important role in the dissemination of resistance. Mapping of antibiotic resistance genes (ARGS) in environment is essential for mitigating antimicrobial resistance (AMR) widespread. Therefore, the review article emphasizes on the application of metagenomics for the surveillance of antimicrobial resistance. Metagenomics is the next generation tool which is being used for cataloging the resistome of diverse environments. We summarize the different metagenomic tools that can be used for mining of ARGs and acquired AMR present in the metagenomic data. Also, we recommend application of targeted sequencing/ capture platform for mapping of resistome with higher specificity and selectivity.

The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities

The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) contains annotated isolate genome and metagenome datasets sequenced at the DOE's Joint Genome Institute (JGI), submitted by external users, or imported from public sources such as NCBI. IMG v 6.0 includes advanced search functions and a new tool for statistical analysis of mixed sets of genomes and metagenome bins. The new IMG web user interface also has a new Help page with additional documentation and webinar tutorials to help users better understand how to use various IMG functions and tools for their research. New datasets have been processed with the prokaryotic annotation pipeline v.5, which includes extended protein family assignments.

New developments in RiPP discovery, enzymology and engineering

Covering: up to June 2020Ribosomally-synthesized and post-translationally modified peptides (RiPPs) are a large group of natural products. A community-driven review in 2013 described the emerging commonalities in the biosynthesis of RiPPs and the opportunities they offered for bioengineering and genome mining. Since then, the field has seen tremendous advances in understanding of the mechanisms by which nature assembles these compounds, in engineering their biosynthetic machinery for a wide range of applications, and in the discovery of entirely new RiPP families using bioinformatic tools developed specifically for this compound class. The First International Conference on RiPPs was held in 2019, and the meeting participants assembled the current review describing new developments since 2013. The review discusses the new classes of RiPPs that have been discovered, the advances in our understanding of the installation of both primary and secondary post-translational modifications, and the mechanisms by which the enzymes recognize the leader peptides in their substrates. In addition, genome mining tools used for RiPP discovery are discussed as well as various strategies for RiPP engineering. An outlook section presents directions for future research.

Expansion of Gamma-Butyrolactone Signaling Molecule Biosynthesis to Phosphotriester Natural Products

Bacterial hormones, such as the iconic gamma-butyrolactone A-factor, are essential signaling molecules that regulate diverse physiological processes, including specialized metabolism. These low molecular weight compounds are common in Streptomyces species and display species-specific structural differences. Recently, unusual gamma-butyrolactone natural products called salinipostins were isolated from the marine actinomycete genus Salinispora based on their antimalarial properties. As the salinipostins possess a rare phosphotriester motif of unknown biosynthetic origin, we set out to explore its construction by the widely conserved 9-gene spt operon in Salinispora species. We show through a series of in vivo and in vitro studies that the spt gene cluster dually encodes the salinipostins and newly identified natural A-factor-like gamma-butyrolactones (Sal-GBLs). Remarkably, homologous biosynthetic gene clusters are widely distributed among many actinomycete genera, including Streptomyces, suggesting the significance of this operon in bacteria.

Antimicrobial activity and biosynthetic potential of cultivable actinomycetes associated with Lichen symbiosis from Qinghai-Tibet Plateau

Actinobacteria that inhabit lichen symbionts are considered a promising yet previously underexplored source of novel compounds. Here, for the first time, we conducted a comprehensive investigation with regard to strain isolation and identification of lichen-associated actinobacteria from Tibet Plateau, antimicrobial activity screening, biosynthetic genes detection, bioactive metabolites identification and activity prediction. A large number of culturable actinomycetes were isolated from lichens around Qinghai Lake, in Qinghai-Tibet Plateau. Twenty-seven strains with distinct morphological characteristics were preliminarily studied. 16S rRNA gene identification showed that 13 strains were new species. The PCR-screening of specific biosynthetic genes indicated that these 27 isolates had abundant intrinsic biosynthetic potential. The antimicrobial activity experiment screened out some potential biological control antagonistic bacteria. The metabolites of 13 strains of Streptomyces with antibacterial activity were analyzed by LC-HRMS, and further 18 compounds were identified by NMR and / or LC-HRMS. The identified compounds were mainly pyrrolidine and indole derivatives, as well as anthracyclines. Seven compounds were identified with less biological activity, then predicted and evaluated their biological activity. The predicted results showed that compound 2 had excellent inhibitory activity on HIV-1 reverse transcriptase. Overall, the results indicate actinobacteria isolated from unexploited plateau lichen are promising sources of biological active metabolite, which could provide important bioactive compounds as potential antibiotic drugs.

Bioinformatic and Reactivity-Based Discovery of Linaridins

Linaridins are members of the ribosomally synthesized and post-translationally modified peptide (RiPP) family of natural products. Five linaridins have been reported, which are defined by the presence of dehydrobutyrine, a dehydrated, alkene-containing amino acid derived from threonine. This work describes the development of a linaridin-specific scoring module for Rapid ORF Description and Evaluation Online (RODEO), a genome-mining tool tailored toward RiPP discovery. Upon mining publicly accessible genomes available in the NCBI database, RODEO identified 561 (382 nonredundant) linaridin biosynthetic gene clusters. Linaridin BGCs with unique gene architectures and precursor sequences markedly different from previous predictions were uncovered during these efforts. To aid in data set validation, two new linaridins, pegvadin A and B, were detected through reactivity-based screening and isolated from Streptomyces noursei and Streptomyces auratus, respectively. Reactivity-based screening involves the use of a probe that chemoselectively modifies an organic functional group present in the natural product. The dehydrated amino acids present in linaridins as α/β-unsaturated carbonyls were appropriate electrophiles for nucleophilic 1,4-addition using a thiol-functionalized probe. The data presented within significantly expand the number of predicted linaridin biosynthetic gene clusters and serve as a roadmap for future work in the area. The combination of bioinformatics and reactivity-based screening is a powerful approach to accelerate natural product discovery.

Bacterial genome editing by coupling Cre-lox and CRISPR-Cas9 systems

The past decade has been a golden age for microbiology, marked by the discovery of an unprecedented increase in the number of novel bacterial species. Yet gaining biological knowledge of those organisms has not kept pace with sequencing efforts. To unlock this genetic potential there is an urgent need for generic (i.e. non-species specific) genetic toolboxes. Recently, we developed a method, termed chassis-independent recombinase-assisted genome engineering (CRAGE), enabling the integration and expression of large complex gene clusters directly into the chromosomes of diverse bacteria. Here we expand upon this technology by incorporating CRISPR-Cas9 allowing precise genome editing across multiple bacterial species. To do that we have developed a landing pad that carries one wild-type and two mutant lox sites to allow integration of foreign DNA at two locations through Cre-lox recombinase-mediated cassette exchange (RMCE). The first RMCE event is to integrate the Cas9 and the DNA repair protein genes RecET, and the second RMCE event enables the integration of customized sgRNA and a repair template. Following this workflow, we achieved precise genome editing in four different gammaproteobacterial species. We also show that the inserted landing pad and the entire editing machinery can be removed scarlessly after editing. We report here the construction of a single landing pad transposon and demonstrate its functionality across multiple species. The modular design of the landing pad and accessory vectors allows design and assembly of genome editing platforms for other organisms in a similar way. We believe this approach will greatly expand the list of bacteria amenable to genetic manipulation and provides the means to advance our understanding of the microbial world.

Environmental and Intestinal Phylum Firmicutes Bacteria Metabolize the Plant Sugar Sulfoquinovose via a 6-Deoxy-6-sulfofructose Transaldolase Pathway

Bacterial degradation of the sugar sulfoquinovose (SQ, 6-deoxy-6-sulfoglucose) produced by plants, algae, and cyanobacteria, is an important component of the biogeochemical carbon and sulfur cycles. Here, we reveal a third biochemical pathway for primary SQ degradation in an aerobic Bacillus aryabhattai strain. An isomerase converts SQ to 6-deoxy-6-sulfofructose (SF). A novel transaldolase enzyme cleaves the SF to 3-sulfolactaldehyde (SLA), while the non-sulfonated C3-(glycerone)-moiety is transferred to an acceptor molecule, glyceraldehyde phosphate (GAP), yielding fructose-6-phosphate (F6P). Intestinal anaerobic bacteria such as Enterococcus gilvus, Clostridium symbiosum, and Eubacterium rectale strains also express transaldolase pathway gene clusters during fermentative growth with SQ. The now three known biochemical strategies for SQ catabolism reflect adaptations to the aerobic or anaerobic lifestyle of the different bacteria. The occurrence of these pathways in intestinal (family) Enterobacteriaceae and (phylum) Firmicutes strains further highlights a potential importance of metabolism of green-diet SQ by gut microbial communities to, ultimately, hydrogen sulfide.

A Versatile Transcription-Translation in One Approach for Activation of Cryptic Biosynthetic Gene Clusters

The ever-growing drug resistance problem worldwide highlights the urgency to discover and develop new drugs. Microbial natural products are a prolific source of drugs. Genome sequencing has revealed a tremendous amount of uncharacterized natural product biosynthetic gene clusters (BGCs) encoded within microbial genomes, most of which are cryptic or express at very low levels under standard culture conditions. Therefore, developing effective strategies to awaken these cryptic BGCs is of great interest for natural product discovery. In this study, we designed and validated a Transcription-Translation in One (TTO) approach for activation of cryptic BGCs. This approach aims to alter the metabolite profiles of target strains by directly overexpressing exogenous rpsL (encoding ribosomal protein S12) and rpoB (encoding RNA polymerase β subunit) genes containing beneficial mutations for natural product production using a plug-and-play plasmid system. As a result, this approach bypasses the tedious screening work and overcomes the false positive problem in the traditional ribosome engineering approach. In this work, the TTO approach was successfully applied to activating cryptic BGCs in three Streptomyces strains, leading to the discovery of two aromatic polyketide antibiotics, piloquinone and homopiloquinone. We further identified a single BGC responsible for the biosynthesis of both piloquinone and homopiloquinone, which features an unusual starter unit incorporation step. This powerful strategy can be further exploited for BGC activation in strains even beyond streptomycetes, thus facilitating natural product discovery research in the future.

IMG-ABC v.5.0: an update to the IMG/Atlas of Biosynthetic Gene Clusters Knowledgebase

Microbial secondary metabolism is a reservoir of bioactive compounds of immense biotechnological and biomedical potential. The biosynthetic machinery responsible for the production of these secondary metabolites (SMs) (also called natural products) is often encoded by collocated groups of genes called biosynthetic gene clusters (BGCs). High-throughput genome sequencing of both isolates and metagenomic samples combined with the development of specialized computational workflows is enabling systematic identification of BGCs and the discovery of novel SMs. In order to advance exploration of microbial secondary metabolism and its diversity, we developed the largest publicly available database of predicted BGCs combined with experimentally verified BGCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc-public). Here we describe the first major content update of the IMG-ABC knowledgebase, since its initial release in 2015, refreshing the BGC prediction pipeline with the latest version of antiSMASH (v5) as well as presenting the data in the context of underlying environmental metadata sourced from GOLD (https://gold.jgi.doe.gov/). This update has greatly improved the quality and expanded the types of predicted BGCs compared to the previous version.

MIBiG 2.0: a repository for biosynthetic gene clusters of known function

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.

IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes

The Integrated Microbial Genomes & Microbiomes system v.5.0 (IMG/M: https://img.jgi.doe.gov/m/) contains annotated datasets categorized into: archaea, bacteria, eukarya, plasmids, viruses, genome fragments, metagenomes, cell enrichments, single particle sorts, and metatranscriptomes. Source datasets include those generated by the DOE's Joint Genome Institute (JGI), submitted by external scientists, or collected from public sequence data archives such as NCBI. All submissions are typically processed through the IMG annotation pipeline and then loaded into the IMG data warehouse. IMG's web user interface provides a variety of analytical and visualization tools for comparative analysis of isolate genomes and metagenomes in IMG. IMG/M allows open access to all public genomes in the IMG data warehouse, while its expert review (ER) system (IMG/MER: https://img.jgi.doe.gov/mer/) allows registered users to access their private genomes and to store their private datasets in workspace for sharing and for further analysis. IMG/M data content has grown by 60% since the last report published in the 2017 NAR Database Issue. IMG/M v.5.0 has a new and more powerful genome search feature, new statistical tools, and supports metagenome binning.

Genome Mining as New Challenge in Natural Products Discovery

Drug discovery is based on bioactivity screening of natural sources, traditionally represented by bacteria fungi and plants. Bioactive natural products and their secondary metabolites have represented the main source for new therapeutic agents, used as drug leads for new antibiotics and anticancer agents. After the discovery of the first biosynthetic genes in the last decades, the researchers had in their hands the tool to understand the biosynthetic logic and genetic basis leading to the production of these compounds. Furthermore, in the genomic era, in which the number of available genomes is increasing, genome mining joined to synthetic biology are offering a significant help in drug discovery. In the present review we discuss the importance of genome mining and synthetic biology approaches to identify new natural products, also underlining considering the possible advantages and disadvantages of this technique. Moreover, we debate the associated techniques that can be applied following to genome mining for validation of data. Finally, we review on the literature describing all novel natural drugs isolated from bacteria, fungi, and other living organisms, not only from the marine environment, by a genome-mining approach, focusing on the literature available in the last ten years.

Targeting Bacterial Genomes for Natural Product Discovery

Bacterial natural products (NPs) and their analogs constitute more than half of the new small molecule drugs developed over the past few decades. Despite this success, interest in natural products from major pharmaceutical companies has decreased even as genomics has uncovered the large number of biosynthetic gene clusters (BGCs) that encode for novel natural products. To date, there is still a lack of universal strategies and enabling technologies to discover natural products at scale and speed. This review highlights several of the opportunities provided by genome sequencing and bioinformatics, challenges associated with translating genomes into natural products, and examples of successful strain prioritization and BGC activation strategies that have been used in the genomic era for natural product discovery from cultivatable bacteria.

Exploration and genome mining of natural products from marine Streptomyces

Marine Streptomyces sp. are an important source of bioactive compounds owing to their unique habitats and metabolic pathways. Whole-genome sequencing and bioinformatics analyses have shown that the potential of synthesizing secondary metabolites from marine-derived Streptomyces has been substantially underestimated. Genome mining is an integrated strategy used to discover natural products based on gene cluster sequences and biosynthetic pathways. Its emergence has greatly enhanced the discovery of natural compounds from marine Streptomyces, thereby yielding a large number of bioactive molecules with novel structures and potent activities. In this review, we briefly summarize the current applications of genome mining in marine Streptomyces, such as bioinformatics-based optimization of culture conditions, ribosome engineering, control of regulatory networks, heterologous expression of biosynthetic gene cluster, and combinatorial biosynthesis of natural compounds. Furthermore, we discuss the factors hindering the utilization of marine-derived natural products and conclude with the prospects for this technique.

Natural products discovery and potential for new antibiotics

Microbial natural products have been one of the most important sources for the discovery of potential new antibiotics. However, the decline in the number of new chemical scaffolds discovered and the rediscovery problem of old known molecules has become a limitation for discovery programs developed by an industry confronted by a lack of incentives and a broken economic model. In contrast, the emergence of multidrug resistance in key pathogens has continued to progress and this issue is compounded by a lack of new antibiotics in development to address most of the difficult to treat infections. Advances in genome mining have confirmed the richness of biosynthetic gene clusters (BGCs) in the majority of microbial sources, and this suggests that an untapped chemical diversity is waiting to be discovered. The development of new genome engineering and synthetic biology tools, and the implementation of comparative omic approaches is fostering the development of new integrated culture-based strategies and genomic-driven approaches aimed at delivering new chemical classes of antibiotics.

Loci Encoding Compounds Potentially Active against Drug-Resistant Pathogens amidst a Decreasing Pool of Novel Antibiotics

Carbapenem-resistant P. aeruginosa is difficult to treat and has been deemed by the World Health Organization as a priority one pathogen for which antibiotics are most urgently needed. Although metagenomics and bioinformatic studies suggest that natural bacteria remain a source of novel compounds, the identification of genes and their products specific to activity against MDR pathogens remains problematic. Here, we examine water-derived pseudomonads and identify gene clusters whose compounds inhibit CF-derived MDR pathogens, including carbapenem-resistant P. aeruginosa.

Since the discovery of penicillin, microbes have been a source of antibiotics that inhibit the growth of pathogens. However, with the evolution of multidrug-resistant (MDR) strains, it remains unclear if there is an abundant or limited supply of natural products to be discovered that are effective against MDR isolates. To identify strains that are antagonistic to pathogens, we examined a set of 471 globally derived environmental Pseudomonas strains (env-Ps) for activity against a panel of 65 pathogens including Achromobacter spp., Burkholderia spp., Pseudomonas aeruginosa, and Stenotrophomonas spp. isolated from the lungs of cystic fibrosis (CF) patients. From more than 30,000 competitive interactions, 1,530 individual inhibitory events were observed. While strains from water habitats were not proportionate in antagonistic activity, MDR CF-derived pathogens (CF-Ps) were less susceptible to inhibition by env-Ps, suggesting that fewer natural products are effective against MDR strains. These results advocate for a directed strategy to identify unique drugs. To facilitate discovery of antibiotics against the most resistant pathogens, we developed a workflow in which phylogenetic and antagonistic data were merged to identify strains that inhibit MDR CF-Ps and subjected those env-Ps to transposon mutagenesis. Six different biosynthetic gene clusters (BGCs) were identified from four strains whose products inhibited pathogens including carbapenem-resistant P. aeruginosa. BGCs were rare in databases, suggesting the production of novel antibiotics. This strategy can be utilized to facilitate the discovery of needed antibiotics that are potentially active against the most drug-resistant pathogens.

IMPORTANCE Carbapenem-resistant P. aeruginosa is difficult to treat and has been deemed by the World Health Organization as a priority one pathogen for which antibiotics are most urgently needed. Although metagenomics and bioinformatic studies suggest that natural bacteria remain a source of novel compounds, the identification of genes and their products specific to activity against MDR pathogens remains problematic. Here, we examine water-derived pseudomonads and identify gene clusters whose compounds inhibit CF-derived MDR pathogens, including carbapenem-resistant P. aeruginosa.

Leveraging a large microbial strain collection for natural product discovery

Throughout history, natural products have significantly contributed to the discovery of novel chemistry, drug leads, and tool molecules to probe and address complex challenges in biology and medicine. Recent microbial genome sequencing efforts have uncovered many microbial biosynthetic gene clusters without an associated natural product. This means that the natural products isolated to date do not fully reflect the biosynthetic potential of microbial strains. This observation has rejuvenated the natural product community and inspired a return to microbial strain collections. Mining large microbial strain collections with the most current technologies in genome sequencing, bioinformatics, and high-throughput screening techniques presents new opportunities in natural product discovery. In this review, we report on the newly expanded microbial strain collection at The Scripps Research Institute, which represents one of the largest and most diverse strain collections in the world. Two complementary approaches, i.e. structure-centric and function-centric, are presented here to showcase how to leverage a large microbial strain collection for natural product discovery and to address challenges and harness opportunities for future efforts. Highlighted examples include the discovery of alternative producers of known natural products with superior growth characteristics and high titers, novel analogs of privileged scaffolds, novel natural products, and new activities of known and new natural products. We anticipate that this large microbial strain collection will facilitate the discovery of new natural products for many applications.

Next-Generation Drug Discovery to Combat Antimicrobial Resistance

The widespread emergence of antibiotic-resistant pathogens poses a severe threat to public health. This problem becomes even worse with a coincident decline in the supply of new antibiotics. Conventional bioactivity-guided natural product discovery has failed to meet the urgent need for new antibiotics, largely due to limited resources and high rediscovery rates. Recent advances in cultivation techniques, analytical technologies, and genomics-based approaches have greatly expanded our access to previously underexploited microbial sources. These strategies will enable us to access new reservoirs of microorganisms and unleash their chemical potentials, thus opening new opportunities for the discovery of next-generation drugs to address the growing concerns of antimicrobial resistance.

A Comparative Analysis of Biosynthetic Gene Clusters in Lean and Obese Humans

Obesity is intrinsically linked with the gut microbiome, and studies have identified several obesity-associated microbes. The microbe-microbe interactions can alter the composition of the microbial community and influence host health by producing secondary metabolites (SMs). However, the contribution of these SMs in the prevention and treatment of obesity has been largely ignored. We identified several SM-encoding biosynthetic gene clusters (BGCs) from the metagenomic data of lean and obese individuals and found significant association between some BGCs, including those that produce hitherto unknown SM, and obesity. In addition, the mean abundance of BGCs was positively correlated with obesity, consistent with the lower taxonomic diversity in the gut microbiota of obese individuals. By comparing the BGCs of known SM between obese and nonobese samples, we found that menaquinone produced by Enterobacter cloacae showed the highest correlation with BMI, in agreement with a recent study on human adipose tissue composition. Furthermore, an obesity-related nonribosomal peptide synthetase (NRPS) was negatively associated with Bacteroidetes, indicating that the SMs produced by intestinal microbes in obese individuals can change the microbiome structure. This is the first systemic study of the association between gut microbiome BGCs and obesity and provides new insights into the causes of obesity.

Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters

The biosynthetic machinery responsible for the production of bacterial specialised metabolites is encoded by physically clustered group of genes called biosynthetic gene clusters (BGCs). The experimental characterisation of numerous BGCs has led to the elucidation of subclusters of genes within BGCs, jointly responsible for the same biosynthetic function in different genetic contexts. We developed an unsupervised statistical method able to successfully detect a large number of modules (putative functional subclusters) within an extensive set of predicted BGCs in a systematic and automated manner. Multiple already known subclusters were confirmed by our method, proving its efficiency and sensitivity. In addition, the resulting large collection of newly defined modules provides new insights into the prevalence and putative biosynthetic role of these modular genetic entities. The automated and unbiased identification of hundreds of co-evolving group of genes is an essential breakthrough for the discovery and biosynthetic engineering of high-value compounds.

New voyages to explore the natural product galaxy

Natural products are a large family of diverse and complex chemical molecules that have roles in both primary and secondary metabolism, and over 210,000 natural products have been described. Secondary metabolite natural products are of high commercial and societal value with therapeutic uses as antibiotics, antifungals, antitumor and antiparasitic products and in agriculture as products for crop protection and animal health. There is a resurgence of activity in exploring natural products for a wide range of applications, due to not only increasing antibiotic resistance, but the advent of next-generation genome sequencing and new technologies to interrogate and investigate natural product biosynthesis. Genome mining has revealed a previously undiscovered richness of biosynthetic potential in novel biosynthetic gene clusters for natural products. Complementing these computational processes are new experimental platforms that are being developed and deployed to access new natural products.

Sequence-based classification of type II polyketide synthase biosynthetic gene clusters for antiSMASH

The software antiSMASH examines microbial genome data to identify and analyze biosynthetic gene clusters for a wide range of natural products. So far, type II polyketide synthase (PKS) gene clusters could only be identified, but no detailed predictions for type II PKS gene clusters could be provided. In this study, an antiSMASH module for analyzing type II PKS gene clusters has been developed. The module detects genes/proteins in the type II PKS gene cluster involved with polyketide biosynthesis and is able to make predictions about the aromatic polyketide product. Predictions include the putative starter unit, the number of malonyl elongations during polyketide biosynthesis, the putative class and the molecular weight of the product. Furthermore, putative cyclization patterns are predicted. The accuracy of the predictions generated with the new PKSII antiSMASH module was evaluated using a leave-one-out cross validation. The prediction module is available in antiSMASH version 5 at https://antismash.secondarymetabolites.org .

Applied evolution: phylogeny-based approaches in natural products research

Here we highlight how phylogenetic analyses can be used to facilitate natural product discovery and structure elucidation.

Tandem mass spectrometry methods to accelerate the identification of phytotoxic metabolites produced by Streptomyces sp. 39 PL

Natural products isolated by microorganisms are interesting in the search for new compounds with several biological activities. However, low concentration and structural diversity make the isolation a time-consuming step. Tandem mass spectrometry is a well-established technology for the identification and characterization of target microbial natural products due to high sensitivity and selectivity of these experiments. We developed a method employing neutral loss experiments (LC-ESI-MS/MS) to identify luminacins in microbial crude extracts. The luminacins class exhibited conserved fragmentation pattern with loss at 172 Da relative to glycosides fragment and this loss was used in searching for compounds belonging to this class. Therefore, the crude extract produced by Streptomyces sp. 39 PL was analysed and five luminacins were isolated - one is a novel luminacin I at 466 Da.

Genome Sequences of Actinobacteria from Extreme Environments in Colombia

We sequenced six actinobacterial genomes isolated from a salt mine and from soil in a high-mountain Páramo ecosystem. The strains belonged to the genera Streptomyces, Nesterenkonia, and Isoptericola and were sequenced due to their antimicrobial and cytotoxic activities.

We sequenced six actinobacterial genomes isolated from a salt mine and from soil in a high-mountain Páramo ecosystem. The strains belonged to the genera Streptomyces, Nesterenkonia, and Isoptericola and were sequenced due to their antimicrobial and cytotoxic activities.

Aquatic Pseudomonads Inhibit Oomycete Plant Pathogens of Glycine max

Seedling root rot of soybeans caused by the host-specific pathogen Phytophthora sojae, and a large number of Pythium species, is an economically important disease across the Midwest United States that negatively impacts soybean yields. Research on biocontrol strategies for crop pathogens has focused on compounds produced by microbes from soil, however, recent studies suggest that aquatic bacteria express distinct compounds that efficiently inhibit a wide range of pathogens. Based on these observations, we hypothesized that freshwater strains of pseudomonads might be producing novel antagonistic compounds that inhibit the growth of oomycetes. To test this prediction, we utilized a collection of 330 Pseudomonas strains isolated from soil and freshwater habitats, and determined their activity against a panel of five oomycetes: Phytophthora sojae, Pythium heterothalicum, Pythium irregulare, Pythium sylvaticum, and Pythium ultimum, all of which are pathogenic on soybeans. Among the bacterial strains, 118 exhibited antagonistic activity against at least one oomycete species, and 16 strains were inhibitory to all pathogens. Antagonistic activity toward oomycetes was significantly more common for aquatic isolates than for soil isolates. One water-derived strain, 06C 126, was predicted to express a siderophore and exhibited diverse antagonistic profiles when tested on nutrient rich and iron depleted media suggesting that more than one compound was produced that effectively inhibited oomycetes. These results support the concept that aquatic strains are an efficient source of compounds that inhibit pathogens. We outline a strategy to identify other strains that express unique compounds that may be useful biocontrol agents.

Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms

Natural products produced by microorganisms represent the main source of bioactive molecules. The development of high-throughput (omics) techniques have importantly contributed to the renaissance of new antibiotic discovery increasing our understanding of complex mechanisms controlling the expression of biosynthetic gene clusters (BGCs) encoding secondary metabolites. In this context this review highlights recent progress in the use and integration of 'omics' approaches with focuses on genomics, transcriptomics, proteomics metabolomics meta-omics and combined omics as powerful strategy to discover new antibiotics.

Increased diversity of peptidic natural products revealed by modification-tolerant database search of mass spectra

Peptidic natural products (PNPs) include many antibiotics and other bioactive compounds. While the recent launch of the Global Natural Products Social (GNPS) molecular networking infrastructure is transforming PNP discovery into a high-throughput technology, PNP identification algorithms are needed to realize the potential of the GNPS project. GNPS relies on the assumption that each connected component of a molecular network (representing related metabolites) illuminates the 'dark matter of metabolomics' as long as it contains a known metabolite present in a database. We reveal a surprising diversity of PNPs produced by related bacteria and show that, contrary to the 'comparative metabolomics' assumption, two related bacteria are unlikely to produce identical PNPs (even though they are likely to produce similar PNPs). Since this observation undermines the utility of GNPS, we developed a PNP identification tool, VarQuest, that illuminates the connected components in a molecular network even if they do not contain known PNPs and only contain their variants. VarQuest reveals an order of magnitude more PNP variants than all previous PNP discovery efforts and demonstrates that GNPS already contains spectra from 41% of the currently known PNP families. The enormous diversity of PNPs suggests that biosynthetic gene clusters in various microorganisms constantly evolve to generate a unique spectrum of PNP variants that differ from PNPs in other species.