BGC Atlas: a web resource for exploring the global chemical diversity encoded in bacterial genomes.

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.

Genomic insights into the evolution of secondary metabolism of Escovopsis and its allies, specialized fungal symbionts of fungus-farming ants.

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.

Unlocking the biosynthetic potential and taxonomy of the Antarctic microbiome along temporal and spatial gradients.

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.

Integrating research on bacterial pathogens and commensals to fight infections-an ecological perspective.

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.

Resurrecting ancestral antibiotics: unveiling the origins of modern lipid II targeting glycopeptides.

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.

Artificial intelligence for natural product drug discovery.

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.

FunARTS, the Fungal bioActive compound Resistant Target Seeker, an exploration engine for target-directed genome mining in fungi.

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.

Origin of the 3-methylglutaryl moiety in caprazamycin biosynthesis.

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.

The Natural Product Domain Seeker version 2 (NaPDoS2) webtool relates ketosynthase phylogeny to biosynthetic function.

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.

The leaf beetle Chelymorpha alternans propagates a plant pathogen in exchange for pupal protection.

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.

Secondary Metabolite Transcriptomic Pipeline (SeMa-Trap), an expression-based exploration tool for increased secondary metabolite production in bacteria.

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.

Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes.

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.

A rapid and efficient strategy to identify and recover biosynthetic gene clusters from soil metagenomes.

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.

ARTS-DB: a database for antibiotic resistant targets.

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 confluence of big data and evolutionary genome mining for the discovery of natural products.

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.

Metagenomic Sequencing of Multiple Soil Horizons and Sites in Close Vicinity Revealed Novel Secondary Metabolite Diversity.

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.

Evaluating the Distribution of Bacterial Natural Product Biosynthetic Genes across Lake Huron Sediment.

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.

Mining Indonesian Microbial Biodiversity for Novel Natural Compounds by a Combined Genome Mining and Molecular Networking Approach.

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.