Comparative Metabologenomics Analysis of Polar Actinomycetes

Taxonomic and metabolic diversity of Actinomycetota isolated from faeces of a 28,000-year-old mammoth

Ancient environmental samples, including permafrost soils and frozen animal remains, represent an archive with microbial communities that have barely been explored. This yet unexplored microbial world is a genetic resource that may provide us with new evolutionary insights into recent genomic changes, as well as novel metabolic pathways and chemistry. Here, we describe Actinomycetota Micromonospora, Oerskovia, Saccharopolyspora, Sanguibacter and Streptomyces species were successfully revived and their genome sequences resolved. Surprisingly, the genomes of these bacteria from an ancient source show a large phylogenetic distance to known strains and harbour many novel biosynthetic gene clusters that may well represent uncharacterised biosynthetic potential. Metabolic profiles of the strains display the production of known molecules like antimycin, conglobatin and macrotetrolides, but the majority of the mass features could not be dereplicated. Our work provides insights into Actinomycetota isolated from an ancient source, yielding unexplored genomic information that is not yet present in current databases.

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

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

The undiscovered biosynthetic potential of the Greenland Ice Sheet microbiome

The Greenland Ice Sheet is a biome which is mainly microbially driven. Several different niches can be found within the glacial biome for those microbes able to withstand the harsh conditions, e.g., low temperatures, low nutrient conditions, high UV radiation in summer, and contrasting long and dark winters. Eukaryotic algae can form blooms during the summer on the ice surface, interacting with communities of bacteria, fungi, and viruses. Cryoconite holes and snow are also habitats with their own microbial community. Nevertheless, the microbiome of supraglacial habitats remains poorly studied, leading to a lack of representative genomes from these environments. Under-investigated extremophiles, like those living on the Greenland Ice Sheet, may provide an untapped reservoir of chemical diversity that is yet to be discovered. In this study, an inventory of the biosynthetic potential of these organisms is made, through cataloging the presence of biosynthetic gene clusters in their genomes. There were 133 high-quality metagenome-assembled genomes (MAGs) and 28 whole genomes of bacteria obtained from samples of the ice sheet surface, cryoconite, biofilm, and snow using culturing-dependent and -independent approaches. AntiSMASH and BiG-SCAPE were used to mine these genomes and subsequently analyze the resulting predicted gene clusters. Extensive sets of predicted Biosynthetic Gene Clusters (BGCs) were collected from the genome collection, with limited overlap between isolates and MAGs. Additionally, little overlap was found in the biosynthetic potential among different environments, suggesting specialization of organisms in specific habitats. The median number of BGCs per genome was significantly higher for the isolates compared to the MAGs. The most talented producers were found among Proteobacteria. We found evidence for the capacity of these microbes to produce antimicrobials, carotenoid pigments, siderophores, and osmoprotectants, indicating potential survival mechanisms to cope with extreme conditions. The majority of identified BGCs, including those in the most prevalent gene cluster families, have unknown functions, presenting a substantial potential for bioprospecting. This study underscores the diverse biosynthetic potential in Greenland Ice Sheet genomes, revealing insights into survival strategies and highlighting the need for further exploration and characterization of these untapped resources.

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.

Prioritization of Microorganisms Isolated from the Indian Ocean Sponge Scopalina hapalia Based on Metabolomic Diversity and Biological Activity for the Discovery of Natural Products

Despite considerable advances in medicine and technology, humanity still faces many deadly diseases such as cancer and malaria. In order to find appropriate treatments, the discovery of new bioactive substances is essential. Therefore, research is now turning to less frequently explored habitats with exceptional biodiversity such as the marine environment. Many studies have demonstrated the therapeutic potential of bioactive compounds from marine macro- and microorganisms. In this study, nine microbial strains isolated from an Indian Ocean sponge, Scopalina hapalia, were screened for their chemical potential. The isolates belong to different phyla, some of which are already known for their production of secondary metabolites, such as the actinobacteria. This article aims at describing the selection method used to identify the most promising microorganisms in the field of active metabolites production. The method is based on the combination of their biological and chemical screening, coupled with the use of bioinformatic tools. The dereplication of microbial extracts and the creation of a molecular network revealed the presence of known bioactive molecules such as staurosporin, erythromycin and chaetoglobosins. Molecular network exploration indicated the possible presence of novel compounds in clusters of interest. The biological activities targeted in the study were cytotoxicity against the HCT-116 and MDA-MB-231 cell lines and antiplasmodial activity against Plasmodium falciparum 3D7. Chaetomium globosum SH-123 and Salinispora arenicola SH-78 strains actually showed remarkable cytotoxic and antiplasmodial activities, while Micromonospora fluostatini SH-82 demonstrated promising antiplasmodial effects. The ranking of the microorganisms as a result of the different screening steps allowed the selection of a promising strain, Micromonospora fluostatini SH-82, as a premium candidate for the discovery of new drugs.

Micrococcus spp. as a promising source for drug discovery: A review

Historically, bacteria of the phylum, Actinobacteria have been a very prominent source of bioactive compounds for drug discovery. Among the actinobacterial genera, Micrococcus has not generally been prioritized in the search for novel drugs. The bacteria in this genus are known to have very small genomes (generally < 3 Mb). Actinobacteria with small genomes seldom contain the well-characterized biosynthetic gene clusters such as those encoding polyketide synthases and nonribosomal peptide synthetases that current genome mining algorithms are optimized to detect. Nevertheless, there are many reports of substantial pharmaceutically relevant bioactivity of Micrococcus extracts. On the other hand, there are remarkably few descriptions of fully characterized and structurally elucidated bioactive compounds from Micrococcus spp. This review provides a comprehensive summary of the bioactivity of Micrococcus spp. that encompasses antibacterial, antifungal, cytotoxic, antioxidant, and anti-inflammatory activities. This review uncovers the considerable biosynthetic potential of this genus and highlights the need for a re-examination of these bioactive strains, with a particular emphasis on marine isolates, because of their potent bioactivity and high potential for encoding unique molecular scaffolds.

Enhanced correlation-based linking of biosynthetic gene clusters to their metabolic products through chemical class matching

BACKGROUND

It is well-known that the microbiome produces a myriad of specialised metabolites with diverse functions. To better characterise their structures and identify their producers in complex samples, integrative genome and metabolome mining is becoming increasingly popular. Metabologenomic co-occurrence-based correlation scoring methods facilitate the linking of metabolite mass fragmentation spectra (MS/MS) to their cognate biosynthetic gene clusters (BGCs) based on shared absence/presence patterns of metabolites and BGCs in paired omics datasets of multiple strains. Recently, these methods have been made more readily accessible through the NPLinker platform. However, co-occurrence-based approaches usually result in too many candidate links to manually validate. To address this issue, we introduce a generic feature-based correlation method that matches chemical compound classes between BGCs and MS/MS spectra.

RESULTS

To automatically reduce the long lists of potential BGC-MS/MS spectrum links, we match natural product (NP) ontologies previously independently developed for genomics and metabolomics and developed NPClassScore: an empirical class matching score that we also implemented in the NPLinker platform. By applying NPClassScore on three paired omics datasets totalling 189 bacterial strains, we show that the number of links is reduced by on average 63% as compared to using a co-occurrence-based strategy alone. We further demonstrate that 96% of experimentally validated links in these datasets are retained and prioritised when using NPClassScore.

CONCLUSION

The matching genome-metabolome class ontologies provide a starting point for selecting plausible candidates for BGCs and MS/MS spectra based on matching chemical compound class ontologies. NPClassScore expedites genome/metabolome data integration, as relevant BGC-metabolite links are prioritised, and researchers are faced with substantially fewer proposed BGC-MS/MS links to manually inspect. We anticipate that our addition to the NPLinker platform will aid integrative omics mining workflows in discovering novel NPs and understanding complex metabolic interactions in the microbiome. Video Abstract.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

Covering: 2016 up to 2021Mass spectrometry (MS) is an essential technology in natural products research with MS fragmentation (MS/MS) approaches becoming a key tool. Recent advancements in MS yield dense metabolomics datasets which have been, conventionally, used by individual labs for individual projects; however, a shift is brewing. The movement towards open MS data (and other structural characterization data) and accessible data mining tools is emerging in natural products research. Over the past 5 years, this movement has rapidly expanded and evolved with no slowdown in sight; the capabilities of today vastly exceed those of 5 years ago. Herein, we address the analysis of individual datasets, a situation we are calling the '2021 status quo', and the emergent framework to systematically capture sample information (metadata) and perform repository-scale analyses. We evaluate public data deposition, discuss the challenges of working in the repository scale, highlight the challenges of metadata capture and provide illustrative examples of the power of utilizing repository data and the tools that enable it. We conclude that the advancements in MS data collection must be met with advancements in how we utilize data; therefore, we argue that open data and data mining is the next evolution in obtaining the maximum potential in natural products research.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

Integrating perspectives in actinomycete research: an ActinoBase review of 2020-21

Last year ActinoBase, a Wiki-style initiative supported by the UK Microbiology Society, published a review highlighting the research of particular interest to the actinomycete community. Here, we present the second ActinoBase review showcasing selected reports published in 2020 and early 2021, integrating perspectives in the actinomycete field. Actinomycetes are well-known for their unsurpassed ability to produce specialised metabolites, of which many are used as therapeutic agents with antibacterial, antifungal, or immunosuppressive activities. Much research is carried out to understand the purpose of these metabolites in the environment, either within communities or in host interactions. Moreover, many efforts have been placed in developing computational tools to handle big data, simplify experimental design, and find new biosynthetic gene cluster prioritisation strategies. Alongside, synthetic biology has provided advances in tools to elucidate the biosynthesis of these metabolites. Additionally, there are still mysteries to be uncovered in understanding the fundamentals of filamentous actinomycetes' developmental cycle and regulation of their metabolism. This review focuses on research using integrative methodologies and approaches to understand the bigger picture of actinomycete biology, covering four research areas: i) technology and methodology; ii) specialised metabolites; iii) development and regulation; and iv) ecology and host interactions.

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.

Combining OSMAC Approach and Untargeted Metabolomics for the Identification of New Glycolipids with Potent Antiviral Activity Produced by a Marine Rhodococcus

Natural products of microbial origin have inspired most of the commercial pharmaceuticals, especially those from Actinobacteria. However, the redundancy of molecules in the discovery process represents a serious issue. The untargeted approach, One Strain Many Compounds (OSMAC), is one of the most promising strategies to induce the expression of silent genes, especially when combined with genome mining and advanced metabolomics analysis. In this work, the whole genome of the marine isolate Rhodococcus sp. I2R was sequenced and analyzed by antiSMASH for the identification of biosynthetic gene clusters. The strain was cultivated in 22 different growth media and the generated extracts were subjected to metabolomic analysis and functional screening. Notably, only a single growth condition induced the production of unique compounds, which were partially purified and structurally characterized by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). This strategy led to identifying a bioactive fraction containing >30 new glycolipids holding unusual functional groups. The active fraction showed a potent antiviral effect against enveloped viruses, such as herpes simplex virus and human coronaviruses, and high antiproliferative activity in PC3 prostate cancer cell line. The identified compounds belong to the biosurfactants class, amphiphilic molecules, which play a crucial role in the biotech and biomedical industry.

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.

Coupling Mass Spectral and Genomic Information to Improve Bacterial Natural Product Discovery Workflows

Bacterial natural products possess potent bioactivities and high structural diversity and are typically encoded in biosynthetic gene clusters. Traditional natural product discovery approaches rely on UV- and bioassay-guided fractionation and are limited in terms of dereplication. Recent advances in mass spectrometry, sequencing and bioinformatics have led to large-scale accumulation of genomic and mass spectral data that is increasingly used for signature-based or correlation-based mass spectrometry genome mining approaches that enable rapid linking of metabolomic and genomic information to accelerate and rationalize natural product discovery. In this mini-review, these approaches are presented, and discovery examples provided. Finally, future opportunities and challenges for paired omics-based natural products discovery workflows are discussed.