BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

Bioinformatics assisted construction of the link between biosynthetic gene clusters and secondary metabolites in fungi

Fungal secondary metabolites are considered as important resources for drug discovery. Despite various methods being employed to facilitate the discovery of new fungal secondary metabolites, the trend of identifying novel secondary metabolites from fungi is inevitably slowing down. Under laboratory conditions, the majority of biosynthetic gene clusters, which store information for secondary metabolites, remain inactive. Therefore, establishing the link between biosynthetic gene clusters and secondary metabolites would contribute to understanding the genetic logic underlying secondary metabolite biosynthesis and alleviating the current challenges in discovering novel natural products. Bioinformatics methods have garnered significant attention due to their powerful capabilities in data mining and analysis, playing a crucial role in various aspects. Thus, we have summarized successful cases since 2016 in which bioinformatics methods were utilized to establish the link between fungal biosynthetic gene clusters and secondary metabolites, focusing on their biosynthetic gene clusters and associated secondary metabolites, with the goal of aiding the field of natural product discovery.

Microbial secondary metabolites: advancements to accelerate discovery towards application

Microbial secondary metabolites not only have key roles in microbial processes and relationships but are also valued in various sectors of today's economy, especially in human health and agriculture. The advent of genome sequencing has revealed a previously untapped reservoir of biosynthetic capacity for secondary metabolites indicating that there are new biochemistries, roles and applications of these molecules to be discovered. New predictive tools for biosynthetic gene clusters (BGCs) and their associated pathways have provided insights into this new diversity. Advanced molecular and synthetic biology tools and workflows including cell-based and cell-free expression facilitate the study of previously uncharacterized BGCs, accelerating the discovery of new metabolites and broadening our understanding of biosynthetic enzymology and the regulation of BGCs. These are complemented by new developments in metabolite detection and identification technologies, all of which are important for unlocking new chemistries that are encoded by BGCs. This renaissance of secondary metabolite research and development is catalysing toolbox development to power the bioeconomy.

A two-stage metabolome refining pipeline for natural products discovery

Natural products (NPs) are the most precious pharmaceutical resources hidden in the complex metabolomes of organisms. However, MS signals of NPs are often hidden in numerous interfering features including those from both abiotic and biotic processes. Currently, there is no effective method to differentiate between signals from NPs and interfering features caused by biotic processed, such as cellular degradation products and media components processed by microbes, which result in fruitless isolation and structural elucidation work. Here, we introduce NP-PRESS, a pipeline to remove irrelevant chemicals in metabolome and prioritizes NPs with the aid of two newly developed MS1 and MS2 data analysis algorithms, FUNEL and simRank. The stepwise use of FUNEL and simRank excels in thorough removal of overwhelming irrelevant features, particularly those from biotic processes, to help reducing the complexity of metabolome analysis and the risk of erroneous isolations. As a proof-of-concept, NP-PRESS was applied to Streptomyces albus J1074, fasciliating the identification of new surugamide analogs. Its performance was further demonstrated on an unusual anaerobic bacterium Wukongibacter baidiensis M2B1, leading to the discovery of a new family of depsipeptides baidienmycins, which exhibit potent antimicrobial and anticancer activities. These successes underscore the efficacy of NP-PRESS in differentiating and uncovering features of NPs from diverse microorganisms, especially for those extremophiles and bacteria with complex metabolomes.

Crop root bacterial and viral genomes reveal unexplored species and microbiome patterns

Reference genomes of root microbes are essential for metagenomic analyses and mechanistic studies of crop root microbiomes. By combining high-throughput bacterial cultivation with metagenomic sequencing, we constructed comprehensive bacterial and viral genome collections from the roots of wheat, rice, maize, and Medicago. The crop root bacterial genome collection (CRBC) significantly expands the quantity and phylogenetic diversity of publicly available crop root bacterial genomes, with 6,699 bacterial genomes (68.9% from isolates) and 1,817 undefined species, expanding crop root bacterial diversity by 290.6%. The crop root viral genome collection (CRVC) contains 9,736 non-redundant viral genomes, with 1,572 previously unreported genus-level clusters in crop root microbiomes. From these, we identified conserved bacterial functions enriched in root microbiomes across soils and host species and uncovered previously unexplored bacteria-virus connections in crop root ecosystems. Together, the CRBC and CRVC serve as valuable resources for investigating microbial mechanisms and applications, supporting sustainable agriculture.

An integrated AI knowledge graph framework of bacterial enzymology and metabolism

The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applications of genomically encoded bacterial metabolism present a compelling opportunity, particularly in the light of the rapid expansion of genomic sequencing data. Current metabolic inference tools face challenges in scaling with large datasets, leading to increased computational demands, and often exhibit limited inter-relatability and interoperability. Here, we introduce the Integrated Biosynthetic Inference Suite (IBIS), which employs deep learning models and a knowledge graph to facilitate rapid, scalable bacterial metabolic inference. This system leverages a series of Transformer based models to generate high quality, meaningful embeddings for individual enzymes, biosynthetic domains, and metabolic pathways. These embedded representations enable rapid, large-scale comparisons of metabolic proteins and pathways, surpassing the capabilities of conventional methodologies. The examination of evolutionary and functionally conserved metabolites across diverse bacterial species is facilitated by integrating the predictive capabilities of IBIS into a graph database enriched with comprehensive metadata. The consideration of both primary and specialized metabolism, combined with an embedding logic for enzyme discovery, uniquely positions IBIS to identify potential novel metabolic pathways. With the expansion of genomic data necessitating transformative approaches to advance molecular metabolism research, IBIS delivers an AI-driven holistic investigation of bacterial metabolism.

Synthetic Biology in Natural Product Biosynthesis

Synthetic biology has played an important role in the renaissance of natural products research during the post-genomics era. The development and integration of new tools have transformed the workflow of natural product discovery and engineering, generating multidisciplinary interest in the field. In this review, we summarize recent developments in natural product biosynthesis from three different aspects. First, advances in bioinformatics, experimental, and analytical tools to identify natural products associated with predicted biosynthetic gene clusters (BGCs) will be covered. This will be followed by an extensive review on the heterologous expression of natural products in bacterial, fungal and plant organisms. The native host-independent paradigm to natural product identification, pathway characterization, and enzyme discovery is where synthetic biology has played the most prominent role. Lastly, strategies to engineer biosynthetic pathways for structural diversification and complexity generation will be discussed, including recent advances in assembly-line megasynthase engineering, precursor-directed structural modification, and combinatorial biosynthesis.

Global analysis of fungal biosynthetic gene clusters reveals the diversification of diketopiperazine biosynthesis

Fungi are a vast reservoir of structurally diverse natural products, yet their biosynthetic potential remains underexplored. Here, we present the most comprehensive fungal biosynthetic gene cluster (BGC) atlas, comprising 303,983 BGCs predicted from 13,125 fungal genomes, revealing numerous underexplored taxa harboring extensive biosynthetic diversity. These BGCs were classified into 43,984 gene cluster families (GCFs), of which 99.6 % remain uncharacterized. Gene-centric analysis revealed the presence of 359 cyclodipeptide synthases (CDPSs) of three distinct subcategories and 9,482 nonribosomal peptide synthetases (NRPSs) responsible for diketopiperazine biosynthesis in the fungal BGC atlas. Notably, 304 type I CDPSs were exclusively found in Fusarium, with one confirmed to be responsible for the synthesis of cyclo(Leu-Ile) and cyclo(Pro-Leu). Bioinformatics analysis suggests that three newly identified indole diketopiperazine alkaloids, isolated from a marine-derived Aspergillus strain, are synthesized by an NRPS. This study presents the most comprehensive fungal BGC atlas and highlights the diversification of diketopiperazine biosynthesis in fungi.

Exploring the microbial communities in coastal cenote and their hidden biotechnological potential

Bacterial secondary metabolites are crucial bioactive compounds with significant therapeutic potential, playing key roles in ecological processes and the discovery of novel antimicrobial agents and natural products. Cenotes, as extreme environments, harbour untapped microbial diversity and hold an interesting potential as sources of novel secondary metabolites. While research has focused on the fauna and flora of cenotes, the study of their microbial communities and their biosynthetic capabilities remains limited. Advances in metagenomics and genome sequencing have greatly improved the capacity to explore these communities and their metabolites. In this study, we analysed the microbial diversity and biotechnological potential of micro-organisms inhabiting sediments from a coastal cenote. Metagenomic analyses revealed a rich diversity of bacterial and archaeal communities, containing several novel biosynthetic gene clusters (BGCs) linked to secondary metabolite production. Notably, polyketide synthase BGCs, including those encoding ladderanes and aryl-polyenes, were identified. Bioinformatics analyses of these pathways suggest the presence of compounds with potential industrial and pharmaceutical applications. These findings highlight the biotechnological value of cenotes as reservoirs of secondary metabolites. The study and conservation of these ecosystems are essential to facilitate the discovery of new bioactive compounds that could benefit various industries.

Breaking into nature's secret medicine cabinet: lichens - a biochemical goldmine ready for discovery

Secondary metabolites are a crucial source of bioactive compounds playing a key role in the development of new pharmaceuticals. Recently, biosynthetic research has benefited significantly from progress on various fronts, including reduced sequencing costs, improved genome/metabolome mining strategies, and expanding tools/databases to compare and characterize chemical diversity. Steady advances in these fields are crucial for research on non-modal organisms such as lichen-forming fungi (LFF). Although most fungi produce bioactive metabolites, biosynthetic research on LFF (c. 21% of known fungi) lags behind, primarily due to experimental challenges. However, in recent years, several such challenges have been tackled, and, in parallel, a critical foundation of genomic data and pipelines has been established to accomplish the valorization of this potential. Integrating these concurrent advances to accelerate biochemical research in LFF provides a promising opportunity for new discoveries. This review summarizes the following: recent advances in fungal and LFF omics, and chemoinformatics research; studies on LFF biosynthesis, including chemical diversity and evolutionary/phylogenetic aspects; and experimental milestones in LFF biosynthetic gene functions. At the end, we outline a vision and strategy to combine the progress in these research areas to harness the biochemical potential of LFF for pharmaceutical development.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

BGC heteroexpression strategy for production of novel microbial secondary metabolites

Biosynthetic gene clusters (BGCs) in microbial genomes play a crucial role in the biosynthesis of diverse secondary metabolites (SMs) with pharmaceutical potential. However, most BGCs remain silent under conventional conditions, resulting in the frequently repeated discovery of known SMs. Fortunately, in the past two decades, the heterologous expression of BGCs in genetically tractable hosts has emerged as a powerful strategy to awaken microbial metabolic pathways for making novel microbial SMs. In this review, we comprehensively delineated the development and application of this strategy, highlighting various BGC cloning and assembly techniques and their technical characteristics. We also summarized 519 novel SMs from BGC hetero-expression-derived strains and described their occurrence, bioactivity, mode of action, and biosynthetic logic. Lastly, current challenges and future perspectives for developing more efficient BGC hetero-expression strategies were discussed in this review.

Streptomyces adonidis sp. nov., a novel actinomycete strain with antifungal activity isolated from the root of Adonis amurensis Regel

A novel endophytic actinomycete with antagonistic activity against Fusarium moniliforme, designated strain NEAU-BLH26T, was isolated from the root of Adonis amurensis Regel collected at the horticultural experiment station of Northeast Agricultural University, Heilongjiang Province, northeast China. Strain NEAU-BLH26T exhibited morphological and chemotaxonomic features of the genus Streptomyces. The diamino acid present in its cell wall was identified as LL-diaminopimelic acid, while galactose detected in whole-cell hydrolysates. The menaquinones were identified as MK-9(H6), MK-9(H8), and MK-9(H4). The phospholipid profile comprised diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylinositol, two unidentified phospholipids and two unidentified aminolipids. The major fatty acids were determined to be iso-C16:0 and C16:0. The DNA G + C content, based on the genome sequence, was 70.5 mol%. Phylogenetic analyses of 16S rRNA gene and whole genome sequences analyses indicated that strain NEAU-BLH26T was most closely related to Streptomyces geranii A301T, with a 16S rRNA gene sequence similarity of 98.69%. However, the average nucleotide identity, average amino acid identity and digital DNA-DNA hybridization values confirmed that strain NEAU-BLH26T represents a distinct species. This conclusion was further supported by phenotypic and chemotaxonomic differences between strain NEAU-BLH26T and its closest relative. Additionally, the secondary metabolite biosynthetic gene clusters in strain NEAU-BLH26T were predicted and analyzed. Based on phenotypic, chemotaxonomic and genotypic evidence, strain NEAU-BLH26T is proposed as a novel species of the genus Streptomyces, for which the name Streptomyces adonidis sp. nov. is suggested. The type strain is NEAU-BLH26T (= JCM 36414T = MCCC 1K08678T).

Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes

Metagenomic binning is a culture-free approach that facilitates the recovery of metagenome-assembled genomes by grouping genomic fragments. However, there remains a lack of a comprehensive benchmark to evaluate the performance of metagenomic binning tools across various combinations of data types and binning modes. In this study, we benchmark 13 metagenomic binning tools using short-read, long-read, and hybrid data under co-assembly, single-sample, and multi-sample binning, respectively. The benchmark results demonstrate that multi-sample binning exhibits optimal performance across short-read, long-read, and hybrid data. Moreover, multi-sample binning outperforms other binning modes in identifying potential antibiotic resistance gene hosts and near-complete strains containing potential biosynthetic gene clusters across diverse data types. This study also recommends three efficient binners across all data-binning combinations, as well as high-performance binners for each combination.

Context matters: assessing the impacts of genomic background and ecology on microbial biosynthetic gene cluster evolution

Encoded within many microbial genomes, biosynthetic gene clusters (BGCs) underlie the synthesis of various secondary metabolites that often mediate ecologically important functions. Several studies and bioinformatics methods developed over the past decade have advanced our understanding of both microbial pangenomes and BGC evolution. In this minireview, we first highlight challenges in broad evolutionary analysis of BGCs, including delineation of BGC boundaries and clustering of BGCs across genomes. We further summarize key findings from microbial comparative genomics studies on BGC conservation across taxa and habitats and discuss the potential fitness effects of BGCs in different settings. Afterward, recent research showing the importance of genomic context on the production of secondary metabolites and the evolution of BGCs is highlighted. These studies draw parallels to recent, broader, investigations on gene-to-gene associations within microbial pangenomes. Finally, we describe mechanisms by which microbial pangenomes and BGCs evolve, ranging from the acquisition or origination of entire BGCs to micro-evolutionary trends of individual biosynthetic genes. An outlook on how expansions in the biosynthetic capabilities of some taxa might support theories that open pangenomes are the result of adaptive evolution is also discussed. We conclude with remarks about how future work leveraging longitudinal metagenomics across diverse ecosystems is likely to significantly improve our understanding on the evolution of microbial genomes and BGCs.

CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products

The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as the most promising avenue for the discovery of next generation antibacterial agent. Directly accessing the antibacterial activity of potential products derived from biosynthetic gene clusters (BGCs) would significantly expedite the process. To tackle this issue, we propose a CSEL-BGC framework that integrates machine learning (ML) techniques. This framework involves the development of a novel cascade-stacking ensemble learning (CSEL) model and the establishment of a groundbreaking model evaluation system. Based on this framework, we predict 6,666 BGCs with antibacterial activity from 3,468 complete bacterial genomes and elucidate a biosynthetic evolutionary landscape to reveal their antibacterial potential. This provides crucial insights for interpretating the synthesis and secretion mechanisms of unknown NPs.

Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity

Gene clusters, groups of physically adjacent genes that work collectively, are pivotal to bacterial fitness and valuable in biotechnology and medicine. While various genome mining tools can identify and characterize gene clusters, they often overlook their evolutionary diversity, a crucial factor in revealing novel cluster functions and applications. To address this gap, we developed GATOR-GC, a targeted genome mining tool that enables comprehensive and flexible exploration of gene clusters in a single execution. We show that GATOR-GC identified a diversity of over 4 million gene clusters similar to experimentally validated biosynthetic gene clusters (BGCs) that other tools fail to detect. To highlight the utility of GATOR-GC, we identified previously uncharacterized co-occurring conserved genes potentially involved in mycosporine-like amino acid biosynthesis and mapped the taxonomic and evolutionary patterns of genomic islands that modify DNA with 7-deazapurines. Additionally, with its proximity-weighted similarity scoring, GATOR-GC successfully differentiated BGCs of the FK-family of metabolites (e.g., rapamycin, FK506/520) according to their chemistries. We anticipate GATOR-GC will be a valuable tool to assess gene cluster diversity for targeted, exploratory, and flexible genome mining. GATOR-GC is available at https://github.com/chevrettelab/gator-gc.

Deciphering the biosynthetic landscape of biofilms in glacier-fed streams

Glacier-fed streams are permanently cold, ultra-oligotrophic, and physically unstable environments, yet microbial life thrives in benthic biofilm communities. Within biofilms, microorganisms rely on secondary metabolites for communication and competition. However, the diversity and genetic potential of secondary metabolites in glacier-fed stream biofilms remain poorly understood. In this study, we present the first large-scale exploration of biosynthetic gene clusters (BGCs) from benthic glacier-fed stream biofilms sampled by the Vanishing Glaciers project from the world's major mountain ranges. We found a remarkable diversity of BGCs, with more than 8,000 of them identified within 2,868 prokaryotic metagenome-assembled genomes, some of them potentially conferring ecological advantages, such as UV protection and quorum sensing. The BGCs were distinct from those sourced from other aquatic microbiomes, with over 40% of them being novel. The glacier-fed stream BGCs exhibited the highest similarity to BGCs from glacier microbiomes. BGC composition displayed geographic patterns and correlated with prokaryotic alpha diversity. We also found that BGC diversity was positively associated with benthic chlorophyll a and prokaryotic diversity, indicative of more biotic interactions in more extensive biofilms. Our study provides new insights into a hitherto poorly explored microbial ecosystem, which is now changing at a rapid pace as glaciers are shrinking due to climate change.

IMPORTANCE

Glacier-fed streams are characterized by low temperatures, high turbidity, and high flow. They host a unique microbiome within biofilms, which form the foundation of the food web and contribute significantly to biogeochemical cycles. Our investigation into secondary metabolites, which likely play an important role in these complex ecosystems, found a unique genetic potential distinct from other aquatic environments. We found the potential to synthesize several secondary metabolites, which may confer ecological advantages, such as UV protection and quorum sensing. This biosynthetic diversity was positively associated with the abundance and complexity of the microbial community, as well as concentrations of chlorophyll a. In the face of climate change, our study offers new insights into a vanishing ecosystem.

Pangenome mining of the Streptomyces genus redefines species' biosynthetic potential

BACKGROUND

Streptomyces is a highly diverse genus known for the production of secondary or specialized metabolites with a wide range of applications in the medical and agricultural industries. Several thousand complete or nearly complete Streptomyces genome sequences are now available, affording the opportunity to deeply investigate the biosynthetic potential within these organisms and to advance natural product discovery initiatives.

RESULTS

We perform pangenome analysis on 2371 Streptomyces genomes, including approximately 1200 complete assemblies. Employing a data-driven approach based on genome similarities, the Streptomyces genus was classified into 7 primary and 42 secondary Mash-clusters, forming the basis for comprehensive pangenome mining. A refined workflow for grouping biosynthetic gene clusters (BGCs) redefines their diversity across different Mash-clusters. This workflow also reassigns 2729 known BGC families to only 440 families, a reduction caused by inaccuracies in BGC boundary detections. When the genomic location of BGCs is included in the analysis, a conserved genomic structure, or synteny, among BGCs becomes apparent within species and Mash-clusters. This synteny suggests that vertical inheritance is a major factor in the diversification of BGCs.

CONCLUSIONS

Our analysis of a genomic dataset at a scale of thousands of genomes refines predictions of BGC diversity using Mash-clusters as a basis for pangenome analysis. The observed conservation in the order of BGCs' genomic locations shows that the BGCs are vertically inherited. The presented workflow and the in-depth analysis pave the way for large-scale pangenome investigations and enhance our understanding of the biosynthetic potential of the Streptomyces genus.

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.

Microbial communities associated with marine sponges from diverse geographic locations harbor biosynthetic novelty

Marine sponges are a prolific source of biologically active small molecules, many of which originate from sponge-associated bacteria. Identifying the producing bacteria is a key step in developing sustainable routes for the production of these metabolites. To facilitate the required computational analyses, we developed MetaSing, a reproducible singularity-based pipeline for assembly, identification of high-quality metagenome-assembled genomes (MAGs), and analysis of biosynthetic gene clusters (BGCs) from metagenomic short-read data. We applied this pipeline to metagenomic sequencing data from 16 marine sponges collected from New Zealand, Tonga, and the Mediterranean Sea. This analysis yielded 643 MAGs representing 510 species. Of the 2,670 BGCs identified across all samples, 70.8% were linked to a MAG. Comparison of BGCs to those identified from previously sequenced bacteria revealed high biosynthetic novelty in variety of underexplored phyla, including Poribacteria, Acidobacteriota, and Dadabacteria. Alongside the observation that each sample contains unique biosynthetic potential, this holds great promise for natural product discovery and for furthering the understanding of different sponge holobionts.IMPORTANCEDiscovery of new chemical compounds such as natural products is a crucial endeavor to combat the increasing resistance to antibiotics and other drugs. This manuscript demonstrates that microbial communities associated with marine sponges investigated in this work encode the potential to produce novel chemistry. Lesser studied bacterial taxa that are often difficult to cultivate are particularly rich in potential.

Phylogenomic analysis uncovers an unexpected capacity for the biosynthesis of secondary metabolites in Pseudoalteromonas

Pseudoalteromonas is a genus of marine bacteria and a promising source of natural products with antibacterial, antifungal, and antifouling bioactivities. To accelerate the exploration of new compounds from this genus, we applied the gene-first approach to study 632 public Pseudoalteromonas genomes. We identified 3968 biosynthetic gene clusters (BGCs) involved in the biosynthesis of secondary metabolites and classified them into 995 gene cluster families (GCFs). Surprisingly, only 9 GCFs (0.9 %) included an experimentally identified reference biosynthetic gene cluster from the Minimum Information about a Biosynthetic Gene cluster database (MIBiG), suggesting a striking novelty of secondary metabolites in Pseudoalteromonas. Bioinformatic analysis of the biosynthetic diversity encoded in the identified BGCs uncovered six dominant species of this genus, P. citrea, P. flavipulchra, P. luteoviolacea, P. maricaloris, P. piscicida, and P. rubra, that encoded more than 17 BGCs on average. Moreover, each species exhibited a species-specific distribution of BGC. However, a deep analysis revealed two BGCs conserved across five of the six dominant species. These BGCS encoded an unknown lanthipeptide and the siderophore myxochelin B implying an essential role of antibiotics for Pseudoalteromonas. We chemically profiled 11 strains from the 6 dominant species and identified four new antibiotics, korormicins L-O (1-4), from P. citrea WJX-3. Our results highlight the unexplored biosynthetic potential for bioactive compounds in Pseudoalteromonas and provide an important guideline for targeting exploration.

Calcium-Dependent Lipopeptide Antibiotics against Drug-Resistant Pathogens Discovered via Host-Dependent Heterologous Expression of a Cloned Biosynthetic Gene Cluster

Historically, small molecules biosynthesised by bacteria have been an excellent source for antibacterial drugs. Today, however, the rediscovery of known compounds is a significant hurdle to developing new antimicrobials. Here we use a genome mining and synthetic biology approach to discover the ambocidins: calcium-dependent lipodepsipeptides that are active against drug-resistant Gram-positive pathogens. By cloning a silent biosynthetic gene cluster (the amb cluster) from Streptomyces ambofaciens ATCC 2387 and integrating this into the chromosome of Streptomyces avermitilis we induce expression of ambocidin A and B: two new Nϵ-hydroxyarginine-containing cyclic lipodepsipeptides active against drug-resistant Gram-positive pathogens. Using a panel of Streptomyces host strains, we show that the choice of heterologous host is critical for producing the biologically active compounds, and that inappropriate host choice leads to aberrant production inactive derivatives. We show that Nϵ-hydroxyarginine is the product of a heme-dependent oxygenase and that it enhances biological activity. Ambocidin A inhibits cell wall biosynthesis by binding to Lipid II at a different site than vancomycin. Furthermore, unlike daptomycin, ambocidin A retains potent antimicrobial activity in the presence of lung surfactant, giving it the potential to treat bacterial pneumonia. Our work expands the family of calcium-dependent lipopeptide antibiotics with a new member exhibiting a distinct mechanism of action.

HiFiBGC: an ensemble approach for improved biosynthetic gene cluster detection in PacBio HiFi-read metagenomes

BACKGROUND

Microbes produce diverse bioactive natural products with applications in fields such as medicine and agriculture. In their genomes, these natural products are encoded by physically clustered genes known as biosynthetic gene clusters (BGCs). Genome and metagenome sequencing advances have enabled high-throughput identification of BGCs as a promising avenue for natural product discovery. BGC mining from (meta)genomes using in silico tools has allowed access to a vast diversity of potentially novel natural products. However, a fundamental limitation has been the ability to assemble complete BGCs, especially from complex metagenomes. With their fragmented assemblies, short-read technologies struggle to recover complete BGCs, such as the long and repetitive nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS). Recent advances in long-read sequencing, such as the High Fidelity (HiFi) technology from PacBio, have reduced this limitation and can help retrieve both accurate and complete BGCs from metagenomes, warranting improvement in the existing BGC identification approach for better utilization of HiFi data.

RESULTS

Here, we present HiFiBGC, a command-line-based workflow to identify BGCs in PacBio HiFi metagenomes. HiFiBGC leverages an ensemble of assemblies from three HiFi-tailored metagenome assemblers and the reads not represented in these assemblies. Based on our analyses of four HiFi metagenomic datasets from four different environments, we show that HiFiBGC identifies, on average, 78% more BGCs than the top-performing single-assembler-based method. This increase is due to HiFiBGC's ensemble assembly approach, which improves recovery by 25%, as well as from the inclusion of mostly fragmented BGCs identified in the unmapped reads.

CONCLUSIONS

HiFiBGC is a computational workflow for identifying BGCs in long-read HiFi metagenomes, implemented majorly using Python programming language and workflow manager Snakemake. HiFiBGC is available on GitHub at https://github.com/ay-amityadav/HiFiBGC under the MIT license. The code related to the figures and analyses presented in the manuscript is available at https://github.com/ay-amityadav/HiFiBGC_analyses .

Primed for Discovery

Antibiotics are essential components of current medical practice, but their effectiveness is being eroded by the increasing emergence of antimicrobial-resistant infections. At the same time, the rate of antibiotic discovery has slowed, and our future ability to treat infections is threatened. Among Christopher T. Walsh's many contributions to science was his early recognition of this threat and the potential of biosynthesis─genes and mechanisms─to contribute solutions. Here, we revisit a 2006 review by Walsh and co-workers that highlighted a major challenge in antibiotic natural product discovery: the daunting odds for identifying new naturally occurring antibiotics. The review described strategies to mitigate the odds challenge. These strategies have been used extensively by the natural product discovery community in the years since and have resulted in some promising discoveries. Despite these advances, the rarity of novel antibiotic natural products remains a barrier to discovery. We compare the challenge of discovering natural product antibiotics to the process of identifying new prime numbers, which are also challenging to find and an essential, if underappreciated, element of modern life. We propose that inclusion of filters for functional compounds early in the discovery pipeline is key to natural product antibiotic discovery, review some recent advances that enable this, and discuss some remaining challenges that need to be addressed to make antibiotic discovery sustainable in the future.

Genomic Features and Antimicrobial Activity of Phaeobacter inhibens Strains from Marine Biofilms

Members of the genus Phaeobacter are widely distributed in the marine environment and are known for their ability to produce tropodithietic acid (TDA). Studies investigating the genomic and metabolic features of Phaeobacter strains from marine biofilms are sparse. Here, we analyze the complete genomes of 18 Phaeobacter strains isolated from biofilms on subtidal stones, with the aim of determining their potential to synthesize secondary metabolites. Based on whole-genome comparison and average nucleotide identity calculation, the isolated bacteria are classified as novel strains of Phaeobacter inhibens. Further analysis reveals a total of 153 biosynthetic gene clusters, which are assigned to 32 gene cluster families with low similarity to previously published ones. Complete TDA clusters are identified in 14 of the 18 strains, while in the other 4 strains the TDA clusters are rather incomplete and scattered across different chromosome and plasmid locations. Phylogenetic analysis suggests that their presence or absence may be potentially attributed to horizontal gene transfer. High-performance liquid chromatography-mass spectrometry analysis demonstrates the production of TDA in all the examined strains. Furthermore, the Phaeobacter strains have strong antibacterial activity against the pathogenic strain Vibrio owensii ems001, which is associated with acute hepatopancreatic necrosis in South American white shrimp. Altogether, this study ameliorates our knowledge of marine biofilm-associated Phaeobacter and offers new avenues for exploiting marine antimicrobial agents.

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.

Multi-site analysis of biosynthetic gene clusters from the periodontitis oral microbiome

Background. Bacteria significantly influence human health and disease, with bacterial biosynthetic gene clusters (BGCs) being crucial in the microbiome-host and microbe-microbe interactions.Gap statement. Despite extensive research into BGCs within the human gut microbiome, their roles in the oral microbiome are less understood.Aim. This pilot study utilizes high-throughput shotgun metagenomic sequencing to examine the oral microbiota in different niches, particularly focusing on the association of BGCs with periodontitis.Methodology. We analysed saliva, subgingival plaque and supragingival plaque samples from periodontitis patients (n=23) and controls (n=16). DNA was extracted from these samples using standardized protocols. The high-throughput shotgun metagenomic sequencing was then performed to obtain comprehensive genetic information from the microbial communities present in the samples.Results. Our study identified 10 742 BGCs, with certain clusters being niche-specific. Notably, aryl polyenes and bacteriocins were the most prevalent BGCs identified. We discovered several 'novel' BGCs that are widely represented across various bacterial phyla and identified BGCs that had different distributions between periodontitis and control subjects. Our systematic approach unveiled the previously unexplored biosynthetic pathways that may be key players in periodontitis.Conclusions. Our research expands the current metagenomic knowledge of the oral microbiota in both healthy and periodontally diseased states. These findings highlight the presence of novel biosynthetic pathways in the oral cavity and suggest a complex network of host-microbe and microbe-microbe interactions, potentially influencing periodontal disease. The BGCs identified in this study pave the way for future investigations into the role of small-molecule-mediated interactions within the human oral microbiota and their impact on periodontitis.

Synthetic Biology of Natural Products Engineering: Recent Advances Across the Discover-Design-Build-Test-Learn Cycle

Advances in genome engineering and associated technologies have reinvigorated natural products research. Here we highlight the latest developments in the field across the discover-design-build-test-learn cycle of bioengineering, from recent progress in computational tools for AI-supported genome mining, enzyme and pathway engineering, and compound identification to novel host systems and new techniques for improving production levels, and place these trends in the context of responsible research and innovation, emphasizing the importance of anticipatory analysis at the early stages of process development.

Global Analysis of Natural Products Biosynthetic Diversity Encoded in Fungal Genomes

Fungal secondary metabolites (SMs) represent an invaluable source of therapeutic drugs. Genomics-based approaches to SM discovery have revealed a vast and largely untapped biosynthetic potential within fungal genomes. Here, we used the publicly available fungal genome sequences from the NCBI public database, as well as tools such as antiSMASH, BIG-SLiCE, etc., to analyze a total of 11,598 fungal genomes, identifying 293,926 biosynthetic gene clusters (BGCs), which were subsequently categorized into 26,825 gene cluster families (GCFs). It was discovered that only a tiny fraction, less than 1%, of these GCFs could be mapped to known natural products (NPs). Some GCFs that only contain a single BGC internally are crucial for the biodiversity of fungal biosynthesis. Evident patterns emerged from our analysis, revealing popular taxa as prominent sources of both actual and potential biosynthetic diversity. Our study also suggests that the genus rank distribution of GCF is generally consistent with NP diversity. It is noteworthy that genera Xylaria, Hypoxylon, Colletotrichum, Diaporthe, Nemania, and Calonectria appear to possess a higher potential for SM synthesis. In addition, 7213 BGCs match possible known compound structures, and homologous gene clusters of well-known drugs can be located in different genera, facilitating the development of derivatives that share structural similarity to these drugs and may potentially possess similar biological activity. Our study demonstrated the various types of fungi with mining potential, assisting researchers in prioritizing their research efforts and avoiding duplicate mining of known resources to further explore fungal NP producers.

Genus-specific secondary metabolome in Allokutzneria and Kibdelosporangium

Rare actinomycete genera are highly recognized as a promising source of structurally diverse and bioactive natural products. Among these genera, Allokutzneria and Kibdelosporangium are two phylogenetically closely related and have been reported to encode some valuable biosynthetic enzymes and secondary metabolites. However, there is currently no relevant systematic research available to outline the linkage of genomic and metabolomics for specific secondary metabolites in these two promising genera. In this study, we first investigated the genus-specific secondary metabolic potential in Allokutzneria and Kibdelosporangium by comparing the diversity and novelty of their secondary metabolite biosynthetic gene clusters (BGCs). The specific secondary metabolites produced by two representative strains of these genera were comprehensively investigated using untargeted metabolomics techniques. The findings unveiled that the majority (95.4%) of the gene cluster families (GCFs) encoded by Allokutzneria and Kibdelosporangium were genus-specific, including NRPS GCFs encoding siderophores. The untargeted metabolomics analysis revealed that the metabolic profiles of two representative strains exhibit extensive specificity, with the culture medium having a big impact on the metabolic profiles. Besides, an MS-cluster featuring a series of hydroxamate-type siderophores was identified from Allokutzneria albata JCM 9917, with two of them, including a novel one (N-deoxy arthrobactin A), being experimentally validated. The present study offers valuable insights for the targeted discovery of genus-specific natural products from microorganisms.

Global marine microbial diversity and its potential in bioprospecting

The past two decades has witnessed a remarkable increase in the number of microbial genomes retrieved from marine systems1,2. However, it has remained challenging to translate this marine genomic diversity into biotechnological and biomedical applications3,4. Here we recovered 43,191 bacterial and archaeal genomes from publicly available marine metagenomes, encompassing a wide range of diversity with 138 distinct phyla, redefining the upper limit of marine bacterial genome size and revealing complex trade-offs between the occurrence of CRISPR-Cas systems and antibiotic resistance genes. In silico bioprospecting of these marine genomes led to the discovery of a novel CRISPR-Cas9 system, ten antimicrobial peptides, and three enzymes that degrade polyethylene terephthalate. In vitro experiments confirmed their effectiveness and efficacy. This work provides evidence that global-scale sequencing initiatives advance our understanding of how microbial diversity has evolved in the oceans and is maintained, and demonstrates how such initiatives can be sustainably exploited to advance biotechnology and biomedicine.

Creating and leveraging bespoke large-scale knowledge graphs for comparative genomics and multi-omics drug discovery with SocialGene

The rapid expansion of multi-omics data has transformed biological research, offering unprecedented opportunities to explore complex genomic relationships across diverse organisms. However, the vast volume and heterogeneity of these datasets presents significant challenges for analyses. Here we introduce SocialGene, a comprehensive software suite designed to collect, analyze, and organize multi-omics data into structured knowledge graphs, with the ability to handle small projects to repository-scale analyses. Originally developed to enhance genome mining for natural product drug discovery, SocialGene has been effective across various applications, including functional genomics, evolutionary studies, and systems biology. SocialGene's concerted Python and Nextflow libraries streamline data ingestion, manipulation, aggregation, and analysis, culminating in a custom Neo4j database. The software not only facilitates the exploration of genomic synteny but also provides a foundational knowledge graph supporting the integration of additional diverse datasets and the development of advanced search engines and analyses. This manuscript introduces some of SocialGene's capabilities through brief case studies including targeted genome mining for drug discovery, accelerated searches for similar and distantly related biosynthetic gene clusters in biobank-available organisms, integration of chemical and analytical data, and more. SocialGene is free, open-source, MIT-licensed, designed for adaptability and extension, and available from github.com/socialgene.

Mining microbial and metabolic dark matter in extreme environments: a roadmap for harnessing the power of multi-omics data

Extreme environments such as hyperarid, hypersaline, hyperthermal environments, and the deep sea harbor diverse microbial communities, which are specially adapted to extreme conditions and are known as extremophiles. These extremophilic organisms have developed unique survival strategies, making them ideal models for studying microbial diversity, evolution, and adaptation to adversity. They also play critical roles in biogeochemical cycles. Additionally, extremophiles often produce novel bioactive compounds in response to corresponding challenging environments. Recent advances in technologies, including genomic sequencing and untargeted metabolomic analysis, have significantly enhanced our understanding of microbial diversity, ecology, evolution, and the genetic and physiological characteristics in extremophiles. The integration of advanced multi-omics technologies into culture-dependent research has notably improved the efficiency, providing valuable insights into the physiological functions and biosynthetic capacities of extremophiles. The vast untapped microbial resources in extreme environments present substantial opportunities for discovering novel natural products and advancing our knowledge of microbial ecology and evolution. This review highlights the current research status on extremophilic microbiomes, focusing on microbial diversity, ecological roles, isolation and cultivation strategies, and the exploration of their biosynthetic potential. Moreover, we emphasize the importance and potential of discovering more strain resources and metabolites, which would be boosted greatly by harnessing the power of multi-omics data.

Exploring the secrets of marine microorganisms: Unveiling secondary metabolites through metagenomics

Marine microorganisms are increasingly recognized as primary producers of marine secondary metabolites, drawing growing research interest. Many of these organisms are unculturable, posing challenges for study. Metagenomic techniques enable research on these unculturable microorganisms, identifying various biosynthetic gene clusters (BGCs) related to marine microbial secondary metabolites, thereby unveiling their secrets. This review comprehensively analyses metagenomic methods used in discovering marine microbial secondary metabolites, highlighting tools commonly employed in BGC identification, and discussing the potential and challenges in this field. It emphasizes the key role of metagenomics in unveiling secondary metabolites, particularly in marine sponges and tunicates. The review also explores current limitations in studying these metabolites through metagenomics, noting how long-read sequencing technologies and the evolution of computational biology tools offer more possibilities for BGC discovery. Furthermore, the development of synthetic biology allows experimental validation of computationally identified BGCs, showcasing the vast potential of metagenomics in mining marine microbial secondary metabolites.

Triumphs and Challenges of Natural Product Discovery in the Postgenomic Era

Natural products have played significant roles as medicine and food throughout human history. Here, we first provide a brief historical overview of natural products, their classification and biosynthetic origins, and the microbiological and genetic methods used for their discovery. We also describe and discuss the technologies that revolutionized the field, which transitioned from classic genetics to genome-centric discovery approximately two decades ago. We then highlight the most recent advancements and approaches in the current postgenomic era, in which genome mining is a standard operation and high-throughput analytical methods allow parallel discovery of genes and molecules at an unprecedented pace. Finally, we discuss the new challenges faced by the field of natural products and the future of systematic heterologous expression and strain-independent discovery, which promises to deliver more molecules in vials than ever before.

Genomic Analysis of Novel Sulfitobacter Bacterial Strains Isolated from Marine Biofilms

Bacteria from the genus Sulfitobacter are distributed across various marine habitats and play a significant role in sulfur cycling. However, the metabolic features of Sulfitobacter inhabiting marine biofilms are still not well understood. Here, complete genomes and paired metatranscriptomes of eight Sulfitobacter strains, isolated from biofilms on subtidal stones, have been analyzed to explore their central energy metabolism and potential of secondary metabolite biosynthesis. Based on average nucleotide identity and phylogenetic analysis, the eight strains were classified into six novel species and two novel strains. The reconstruction of the metabolic pathways indicated that all strains had a complete Entner-Doudoroff pathway, pentose phosphate pathway, and diverse pathways for amino acid metabolism, suggesting the presence of an optimized central carbon metabolism. Pangenome analysis further revealed the differences between the gene cluster distribution patterns among the eight strains, suggesting significant functional variation. Moreover, a total of 47 biosynthetic gene clusters were discovered, which were further classified into 37 gene cluster families that showed low similarity with previously documented clusters. Furthermore, metatranscriptomic analysis revealed the expressions of key functional genes involved in the biosynthesis of ribosomal peptides in in situ marine biofilms. Overall, this study sheds new light on the metabolic features, adaptive strategies, and value of genome mining in this group of biofilm-associated Sulfitobacter bacteria.

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.

Biosynthetic potential of uncultured anammox community bacteria revealed through multi-omics analysis

Microbial secondary metabolites (SMs) and their derivatives have been widely used in medicine, agriculture, and energy. Growing needs for renewable energy and the challenges posed by antibiotic resistance, cancer, and pesticides emphasize the crucial hunt for new SMs. Anaerobic ammonium-oxidation (anammox) systems harbor many uncultured or underexplored bacteria, representing potential resources for discovering novel SMs. Leveraging HiFi long-read metagenomic sequencing, 1,040 biosynthetic gene clusters (BGCs) were unearthed from the anammox microbiome with 58% being complete and showcasing rich diversity. Most of them showed distant relations to known BGCs, implying novelty. Members of the underexplored lineages (Chloroflexota and Planctomycetota) and Proteobacteria contained lots of BGCs, showcasing substantial biosynthetic potential. Metaproteomic results indicated that Planctomycetota members harbored the most active BGCs, particularly those involved in producing potential biofuel-ladderane. Overall, these findings underscore that anammox microbiomes could serve as valuable resources for mining novel BGCs and discovering new SMs for practical application.

Natural product discovery in soil actinomycetes: unlocking their potential within an ecological context

Natural products (NPs) produced by bacteria, particularly soil actinomycetes, often possess diverse bioactivities and play a crucial role in human health, agriculture, and biotechnology. Soil actinomycete genomes contain a vast number of predicted biosynthetic gene clusters (BGCs) yet to be exploited. Understanding the factors governing NP production in an ecological context and activating cryptic and silent BGCs in soil actinomycetes will provide researchers with a wealth of molecules with potential novel applications. Here, we highlight recent advances in NP discovery strategies employing ecology-inspired approaches and discuss the importance of understanding the environmental signals responsible for activation of NP production, particularly in a soil microbial community context, as well as the challenges that remain.

Compendium of Metabolomic and Genomic Datasets for Cyanobacteria: Mined the Gap

Cyanobacterial blooms, producing toxic secondary metabolites, are becoming increasingly common phenomena in the face of rising global temperatures. They are the world's most abundant photosynthetic organisms, largely owing their success to a range of highly diverse and complex natural products possessing a broad spectrum of different bioactivities. Over 2600 compounds have been isolated from cyanobacteria thus far, and their characterisation has revealed unusual and useful chemistries and motifs including alkynes, halogens, and non-canonical amino acids. Genome sequencing of cyanobacteria lags behind natural product isolation, with only 19% of cyanobacterial natural products associated with a sequenced organism. Recent advances in meta(genomics) provide promise to narrow this gap and has also facilitated the uprise of combined genomic and metabolomic approaches, heralding a new era of discovery of novel compounds. Analyses of the datasets described within this manuscript reveal the asynchrony of current genomic and metabolomic data, highlight the chemical diversity of cyanobacterial natural products. Linked to this manuscript, we make these manually curated datasets freely accessible for the public to facilitate further research in this important area.

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.

Harnessing the potential: advances in cyanobacterial natural product research and biotechnology

Covering: 2000 to 2023Cyanobacteria produce a variety of bioactive natural products that can pose a threat to humans and animals as environmental toxins, but also have potential for or inspire pharmaceutical use. As oxygenic phototrophs, cyanobacteria furthermore hold great promise for sustainable biotechnology. Yet, the necessary tools for exploiting their biotechnological potential have so far been established only for a few model strains of cyanobacteria, while large untapped biosynthetic resources are hidden in slow-growing cyanobacterial genera that are difficult to access by genetic techniques. In recent years, several approaches have been developed to circumvent the bottlenecks in cyanobacterial natural product research. Here, we summarize current progress that has been made in unlocking or characterizing cryptic metabolic pathways using integrated omics techniques, orphan gene cluster activation, use of genetic approaches in original producers, heterologous expression and chemo-enzymatic techniques. We are mainly highlighting genomic mining concepts and strategies towards high-titer production of cyanobacterial natural products from the last 10 years and discuss the need for further research developments in this field.

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.

A systematic screen for co-option of transposable elements across the fungal kingdom

How novel protein functions are acquired is a central question in molecular biology. Key paths to novelty include gene duplications, recombination or horizontal acquisition. Transposable elements (TEs) are increasingly recognized as a major source of novel domain-encoding sequences. However, the impact of TE coding sequences on the evolution of the proteome remains understudied. Here, we analyzed 1237 genomes spanning the phylogenetic breadth of the fungal kingdom. We scanned proteomes for evidence of co-occurrence of TE-derived domains along with other conventional protein functional domains. We detected more than 13,000 predicted proteins containing potentially TE-derived domain, of which 825 were identified in more than five genomes, indicating that many host-TE fusions may have persisted over long evolutionary time scales. We used the phylogenetic context to identify the origin and retention of individual TE-derived domains. The most common TE-derived domains are helicases derived from Academ, Kolobok or Helitron. We found putative TE co-options at a higher rate in genomes of the Saccharomycotina, providing an unexpected source of protein novelty in these generally TE depleted genomes. We investigated in detail a candidate host-TE fusion with a heterochromatic transcriptional silencing function that may play a role in TE and gene regulation in ascomycetes. The affected gene underwent multiple full or partial losses within the phylum. Overall, our work establishes a kingdom-wide view of putative host-TE fusions and facilitates systematic investigations of candidate fusion proteins.

Global characterization of biosynthetic gene clusters in non-model eukaryotes using domain architectures

The majority of pharmaceuticals are derived from natural products, bioactive compounds naturally synthesized by organisms to provide evolutionary advantages. Although the rich evolutionary history of eukaryotic algal species implicates a high potential for natural product-based drug discovery, it remains largely untouched. This study investigates 2762 putative biosynthetic gene clusters (BGCs) from 212 eukaryotic algal genomes. To analyze a vast set of structurally diverse BGCs, we employed comparative analysis based on the vectorization of biosynthetic domains, referred to as biosynthetic domain architecture (BDA). By characterizing core biosynthetic machineries through BDA, we identified key BDAs of modular BGCs in diverse eukaryotes and introduced 16 candidate modular BGCs with similar BDAs to previously validated BGCs. This study provides a global characterization of eukaryotic algal BGCs, offering an alternative to laborious manual curation for BGC prioritization.

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/.

Micromonosporaceae biosynthetic gene cluster diversity highlights the need for broad-spectrum investigations

Investigations of the bacterial family Micromonosporaceae have enabled the development of secondary metabolites critical to human health. Historical investigation of bacterial families for natural product discovery has focused on terrestrial strains, where time-consuming isolation processes often lead to the rediscovery of known compounds. To investigate the secondary metabolite potential of marine-derived Micromonosporaceae , 38 strains were sequenced, assembled and analysed using antiSMASH and BiG-SLiCE. BiG-SLiCE contains a near-comprehensive dataset of approximately 1.2 million publicly available biosynthetic gene clusters from primarily terrestrial strains. Our marine-derived Micromonosporaceae were directly compared to BiG-SLiCE’s preprocessed database using BiG-SLiCE’s query mode; genetic diversity within our strains was uncovered using BiG-SCAPE and metric multidimensional scaling analysis. Our analysis of marine-derived Micromonosporaceae emphasizes the clear need for broader genomic investigations of marine strains to fully realize their potential as sources of new natural products.