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Abstract Background The colonial cyanobacterium Microcystis proliferates in a wide range of freshwater ecosystems and is exposed to changing environmental factors during its life cycle. Microcystis blooms are often toxic, potentially fatal to animals and humans, and may cause environmental problems. There has been little investigation of the genomics of these cyanobacteria. Results Deciphering the 5,172,804 bp sequence of Microcystis aeruginosa PCC 7806 has revealed the high plasticity of its genome: 11.7% DNA repeats containing more than 1,000 bases, 6.8% putative transposases and 21 putative restriction enzymes. Compared to the genomes of other cyanobacterial lineages, strain PCC 7806 contains a large number of atypical genes that may have been acquired by lateral transfers. Metabolic pathways, such as fermentation and a methionine salvage pathway, have been identified, as have genes for programmed cell death that may be related to the rapid disappearance of Microcystis blooms in nature. Analysis of the PCC 7806 genome also reveals striking novel biosynthetic features that might help to elucidate the ecological impact of secondary metabolites and lead to the discovery of novel metabolites for new biotechnological applications. M. aeruginosa and other large cyanobacterial genomes exhibit a rapid loss of synteny in contrast to other microbial genomes. Conclusion Microcystis aeruginosa PCC 7806 appears to have adopted an evolutionary strategy relying on unusual genome plasticity to adapt to eutrophic freshwater ecosystems, a property shared by another strain of M. aeruginosa (NIES-843). Comparisons of the genomes of PCC 7806 and other cyanobacterial strains indicate that a similar strategy may have also been used by the marine strain Crocosphaera watsonii WH8501 to adapt to other ecological niches, such as oligotrophic open oceans.

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The realm of natural product (NP) research is constantly expanding, with diverse applications in both medicine and industry. In this interdisciplinary field, scientists collaborate to investigate various aspects of NPs, including understanding the mode of action of these compounds, unraveling their biosynthetic pathways, studying evolutionary aspects, and biochemically characterizing the enzymes involved. However, this collaboration can be challenging as all parties involved come from very different backgrounds (such as microbiology, synthetic chemistry, biochemistry, or bioinformatics) and may not use the same terminology. Fortunately, contemporary technologies, such as videos, provide novel avenues for effective engagement. Recognizing the potency of visual stimuli in explaining complex processes, we envision a future where animations become more and more common in interdisciplinary communication, accompanying perspectives, and reviews. To demonstrate how such approaches can enhance the understanding of complex processes, we have animated the biosynthesis of the glycopeptide antibiotic vancomycin (https://youtu.be/TGAgC4c8hvo)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Microbial biosurfactants have garnered significant interest from industry due to their lower toxicity, biodegradability, activity at lower concentrations and higher resistance compared to synthetic surfactants. The deep-sea Rhodococcus sp. I2R has been identified as a producer of glycolipid biosurfactants, specifically succinoyl trehalolipids, which exhibit antiviral activity. However, genome mining of this bacterium has revealed a still unexplored repertoire of biosurfactants. The microbial genome was found to host five non-ribosomal peptide synthetase (NRPS) gene clusters containing starter condensation domains that direct lipopeptide biosynthesis. Genomics and mass spectrometry (MS)-based metabolomics enabled the linking of two NRPS gene clusters to the corresponding lipopeptide families, leading to the identification of 20 new cyclolipopeptides, designated as rhodoheptins, and 33 new glycolipopeptides, designated as rhodamides. An integrated in silico gene cluster and high-resolution MS/MS data analysis allowed us to elucidate the planar structure, inference of stereochemistry and reconstruction of the biosynthesis of rhodoheptins and rhodamides. Rhodoheptins are cyclic heptapeptides where the N-terminus is bonded to a β-hydroxy fatty acid forming a macrolactone ring with the C-terminal amino acid residue. Rhodamides are linear 14-mer glycolipopeptides with a serine- and alanine-rich peptide backbone, featuring a distinctive pattern of acetylation, glycosylation and succinylation. These molecules exhibited biosurfactant activity in the oil-spreading assay and showed moderate antiproliferative effects against human A375 melanoma cells., (© 2024 The Author(s). Microbial Biotechnology published by John Wiley & Sons Ltd.)

Secondary metabolites are compounds not essential for an organism's development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35 000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The incidence of antibiotic-resistant bacterial infections is increasing, and development of new antibiotics has been deprioritised by the pharmaceutical industry. Interdisciplinary research approaches, based on the ecological principles of bacterial fitness, competition, and transmission, could open new avenues to combat antibiotic-resistant infections. Many facultative bacterial pathogens use human mucosal surfaces as their major reservoirs and induce infectious diseases to aid their lateral transmission to new host organisms under some pathological states of the microbiome and host. Beneficial bacterial commensals can outcompete specific pathogens, thereby lowering the capacity of the pathogens to spread and cause serious infections. Despite the clinical relevance, however, the understanding of commensal-pathogen interactions in their natural habitats remains poor. In this Personal View, we highlight directions to intensify research on the interactions between bacterial pathogens and commensals in the context of human microbiomes and host biology that can lead to the development of innovative and sustainable ways of preventing and treating infectious diseases., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The metabolic intimacy of symbiosis often demands the work of specialists. Natural products and defensive secondary metabolites can drive specificity by ensuring infection and propagation across host generations. But in contrast to bacteria, little is known about the diversity and distribution of natural product biosynthetic pathways among fungi and how they evolve to facilitate symbiosis and adaptation to their host environment. In this study, we define the secondary metabolism of Escovopsis and closely related genera, symbionts in the gardens of fungus-farming ants. We ask how the gain and loss of various biosynthetic pathways correspond to divergent lifestyles. Long-read sequencing allowed us to define the chromosomal features of representative Escovopsis strains, revealing highly reduced genomes composed of seven to eight chromosomes. The genomes are highly syntenic with macrosynteny decreasing with increasing phylogenetic distance, while maintaining a high degree of mesosynteny. An ancestral state reconstruction analysis of biosynthetic pathways revealed that, while many secondary metabolites are shared with non-ant-associated Sordariomycetes , 56 pathways are unique to the symbiotic genera. Reflecting adaptation to diverging ant agricultural systems, we observe that the stepwise acquisition of these pathways mirrors the ecological radiations of attine ants and the dynamic recruitment and replacement of their fungal cultivars. As different clades encode characteristic combinations of biosynthetic gene clusters, these delineating profiles provide important insights into the possible mechanisms underlying specificity between these symbionts and their fungal hosts. Collectively, our findings shed light on the evolutionary dynamic nature of secondary metabolism in Escovopsis and its allies, reflecting adaptation of the symbionts to an ancient agricultural system.IMPORTANCEMicrobial symbionts interact with their hosts and competitors through a remarkable array of secondary metabolites and natural products. Here, we highlight the highly streamlined genomic features of attine-associated fungal symbionts. The genomes of Escovopsis species, as well as species from other symbiont genera, many of which are common with the gardens of fungus-growing ants, are defined by seven chromosomes. Despite a high degree of metabolic conservation, we observe some variation in the symbionts' potential to produce secondary metabolites. As the phylogenetic distribution of the encoding biosynthetic gene clusters coincides with attine transitions in agricultural systems, we highlight the likely role of these metabolites in mediating adaptation by a group of highly specialized symbionts., Competing Interests: The authors declare no conflict of interest.

Extreme environments, such as Antarctica, select microbial communities that display a range of evolutionary strategies to survive and thrive under harsh environmental conditions. These include a diversity of specialized metabolites, which have the potential to be a source for new natural product discovery. Efforts using (meta)genome mining approaches to identify and understand biosynthetic gene clusters in Antarctica are still scarce, and the extent of their diversity and distribution patterns in the environment have yet to be discovered. Herein, we investigated the biosynthetic gene diversity of the biofilm microbial community of Whalers Bay, Deception Island, in the Antarctic Peninsula and revealed its distribution patterns along spatial and temporal gradients by applying metagenome mining approaches and multivariable analysis. The results showed that the Whalers Bay microbial community harbors a great diversity of biosynthetic gene clusters distributed into seven classes, with terpene being the most abundant. The phyla Proteobacteria and Bacteroidota were the most abundant in the microbial community and contributed significantly to the biosynthetic gene abundances in Whalers Bay. Furthermore, the results highlighted a significant correlation between the distribution of biosynthetic genes and taxonomic diversity, emphasizing the intricate interplay between microbial taxonomy and their potential for specialized metabolite production.IMPORTANCEThis research on antarctic microbial biosynthetic diversity in Whalers Bay, Deception Island, unveils the hidden potential of extreme environments for natural product discovery. By employing metagenomic techniques, the research highlights the extensive diversity of biosynthetic gene clusters and identifies key microbial phyla, Proteobacteria and Bacteroidota, as significant contributors. The correlation between taxonomic diversity and biosynthetic gene distribution underscores the intricate interplay governing specialized metabolite production. These findings are crucial for understanding microbial adaptation in extreme environments and hold significant implications for bioprospecting initiatives. The study opens avenues for discovering novel bioactive compounds with potential applications in medicine and industry, emphasizing the importance of preserving and exploring these polyextreme ecosystems to advance biotechnological and pharmaceutical research., Competing Interests: The authors declare no conflicts of interest.

Antibiotics are central to modern medicine, and yet they are mainly the products of intra and inter-kingdom evolutionary warfare. To understand how nature evolves antibiotics around a common mechanism of action, we investigated the origins of an extremely valuable class of compounds, lipid II targeting glycopeptide antibiotics (GPAs, exemplified by teicoplanin and vancomycin), which are used as last resort for the treatment of antibiotic resistant bacterial infections. Using a molecule-centred approach and computational techniques, we first predicted the nonribosomal peptide synthetase assembly line of paleomycin, the ancestral parent of lipid II targeting GPAs. Subsequently, we employed synthetic biology techniques to produce the predicted peptide and validated its antibiotic activity. We revealed the structure of paleomycin, which enabled us to address how nature morphs a peptide antibiotic scaffold through evolution. In doing so, we obtained temporal snapshots of key selection domains in nonribosomal peptide synthesis during the biosynthetic journey from ancestral, teicoplanin-like GPAs to modern GPAs such as vancomycin. Our study demonstrates the synergy of computational techniques and synthetic biology approaches enabling us to journey back in time, trace the temporal evolution of antibiotics, and revive these ancestral molecules. It also reveals the optimisation strategies nature has applied to evolve modern GPAs, laying the foundation for future efforts to engineer this important class of antimicrobial agents., (© 2023. The Author(s).)

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation., (© 2023. Springer Nature Limited.)

Background: High rates of clinical alarms in the intensive care unit can result in alarm fatigue among staff. Individualization of alarm thresholds is regarded as one measure to reduce non-actionable alarms. The aim of this study was to investigate staff's perceptions of alarm threshold individualization according to patient characteristics and disease status., Methods: This is a cross-sectional survey study (February-July 2020). Intensive care nurses and physicians were sampled by convenience. Data was collected using an online questionnaire., Results: Staff view the individualization of alarm thresholds in the monitoring of vital signs as important. The extent to which alarm thresholds are adapted from the normal range varies depending on the vital sign monitored, the reason for clinical deterioration, and the professional group asked. Vital signs used for hemodynamic monitoring (heart rate and blood pressure) were most subject to alarm individualizations. Staff are ambivalent regarding the integration of novel technological features into alarm management., Conclusions: All relevant stakeholders, including clinicians, hospital management, and industry, must collaborate to establish a "standard for individualization," moving away from ad hoc alarm management to an intelligent, data-driven alarm management. Making alarms meaningful and trustworthy again has the potential to mitigate alarm fatigue - a major cause of stress in clinical staff and considerable hazard to patient safety., Trial Registration: The study was registered at ClinicalTrials.gov (NCT03514173) on 02/05/2018., (© 2023. The Author(s).)

There is an urgent need to diversify the pipeline for discovering novel natural products due to the increase in multi-drug resistant infections. Like bacteria, fungi also produce secondary metabolites that have potent bioactivity and rich chemical diversity. To avoid self-toxicity, fungi encode resistance genes which are often present within the biosynthetic gene clusters (BGCs) of the corresponding bioactive compounds. Recent advances in genome mining tools have enabled the detection and prediction of BGCs responsible for the biosynthesis of secondary metabolites. The main challenge now is to prioritize the most promising BGCs that produce bioactive compounds with novel modes of action. With target-directed genome mining methods, it is possible to predict the mode of action of a compound encoded in an uncharacterized BGC based on the presence of resistant target genes. Here, we introduce the 'fungal bioactive compound resistant target seeker' (FunARTS) available at https://funarts.ziemertlab.com. This is a specific and efficient mining tool for the identification of fungal bioactive compounds with interesting and novel targets. FunARTS rapidly links housekeeping and known resistance genes to BGC proximity and duplication events, allowing for automated, target-directed mining of fungal genomes. Additionally, FunARTS generates gene cluster networking by comparing the similarity of BGCs from multi-genomes., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Caprazamycins are liponucleoside antibiotics showing bioactivity against Gram-positive bacteria including clinically relevant Mycobacterium tuberculosis by targeting the bacterial MraY-translocase. Their chemical structure contains a unique 3-methylglutaryl moiety which they only share with the closely related liposidomycins. Although the biosynthesis of caprazamycin is understood to some extent, the origin of 3-methylglutaryl-CoA for caprazamycin biosynthesis remains elusive., Results: In this work, we demonstrate two pathways of the heterologous producer Streptomyces coelicolor M1154 capable of supplying 3-methylglutaryl-CoA: One is encoded by the caprazamycin gene cluster itself including the 3-hydroxy-3-methylglutaryl-CoA synthase Cpz5. The second pathway is part of primary metabolism of the host cell and encodes for the leucine/isovalerate utilization pathway (Liu-pathway). We could identify the liu cluster in S. coelicolor M1154 and gene deletions showed that the intermediate 3-methylglutaconyl-CoA is used for 3-methylglutaryl-CoA biosynthesis. This is the first report of this intermediate being hijacked for secondary metabolite biosynthesis. Furthermore, Cpz20 and Cpz25 from the caprazamycin gene cluster were found to be part of a common route after both individual pathways are merged together., Conclusions: The unique 3-methylglutaryl moiety in caprazamycin originates both from the caprazamycin gene cluster and the leucine/isovalerate utilization pathway of the heterologous host. Our study enhanced the knowledge on the caprazamycin biosynthesis and points out the importance of primary metabolism of the host cell for biosynthesis of natural products., (© 2022. The Author(s).)

Many insects rely on microbial protection in the early stages of their development. However, in contrast to symbiont-mediated defense of eggs and young instars, the role of microbes in safeguarding pupae remains relatively unexplored, despite the susceptibility of the immobile stage to antagonistic challenges. Here, we outline the importance of symbiosis in ensuring pupal protection by describing a mutualistic partnership between the ascomycete Fusarium oxysporum and Chelymorpha alternans, a leaf beetle. The symbiont rapidly proliferates at the onset of pupation, extensively and conspicuously coating C. alternans during metamorphosis. The fungus confers defense against predation as symbiont elimination results in reduced pupal survivorship. In exchange, eclosing beetles vector F. oxysporum to their host plants, resulting in a systemic infection. By causing wilt disease, the fungus retained its phytopathogenic capacity in light of its symbiosis with C. alternans. Despite possessing a relatively reduced genome, F. oxysporum encodes metabolic pathways that reflect its dual lifestyle as a plant pathogen and a defensive insect symbiont. These include virulence factors underlying plant colonization, along with mycotoxins that may contribute to the defensive biochemistry of the insect host. Collectively, our findings shed light on a mutualism predicated on pupal protection of an herbivorous beetle in exchange for symbiont dissemination and propagation., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The Natural Product Domain Seeker (NaPDoS) webtool detects and classifies ketosynthase (KS) and condensation domains from genomic, metagenomic, and amplicon sequence data. Unlike other tools, a phylogeny-based classification scheme is used to make broader predictions about the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) genes in which these domains are found. NaPDoS is particularly useful for the analysis of incomplete biosynthetic genes or gene clusters, as are often observed in poorly assembled genomes and metagenomes, or when loci are not clustered, as in eukaryotic genomes. To help support the growing interest in sequence-based analyses of natural product biosynthetic diversity, here we introduce version 2 of the webtool, NaPDoS2, available at http://napdos.ucsd.edu/napdos2. This update includes the addition of 1417 KS sequences, representing a major expansion of the taxonomic and functional diversity represented in the webtool database. The phylogeny-based KS classification scheme now recognizes 41 class and subclass assignments, including new type II PKS subclasses. Workflow modifications accelerate run times, allowing larger datasets to be analyzed. In addition, default parameters were established using statistical validation tests to maximize KS detection and classification accuracy while minimizing false positives. We further demonstrate the applications of NaPDoS2 to assess PKS biosynthetic potential using genomic, metagenomic, and PCR amplicon datasets. These examples illustrate how NaPDoS2 can be used to predict biosynthetic potential and detect genes involved in the biosynthesis of specific structure classes or new biosynthetic mechanisms., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the 'Secondary Metabolite Transcriptomic Pipeline' (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Bacterial specialized metabolites are a proven source of antibiotics and cancer therapies, but whether we have sampled all the secondary metabolite chemical diversity of cultivated bacteria is not known. We analysed ~170,000 bacterial genomes and ~47,000 metagenome assembled genomes (MAGs) using a modified BiG-SLiCE and the new clust-o-matic algorithm. We estimate that only 3% of the natural products potentially encoded in bacterial genomes have been experimentally characterized. We show that the variation in secondary metabolite biosynthetic diversity drops significantly at the genus level, identifying it as an appropriate taxonomic rank for comparison. Equal comparison of genera based on relative evolutionary distance revealed that Streptomyces bacteria encode the largest biosynthetic diversity by far, with Amycolatopsis, Kutzneria and Micromonospora also encoding substantial diversity. Finally, we find that several less-well-studied taxa, such as Weeksellaceae (Bacteroidota), Myxococcaceae (Myxococcota), Pleurocapsa and Nostocaceae (Cyanobacteria), have potential to produce highly diverse sets of secondary metabolites that warrant further investigation., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Culture-independent metagenomic approaches offer a promising solution to the discovery of therapeutically relevant compounds such as antibiotics by enabling access to the hidden biosynthetic potential of microorganisms. These strategies, however, often entail laborious, multi-step, and time-consuming procedures to recover the biosynthetic gene clusters (BGCs) from soil metagenomes for subsequent heterologous expression. Here, we developed an efficient method we called single Nanopore read cluster mining (SNRCM), which enables the fast recovery of complete BGCs from a soil metagenome using long- and short-read sequencing. A metagenomic fosmid library of 83,700 clones was generated and sequenced using Nanopore as well as Illumina technologies. Hybrid assembled contigs of the sequenced fosmid library were subsequently analyzed to identify BGCs encoding secondary metabolites. Using SNRCM, we aligned the identified BGCs directly to Nanopore long-reads and were able to detect complete BGCs on single fosmids. This enabled us to select for and recover BGCs of interest for subsequent heterologous expression attempts. Additionally, the sequencing data of the fosmid library and its corresponding metagenomic DNA enabled us to assemble and recover a large nonribosomal peptide synthetase (NRPS) BGC from three different fosmids of our library and to directly amplify and recover a complete lasso peptide BGC from the high-quality metagenomic DNA. Overall, the strategies presented here provide a useful tool for accelerating and facilitating the identification and production of potentially interesting bioactive compounds from soil metagenomes. KEY POINTS: • An efficient approach for the recovery of BGCs from soil metagenomes was developed to facilitate natural product discovery. • A fosmid library was constructed from soil metagenomic HMW DNA and sequenced via Illumina and Nanopore. • Nanopore long-reads enabled the direct identification and recovery of complete BGCs on single fosmids., (© 2022. The Author(s).)

As a result of the continuous evolution of drug resistant bacteria, new antibiotics are urgently needed. Encoded by biosynthetic gene clusters (BGCs), antibiotic compounds are mostly produced by bacteria. With the exponential increase in the number of publicly available, sequenced genomes and the advancements of BGC prediction tools, genome mining algorithms have uncovered millions of uncharacterized BGCs for further evaluation. Since compound identification and characterization remain bottlenecks, a major challenge is prioritizing promising BGCs. Recently, researchers adopted self-resistance based strategies allowing them to predict the biological activities of natural products encoded by uncharacterized BGCs. Since 2017, the Antibiotic Resistant Target Seeker (ARTS) facilitated this so-called target-directed genome mining (TDGM) approach for the prioritization of BGCs encoding potentially novel antibiotics. Here, we present the ARTS database, available at https://arts-db.ziemertlab.com/. The ARTS database provides pre-computed ARTS results for >70,000 genomes and metagenome assembled genomes in total. Advanced search queries allow users to rapidly explore the fundamental criteria of TDGM such as BGC proximity, duplication and horizontal gene transfers of essential housekeeping genes. Furthermore, the ARTS database provides results interconnected throughout the bacterial kingdom as well as links to known databases in natural product research., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Environmental microorganisms continue to serve as a major source of bioactive natural products (NPs) and as an inspiration for many other scaffolds in the toolbox of modern medicine. Nearly all microbial NP-inspired therapies can be traced to field expeditions to collect samples from the environment. Despite the importance of these expeditions in the search for new drugs, few studies have attempted to document the extent to which NPs or their corresponding production genes are distributed within a given environment. To gain insights into this, the geographic occurrence of NP ketosynthase (KS) and adenylation (A) domains was documented across 53 and 58 surface sediment samples, respectively, covering 59,590 square kilometers of Lake Huron. Overall, no discernible NP geographic distribution patterns were observed for 90,528 NP classes of nonribosomal peptides and polyketides detected in the survey. While each sampling location harbored a similar number of A domain operational biosynthetic units (OBUs), a limited overlap of OBU type was observed, suggesting that at the sequencing depth used in this study, no single location served as a NP "hotspot". These data support the hypothesis that there is ample variation in NP occurrence between sampling sites and suggest that extensive sample collection efforts are required to fully capture the functional chemical diversity of sediment microbial communities on a regional scale.

This review covers literature between 2003-2021The development and application of genome mining tools has given rise to ever-growing genetic and chemical databases and propelled natural products research into the modern age of Big Data. Likewise, an explosion of evolutionary studies has unveiled genetic patterns of natural products biosynthesis and function that support Darwin's theory of natural selection and other theories of adaptation and diversification. In this review, we aim to highlight how Big Data and evolutionary thinking converge in the study of natural products, and how this has led to an emerging sub-discipline of evolutionary genome mining of natural products. First, we outline general principles to best utilize Big Data in natural products research, addressing key considerations needed to provide evolutionary context. We then highlight successful examples where Big Data and evolutionary analyses have been combined to provide bioinformatic resources and tools for the discovery of novel natural products and their biosynthetic enzymes. Rather than an exhaustive list of evolution-driven discoveries, we highlight examples where Big Data and evolutionary thinking have been embraced for the evolutionary genome mining of natural products. After reviewing the nascent history of this sub-discipline, we discuss the challenges and opportunities of genomic and metabolomic tools with evolutionary foundations and/or implications and provide a future outlook for this emerging and exciting field of natural product research.

Discovery of novel antibiotics is crucial for combating rapidly spreading antimicrobial resistance and new infectious diseases. Most of the clinically used antibiotics are natural products-secondary metabolites produced by soil microbes that can be cultured in the lab. Rediscovery of these secondary metabolites during discovery expeditions costs both time and resources. Metagenomics approaches can overcome this challenge by capturing both culturable and unculturable hidden microbial diversity. To be effective, such an approach should address questions like the following. Which sequencing method is better at capturing the microbial diversity and biosynthesis potential? What part of the soil should be sampled? Can patterns and correlations from such big-data explorations guide future novel natural product discovery surveys? Here, we address these questions by a paired amplicon and shotgun metagenomic sequencing survey of samples from soil horizons of multiple forest sites very close to each other. Metagenome mining identified numerous novel biosynthetic gene clusters (BGCs) and enzymatic domain sequences. Hybrid assembly of both long reads and short reads improved the metagenomic assembly and resulted in better BGC annotations. A higher percentage of novel domains was recovered from shotgun metagenome data sets than from amplicon data sets. Overall, in addition to revealing the biosynthetic potential of soil microbes, our results suggest the importance of sampling not only different soils but also their horizons to capture microbial and biosynthetic diversity and highlight the merits of metagenome sequencing methods. IMPORTANCE This study helped uncover the biosynthesis potential of forest soils via exploration of shotgun metagenome and amplicon sequencing methods and showed that both methods are needed to expose the full microbial diversity in soil. Based on our metagenome mining results, we suggest revising the historical strategy of sampling soils from far-flung places, as we found a significant number of novel and diverse BGCs and domains even in different soils that are very close to each other. Furthermore, sampling of different soil horizons can reveal the additional diversity that often remains hidden and is mainly caused by differences in environmental key parameters such as soil pH and nutrient content. This paired metagenomic survey identified diversity patterns and correlations, a step toward developing a rational approach for future natural product discovery surveys.

Indonesia is one of the most biodiverse countries in the world and a promising resource for novel natural compound producers. Actinomycetes produce about two thirds of all clinically used antibiotics. Thus, exploiting Indonesia's microbial diversity for actinomycetes may lead to the discovery of novel antibiotics. A total of 422 actinomycete strains were isolated from three different unique areas in Indonesia and tested for their antimicrobial activity. Nine potent bioactive strains were prioritized for further drug screening approaches. The nine strains were cultivated in different solid and liquid media, and a combination of genome mining analysis and mass spectrometry (MS)-based molecular networking was employed to identify potential novel compounds. By correlating secondary metabolite gene cluster data with MS-based molecular networking results, we identified several gene cluster-encoded biosynthetic products from the nine strains, including naphthyridinomycin, amicetin, echinomycin, tirandamycin, antimycin, and desferrioxamine B. Moreover, 16 putative ion clusters and numerous gene clusters were detected that could not be associated with any known compound, indicating that the strains can produce novel secondary metabolites. Our results demonstrate that sampling of actinomycetes from unique and biodiversity-rich habitats, such as Indonesia, along with a combination of gene cluster networking and molecular networking approaches, accelerates natural product identification.

Non-ribosomal peptide synthetases are important enzymes for the assembly of complex peptide natural products. Within these multi-modular assembly lines, condensation domains perform the central function of chain assembly, typically by forming a peptide bond between two peptidyl carrier protein (PCP)-bound substrates. In this work, we report structural snapshots of a condensation domain in complex with an aminoacyl-PCP acceptor substrate. These structures allow the identification of a mechanism that controls access of acceptor substrates to the active site in condensation domains. The structures of this complex also allow us to demonstrate that condensation domain active sites do not contain a distinct pocket to select the side chain of the acceptor substrate during peptide assembly but that residues within the active site motif can instead serve to tune the selectivity of these central biosynthetic domains.

Elaborate fragments of the proposed stereostructure of the complex polyketide antibiotic vancoresmycin have been synthesized in a stereoselective fashion based on a modular and convergent approach. Significant nuclear magnetic resonance differences in one of these subunits compared with the natural product question the proposed stereoconfiguration. Consequently, an extensive bioinformatics analysis of the biosynthetic gene cluster was carried out, leading to a revised stereoconfigurational proposal for this highly potent antibiotic.

The development of new antibacterial drugs has become one of the most important tasks of the century in order to overcome the posing threat of drug resistance in pathogenic bacteria. Many antibiotics originate from natural products produced by various microorganisms. Over the last decades, bioinformatical approaches have facilitated the discovery and characterization of these small compounds using genome mining methodologies. A key part of this process is the identification of the most promising biosynthetic gene clusters (BGCs), which encode novel natural products. In 2017, the Antibiotic Resistant Target Seeker (ARTS) was developed in order to enable an automated target-directed genome mining approach. ARTS identifies possible resistant target genes within antibiotic gene clusters, in order to detect promising BGCs encoding antibiotics with novel modes of action. Although ARTS can predict promising targets based on multiple criteria, it provides little information about the cluster structures of possible resistant genes. Here, we present SYN-view. Based on a phylogenetic approach, SYN-view allows for easy comparison of gene clusters of interest and distinguishing genes with regular housekeeping functions from genes functioning as antibiotic resistant targets. Our aim is to implement our proposed method into the ARTS web-server, further improving the target-directed genome mining strategy of the ARTS pipeline.

We report a genomics-guided exploration of the metabolic potential of the brasilicardin producer strain Nocardia terpenica IFM 0406. Bioinformatics analysis of the whole genome sequence revealed the presence of a biosynthetic gene cluster presumably responsible for the generation of formerly unknown nocobactin derivatives. Mass spectrometry-assisted isolation led to the identification of three new siderophores, terpenibactins A (1), B (2) and C (3), which belong to the class of nocobactins. Their structures were elucidated by employing spectroscopic techniques. Compounds 1-3 demonstrated inhibitory activity towards the muscarinic M3 receptor, while exhibiting only a low cytotoxicity., (© 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

This review covers the development of the genus Micromonospora as a model for natural product research and the timeline of discovery progress from the classical bioassay-guided approaches through the application of genome mining and genetic engineering techniques that target specific products. It focuses on the reported chemical structures along with their biological activities and the synthetic and biosynthetic studies they have inspired. This survey summarizes the extraordinary biosynthetic diversity that can emerge from a widely distributed actinomycete genus and supports future efforts to explore under-explored species in the search for novel natural products., Competing Interests: The authors declare no conflict of interest., (This journal is © The Royal Society of Chemistry.)

Using automated genome analysis tools, it is often unclear to what degree genetic variability in homologous biosynthetic pathways relates to structural variation. This hampers strain prioritization and compound identification and can lead to overinterpretation of chemical diversity. Here, we assessed the metabolic potential of Nocardia , an underinvestigated actinobacterial genus that is known to comprise opportunistic human pathogens. Our analysis revealed a plethora of putative biosynthetic gene clusters of various classes, including polyketide, nonribosomal peptide, and terpenoid pathways. Furthermore, we used the highly conserved biosynthetic pathway for nocobactin-like siderophores to investigate how gene cluster differences correlate to structural differences in the produced compounds. Sequence similarity networks generated by BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) showed the presence of several distinct gene cluster families. Metabolic profiling of selected Nocardia strains using liquid chromatography-mass spectrometry (LC-MS) metabolomics data, nuclear magnetic resonance (NMR) spectroscopy, and GNPS (Global Natural Product Social molecular networking) revealed that nocobactin-like biosynthetic gene cluster (BGC) families above a BiG-SCAPE threshold of 70% can be assigned to distinct structural types of nocobactin-like siderophores. IMPORTANCE Our work emphasizes that Nocardia represent a prolific source for natural products rivaling better-characterized genera such as Streptomyces or Amycolatopsis Furthermore, we showed that large-scale analysis of biosynthetic gene clusters using similarity networks with high stringency allows the distinction and prediction of natural product structural variations. This will facilitate future genomics-driven drug discovery campaigns., (Copyright © 2020 Männle et al.)

The increasing threat posed by multiresistant bacterial pathogens necessitates the discovery of novel antibacterials with unprecedented modes of action. ADEP1, a natural compound produced by Streptomyces hawaiiensis NRRL 15010, is the prototype for a new class of acyldepsipeptide (ADEP) antibiotics. ADEP antibiotics deregulate the proteolytic core ClpP of the bacterial caseinolytic protease, thereby exhibiting potent antibacterial activity against Gram-positive bacteria, including multiresistant pathogens. ADEP1 and derivatives, here collectively called ADEP, have been previously investigated for their antibiotic potency against different species, structure-activity relationship, and mechanism of action; however, knowledge on the biosynthesis of the natural compound and producer self-resistance have remained elusive. In this study, we identified and analyzed the ADEP biosynthetic gene cluster in S. hawaiiensis NRRL 15010, which comprises two NRPSs, genes necessary for the biosynthesis of (4 S ,2 R )-4-methylproline, and a type II polyketide synthase (PKS) for the assembly of highly reduced polyenes. While no resistance factor could be identified within the gene cluster itself, we discovered an additional clpP homologous gene (named clpP ADEP ) located further downstream of the biosynthetic genes, separated from the biosynthetic gene cluster by several transposable elements. Heterologous expression of ClpP ADEP in three ADEP-sensitive Streptomyces species proved its role in conferring ADEP resistance, thereby revealing a novel type of antibiotic resistance determinant. IMPORTANCE Antibiotic acyldepsipeptides (ADEPs) represent a promising new class of potent antibiotics and, at the same time, are valuable tools to study the molecular functioning of their target, ClpP, the proteolytic core of the bacterial caseinolytic protease. Here, we present a straightforward purification procedure for ADEP1 that yields substantial amounts of the pure compound in a time- and cost-efficient manner, which is a prerequisite to conveniently study the antimicrobial effects of ADEP and the operating mode of bacterial ClpP machineries in diverse bacteria. Identification and characterization of the ADEP biosynthetic gene cluster in Streptomyces hawaiiensis NRRL 15010 enables future bioinformatics screenings for similar gene clusters and/or subclusters to find novel natural compounds with specific substructures. Most strikingly, we identified a cluster-associated clpP homolog (named clpP ADEP ) as an ADEP resistance gene. ClpP ADEP constitutes a novel bacterial resistance factor that alone is necessary and sufficient to confer high-level ADEP resistance to Streptomyces across species., (Copyright © 2019 American Society for Microbiology.)

Covering: up to 2019Phylogenetic methods become increasingly important in natural product research. The growing amount of genetic data available today is enabling us to infer the evolutionary history of secondary metabolite gene clusters and their encoded compounds. We are starting to understand patterns and mechanisms of how the enormous diversity of chemical compounds produced by nature has evolved and are able to use phylogenetic inference to facilitate functional predictions of involved enzymes. In this review, we highlight how phylogenetic methods can aid natural product discovery and predictions and demonstrate several examples how these have been used in the past. We are featuring a number of easy to use tools that aid tree building and analysis and are providing a short overview how to create and interpret a phylogenetic tree.

Understanding the evolutionary background of a bacterial isolate has applications for a wide range of research. However generating an accurate species phylogeny remains challenging. Reliance on 16S rDNA for species identification currently remains popular. Unfortunately, this widespread method suffers from low resolution at the species level due to high sequence conservation. Currently, there is now a wealth of genomic data that can be used to yield more accurate species designations via modern phylogenetic methods and multiple genetic loci. However, these often require extensive expertise and time. The Automated Multi-Locus Species Tree (autoMLST) was thus developed to provide a rapid 'one-click' pipeline to simplify this workflow at: https://automlst.ziemertlab.com. This server utilizes Multi-Locus Sequence Analysis (MLSA) to produce high-resolution species trees; this does not preform multi-locus sequence typing (MLST), a related classification method. The resulting phylogenetic tree also includes helpful annotations, such as species clade designations and secondary metabolite counts to aid natural product prospecting. Distinct from currently available web-interfaces, autoMLST can automate selection of reference genomes and out-group organisms based on one or more query genomes. This enables a wide range of researchers to perform rigorous phylogenetic analyses more rapidly compared to manual MLSA workflows., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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, β-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., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Kistamicin is a divergent member of the glycopeptide antibiotics, a structurally complex class of important, clinically relevant antibiotics often used as the last resort against resistant bacteria. The extensively crosslinked structure of these antibiotics that is essential for their activity makes their chemical synthesis highly challenging and limits their production to bacterial fermentation. Kistamicin contains three crosslinks, including an unusual 15-membered A-O-B ring, despite the presence of only two Cytochrome P450 Oxy enzymes thought to catalyse formation of such crosslinks within the biosynthetic gene cluster. In this study, we characterise the kistamicin cyclisation pathway, showing that the two Oxy enzymes are responsible for these crosslinks within kistamicin and that they function through interactions with the X-domain, unique to glycopeptide antibiotic biosynthesis. We also show that the kistamicin OxyC enzyme is a promiscuous biocatalyst, able to install multiple crosslinks into peptides containing phenolic amino acids.

The peptidoglycan of the bacterial cell wall undergoes a permanent turnover during cell growth and differentiation. In the Gram-positive pathogen Staphylococcus aureus , the major peptidoglycan hydrolase Atl is required for accurate cell division, daughter cell separation and autolysis. Atl is a bifunctional N -acetylmuramoyl-L-alanine amidase/endo-β- N -acetylglucosaminidase that releases peptides and the disaccharide N -acetylmuramic acid-β-1,4- N -acetylglucosamine (MurNAc-GlcNAc) from the peptido-glycan. Here we revealed the recycling pathway of the cell wall turnover product MurNAc-GlcNAc in S. aureus . The latter disaccharide is internalized and concomitantly phosphorylated by the phosphotransferase system (PTS) transporter MurP, which had been implicated previously in the uptake and phosphorylation of MurNAc. Since MurP mutant cells accumulate MurNAc-GlcNAc and not MurNAc in the culture medium during growth, the disaccharide represents the physiological substrate of the PTS transporter. We further identified and characterized a novel 6-phospho- N -acetylmuramidase, named MupG, which intracellularly hydrolyses MurNAc 6-phosphate-GlcNAc, the product of MurP-uptake and phosphorylation, yielding MurNAc 6-phosphate and GlcNAc. MupG is the first characterized representative of a novel family of glycosidases containing domain of unknown function 871 (DUF871). The corresponding gene mupG ( SAUSA300_0192 ) of S. aureus strain USA300 is the first gene within a putative operon that also includes genes encoding the MurNAc 6-phosphate etherase MurQ, MurP, and the putative transcriptional regulator MurR. Using mass spectrometry, we observed cytoplasmic accumulation of MurNAc 6-phosphate-GlcNAc in Δ mupG and Δ mupGmurQ markerless non-polar deletion mutants, but not in the wild type or in the complemented Δ mupG strain. MurNAc 6-phosphate-GlcNAc levels in the mutants increased during stationary phase, in accordance with previous observations regarding peptidoglycan recycling in S. aureus .