The antiSMASH database version 4: additional genomes and BGCs, new sequence-based searches and more.

Many microorganisms produce natural products that are frequently used in the development of medicines and crop protection agents. Genome mining has evolved into a prominent method to access this potential. antiSMASH is the most popular tool for this task. Here we present version 4 of the antiSMASH database, providing biosynthetic gene clusters detected by antiSMASH 7.1 in publicly available, dereplicated, high-quality microbial genomes via an interactive graphical user interface. In version 4, the database contains 231 534 high quality BGC regions from 592 archaeal, 35 726 bacterial and 236 fungal genomes and is available at https://antismash-db.secondarymetabolites.org/.

BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets.

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as: (i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge from the ever-growing genomic datasets of biotechnologically relevant bacterial species.

A treasure trove of 1034 actinomycete genomes.

Filamentous Actinobacteria, recently renamed Actinomycetia, are the most prolific source of microbial bioactive natural products. Studies on biosynthetic gene clusters benefit from or require chromosome-level assemblies. Here, we provide DNA sequences from >1000 isolates: 881 complete genomes and 153 near-complete genomes, representing 28 genera and 389 species, including 244 likely novel species. All genomes are from filamentous isolates of the class Actinomycetia from the NBC culture collection. The largest genus is Streptomyces with 886 genomes including 742 complete assemblies. We use this data to show that analysis of complete genomes can bring biological understanding not previously derived from more fragmented sequences or less systematic datasets. We document the central and structured location of core genes and distal location of specialized metabolite biosynthetic gene clusters and duplicate core genes on the linear Streptomyces chromosome, and analyze the content and length of the terminal inverted repeats which are characteristic for Streptomyces. We then analyze the diversity of trans-AT polyketide synthase biosynthetic gene clusters, which encodes the machinery of a biotechnologically highly interesting compound class. These insights have both ecological and biotechnological implications in understanding the importance of high quality genomic resources and the complex role synteny plays in Actinomycetia biology.

teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation.

Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

Pepticinnamins N, O, and P, Nonribosomal Peptides from the Soil-Derived Streptomyces mirabilis P8-A2.

Cinnamoyl moiety containing nonribosomal peptides represented by pepticinnamin E are a growing family of natural products isolated from different Streptomyces species and possess diverse bioactivities. The soil bacterium Streptomyces mirabilis P8-A2 harbors a cryptic pepticinnamin biosynthetic gene cluster, producing azodyrecins as major products. Inactivation of the azodyrecin biosynthetic gene cluster by CRISPR-BEST base editing led to the activation and production of pepticinnamin E (1) and its analogues, pepticinnamins N, O, and P (2-4), the structures of which were determined by detailed NMR spectroscopy, HRMS data, and Marfey's reactions. These new compounds did not show a growth inhibitory effect against the LNCaP and C4-2B prostate cancer lines, respectively.

Genome-Driven Discovery of Hygrocins in Streptomyces rapamycinicus.

Ansamycins, represented by the antituberculosis drug rifamycin, are an important family of natural products. To obtain new ansamycins, Streptomyces rapamycinicus IMET 43975 harboring an ansamycin biosynthetic gene cluster was fermented in a 50 L scale, and subsequent purification work led to the isolation of five known and four new analogues, where hygrocin W (2) belongs to benzoquinonoid ansamycins, and the other three hygrocins, hygrocins X-Z (6-8), are new seco-hygrocins. The structures of ansamycins (1-8) were determined by the analysis of spectroscopic (1D/2D NMR and ECD) and MS spectrometric data. The Baeyer-Villiger enzyme which catalyzed the ester formation in the ansa-ring was confirmed through in vivo CRISPR base editing. The discovery of these compounds further enriches the structural diversity of ansamycins.

The antiSMASH database version 3: increased taxonomic coverage and new query features for modular enzymes.

Microorganisms produce natural products that are frequently used in the development of antibacterial, antiviral, and anticancer drugs, pesticides, herbicides, or fungicides. In recent years, genome mining has evolved into a prominent method to access this potential. antiSMASH is one of the most popular tools for this task. Here, we present version 3 of the antiSMASH database, providing a means to access and query precomputed antiSMASH-5.2-detected biosynthetic gene clusters from representative, publicly available, high-quality microbial genomes via an interactive graphical user interface. In version 3, the database contains 147 517 high quality BGC regions from 388 archaeal, 25 236 bacterial and 177 fungal genomes and is available at https://antismash-db.secondarymetabolites.org/.

BiG-FAM: the biosynthetic gene cluster families database.

Computational analysis of biosynthetic gene clusters (BGCs) has revolutionized natural product discovery by enabling the rapid investigation of secondary metabolic potential within microbial genome sequences. Grouping homologous BGCs into Gene Cluster Families (GCFs) facilitates mapping their architectural and taxonomic diversity and provides insights into the novelty of putative BGCs, through dereplication with BGCs of known function. While multiple databases exist for exploring BGCs from publicly available data, no public resources exist that focus on GCF relationships. Here, we present BiG-FAM, a database of 29,955 GCFs capturing the global diversity of 1,225,071 BGCs predicted from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs). The database offers rich functionalities, such as multi-criterion GCF searches, direct links to BGC databases such as antiSMASH-DB, and rapid GCF annotation of user-supplied BGCs from antiSMASH results. BiG-FAM can be accessed online at https://bigfam.bioinformatics.nl.

antiSMASH 6.0: improving cluster detection and comparison capabilities.

Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the "antibiotics and secondary metabolite analysis shell-antiSMASH" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters.

High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917.

BACKGROUND: Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation. RESULTS: An updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways (enterobactin, salmochelins, aerobactin, yersiniabactin, and colibactin). The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published 13C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites. CONCLUSION: iHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts.

ARTS 2.0: feature updates and expansion of the Antibiotic Resistant Target Seeker for comparative genome mining.

Multi-drug resistant pathogens have become a major threat to human health and new antibiotics are urgently needed. Most antibiotics are derived from secondary metabolites produced by bacteria. In order to avoid suicide, these bacteria usually encode resistance genes, in some cases within the biosynthetic gene cluster (BGC) of the respective antibiotic compound. Modern genome mining tools enable researchers to computationally detect and predict BGCs that encode the biosynthesis of secondary metabolites. The major challenge now is the prioritization of the most promising BGCs encoding antibiotics with novel modes of action. A recently developed target-directed genome mining approach allows researchers to predict the mode of action of the encoded compound of an uncharacterized BGC based on the presence of resistant target genes. In 2017, we introduced the 'Antibiotic Resistant Target Seeker' (ARTS). ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets by rapidly linking housekeeping and known resistance genes to BGC proximity, duplication and horizontal gene transfer (HGT) events. Here, we present ARTS 2.0 available at http://arts.ziemertlab.com. ARTS 2.0 now includes options for automated target directed genome mining in all bacterial taxa as well as metagenomic data. Furthermore, it enables comparison of similar BGCs from different genomes and their putative resistance genes.

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/.

Highly efficient DSB-free base editing for streptomycetes with CRISPR-BEST.

Streptomycetes serve as major producers of various pharmacologically and industrially important natural products. Although CRISPR-Cas9 systems have been developed for more robust genetic manipulations, concerns of genome instability caused by the DNA double-strand breaks (DSBs) and the toxicity of Cas9 remain. To overcome these limitations, here we report development of the DSB-free, single-nucleotide-resolution genome editing system CRISPR-BEST (CRISPR-Base Editing SysTem), which comprises a cytidine (CRISPR-cBEST) and an adenosine (CRISPR-aBEST) deaminase-based base editor. Specifically targeted by an sgRNA, CRISPR-cBEST can efficiently convert a C:G base pair to a T:A base pair and CRISPR-aBEST can convert an A:T base pair to a G:C base pair within a window of approximately 7 and 6 nucleotides, respectively. CRISPR-BEST was validated and successfully used in different Streptomyces species. Particularly in nonmodel actinomycete Streptomyces collinus Tü365, CRISPR-cBEST efficiently inactivated the 2 copies of kirN gene that are in the duplicated kirromycin biosynthetic pathways simultaneously by STOP codon introduction. Generating such a knockout mutant repeatedly failed using the conventional DSB-based CRISPR-Cas9. An unbiased, genome-wide off-target evaluation indicates the high fidelity and applicability of CRISPR-BEST. Furthermore, the system supports multiplexed editing with a single plasmid by providing a Csy4-based sgRNA processing machinery. To simplify the protospacer identification process, we also updated the CRISPy-web (https://crispy.secondarymetabolites.org), and now it allows designing sgRNAs specifically for CRISPR-BEST applications.

Antitumor astins originate from the fungal endophyte Cyanodermella asteris living within the medicinal plant Aster tataricus.

Medicinal plants are a prolific source of natural products with remarkable chemical and biological properties, many of which have considerable remedial benefits. Numerous medicinal plants are suffering from wildcrafting, and thus biotechnological production processes of their natural products are urgently needed. The plant Aster tataricus is widely used in traditional Chinese medicine and contains unique active ingredients named astins. These are macrocyclic peptides showing promising antitumor activities and usually containing the highly unusual moiety 3,4-dichloroproline. The biosynthetic origins of astins are unknown despite being studied for decades. Here we show that astins are produced by the recently discovered fungal endophyte Cyanodermella asteris. We were able to produce astins in reasonable and reproducible amounts using axenic cultures of the endophyte. We identified the biosynthetic gene cluster responsible for astin biosynthesis in the genome of C. asteris and propose a production pathway that is based on a nonribosomal peptide synthetase. Striking differences in the production profiles of endophyte and host plant imply a symbiotic cross-species biosynthesis pathway for astin C derivatives, in which plant enzymes or plant signals are required to trigger the synthesis of plant-exclusive variants such as astin A. Our findings lay the foundation for the sustainable biotechnological production of astins independent from aster plants.

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters.

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

Distribution of ε-Poly-l-Lysine Synthetases in Coryneform Bacteria Isolated from Cheese and Human Skin.

ε-Poly-l-lysine is a potent antimicrobial produced through fermentation of Streptomyces and used in many Asian countries as a food preservative. It is synthesized and excreted by a special nonribosomal peptide synthetase (NRPS)-like enzyme called Pls. In this study, we discovered a gene from cheese bacterium Corynebacterium variabile that showed high similarity to the Pls from Streptomyces in terms of domain architecture and gene context. By cloning it into Streptomyces coelicolor with a Streptomyces albulus Pls promoter, we confirmed that its product is indeed ε-poly-l-lysine. A comprehensive sequence analysis suggested that Pls genes are widely spread among coryneform actinobacteria isolated from cheese and human skin; 14 out of 15 Brevibacterium isolates and 10 out of 12 Corynebacterium isolates contain it in their genomes. This finding raises the possibility that ε-poly-l-lysine as a bioactive secondary metabolite might be produced and play a role in the cheese and skin ecosystems.IMPORTANCE Every year, microbial contamination causes billions of tons of food wasted and millions of cases of illness. ε-Poly-l-lysine has potent, wide-spectrum inhibitory activity and is heat stable and biodegradable. It has been approved for food preservation by an increasing number of countries. ε-Poly-l-lysine is produced from soil bacteria of the genus Streptomyces, also producers of various antibiotic drugs and toxins and not considered to be a naturally occurring food component. The frequent finding of pls in cheese and skin bacteria suggests that ε-poly-l-lysine may naturally exist in cheese and on our skin, and ε-poly-l-lysine producers are not limited to filamentous actinobacteria.

The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters.

Natural products originating from microorganisms are frequently used in antimicrobial and anticancer drugs, pesticides, herbicides or fungicides. In the last years, the increasing availability of microbial genome data has made it possible to access the wealth of biosynthetic clusters responsible for the production of these compounds by genome mining. antiSMASH is one of the most popular tools in this field. The antiSMASH database provides pre-computed antiSMASH results for many publicly available microbial genomes and allows for advanced cross-genome searches. The current version 2 of the antiSMASH database contains annotations for 6200 full bacterial genomes and 18,576 bacterial draft genomes and is available at https://antismash-db.secondarymetabolites.org/.

antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline.

Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, ß-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.

A Regulator Based "Semi-Targeted" Approach to Activate Silent Biosynthetic Gene Clusters.

By culturing microorganisms under standard laboratory conditions, most biosynthetic gene clusters (BGCs) are not expressed, and thus, the products are not produced. To explore this biosynthetic potential, we developed a novel "semi-targeted" approach focusing on activating "silent" BGCs by concurrently introducing a group of regulator genes into streptomycetes of the Tübingen strain collection. We constructed integrative plasmids containing two classes of regulatory genes under the control of the constitutive promoter ermE*p (cluster situated regulators (CSR) and Streptomyces antibiotic regulatory proteins (SARPs)). These plasmids were introduced into Streptomyces sp. TÜ17, Streptomyces sp. TÜ10 and Streptomyces sp. TÜ102. Introduction of the CSRs-plasmid into strain S. sp. TÜ17 activated the production of mayamycin A. By using the individual regulator genes, we proved that Aur1P, was responsible for the activation. In strain S. sp. TÜ102, the introduction of the SARP-plasmid triggered the production of a chartreusin-like compound. Insertion of the CSRs-plasmid into strain S. sp. TÜ10 resulted in activating the warkmycin-BGC. In both recombinants, activation of the BGCs was only possible through the simultaneous expression of aur1PR3 and griR in S. sp. TÜ102 and aur1P and pntR in of S. sp. TÜ10.