BiG-FAM: the biosynthetic gene cluster families database

A systematic comparison of natural product potential, with an emphasis on RiPPs, by mining of bacteria of three large ecosystems

The implementation of several global microbiome studies has yielded extensive insights into the biosynthetic potential of natural microbial communities. However, studies on the distribution of several classes of ribosomally synthesized and post-translationally modified peptides (RiPPs), non-ribosomal peptides (NRPs) and polyketides (PKs) in different large microbial ecosystems have been very limited. Here, we collected a large set of metagenome-assembled bacterial genomes from marine, freshwater and terrestrial ecosystems to investigate the biosynthetic potential of these bacteria. We demonstrate the utility of public dataset collections for revealing the different secondary metabolite biosynthetic potentials among these different living environments. We show that there is a higher occurrence of RiPPs in terrestrial systems, while in marine systems, we found relatively more terpene-, NRP-, and PK encoding gene clusters. Among the many new biosynthetic gene clusters (BGCs) identified, we analyzed various Nif-11-like and nitrile hydratase leader peptide (NHLP) containing gene clusters that would merit further study, including promising products, such as mersacidin-, LAP- and proteusin analogs. This research highlights the significance of public datasets in elucidating the biosynthetic potential of microbes in different living environments and underscores the wide bioengineering opportunities within the RiPP family.

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.

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.

Clostridium aquiflavi sp. nov., isolated from yellow water of Nongxiangxing baijiu in the Yibin region of China

A Gram-stain-positive, strictly anaerobic, endospore-forming and rod-shaped (0.6-0.8×2.7-13.1 µm) bacterium, designated as 5 N-1T, was isolated from a yellow water sample collected from the manufacturing process of Nongxiangxing baijiu in the Yibin region of Sichuan, PR China. Growth occurred at 15-40 °C (optimum growth at 37 °C), at pH 6.0-9.0 (optimum growth at pH 7.0) and in NaCl concentrations of 0-1 % (w/v) and ethanol concentrations of 0-2 % (v/v). The major fatty acids in strain 5 N-1T were C16 : 0, C18 : 0 and C14 : 0. The polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, four unidentified aminophospholipids and one unidentified lipid. Phylogenetic analysis of its 16S rRNA gene sequence indicated that strain 5 N-1T was most closely related to Clostridium weizhouense YB-6T (97.70 %) and Clostridium uliginosum DSM 12992T (97.56 %). The average nucleotide identity and digital DNA‒DNA hybridization values between strain 5 N-1T and the above two type strains were 80.89 and 80.05 % and 25.80 and 25.30 %, respectively, which were all below the species thresholds. The genome size of strain 5 N-1T was 3.5 Mbp and the DNA G+C content was 27.5 mol%. Based on the results of phenotypic and genotypic analyses, strain 5 N-1T represents a novel species of the genus Clostridium, for which the name Clostridium aquiflavi sp. nov. is proposed. The type strain is Clostridium aquiflavi 5 N-1T (=CICC 24886T=JCM 35355T).

The porcine skin microbiome exhibits broad fungal antagonism

The skin and its microbiome function to protect the host from pathogen colonization and environmental stressors. In this study, using the Wisconsin Miniature Swine™ model, we characterize the porcine skin fungal and bacterial microbiomes, identify bacterial isolates displaying antifungal activity, and use whole-genome sequencing to identify biosynthetic gene clusters encoding for secondary metabolites that may be responsible for the antagonistic effects on fungi. Through this comprehensive approach of paired microbiome sequencing with culturomics, we report the discovery of novel species of Corynebacterium and Rothia. Further, this study represents the first comprehensive evaluation of the porcine skin mycobiome and the evaluation of bacterial-fungal interactions on this surface. Several diverse bacterial isolates exhibit potent antifungal properties against opportunistic fungal pathogens in vitro. Genomic analysis of inhibitory species revealed a diverse repertoire of uncharacterized biosynthetic gene clusters suggesting a reservoir of novel chemical and biological diversity. Collectively, the porcine skin microbiome represents a potential unique source of novel antifungals.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Enrichable consortia of microbial symbionts degrade macroalgal polysaccharides in Kyphosus fish

Coastal herbivorous fishes consume macroalgae, which is then degraded by microbes along their digestive tract. However, there is scarce genomic information about the microbiota that perform this degradation. This study explores the potential of Kyphosus gastrointestinal microbial symbionts to collaboratively degrade and ferment polysaccharides from red, green, and brown macroalgae through in silico study of carbohydrate-active enzyme and sulfatase sequences. Recovery of metagenome-assembled genomes (MAGs) from previously described Kyphosus gut metagenomes and newly sequenced bioreactor enrichments reveals differences in enzymatic capabilities between the major microbial taxa in Kyphosus guts. The most versatile of the recovered MAGs were from the Bacteroidota phylum, whose MAGs house enzyme collections able to decompose a variety of algal polysaccharides. Unique enzymes and predicted degradative capacities of genomes from the Bacillota (genus Vallitalea) and Verrucomicrobiota (order Kiritimatiellales) highlight the importance of metabolic contributions from multiple phyla to broaden polysaccharide degradation capabilities. Few genomes contain the required enzymes to fully degrade any complex sulfated algal polysaccharide alone. The distribution of suitable enzymes between MAGs originating from different taxa, along with the widespread detection of signal peptides in candidate enzymes, is consistent with cooperative extracellular degradation of these carbohydrates. This study leverages genomic evidence to reveal an untapped diversity at the enzyme and strain level among Kyphosus symbionts and their contributions to macroalgae decomposition. Bioreactor enrichments provide a genomic foundation for degradative and fermentative processes central to translating the knowledge gained from this system to the aquaculture and bioenergy sectors.IMPORTANCESeaweed has long been considered a promising source of sustainable biomass for bioenergy and aquaculture feed, but scalable industrial methods for decomposing terrestrial compounds can struggle to break down seaweed polysaccharides efficiently due to their unique sulfated structures. Fish of the genus Kyphosus feed on seaweed by leveraging gastrointestinal bacteria to degrade algal polysaccharides into simple sugars. This study reconstructs metagenome-assembled genomes for these gastrointestinal bacteria to enhance our understanding of herbivorous fish digestion and fermentation of algal sugars. Investigations at the gene level identify Kyphosus guts as an untapped source of seaweed-degrading enzymes ripe for further characterization. These discoveries set the stage for future work incorporating marine enzymes and microbial communities in the industrial degradation of algal polysaccharides.

The Natural Products Discovery Center: Release of the First 8490 Sequenced Strains for Exploring Actinobacteria Biosynthetic Diversity

Actinobacteria, the bacterial phylum most renowned for natural product discovery, has been established as a valuable source for drug discovery and biotechnology but is underrepresented within accessible genome and strain collections. Herein, we introduce the Natural Products Discovery Center (NPDC), featuring 122,449 strains assembled over eight decades, the genomes of the first 8490 NPDC strains (7142 Actinobacteria), and the online NPDC Portal making both strains and genomes publicly available. A comparative survey of RefSeq and NPDC Actinobacteria highlights the taxonomic and biosynthetic diversity within the NPDC collection, including three new genera, hundreds of new species, and ~7000 new gene cluster families. Selected examples demonstrate how the NPDC Portal's strain metadata, genomes, and biosynthetic gene clusters can be leveraged using genome mining approaches. Our findings underscore the ongoing significance of Actinobacteria in natural product discovery, and the NPDC serves as an unparalleled resource for both Actinobacteria strains and genomes.

Diversity and distribution of biosynthetic gene clusters in agricultural soil microbiomes

Bacterial secondary metabolites serve as an important source of molecules for drug discovery. They also play an important function in mediating the interactions of microbial producers with their living environment and surrounding organisms. However, little is known about the genetic novelty, distribution, and community-level impacts of soil bacterial biosynthetic potential on a large geographic scale. Here, we constructed the first catalog of 11,149 biosynthetic gene clusters (BGCs) from agricultural soils across China and unearthed hidden biosynthetic potential for new natural product discovery from the not-yet-cultivated soil bacteria. Notably, we revealed soil pH as the strongest environmental driver of BGC biogeography and predicted that soil acidification and global climate change could damage the biosynthetic potential of the soil microbiome. The co-occurrence network of bacterial genomes revealed two BGC-rich species, i.e., Nocardia niigatensis from Actinobacteriota and PSRF01 from Acidobacteriota, as the module hub and connector, respectively, indicating their keystone positions in the soil microbial communities. We also uncovered a dominant role of BGC-inferred biotic interactions over environmental drivers in structuring the soil microbiome. Overall, this study achieved novel insights into the BGC landscape in agricultural soils of China, substantially expanding our understanding of the diversity and novelty of bacterial secondary metabolism and the potential role of secondary metabolites in microbiota assembly.IMPORTANCEBacterial secondary metabolites not only serve as the foundation for numerous therapeutics (e.g., antibiotics and anticancer drugs), but they also play critical ecological roles in mediating microbial interactions (e.g., competition and communication). However, our knowledge of bacterial secondary metabolism is limited to only a small fraction of cultured strains, thus restricting our comprehensive understanding of their diversity, novelty, and potential ecological roles in soil ecosystems. Here, we used culture-independent metagenomics to explore biosynthetic potentials in agricultural soils of China. Our analyses revealed a high degree of genetic diversity and novelty within biosynthetic gene clusters in agricultural soil environments, offering valuable insights for biochemists seeking to synthesize novel bioactive products. Furthermore, we uncovered the pivotal role of BGC-rich species in microbial communities and the significant relationship between BGC richness and microbial phylogenetic turnover. This information emphasizes the importance of biosynthetic potential in the assembly of microbial communities.

A Timeline of Biosynthetic Gene Cluster Discovery in Aspergillus fumigatus: From Characterization to Future Perspectives

In 1999, the first biosynthetic gene cluster (BGC), synthesizing the virulence factor DHN melanin, was characterized in Aspergillus fumigatus. Since then, 19 additional BGCs have been linked to specific secondary metabolites (SMs) in this species. Here, we provide a comprehensive timeline of A. fumigatus BGC discovery and find that initial advances centered around the commonly expressed SMs where chemical structure informed rationale identification of the producing BGC (e.g., gliotoxin, fumigaclavine, fumitremorgin, pseurotin A, helvolic acid, fumiquinazoline). Further advances followed the transcriptional profiling of a ΔlaeA mutant, which aided in the identification of endocrocin, fumagillin, hexadehydroastechrome, trypacidin, and fumisoquin BGCs. These SMs and their precursors are the commonly produced metabolites in most A. fumigatus studies. Characterization of other BGC/SM pairs required additional efforts, such as induction treatments, including co-culture with bacteria (fumicycline/neosartoricin, fumigermin) or growth under copper starvation (fumivaline, fumicicolin). Finally, four BGC/SM pairs were discovered via overexpression technologies, including the use of heterologous hosts (fumicycline/neosartoricin, fumihopaside, sphingofungin, and sartorypyrone). Initial analysis of the two most studied A. fumigatus isolates, Af293 and A1160, suggested that both harbored ca. 34-36 BGCs. However, an examination of 264 available genomes of A. fumigatus shows up to 20 additional BGCs, with some strains showing considerable variations in BGC number and composition. These new BGCs present a new frontier in the future of secondary metabolism characterization in this important species.

Discovery of Acyl-Surugamide A2 from Marine Streptomyces albidoflavus RKJM-0023-A New Cyclic Nonribosomal Peptide Containing an N-ε-acetyl-L-lysine Residue

We report the discovery of a novel cyclic nonribosomal peptide (NRP), acyl-surugamide A2, from a marine-derived Streptomyces albidoflavus RKJM-0023 (CP133227). The structure of acyl-surugamide A2 was elucidated using a combination of NMR spectroscopy, MS2 fragmentation analysis, and comparative analysis of the sur biosynthetic gene cluster. Acyl-surugamide A2 contains all eight core amino acids of surugamide A, with a modified N-ε-acetyl-L-lysine residue. Our study highlights the potential of marine Streptomyces strains to produce novel natural products with potential therapeutic applications. The structure of cyclic peptides can be solved using MS2 spectra and analysis of their biosynthetic gene clusters.

A genome and gene catalog of the aquatic microbiomes of the Tibetan Plateau

The Tibetan Plateau supplies water to nearly 2 billion people in Asia, but climate change poses threats to its aquatic microbial resources. Here, we construct the Tibetan Plateau Microbial Catalog by sequencing 498 metagenomes from six water ecosystems (saline lakes, freshwater lakes, rivers, hot springs, wetlands and glaciers). Our catalog expands knowledge of regional genomic diversity by presenting 32,355 metagenome-assembled genomes that de-replicated into 10,723 representative genome-based species, of which 88% were unannotated. The catalog contains nearly 300 million non-redundant gene clusters, of which 15% novel, and 73,864 biosynthetic gene clusters, of which 50% novel, thus expanding known functional diversity. Using these data, we investigate the Tibetan Plateau aquatic microbiome's biogeography along a distance of 2,500 km and >5 km in altitude. Microbial compositional similarity and the shared gene count with the Tibetan Plateau microbiome decline along with distance and altitude difference, suggesting a dispersal pattern. The Tibetan Plateau Microbial Catalog stands as a substantial repository for high-altitude aquatic microbiome resources, providing potential for discovering novel lineages and functions, and bridging knowledge gaps in microbiome biogeography.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

OMA orthology in 2024: improved prokaryote coverage, ancestral and extant GO enrichment, a revamped synteny viewer and more in the OMA Ecosystem

In this update paper, we present the latest developments in the OMA browser knowledgebase, which aims to provide high-quality orthology inferences and facilitate the study of gene families, genomes and their evolution. First, we discuss the addition of new species in the database, particularly an expanded representation of prokaryotic species. The OMA browser now offers Ancestral Genome pages and an Ancestral Gene Order viewer, allowing users to explore the evolutionary history and gene content of ancestral genomes. We also introduce a revamped Local Synteny Viewer to compare genomic neighborhoods across both extant and ancestral genomes. Hierarchical Orthologous Groups (HOGs) are now annotated with Gene Ontology annotations, and users can easily perform extant or ancestral GO enrichments. Finally, we recap new tools in the OMA Ecosystem, including OMAmer for proteome mapping, OMArk for proteome quality assessment, OMAMO for model organism selection and Read2Tree for phylogenetic species tree construction from reads. These new features provide exciting opportunities for orthology analysis and comparative genomics. OMA is accessible at https://omabrowser.org.

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/.

Identification of siderophores blocking infection of Pseudomonas aeruginosa from Kitasatospora sp. LS1784

Siderophores are low-molecular-mass, high-affinity chelators of Fe3+ ions that are critical for the survival of bacteria in ferric deficient environment. Exogenous siderophores are potential bacteriostat by disrupting the iron-uptake process of pathogens. In our previous work to discover siderophores, strain LS1784 was previously predicted to produce new catecholate-type siderophores by genome analysis but no compounds were obtained. In this work, we reclassified train LS1784 as Kitasatospora sp. LS1784 according to the genome phylogenetic analysis. Then guided by CAS colorimetric assay and molecular network analysis, four catecholate-type siderophores were isolated from the ethyl acetate extract of LS1784 which were coincident with the initial prediction. Notably, compounds 2 and 3 were reported for the first time. Following activity screening, compound 3 showed sufficient anti-Pseudomonas aeruginosa-infection activity in Caenorhabditis elegans infection models, whereas all compounds exhibited no antimicrobial activity. These results indicated that compound 3 can enhance the survival of P. aeruginosa infecting C. elegans by reducing the virulence of P. aeruginosa rather than killing P. aeruginosa, which aligns with our previous findings. Moreover, these findings highlight the effectiveness of comprehensive approaches, including genome mining, CAS (Chromeazurol S) testing, and molecular network (MN) analysis, in identifying potential siderophores, thereby expanding the siderophores arsenal in bacteria for the development of anti-infective drugs.

Enrichable consortia of microbial symbionts degrade macroalgal polysaccharides in Kyphosus fish

Coastal herbivorous fishes consume macroalgae, which is then degraded by microbes along their digestive tract. However, there is scarce foundational genomic work on the microbiota that perform this degradation. This study explores the potential of Kyphosus gastrointestinal microbial symbionts to collaboratively degrade and ferment polysaccharides from red, green, and brown macroalgae through in silico study of carbohydrate-active enzyme and sulfatase sequences. Recovery of metagenome-assembled genomes (MAGs) reveals differences in enzymatic capabilities between the major microbial taxa in Kyphosus guts. The most versatile of the recovered MAGs were from the Bacteroidota phylum, whose MAGs house enzymes able to decompose a variety of algal polysaccharides. Unique enzymes and predicted degradative capacities of genomes from the Bacillota (genus Vallitalea) and Verrucomicrobiota (order Kiritimatiellales) suggest the potential for microbial transfer between marine sediment and Kyphosus digestive tracts. Few genomes contain the required enzymes to fully degrade any complex sulfated algal polysaccharide alone. The distribution of suitable enzymes between MAGs originating from different taxa, along with the widespread detection of signal peptides in candidate enzymes, is consistent with cooperative extracellular degradation of these carbohydrates. This study leverages genomic evidence to reveal an untapped diversity at the enzyme and strain level among Kyphosus symbionts and their contributions to macroalgae decomposition. Bioreactor enrichments provide a genomic foundation for degradative and fermentative processes central to translating the knowledge gained from this system to the aquaculture and bioenergy sectors.

Genome mining reveals novel biosynthetic gene clusters in entomopathogenic bacteria

The discovery of novel bioactive compounds produced by microorganisms holds significant potential for the development of therapeutics and agrochemicals. In this study, we conducted genome mining to explore the biosynthetic potential of entomopathogenic bacteria belonging to the genera Xenorhabdus and Photorhabdus. By utilizing next-generation sequencing and bioinformatics tools, we identified novel biosynthetic gene clusters (BGCs) in the genomes of the bacteria, specifically plu00736 and plu00747. These clusters were identified as unidentified non-ribosomal peptide synthetase (NRPS) and unidentified type I polyketide synthase (T1PKS) clusters. These BGCs exhibited unique genetic architecture and encoded several putative enzymes and regulatory elements, suggesting its involvement in the synthesis of bioactive secondary metabolites. Furthermore, comparative genome analysis revealed that these BGCs were distinct from previously characterized gene clusters, indicating the potential for the production of novel compounds. Our findings highlighted the importance of genome mining as a powerful approach for the discovery of biosynthetic gene clusters and the identification of novel bioactive compounds. Further investigations involving expression studies and functional characterization of the identified BGCs will provide valuable insights into the biosynthesis and potential applications of these bioactive compounds.

Streptomyces alleviate abiotic stress in plant by producing pteridic acids

Soil microbiota can confer fitness advantages to plants and increase crop resilience to drought and other abiotic stressors. However, there is little evidence on the mechanisms correlating a microbial trait with plant abiotic stress tolerance. Here, we report that Streptomyces effectively alleviate drought and salinity stress by producing spiroketal polyketide pteridic acid H (1) and its isomer F (2), both of which promote root growth in Arabidopsis at a concentration of 1.3 nM under abiotic stress. Transcriptomics profiles show increased expression of multiple stress responsive genes in Arabidopsis seedlings after pteridic acids treatment. We confirm in vivo a bifunctional biosynthetic gene cluster for pteridic acids and antimicrobial elaiophylin production. We propose it is mainly disseminated by vertical transmission and is geographically distributed in various environments. This discovery reveals a perspective for understanding plant-Streptomyces interactions and provides a promising approach for utilising beneficial Streptomyces and their secondary metabolites in agriculture to mitigate the detrimental effects of climate change.

Phylogenetic classification of natural product biosynthetic gene clusters based on regulatory mechanisms

The natural products (NPs) biosynthetic gene clusters (BGCs) represent the adapting biochemical toolkit for microorganisms to thrive different microenvironments. Despite their high diversity, particularly at the genomic level, detecting them in a shake-flask is challenging and remains the primary obstacle limiting our access to valuable chemicals. Studying the molecular mechanisms that regulate BGC expression is crucial to design of artificial conditions that derive on their expression. Here, we propose a phylogenetic analysis of regulatory elements linked to biosynthesis gene clusters, to classify BGCs to regulatory mechanisms based on protein domain information. We utilized Hidden Markov Models from the Pfam database to retrieve regulatory elements, such as histidine kinases and transcription factors, from BGCs in the MIBiG database, focusing on actinobacterial strains from three distinct environments: oligotrophic basins, rainforests, and marine environments. Despite the environmental variations, our isolated microorganisms share similar regulatory mechanisms, suggesting the potential to activate new BGCs using activators known to affect previously characterized BGCs.

Streptomyces: a natural source of anti-Candida agents

Introduction. There is an urgent need to source new compounds that can combat the current threat of serious infection caused by Candida spp. and contend with the problem of antimicrobial resistance.

Gap. A synthesis of the evidence available from the current literature is needed to identify promising antifungal chemotherapeutics.

Aim. To highlight anti-Candida compounds derived from Streptomyces spp. (a well-known source of antimicrobial compounds) that could translate to potential candidates for future clinical practice.

Methodology. A comprehensive review was conducted across three scientific literature databases spanning a 13-year period.

Results. We identified 151 compounds with anti-Candida activity. Amongst these, 40 were reported with very strong inhibitory activity, having minimum inhibitory concentrations (MICs) against Candida spp. of <3.5 µg ml−1, 66 compounds were considered strong inhibitors and 45 compounds exhibited moderate inhibitory potential. From an analysis of the MICs, we deduced that the actinomycin-like compounds RSP01 and RSP02 were probably the most promising anti-Candida compounds. Other antifungals of note included filipin-like compounds, which demonstrated superior inhibition to amphotericin B and activity against Candida glabrata and Candida krusei, and bafilomycin derivatives, which had substantial inhibition against Candida parapsilosis.

Conclusion. It is essential to recognize the limitations inherent in the quest for new antifungals, which encompass toxicity, in vivo effectiveness and constraints associated with limited data access. However, further investigation through in-depth study and emerging technologies is of paramount importance, given that there are still many more compounds to discover. This review highlights the importance of antifungal compounds derived from Streptomyces , which demonstrate robust inhibition, and, in many cases, low toxicity, making them promising candidates for the development of novel antifungal agents.

Long-Read Metagenomics of Marine Microbes Reveals Diversely Expressed Secondary Metabolites

Microbial secondary metabolites play crucial roles in microbial competition, communication, resource acquisition, antibiotic production, and a variety of other biotechnological processes. The retrieval of full-length BGC (biosynthetic gene cluster) sequences from uncultivated bacteria is difficult due to the technical constraints of short-read sequencing, making it impossible to determine BGC diversity. Using long-read sequencing and genome mining, 339 mainly full-length BGCs were recovered in this study, illuminating the wide range of BGCs from uncultivated lineages discovered in seawater from Aoshan Bay, Yellow Sea, China. Many extremely diverse BGCs were discovered in bacterial phyla such as Proteobacteria, Bacteroidota, Acidobacteriota, and Verrucomicrobiota as well as the previously uncultured archaeal phylum "Candidatus Thermoplasmatota." The data from metatranscriptomics showed that 30.1% of secondary metabolic genes were being expressed, and they also revealed the expression pattern of BGC core biosynthetic genes and tailoring enzymes. Taken together, our results demonstrate that long-read metagenomic sequencing combined with metatranscriptomic analysis provides a direct view into the functional expression of BGCs in environmental processes. IMPORTANCE Genome mining of metagenomic data has become the preferred method for the bioprospecting of novel compounds by cataloguing secondary metabolite potential. However, the accurate detection of BGCs requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until recently with new long-read technologies. We used high-quality metagenome-assembled genomes generated from long-read data to determine the biosynthetic potential of microbes found in the surface water of the Yellow Sea. We recovered 339 highly diverse and mostly full-length BGCs from largely uncultured and underexplored bacterial and archaeal phyla. Additionally, we present long-read metagenomic sequencing combined with metatranscriptomic analysis as a potential method for gaining access to the largely underutilized genetic reservoir of specialized metabolite gene clusters in the majority of microbes that are not cultured. The combination of long-read metagenomic and metatranscriptomic analyses is significant because it can more accurately assess the mechanisms of microbial adaptation to the environment through BGC expression based on metatranscriptomic data.

Chromosomal organization of biosynthetic gene clusters, including those of nine novel species, suggests plasticity of myxobacterial specialized metabolism

Introduction

Natural products discovered from bacteria provide critically needed therapeutic leads for drug discovery, and myxobacteria are an established source for metabolites with unique chemical scaffolds and biological activities. Myxobacterial genomes accommodate an exceptional number and variety of biosynthetic gene clusters (BGCs) which encode for features involved in specialized metabolism.

Methods

In this study, we describe the collection, sequencing, and genome mining of 20 myxobacteria isolated from rhizospheric soil samples collected in North America.

Results

Nine isolates were determined to be novel species of myxobacteria including representatives from the genera Archangium, Myxococcus, Nannocystis, Polyangium, Pyxidicoccus, Sorangium, and Stigmatella. Growth profiles, biochemical assays, and descriptions were provided for all proposed novel species. We assess the BGC content of all isolates and observe differences between Myxococcia and Polyangiia clusters.

Discussion

Continued discovery and sequencing of novel myxobacteria from the environment provide BGCs for the genome mining pipeline. Utilizing complete or near-complete genome sequences, we compare the chromosomal organization of BGCs of related myxobacteria from various genera and suggest that the spatial proximity of hybrid, modular clusters contributes to the metabolic adaptability of myxobacteria.

The Cytotoxic Properties of Extreme Fungi's Bioactive Components-An Updated Metabolic and Omics Overview

Fungi are the most diverse living organisms on planet Earth, where their ubiquitous presence in various ecosystems offers vast potential for the research and discovery of new, naturally occurring medicinal products. Concerning human health, cancer remains one of the leading causes of mortality. While extensive research is being conducted on treatments and their efficacy in various stages of cancer, finding cytotoxic drugs that target tumor cells with no/less toxicity toward normal tissue is a significant challenge. In addition, traditional cancer treatments continue to suffer from chemical resistance. Fortunately, the cytotoxic properties of several natural products derived from various microorganisms, including fungi, are now well-established. The current review aims to extract and consolidate the findings of various scientific studies that identified fungi-derived bioactive metabolites with antitumor (anticancer) properties. The antitumor secondary metabolites identified from extremophilic and extremotolerant fungi are grouped according to their biological activity and type. It became evident that the significance of these compounds, with their medicinal properties and their potential application in cancer treatment, is tremendous. Furthermore, the utilization of omics tools, analysis, and genome mining technology to identify the novel metabolites for targeted treatments is discussed. Through this review, we tried to accentuate the invaluable importance of fungi grown in extreme environments and the necessity of innovative research in discovering naturally occurring bioactive compounds for the development of novel cancer treatments.

Correlative metabologenomics of 110 fungi reveals metabolite-gene cluster pairs

Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics-metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product-GCF linkages to direct future discovery.

Identification of a biosynthetic gene cluster for a red pigment cristazarin produced by a lichen-forming fungus Cladonia metacorallifera

Lichens are known to produce many novel bioactive metabolites. To date, approximately 1,000 secondary metabolites have been discovered, which are predominantly produced by the lichen mycobionts. However, despite the extensive studies on production of lichen secondary metabolites, little is known about the responsible biosynthetic gene clusters (BGCs). Here, we identified a putative BGC that is implicated in production of a red pigment, cristazarin (a naphthazarin derivative), in Cladonia metacorallifera. Previously, cristazarin was shown to be specifically induced in growth media containing fructose as a sole carbon source. Thus, we performed transcriptome analysis of C. metacorallifera growing on different carbon sources including fructose to identify the BGC for cristazarin. Among 39 polyketide synthase (PKS) genes found in the genome of C. metacorallifera, a non-reducing PKS (coined crz7) was highly expressed in growth media containing either fructose or glucose. The borders of a cristazarin gene cluster were delimited by co-expression patterns of neighboring genes of the crz7. BGCs highly conserved to the cristazarin BGC were also found in C. borealis and C. macilenta, indicating that these related species also have metabolic potentials to produce cristazarin. Phylogenetic analysis revealed that the Crz7 is sister to fungal PKSs that biosynthesize an acetylated tetrahydoxynaphthalene as a precursor of melanin pigment. Based on the phylogenetic placement of the Crz7 and putative functions of its neighboring genes, we proposed a plausible biosynthetic route for cristazarin. In this study, we identified a lichen-specific BGC that is likely involved in the biosynthesis of a naphthazarin derivative, cristazarin, and confirmed that transcriptome profiling under inducing and non-inducing conditions is an effective strategy for linking metabolites of interest to biosynthetic genes.

Streptomyces and their specialised metabolites for phytopathogen control - comparative in vitro and in planta metabolic approaches

In the search for new crop protection microbial biocontrol agents, isolates from the genus Streptomyces are commonly found with promising attributes. Streptomyces are natural soil dwellers and have evolved as plant symbionts producing specialised metabolites with antibiotic and antifungal activities. Streptomyces biocontrol strains can effectively suppress plant pathogens via direct antimicrobial activity, but also induce plant resistance through indirect biosynthetic pathways. The investigation of factors stimulating the production and release of Streptomyces bioactive compounds is commonly conducted in vitro, between Streptomyces sp. and a plant pathogen. However, recent research is starting to shed light on the behaviour of these biocontrol agents in planta, where the biotic and abiotic conditions share little similarity to those of controlled laboratory conditions. With a focus on specialised metabolites, this review details (i) the various methods by which Streptomyces biocontrol agents employ specialised metabolites as an additional line of defence against plant pathogens, (ii) the signals shared in the tripartite system of plant, pathogen and biocontrol agent, and (iii) an outlook on new approaches to expedite the identification and ecological understanding of these metabolites under a crop protection lens.

Metagenomics reveals the habitat specificity of biosynthetic potential of secondary metabolites in global food fermentations

BACKGROUND

Fermented foods are considered to be beneficial for human health. Secondary metabolites determined by biosynthetic gene clusters (BGCs) are precious bioactive compounds with various biological activities. However, the diversity and distribution of the biosynthetic potential of secondary metabolites in global food fermentations remain largely unknown. In this study, we performed a large-scale and comprehensive investigation for the BGCs in global food fermentations by metagenomics analysis.

RESULTS

We recovered 653 bacterial metagenome-assembled genomes (MAGs) from 367 metagenomic sequencing datasets covering 15 general food fermentation types worldwide. In total, 2334 secondary metabolite BGCs, including 1003 novel BGCs, were identified in these MAGs. Bacillaceae, Streptococcaceae, Streptomycetaceae, Brevibacteriaceae and Lactobacillaceae contained high abundances of novel BGCs (≥ 60 novel BGCs). Among 2334 BGCs, 1655 were habitat-specific, originating from habitat-specific species (80.54%) and habitat-specific genotypes within multi-habitat species (19.46%) in different food fermentation types. Biological activity analysis suggested that 183 BGC-producing secondary metabolites exhibited high probabilities of antibacterial activity (> 80%). These 183 BGCs were distributed across all 15 food fermentation types, and cheese fermentation contained the most BGC number.

CONCLUSIONS

This study demonstrates that food fermentation systems are an untapped reservoir of BGCs and bioactive secondary metabolites, and it provides novel insights into the potential human health benefits of fermented foods. Video Abstract.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Accessing hidden microbial biosynthetic potential from underexplored sources for novel drug discovery

Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently needed to be developed to combat the growing antimicrobial resistance and other public health problems. The advances in next-generation sequencing technologies and powerful computational tools open up new opportunities to explore microbial biosynthetic potential from underexplored sources, with millions of secondary metabolites awaiting discovery. The review highlights challenges associated with discovery of new chemical entities, rich reservoirs provided by untapped taxa, ecological niches or host microbiomes, emerging synthetic biotechnologies to unearth the hidden microbial biosynthetic potential for novel drug discovery at scale and speed.

Knowledge-guided data mining on the standardized architecture of NRPS: Subtypes, novel motifs, and sequence entanglements

Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with re-engineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries.

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.

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 .

Genome mining for unknown-unknown natural products

Genome mining of biosynthetic pathways with no identifiable core enzymes can lead to discovery of the so-called unknown (biosynthetic route)-unknown (molecular structure) natural products. Here we focused on a conserved fungal biosynthetic pathway that lacks a canonical core enzyme and used heterologous expression to identify the associated natural product, a highly modified cyclo-arginine-tyrosine dipeptide. Biochemical characterization of the pathway led to identification of a new arginine-containing cyclodipeptide synthase (RCDPS), which was previously annotated as a hypothetical protein and has no sequence homology to non-ribosomal peptide synthetase or bacterial cyclodipeptide synthase. RCDPS homologs are widely encoded in fungal genomes; other members of this family can synthesize diverse cyclo-arginine-Xaa dipeptides, and characterization of a cyclo-arginine-tryptophan RCDPS showed that the enzyme is aminoacyl-tRNA dependent. Further characterization of the biosynthetic pathway led to discovery of new compounds whose structures would not have been predicted without knowledge of RCDPS function.

Chromosomal organization of biosynthetic gene clusters suggests plasticity of myxobacterial specialized metabolism including descriptions for nine novel species: Archangium lansinium sp. nov., Myxococcus landrumus sp. nov., Nannocystis bainbridgea sp. nov., Nannocystis poenicansa sp. nov., Nannocystis radixulma sp. nov., Polyangium mundeleinium sp. nov., Pyxidicoccus parkwaysis sp. nov., Sorangium aterium sp. nov., Stigmatella ashevillena sp. nov

Natural products discovered from bacteria provide critically needed therapeutic leads for drug discovery, and myxobacteria are an established source for metabolites with unique chemical scaffolds and biological activities. Myxobacterial genomes accommodate an exceptional number and variety of biosynthetic gene clusters (BGCs) which encode for features involved in specialized metabolism. Continued discovery and sequencing of novel myxobacteria from the environment provides BGCs for the genome mining pipeline. Herein, we describe the collection, sequencing, and genome mining of 20 myxobacteria isolated from rhizospheric soil samples collected in North America. Nine isolates where determined to be novel species of myxobacteria including representatives from the genera Archangium, Myxococcus, Nannocystis, Polyangium, Pyxidicoccus, Sorangium, and Stigmatella. Growth profiles, biochemical assays, and descriptions are provided for all proposed novel species. We assess the BGC content of all isolates and observe differences between Myxococcia and Polyangiia clusters. Utilizing complete or near complete genome sequences we compare the chromosomal organization of BGCs of related myxobacteria from various genera and suggest spatial proximity of hybrid, modular clusters contributes to the metabolic adaptability of myxobacteria.

Current status of secondary metabolite pathways linked to their related biosynthetic gene clusters in Aspergillus section Nigri

Covering: up to the end of 2021Aspergilli are biosynthetically 'talented' micro-organisms and therefore the natural products community has continually been interested in the wealth of biosynthetic gene clusters (BGCs) encoding numerous secondary metabolites related to these fungi. With the rapid increase in sequenced fungal genomes combined with the continuous development of bioinformatics tools such as antiSMASH, linking new structures to unknown BGCs has become much easier when taking retro-biosynthetic considerations into account. On the other hand, in most cases it is not as straightforward to prove proposed biosynthetic pathways due to the lack of implemented genetic tools in a given fungal species. As a result, very few secondary metabolite biosynthetic pathways have been characterized even amongst some of the most well studied Aspergillus spp., section Nigri (black aspergilli). This review will cover all known biosynthetic compound families and their structural diversity known from black aspergilli. We have logically divided this into sub-sections describing major biosynthetic classes (polyketides, non-ribosomal peptides, terpenoids, meroterpenoids and hybrid biosynthesis). Importantly, we will focus the review on metabolites which have been firmly linked to their corresponding BGCs.

EvolClustDB: Exploring Eukaryotic Gene Clusters with Evolutionarily Conserved Genomic Neighbourhoods

Conservation of gene neighbourhood over evolutionary distances is generally indicative of shared regulation or functional association among genes. This concept has been broadly exploited in prokaryotes but its use on eukaryotic genomes has been limited to specific functional classes, such as biosynthetic gene clusters. We here used an evolutionary-based gene cluster discovery algorithm (EvolClust) to pre-compute evolutionarily conserved gene neighbourhoods, which can be searched, browsed and downloaded in EvolClustDB. We inferred ∼35,000 cluster families in 882 different species in genome comparisons of five taxonomically broad clades: Fungi, Plants, Metazoans, Insects and Protists. EvolClustDB allows browsing through the cluster families, as well as searching by protein, species, identifier or sequence. Visualization allows inspecting gene order per species in a phylogenetic context, so that relevant evolutionary events such as gain, loss or transfer, can be inferred. EvolClustDB is freely available, without registration, at http://evolclustdb.org/.

A Genomic Survey of the Natural Product Biosynthetic Potential of Actinomycetes Isolated from New Zealand Lichens

Actinomycetes are prolific producers of industrially valuable and medically important compounds. Historically, the most efficient method of obtaining compounds has been bioactivity-guided isolation and characterization of drug-like molecules from culturable soil actinomycetes. Unfortunately, this pipeline has been met with an increasing number of rediscoveries, to the point where it is no longer considered an attractive approach for drug discovery. To address this challenge and to continue finding new compounds, researchers have increasingly focused on alternative environmental niches and screening methods. Here, we report the genetic investigation of actinomycetes from an underexplored source, New Zealand lichens. In this work, we obtain draft genome sequences for 322 lichen-associated actinomycetes. We then explore this genetic resource with an emphasis on biosynthetic potential. By enumerating biosynthetic gene clusters (BGCs) in our data sets and comparing these to various reference collections, we demonstrate that actinomycetes sourced from New Zealand lichens have the genetic capacity to produce large numbers of natural products, many of which are expected to be broadly different from those identified in previous efforts predominantly based on soil samples. Our data shed light on the actinomycete assemblage in New Zealand lichens and demonstrate that lichen-sourced actinobacteria could serve as reservoirs for discovering new secondary metabolites. IMPORTANCE Lichens are home to complex and distinctive microbial cohorts that have not been extensively explored for the ability to produce novel secondary metabolites. Here, we isolate and obtain genome sequence data for 322 actinomycetes from New Zealand lichens. In doing so, we delineate at least 85 potentially undescribed species, and show that lichen associated actinomycetes have the potential to yield many new secondary metabolites, and as such, might serve as a productive starting point for drug discovery efforts.

Kingella pumchi sp. nov., an organism isolated from human vertebral puncture tissue

A Gram-negative, non-motile rod and strictly aerobic bacterium, designated as 18B16333^T, was isolated from vertebral puncture tissue of a patient at Peking union medical college hospital in China. Growth occurred in NaCl concentrations of 0-1% (w/v) (optimum growth at 0% NaCl), at temperatures of 25-40 °C (optimum growth at 37 °C) and at pH 6.0-9.0 (optimum growth at pH 8.0). Diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine were the predominant polar lipids, and the major fatty acids were C_16:0, C_18:1 ω7c/C_18:1 ω6c and C_16:1 ω7c/C_16:1 ω6c. Phylogenetic analysis based on 16S rRNA gene sequence comparisons indicated that strain 18B16333^T was most closely related to Kingella potus CCUG 49773 ^T (97.3%, 16S rRNA gene sequence identity) and Neisseria bacilliformis CCUG 50858 ^T (96.8%). The ANI values between strain 18B16333^T and the type strains K. potus CCUG 49773 ^T, N. bacilliformis CCUG 50858 ^T, Kingella kingae CCUG 352 ^T and Neisseria gonorrhoeae CCUG 26876 ^T were 77.3%, 79.1%, 72.1% and 75.4%, respectively. The dDDH values between strain 18B16333^T and the four reference strains mentioned above were 24.8%, 26.9%, 24.2% and 20.7%. Further core gene analysis distinctively clustered strain 18B16333^T with four Kingella species but not with Neisseria species. Based on the phenotypic, chemotaxonomic, and phylogenetic properties, strain 18B16333^T represents a novel species of the genus Kingella, for which the name Kingella pumchi sp. nov. is proposed. The type strain is Kingella pumchi 18B16333^T (= CICC 24913 ^T = CCUG 75125 ^T).

Advances in mining and expressing microbial biosynthetic gene clusters

Natural products (NPs) especially the secondary metabolites originated from microbes exhibit great importance in biomedical, industrial and agricultural applications. However, mining biosynthetic gene clusters (BGCs) to produce novel NPs has been hindered owing that a large population of environmental microbes are unculturable. In the past decade, strategies to explore BGCs directly from (meta)genomes have been established along with the fast development of high-throughput sequencing technologies and the powerful bioinformatics data-processing tools, which greatly expedited the exploitations of novel BGCs from unculturable microbes including the extremophilic microbes. In this review, we firstly summarized the popular bioinformatics tools and databases available to mine novel BGCs from (meta)genomes based on either pure cultures or pristine environmental samples. Noticeably, approaches rooted from machine learning and deep learning with focuses on the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs) were dramatically increased in recent years. Moreover, synthetic biology techniques to express the novel BGCs in culturable native microbes or heterologous hosts were introduced. This working pipeline including the discovery and biosynthesis of novel NPs will greatly advance the exploitations of the abundant but unexplored microbial BGCs.

Linking Lichen Metabolites to Genes: Emerging Concepts and Lessons from Molecular Biology and Metagenomics

Lichen secondary metabolites have tremendous pharmaceutical and industrial potential. Although more than 1000 metabolites have been reported from lichens, less than 10 have been linked to the genes coding them. The current biosynthetic research focuses strongly on linking molecules to genes as this is fundamental to adapting the molecule for industrial application. Metagenomic-based gene discovery, which bypasses the challenges associated with culturing an organism, is a promising way forward to link secondary metabolites to genes in non-model, difficult-to-culture organisms. This approach is based on the amalgamation of the knowledge of the evolutionary relationships of the biosynthetic genes, the structure of the target molecule, and the biosynthetic machinery required for its synthesis. So far, metagenomic-based gene discovery is the predominant approach by which lichen metabolites have been linked to their genes. Although the structures of most of the lichen secondary metabolites are well-documented, a comprehensive review of the metabolites linked to their genes, strategies implemented to establish this link, and crucial takeaways from these studies is not available. In this review, I address the following knowledge gaps and, additionally, provide critical insights into the results of these studies, elaborating on the direct and serendipitous lessons that we have learned from them.

Genetically Encoded Detection of Biosynthetic Protease Inhibitors

Proteases are an important class of drug targets that continue to drive inhibitor discovery. These enzymes are prone to resistance mutations, yet their promise for treating viral diseases and other disorders continues to grow. This study develops a general approach for detecting microbially synthesized protease inhibitors and uses it to screen terpenoid pathways for inhibitory compounds. The detection scheme relies on a bacterial two-hybrid (B2H) system that links protease inactivation to the transcription of a swappable reporter gene. This system, which can accomodate multiple biochemical outputs (i.e., luminescence and antibiotic resistance), permitted the facile incorporation of four disease-relevant proteases. A B2H designed to detect the inactivation of the main protease of severe acute respiratory syndrome coronavirus 2 enabled the identification of a terpenoid inhibitor of modest potency. An analysis of multiple pathways that make this terpenoid, however, suggested that its production was necessary but not sufficient to confer a survival advantage in growth-coupled assays. This finding highlights an important challenge associated with the use of genetic selection to search for inhibitors─notably, the influence of pathway toxicity─and underlines the value of including multiple pathways with overlapping product profiles in pathway screens. This study provides a detailed experimental framework for using microbes to screen libraries of biosynthetic pathways for targeted protease inhibitors.

Co-factor independent oxidases ncnN and actVA-3 are involved in the dimerization of benzoisochromanequinone antibiotics in naphthocyclinone and actinorhodin biosynthesis

Streptomyces produce complex bioactive secondary metabolites with remarkable chemical diversity. Benzoisochromanequinone polyketides actinorhodin and naphthocyclinone are formed through dimerization of half-molecules via single or double carbon-carbon bonds, respectively. Here we sequenced the genome of S. arenae DSM40737 to identify the naphthocyclinone gene cluster and established heterologous production in S. albus J1074 by utilizing direct cluster capture techniques. Comparative sequence analysis uncovered ncnN and ncnM gene products as putative enzymes responsible for dimerization. Inactivation of ncnN that is homologous to atypical co-factor independent oxidases resulted in the accumulation of fogacin, which is likely a reduced shunt product of the true substrate for naphthocyclinone dimerization. In agreement, inactivation of the homologous actVA-3 in S. coelicolor M145 also led to significantly reduced production of actinorhodin. Previous work has identified the NAD(P)H-dependent reductase ActVA-4 as the key enzyme in actinorhodin dimerization, but surprisingly inactivation of the homologous ncnM did not abolish naphthocyclinone formation and the mutation may have been complemented by an endogenous gene product. Our data suggests that dimerization of benzoisochromanequinone polyketides require two-component reductase-oxidase systems.

Biosynthetic novelty index reveals the metabolic potential of rare actinobacteria isolated from highly oligotrophic sediments

Calculations predict that testing of 5 000-10 000 molecules and >1 billion US dollars (£0.8 billion, £1=$1.2) are required for one single drug to come to the market. A solution to this problem is to establish more efficient protocols that reduce the high rate of re-isolation and continuous rediscovery of natural products during early stages of the drug development process. The study of 'rare actinobacteria' has emerged as a possible approach for increasing the discovery rate of drug leads from natural sources. Here, we define a simple genomic metric, defined as biosynthetic novelty index (BiNI), that can be used to rapidly rank strains according to the novelty of the subset of encoding biosynthetic clusters. By comparing a subset of high-quality genomes from strains of different taxonomic and ecological backgrounds, we used the BiNI score to support the notion that rare actinobacteria encode more biosynthetic gene cluster (BGC) novelty. In addition, we present the isolation and genomic characterization, focused on specialized metabolites and phenotypic screening, of two isolates belonging to genera Lentzea and Actinokineospora from a highly oligotrophic environment. Our results show that both strains harbour a unique subset of BGCs compared to other members of the genera Lentzea and Actinokineospora. These BGCs are responsible for potent antimicrobial and cytotoxic bioactivity. The experimental data and analysis presented in this study contribute to the knowledge of genome mining analysis in rare actinobacteria and, most importantly, can serve to direct sampling efforts to accelerate early stages of the drug discovery pipeline.

Microbiome and metagenomic analysis of Lake Hillier Australia reveals pigment-rich polyextremophiles and wide-ranging metabolic adaptations

Lake Hillier is a hypersaline lake known for its distinctive bright pink color. The cause of this phenomenon in other hypersaline sites has been attributed to halophiles, Dunaliella, and Salinibacter, however, a systematic analysis of the microbial communities, their functional features, and the prevalence of pigment-producing-metabolisms has not been previously studied. Through metagenomic sequencing and culture-based approaches, our results evidence that Lake Hillier is composed of a diverse set of microorganisms including archaea, bacteria, algae, and viruses. Our data indicate that the microbiome in Lake Hillier is composed of multiple pigment-producer microbes, including Dunaliella, Salinibacter, Halobacillus, Psychroflexus, Halorubrum, many of which are cataloged as polyextremophiles. Additionally, we estimated the diversity of metabolic pathways in the lake and determined that many of these are related to pigment production. We reconstructed complete or partial genomes for 21 discrete bacteria (N = 14) and archaea (N = 7), only 2 of which could be taxonomically annotated to previously observed species. Our findings provide the first metagenomic study to decipher the source of the pink color of Australia's Lake Hillier. The study of this pink hypersaline environment is evidence of a microbial consortium of pigment producers, a repertoire of polyextremophiles, a core microbiome and potentially novel species.

Long-Read Metagenome-Assembled Genomes Improve Identification of Novel Complete Biosynthetic Gene Clusters in a Complex Microbial Activated Sludge Ecosystem

Microorganisms produce a wide variety of secondary/specialized metabolites (SMs), the majority of which are yet to be discovered. These natural products play multiple roles in microbiomes and are important for microbial competition, communication, and success in the environment. SMs have been our major source of antibiotics and are used in a range of biotechnological applications. In silico mining for biosynthetic gene clusters (BGCs) encoding the production of SMs is commonly used to assess the genetic potential of organisms. However, as BGCs span tens to over 200 kb, identifying complete BGCs requires genome data that has minimal assembly gaps within the BGCs, a prerequisite that was previously only met by individually sequenced genomes. Here, we assess the performance of the currently available genome mining platform antiSMASH on 1,080 high-quality metagenome-assembled bacterial genomes (HQ MAGs) previously produced from wastewater treatment plants (WWTPs) using a combination of long-read (Oxford Nanopore) and short-read (Illumina) sequencing technologies. More than 4,200 different BGCs were identified, with 88% of these being complete. Sequence similarity clustering of the BGCs implies that the majority of this biosynthetic potential likely encodes novel compounds, and few BGCs are shared between genera. We identify BGCs in abundant and functionally relevant genera in WWTPs, suggesting a role of secondary metabolism in this ecosystem. We find that the assembly of HQ MAGs using long-read sequencing is vital to explore the genetic potential for SM production among the uncultured members of microbial communities. IMPORTANCE Cataloguing secondary metabolite (SM) potential using genome mining of metagenomic data has become the method of choice in bioprospecting for novel compounds. However, accurate biosynthetic gene cluster (BGC) detection requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until very recently with new long-read technologies. Here, we determined the biosynthetic potential of activated sludge (AS), the microbial community used in resource recovery and wastewater treatment, by mining high-quality metagenome-assembled genomes generated from long-read data. We found over 4,000 BGCs, including BGCs in abundant process-critical bacteria, with no similarity to the BGCs of characterized products. We show how long-read MAGs are required to confidently assemble complete BGCs, and we determined that the AS BGCs from different studies have very little overlap, suggesting that AS is a rich source of biosynthetic potential and new bioactive compounds.

Navigating and expanding the roadmap of natural product genome mining tools

Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.

Investigation of diverse biosynthetic secondary metabolites gene clusters using genome mining of indigenous Streptomyces strains isolated from saline soils in Iran

Background and Objectives

Bioactive secondary metabolites are the products of microbial communities adapting to environmental challenges, which have yet remained anonymous. As a result of demands in the pharmaceutical, agricultural, and food industries, microbial metabolites should be investigated. The most substantial sources of secondary metabolites are Streptomyces strains and are potential candidates for bioactive compound production. So, we used genome mining and bioinformatics to predict the isolates secondary metabolites, biosynthesis, and potential pharmaceuticals.

Materials and Methods

This is a bioinformatics part of our previous experimental research. Here, we aimed to inspect the underlying secondary metabolite properties of 20 phylogenetically diverse Streptomyces species of saline soil by a rationalized computational workflow by several software tools. We examined the Metabolites' cytotoxicity and antibacterial effects using the MTT assay and plate count technique, respectively.

Results

Among Streptomyces species, three were selected for genome mining and predicted novel secondary metabolites and potential drug abilities. All 11 metabolites were cytotoxic to A549, but ectoine (p≤0.5) and geosmin (p≤0.001) significantly operated as an anti-cancer drug. Metabolites of oxytetracycline and phosphinothricin (p≤0.001), 4Z-annimycin and geosmin (p≤0.01), and ectoine (p≤0.5) revealed significant antibacterial activity.

Conclusion

Of all the 11 compounds investigated, annimycin, geosmin, phosphinothricin, and ectoine had antimicrobial properties, but geosmin also showed very significant anti-cancer properties.