MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

Multi-omics analysis of antagonistic interactions among free-living Pseudonocardia from diverse ecosystems.

Actinomycetes are a phylogenetically diverse bacterial group which are widely distributed across terrestrial and aquatic ecosystems. Within this order, the genus Pseudonocardia and their specialised metabolites have been the focus of previous ecological studies due to their antagonistic interactions with other microorganisms and their mutualistic interactions with insects. However, the chemical ecology of free-living Pseudonocardia remains understudied. This study applies a multi-omics approach to investigate the chemical ecology of free-living actinomycetes from the genus Pseudonocardia. In a comparative genomics analysis, it was observed that the biosynthetic gene cluster family distribution was influenced mainly by phylogenetic distance rather than the geographic or ecological origin of strains. This finding was also observed in the mass spectrometry-based metabolomic profiles of nine Pseudonocardia species isolated from marine sediments and two terrestrial species. Antagonist interactions between these 11 species were examined, and matrix-assisted laser desorption/ionisation-mass spectrometry imaging was used to examine in situ chemical interactions between the Southern Ocean strains and their phylogenetically close relatives. Overall, it was demonstrated that phylogeny was the main predictor of antagonistic interactions among free-living Pseudonocardia. Moreover, two features at m/z 441.15 and m/z 332.20 were identified as metabolites related to these interspecies interactions.

The Natural Products Atlas 2.0: a database of microbially-derived natural products.

Within the natural products field there is an increasing emphasis on the study of compounds from microbial sources. This has been fuelled by interest in the central role that microorganisms play in mediating both interspecies interactions and host-microbe relationships. To support the study of natural products chemistry produced by microorganisms we released the Natural Products Atlas, a database of known microbial natural products structures, in 2019. This paper reports the release of a new version of the database which includes a full RESTful application programming interface (API), a new website framework, and an expanded database that includes 8128 new compounds, bringing the total to 32 552. In addition to these structural and content changes we have added full taxonomic descriptions for all microbial taxa and have added chemical ontology terms from both NP Classifier and ClassyFire. We have also performed manual curation to review all entries with incomplete configurational assignments and have integrated data from external resources, including CyanoMetDB. Finally, we have improved the user experience by updating the Overview dashboard and creating a dashboard for taxonomic origin. The database can be accessed via the new interactive website at https://www.npatlas.org.

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions.

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.

Pseudonocardia abyssalis sp. nov. and Pseudonocardia oceani sp. nov., two novel actinomycetes isolated from the deep Southern Ocean.

The actinomycetes strains KRD168T and KRD185T were isolated from sediments collected from the deep Southern Ocean and, in this work, they are described as representing two novel species of the genus Pseudonocardia through a polyphasic approach. Despite sharing >99 % 16S rRNA gene sequence similarity with other members of the genus, comparative genomic analysis allowed species delimitation based on average nucleotide identity and digital DNA-DNA hybridization. The KRD168T genome is characterized by a size of 6.31 Mbp and a G+C content of 73.44 mol%, while the KRD185T genome has a size of 6.82 Mbp and a G+C content of 73.98 mol%. Both strains contain meso-diaminopimelic acid as the diagnostic diamino acid, glucose as the major whole-cell sugar, MK-8(H4) as a major menaquinone and iso-branched hexadecanoic acid as a major fatty acid. Biochemical and fatty acid analyses also revealed differences between these strains and their phylogenetic neighbours, supporting their status as distinct species. The names Pseudonocardia abyssalis sp. nov. (type strain KRD168T=DSM 111918T=NCIMB 15270T) and Pseudonocardia oceani (type strain KRD185T=DSM 111919T=NCIMB 15269T) are proposed.

Comparative Metabologenomics Analysis of Polar Actinomycetes.

Biosynthetic and chemical datasets are the two major pillars for microbial drug discovery in the omics era. Despite the advancement of analysis tools and platforms for multi-strain metabolomics and genomics, linking these information sources remains a considerable bottleneck in strain prioritisation and natural product discovery. In this study, molecular networking of the 100 metabolite extracts derived from applying the OSMAC approach to 25 Polar bacterial strains, showed growth media specificity and potential chemical novelty was suggested. Moreover, the metabolite extracts were screened for antibacterial activity and promising selective bioactivity against drug-persistent pathogens such as Klebsiella pneumoniae and Acinetobacter baumannii was observed. Genome sequencing data were combined with metabolomics experiments in the recently developed computational approach, NPLinker, which was used to link BGC and molecular features to prioritise strains for further investigation based on biosynthetic and chemical information. Herein, we putatively identified the known metabolites ectoine and chrloramphenicol which, through NPLinker, were linked to their associated BGCs. The metabologenomics approach followed in this study can potentially be applied to any large microbial datasets for accelerating the discovery of new (bioactive) specialised metabolites.

Linking genomics and metabolomics to chart specialized metabolic diversity.

Microbial and plant specialized metabolites constitute an immense chemical diversity, and play key roles in mediating ecological interactions between organisms. Also referred to as natural products, they have been widely applied in medicine, agriculture, cosmetic and food industries. Traditionally, the main discovery strategies have centered around the use of activity-guided fractionation of metabolite extracts. Increasingly, omics data is being used to complement this, as it has the potential to reduce rediscovery rates, guide experimental work towards the most promising metabolites, and identify enzymatic pathways that enable their biosynthetic production. In recent years, genomic and metabolomic analyses of specialized metabolic diversity have been scaled up to study thousands of samples simultaneously. Here, we survey data analysis technologies that facilitate the effective exploration of large genomic and metabolomic datasets, and discuss various emerging strategies to integrate these two types of omics data in order to further accelerate discovery.

Awakening ancient polar Actinobacteria: diversity, evolution and specialized metabolite potential.

Polar and subpolar ecosystems are highly vulnerable to global climate change with consequences for biodiversity and community composition. Bacteria are directly impacted by future environmental change and it is therefore essential to have a better understanding of microbial communities in fluctuating ecosystems. Exploration of Polar environments, specifically sediments, represents an exciting opportunity to uncover bacterial and chemical diversity and link this to ecosystem and evolutionary parameters. In terms of specialized metabolite production, the bacterial order Actinomycetales, within the phylum Actinobacteria are unsurpassed, producing 10 000 specialized metabolites accounting for over 45 % of all bioactive microbial metabolites. A selective isolation approach focused on spore-forming Actinobacteria of 12 sediment cores from the Antarctic and sub-Arctic generated a culture collection of 50 strains. This consisted of 39 strains belonging to rare Actinomycetales genera including Microbacterium, Rhodococcus and Pseudonocardia. This study used a combination of nanopore sequencing and molecular networking to explore the community composition, culturable bacterial diversity, evolutionary relatedness and specialized metabolite potential of these strains. Metagenomic analyses using MinION sequencing was able to detect the phylum Actinobacteria across polar sediment cores at an average of 13 % of the total bacterial reads. The resulting molecular network consisted of 1652 parent ions and the lack of known metabolite identification supports the argument that Polar bacteria are likely to produce previously unreported chemistry.

An Integrative Approach to Decipher the Chemical Antagonism between the Competing Endophytes Paraconiothyrium variabile and Bacillus subtilis.

An integrative approach combining traditional natural products chemistry, molecular networking, and mass spectrometry imaging has been undertaken to decipher the molecular dialogue between the fungus Paraconiothyrium variabile and the bacterium Bacillus subtilis, which were isolated as endophytes from the conifer Cephalotaxus harringtonia and are characterized by a strong and mutual antibiosis. From this study, we highlight that bacterial surfactins and a fungal tetronic acid are involved in such competition and that the fungus is able to hydrolyze surfactins to fight against the bacterial partner.

Antibiotics from rare actinomycetes, beyond the genus Streptomyces.

Throughout the golden age of antibiotic discovery, Streptomyces have been unsurpassed for their ability to produce bioactive metabolites. Yet, this success has been hampered by rediscovery. As we enter a new stage of biodiscovery, omics data and existing scientific repositories can enable informed choices on the biodiversity that may yield novel antibiotics. Here, we focus on the chemical potential of rare actinomycetes, defined as bacteria within the order Actinomycetales, but not belonging to the genus Streptomyces. They are named as such due to their less-frequent isolation under standard laboratory practices, yet there is increasing evidence to suggest these biologically diverse genera harbour considerable biosynthetic and chemical diversity. In this review, we focus on examples of successful isolation and genera that have been the focus of more concentrated biodiscovery efforts, we survey the representation of rare actinomycete taxa, compared with Streptomyces, across natural product data repositories in addition to its biosynthetic potential. This is followed by an overview of clinically useful drugs produced by rare actinomycetes and considerations for future biodiscovery efforts. There is much to learn about these underexplored taxa, and mounting evidence suggests that they are a fruitful avenue for the discovery of novel antimicrobials.

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.

Overcoming drug resistance, the natural way.

Colistin is an antibiotic of last resort for treating Gram-negative bacterial infections, but resistance is spreading rapidly. In a recent issue of Nature, Wang et al. use genome mining to identify and synthesize a natural variant that bypasses colistin resistance and offers new hope for tackling antimicrobial resistance.

ActinoBase: tools and protocols for researchers working on Streptomyces and other filamentous actinobacteria.

Actinobacteria is an ancient phylum of Gram-positive bacteria with a characteristic high GC content to their DNA. The ActinoBase Wiki is focused on the filamentous actinobacteria, such as Streptomyces species, and the techniques and growth conditions used to study them. These organisms are studied because of their complex developmental life cycles and diverse specialised metabolism which produces many of the antibiotics currently used in the clinic. ActinoBase is a community effort that provides valuable and freely accessible resources, including protocols and practical information about filamentous actinobacteria. It is aimed at enabling knowledge exchange between members of the international research community working with these fascinating bacteria. ActinoBase is an anchor platform that underpins worldwide efforts to understand the ecology, biology and metabolic potential of these organisms. There are two key differences that set ActinoBase apart from other Wiki-based platforms: [1] ActinoBase is specifically aimed at researchers working on filamentous actinobacteria and is tailored to help users overcome challenges working with these bacteria and [2] it provides a freely accessible resource with global networking opportunities for researchers with a broad range of experience in this field.

Exploring the Chemical Space of Macro- and Micro-Algae Using Comparative Metabolomics.

With more than 156,000 described species, eukaryotic algae (both macro- and micro-algae) are a rich source of biological diversity, however their chemical diversity remains largely unexplored. Specialised metabolites with promising biological activities have been widely reported for seaweeds, and more recently extracts from microalgae have exhibited activity in anticancer, antimicrobial, and antioxidant screens. However, we are still missing critical information on the distinction of chemical profiles between macro- and microalgae, as well as the chemical space these metabolites cover. This study has used an untargeted comparative metabolomics approach to explore the chemical diversity of seven seaweeds and 36 microalgal strains. A total of 1390 liquid chromatography-mass spectrometry (LC-MS) features were detected, representing small organic algal metabolites, with no overlap between the seaweeds and microalgae. An in-depth analysis of four Dunaliella tertiolecta strains shows that environmental factors may play a larger role than phylogeny when classifying their metabolomic profiles.

Linking biosynthetic and chemical space to accelerate microbial secondary metabolite discovery.

Secondary metabolites can be viewed as a chemical language, facilitating communication between microorganisms. From an ecological point of view, this metabolite exchange is in constant flux due to evolutionary and environmental pressures. From a biomedical perspective, the chemistry is unsurpassed for its antibiotic properties. Genome sequencing of microorganisms has revealed a large reservoir of Biosynthetic Gene Clusters (BGCs); however, linking these to the secondary metabolites they encode is currently a major bottleneck to chemical discovery. This linking of genes to metabolites with experimental validation will aid the elicitation of silent or cryptic (not expressed under normal laboratory conditions) BGCs. As a result, this will accelerate chemical dereplication, our understanding of gene transcription and provide a comprehensive resource for synthetic biology. This will ultimately provide an improved understanding of both the biosynthetic and chemical space. In recent years, integrating these complex metabolomic and genomic data sets has been achieved using a spectrum of manual and automated approaches. In this review, we cover examples of these approaches, while addressing current challenges and future directions in linking these data sets.

The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery.

Despite rapid evolution in the area of microbial natural products chemistry, there is currently no open access database containing all microbially produced natural product structures. Lack of availability of these data is preventing the implementation of new technologies in natural products science. Specifically, development of new computational strategies for compound characterization and identification are being hampered by the lack of a comprehensive database of known compounds against which to compare experimental data. The creation of an open access, community-maintained database of microbial natural product structures would enable the development of new technologies in natural products discovery and improve the interoperability of existing natural products data resources. However, these data are spread unevenly throughout the historical scientific literature, including both journal articles and international patents. These documents have no standard format, are often not digitized as machine readable text, and are not publicly available. Further, none of these documents have associated structure files (e.g., MOL, InChI, or SMILES), instead containing images of structures. This makes extraction and formatting of relevant natural products data a formidable challenge. Using a combination of manual curation and automated data mining approaches we have created a database of microbial natural products (The Natural Products Atlas, www.npatlas.org) that includes 24 594 compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. This database is accompanied by an interactive web portal that permits searching by structure, substructure, and physical properties. The Web site also provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. These interactive tools offer a powerful knowledge base for natural products discovery with a central interface for structure and property-based searching and presents new viewpoints on structural diversity in natural products. The Natural Products Atlas has been developed under FAIR principles (Findable, Accessible, Interoperable, and Reusable) and is integrated with other emerging natural product databases, including the Minimum Information About a Biosynthetic Gene Cluster (MIBiG) repository, and the Global Natural Products Social Molecular Networking (GNPS) platform. It is designed as a community-supported resource to provide a central repository for known natural product structures from microorganisms and is the first comprehensive, open access resource of this type. It is expected that the Natural Products Atlas will enable the development of new natural products discovery modalities and accelerate the process of structural characterization for complex natural products libraries.

Temperature Driven Changes in Benthic Bacterial Diversity Influences Biogeochemical Cycling in Coastal Sediments.

Marine sediments are important sites for global biogeochemical cycling, mediated by macrofauna and microalgae. However, it is the microorganisms that drive these key processes. There is strong evidence that coastal benthic habitats will be affected by changing environmental variables (rising temperature, elevated CO2), and research has generally focused on the impact on macrofaunal biodiversity and ecosystem services. Despite their importance, there is less understanding of how microbial community assemblages will respond to environmental changes. In this study, a manipulative mesocosm experiment was employed, using next-generation sequencing to assess changes in microbial communities under future environmental change scenarios. Illumina sequencing generated over 11 million 16S rRNA gene sequences (using a primer set biased toward bacteria) and revealed Bacteroidetes and Proteobacteria dominated the total bacterial community of sediment samples. In this study, the sequencing coverage and depth revealed clear changes in species abundance within some phyla. Bacterial community composition was correlated with simulated environmental conditions, and species level community composition was significantly influenced by the mean temperature of the environmental regime (p = 0.002), but not by variation in CO2 or diurnal temperature variation. Species level changes with increasing mean temperature corresponded with changes in NH4 concentration, suggesting there is no functional redundancy in microbial communities for nitrogen cycling. Marine coastal biogeochemical cycling under future environmental conditions is likely to be driven by changes in nutrient availability as a direct result of microbial activity.

Competitive strategies differentiate closely related species of marine actinobacteria.

Although competition, niche partitioning, and spatial isolation have been used to describe the ecology and evolution of macro-organisms, it is less clear to what extent these principles account for the extraordinary levels of bacterial diversity observed in nature. Ecological interactions among bacteria are particularly challenging to address due to methodological limitations and uncertainties over how to recognize fundamental units of diversity and link them to the functional traits and evolutionary processes that led to their divergence. Here we show that two closely related marine actinomycete species can be differentiated based on competitive strategies. Using a direct challenge assay to investigate inhibitory interactions with members of the bacterial community, we observed a temporal difference in the onset of inhibition. The majority of inhibitory activity exhibited by Salinispora arenicola occurred early in its growth cycle and was linked to antibiotic production. In contrast, most inhibition by Salinispora tropica occurred later in the growth cycle and was more commonly linked to nutrient depletion or other sources. Comparative genomics support these differences, with S. arenicola containing nearly twice the number of secondary metabolite biosynthetic gene clusters as S. tropica, indicating a greater potential for secondary metabolite production. In contrast, S. tropica is enriched in gene clusters associated with the acquisition of growth-limiting nutrients such as iron. Coupled with differences in growth rates, the results reveal that S. arenicola uses interference competition at the expense of growth, whereas S. tropica preferentially employs a strategy of exploitation competition. The results support the ecological divergence of two co-occurring and closely related species of marine bacteria by providing evidence they have evolved fundamentally different strategies to compete in marine sediments.

Using Molecular Networking for Microbial Secondary Metabolite Bioprospecting.

The oceans represent an understudied resource for the isolation of bacteria with the potential to produce novel secondary metabolites. In particular, actinomyces are well known to produce chemically diverse metabolites with a wide range of biological activities. This study characterised spore-forming bacteria from both Scottish and Antarctic sediments to assess the influence of isolation location on secondary metabolite production. Due to the selective isolation method used, all 85 isolates belonged to the phyla Firmicutes and Actinobacteria, with the majority of isolates belonging to the genera Bacillus and Streptomyces. Based on morphology, thirty-eight isolates were chosen for chemical investigation. Molecular networking based on chemical profiles (HR-MS/MS) of fermentation extracts was used to compare complex metabolite extracts. The results revealed 40% and 42% of parent ions were produced by Antarctic and Scottish isolated bacteria, respectively, and only 8% of networked metabolites were shared between these locations, implying a high degree of biogeographic influence upon secondary metabolite production. The resulting molecular network contained over 3500 parent ions with a mass range of m/z 149-2558 illustrating the wealth of metabolites produced. Furthermore, seven fermentation extracts showed bioactivity against epithelial colon adenocarcinoma cells, demonstrating the potential for the discovery of novel bioactive compounds from these understudied locations.

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.