A metabolomics guided exploration of marine natural product chemical space

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Comparison of chromatographic conditions for the targeted tandem mass spectrometric determination of 354 mammalian metabolites

Metabolomics is becoming increasingly popular in livestock research, but no single analytical method can cover the entire metabolome. As such, we compared similar and complementary chromatographic methods with respect to analyte coverage and chromatographic properties of mammalian metabolites. We investigated 354 biologically relevant primary metabolites from 19 compound classes including amino acids, bile acids, biogenic amines, carboxylic acids, lipids, nucleotides and sugars. A total of 2063 selected reaction monitoring transitions were optimized on a triple quadrupole mass spectrometer. We then determined the retention profiles and peak parameters of our compounds using an anion exchange chromatography (AIC), three reversed-phase (RP) and three hydrophilic interaction liquid chromatography (HILIC) methods. On average, HILIC methods covered 54% of all metabolites with retention factors >1, while average RP coverage was 41%. In contrast to RP, HILIC methods could also retain polar metabolites such as amino acids and biogenic amines. Carboxylic acids, nucleotides, and sugar related compounds were best separated by AIC or zwitterionic pHILIC with alkaline eluents. Combining two complementary HILIC and RP methods increased the library coverage to 92%. By further including important short chain fatty acids, a combination of HILIC, RP and AIC methods achieved a coverage of 97%. The resulting dataset of LC and MS/MS parameters will facilitate the development of tailor-made quantitative targeted LC-MS/MS methods to investigate the mammalian metabolome.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Interdisciplinary Overview of Lipopeptide and Protein-Containing Biosurfactants

Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known. Protein-containing biosurfactants are much less studied and could be an interesting and valuable alternative. The harsh temperature, pH, and salinity conditions that target organisms can sustain need to be understood for better implementation. Here, we will explore biotechnological applications via lipopeptide and protein-containing biosurfactants. Also, we discuss their natural role and the organisms that produce them, taking a glimpse into the possibilities of research via meta-omics and machine learning.

Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family

Natural products produced by plants are one of the most investigated natural sources, which substantially contributed to the development of the natural products field. Even though these compounds are widely explored, the literature still lacks comprehensive investigations aiming to explore the evolution of secondary metabolites produced by plants, especially if classical methodologies are employed. The development of sensitive hyphenated techniques and computational tools for data processing has enabled the study of large datasets, being valuable assets for chemosystematic studies. Here, we describe a strategy for chemotaxonomic investigations using the Malpighiaceae botanical family as a model. Our workflow was based on MS/MS untargeted metabolomics, spectral searches, and recently described in silico classification tools, which were mapped into the latest molecular phylogeny accepted for this family. The metabolomic analysis revealed that different ionization modes and extraction protocols significantly impacted the chemical profiles, influencing the chemotaxonomic results. Spectral searches within public databases revealed several clades or genera-specific molecular families, being potential chemical markers for these taxa, while the in silico classification tools were able to expand the Malpighiaceae chemical space. The classes putatively annotated were used for ancestral character reconstructions, which recovered several classes of metabolites as homoplasies (i.e., non-exclusive) or synapomorphies (i.e., exclusive) for all sampled clades and genera. Our workflow combines several approaches to perform a comprehensive evolutionary chemical study. We expect it to be used on further chemotaxonomic investigations to expand chemical knowledge and reveal biological insights for compounds classes in different biological groups.

Metabolomics on the study of marine organisms

BACKGROUND

Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the "omics" technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms.

AIM OF REVIEW

This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

Kimidinomycin, a new antibiotic against Mycobacterium avium complex, produced by Streptomyces sp. KKTA-0263

During our screening for antibiotics against Mycobacterium avium complex (MAC) with a mass spectrometry network-based indexing approach, a new compound named kimidinomycin was isolated from the culture broth of Streptomyces sp. KKTA-0263 by solvent extraction, HP20 column chromatography, and preparative HPLC. From the structural elucidation, the compound possesses a 38-membered macrolide structure with an N-methylguanidyl group at the terminal side chain. The compound exhibited antimycobacterial activity against M. avium, M. intracellulare, M. smegmatis, and M. bovis BCG with respective MIC values of 12.5, 0.78, 12.5, and 25.0 µg ml-1.

ZIF-8@s-EPS as a novel hydrophilic multifunctional biomaterial for efficient scale inhibition, antibacterial and antifouling in water treatment

The hydrophilic biomaterial was constructed based on the soluble extracellular polysaccharides (s-EPS) secreted by Bacillus megaterium and zeolitic imidazolate framework-8 (ZIF-8), namely ZIF-8@s-EPS, wrapped in s-EPS shell with ZIF-8 as the core. ZIF-8@s-EPS was used as a novel multifunctional biomaterial in water treatment for the first time. Unexpectedly, results showed ZIF-8@s-EPS with strong synergistic effect presented multifunctional performances including descaling, antifouling and antibacterial. Scale inhibition efficiency reached 98.63% for CaCO₃ and as high as 99.40% for CaSO₄ at concentration 20.00 mg/L. The synergy of s-EPS and ZIF-8 demonstrated effective antibacterial activity against Pseudomonas aeruginosa and inhibitory effect on biofilms, and result presented that ZIF-8@s-EPS could inhibit the growth of nearly 89.4% P. aeruginosa. Therefore, the obtained insights will shed light on the development of s-EPS modified biomaterials in water treatment.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

The need to innovate sample collection and library generation in microbial drug discovery: a focus on academia

The question of whether culturable microorganisms will continue to be a viable source of new drug leads is inherently married to the strategies used to collect samples from the environment, the methods used to cultivate microorganisms from these samples, and the processes used to create microbial libraries. An academic microbial natural products (NP) drug discovery program with the latest innovative chromatographic and spectroscopic technology, high-throughput capacity, and bioassays will remain at the mercy of the quality of its microorganism source library. This viewpoint will discuss limitations of sample collection and microbial strain library generation practices. Additionally, it will offer suggestions to innovate these areas, particularly through the targeted cultivation of several understudied bacterial phyla and the untargeted use of mass spectrometry and bioinformatics to generate diverse microbial libraries. Such innovations have potential to impact downstream therapeutic discovery, and make its front end more informed, efficient, and less reliant on serendipity. This viewpoint is not intended to be a comprehensive review of contributing literature and was written with a focus on bacteria. Strategies to discover NPs from microbial libraries, including a variety of genomics and "OSMAC" style approaches, are considered downstream of sample collection and library creation, and thus are out of the scope of this viewpoint.

Actinobacteria in natural products research: Progress and prospects

Actinobacteria are well-recognised biosynthetic factories that produce an extensive spectrum of secondary metabolites. Recent genomic insights seem to impact the exploitation of these metabolically versatile bacteria in several aspects. Notably, from the isolation of novel taxa to the discovery of new compounds, different approaches evolve at a steady pace. Here, we systematically discuss the enduring importance of Actinobacteria in the field of drug discovery, the current focus of isolation efforts targeting bioactive Actinobacteria from diverse sources, recent discoveries of novel compounds with different bioactivities, and the relative employment of different strategies in the search for novel compounds. Ultimately, we highlight notable progress that will have profound impacts on future quests for secondary metabolites of Actinobacteria.

Chemical Profiling Provides Insights into the Metabolic Machinery of Hydrocarbon-Degrading Deep-Sea Microbes

Marine microbes are known to degrade hydrocarbons; however, microbes inhabiting deep-sea sediments remain largely unexplored. Previous studies into the classical pathways of marine microbial metabolism reveal diverse chemistries; however, metabolic profiling of marine microbes cultured with hydrocarbons is limited. In this study, taxonomic (amplicon sequencing) profiles of two environmental deep-sea sediments (>1,200 m deep) were obtained, along with taxonomic and metabolomic (mass spectrometry-based metabolomics) profiles of microbes harbored in deep-sea sediments cultured with hydrocarbons as the sole energy source. Samples were collected from the Gulf of México (GM) and cultured for 28 days using simple (toluene, benzene, hexadecane, and naphthalene) and complex (petroleum API 40) hydrocarbon mixtures as the sole energy sources. The sediment samples harbored diverse microbial communities predominantly classified into Woeseiaceae and Kiloniellaceae families, whereas Pseudomonadaceae and Enterobacteriaceae families prevailed after sediments were cultured with hydrocarbons. Chemical profiling of microbial metabolomes revealed diverse chemical groups belonging primarily to the lipids and lipid-like molecules superclass, as well as the organoheterocyclic compound superclass (ClassyFire annotation). Metabolomic data and prediction of functional profiles indicated an increase in aromatic and alkane degradation in samples cultured with hydrocarbons. Previously unreported metabolites, identified as intermediates in the degradation of hydrocarbons, were annotated as hydroxylated polyunsaturated fatty acids and carboxylated benzene derivatives. In summary, this study used mass spectrometry-based metabolomics coupled to chemoinformatics to demonstrate how microbes from deep-sea sediments could be cultured in the presence of hydrocarbons. This study also highlights how this experimental approach can be used to increase the understanding of hydrocarbon degradation by deep-sea sediment microbes.IMPORTANCE High-throughput technologies and emerging informatics tools have significantly advanced knowledge of hydrocarbon metabolism by marine microbes. However, research into microbes inhabiting deep-sea sediments (>1,000 m) is limited compared to those found in shallow waters. In this study, a nontargeted and nonclassical approach was used to examine the diversity of bacterial taxa and the metabolic profiles of hydrocarbon-degrading deep-sea microbes. In conclusion, this study used metabolomics and chemoinformatics to demonstrate that microbes from deep-sea sediment origin thrive in the presence of toxic and difficult-to-metabolize hydrocarbons. Notably, this study provides evidence of previously unreported metabolites and the global chemical repertoire associated with the metabolism of hydrocarbons by deep-sea microbes.

Penicillic Acid Congener, a New Inhibitor of BMP-Induced Alkaline Phosphatase Activity in Myoblasts, Produced by the Fungus Penicillium sp. BF-0343

During our screening for microbial regulators of bone metabolism, a new compound, 6-ethoxy-5,6-dihydropenillic acid (1), was isolated together with a known and structurally related penicillic acid (2) from the culture broth of the soil-derived fungus Penicillium sp. BF-0343. The structure of 1 was elucidated by various spectroscopic data including nuclear magnetic resonance experiments. Compounds 1 and 2 dose-dependently inhibited bone morphogenetic protein–induced alkaline phosphatase activity in myoblasts with half-maximal inhibitory concentration values of 19.8 and 2.1 μM, respectively.

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.

Dark, rare and inspirational microbial matter in the extremobiosphere: 16 000 m of bioprospecting campaigns

The rationale of our bioprospecting campaigns is that the extremobiosphere, particularly the deep sea and hyper-arid deserts, harbours undiscovered biodiversity that is likely to express novel chemistry and biocatalysts thereby providing opportunities for therapeutic drug and industrial process development. We have focused on actinobacteria because of their frequent role as keystone species in soil ecosystems and their unrivalled track record as a source of bioactive compounds. Population numbers and diversity of actinobacteria in the extremobiosphere are traditionally considered to be low, although they often comprise the dominant bacterial biota. Recent metagenomic evaluation of 'the uncultured microbial majority' has now revealed enormous taxonomic diversity among 'dark' and 'rare' actinobacteria in samples as diverse as sediments from the depths of the Mariana Trench and soils from the heights of the Central Andes. The application of innovative culture and screening options that emphasize rigorous dereplication at each stage of the analysis, and strain prioritization to identify 'gifted' organisms, have been deployed to detect and characterize bioactive hit compounds and sought-after catalysts from this hitherto untapped resource. The rewards include first-in-a-class chemical entities with novel modes of action, as well as a growing microbial seed bank that represents a potentially enormous source of biotechnological and therapeutic innovation.

Molecular networking-based for the target discovery of potent antiproliferative polycyclic macrolactam ansamycins from Streptomyces cacaoi subsp. asoensis

Molecular networking-based for the target discovery of potent antiproliferative polycyclic macrolactam ansamycins.

Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery

Covering: 2013 to 2019 The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed.

Computational methods for NMR and MS for structure elucidation II: database resources and advanced methods

Technological advances have contributed to the evolution of the natural product chemistry and drug discovery programs. Recently, computational methods for nuclear magnetic resonance (NMR) and mass spectrometry (MS) have speeded up and facilitated the process of structural elucidation even in high complex biological samples. In this chapter, the current computational tools related to NMR and MS databases and spectral similarity networks, as well as their applications on dereplication and determination of biological biomarkers, are addressed.

Exploring novel secondary metabolites from natural products using pre-processed mass spectral data

Many natural product chemists are working to identify a wide variety of novel secondary metabolites from natural materials and are eager to avoid repeatedly discovering known compounds. Here, we developed liquid chromatography/mass spectrometry (LC/MS) data-processing protocols for assessing high-throughput spectral data from natural sources and scoring the novelty of unknown metabolites from natural products. This approach automatically produces representative MS spectra (RMSs) corresponding to single secondary metabolites in natural sources. In this study, we used the RMSs of Agrimonia pilosa roots and aerial parts as models to reveal the structural similarities of their secondary metabolites and identify novel compounds, as well as isolation of three types of nine new compounds including three pilosanidin- and four pilosanol-type molecules and two 3-hydroxy-3-methylglutaryl (HMG)-conjugated chromones. Furthermore, we devised a new scoring system, the Fresh Compound Index (FCI), which grades the novelty of single secondary metabolites from a natural material using an in-house database constructed from 466 representative medicinal plants from East Asian countries. We expect that the FCIs of RMSs in a sample will help natural product chemists to discover other compounds of interest with similar chemical scaffolds or novel compounds and will provide insights relevant to the structural diversity and novelty of secondary metabolites in natural products.

Chemical Diversity in a Stingless Bee-Plant Symbiosis

Bees are essential pollinators on earth, supporting forest equilibrium and human agriculture. The chemistry of the stingless bee-plant symbiosis is a complex and not completely understood phenomenon. Here, we combined untargeted tandem mass spectrometry, molecular networking, and multivariate statistical analysis to investigate the chemical diversity in colonies of the stingless bee Scaptotrigona depilis. Flavonoids were the most representative and diverse group of plant metabolites detected, indicating the importance of these biologically active natural products to the bees. We unveiled the metabolome, mapped the distribution of plant metabolites in stingless bee colonies, and digitized the chemical data into a public database.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

Minimizing Taxonomic and Natural Product Redundancy in Microbial Libraries Using MALDI-TOF MS and the Bioinformatics Pipeline IDBac

Libraries of microorganisms have been a cornerstone of drug discovery efforts since the mid-1950s, but strain duplication in some libraries has resulted in unwanted natural product redundancy. In the current study, we implemented a workflow that minimizes both the natural product overlap and the total number of bacterial isolates in a library. Using a collection expedition to Iceland as an example, we purified every distinct bacterial colony off isolation plates derived from 86 environmental samples. We employed our mass spectrometry (MS)-based IDBac workflow on these isolates to form groups of taxa based on protein MS fingerprints (3-15 kDa) and further distinguished taxa subgroups based on their degree of overlap within corresponding natural product spectra (0.2-2 kDa). This informed the decision to create a library of 301 isolates spanning 54 genera. This process required only 25 h of data acquisition and 2 h of analysis. In a separate experiment, we reduced the size of an existing library based on the degree of metabolic overlap observed in natural product MS spectra of bacterial colonies (from 833 to 233 isolates, a 72.0% size reduction). Overall, our pipeline allows for a significant reduction in costs associated with library generation and minimizes natural product redundancy entering into downstream biological screening efforts.

Natural products targeting strategies involving molecular networking: different manners, one goal

Covering: up to 2019Landmark advances in bioinformatics tools have recently enhanced the field of natural products research, putting today's natural product chemists in the enviable position of being able to perform the efficient targeting/discovery of previously undescribed molecules by expediting the prioritization of the isolation workflow. Among these advances, MS/MS molecular networking has appeared as a promising approach to dereplicate complex natural product mixtures, leading to a real revolution in the "art of natural product isolation" by accelerating the pace of research of this field. This review illustrates through selected cornerstone studies the new thinking in natural product isolation by drawing a parallel between the different underlying philosophies behind the use of molecular networking in targeting natural products.

Ecological Strategies Behind the Selection of Cultivable Actinomycete Strains from the Yucatan Peninsula for the Discovery of Secondary Metabolites with Antibiotic Activity

The quest for novel natural products has recently focused on the marine environment as a source for novel microorganisms. Although isolation of marine-derived actinomycete strains is now common, understanding their distribution in the oceans and their adaptation to this environment can be helpful in the selection of isolates for further novel secondary metabolite discovery. This study explores the taxonomic diversity of marine-derived actinomycetes from distinct environments in the coastal areas of the Yucatan Peninsula and their adaptation to the marine environment as a first step towards novel natural product discovery. The use of simple ecological principles, for example, phylogenetic relatedness to previously characterized actinomycetes or seawater requirements for growth, to recognize isolates with adaptations to the ocean in an effort to select for marine-derived actinomycete to be used for further chemical studies. Marine microbial environments are an important source of novel bioactive natural products and, together with methods such as genome mining for detection of strains with biotechnological potential, ecological strategies can bring useful insights in the selection and identification of marine-derived actinomycetes for novel natural product discovery.

Metabolomic richness and fingerprints of deep-sea coral species and populations

INTRODUCTION

From shallow water to the deep sea, corals form the basis of diverse communities with significant ecological and economic value. These communities face many anthropogenic stressors including energy and mineral extraction activities, ocean acidification and rising sea temperatures. Corals and their symbionts produce a diverse assemblage of compounds that may help provide resilience to some of these stressors.

OBJECTIVES

We aim to characterize the metabolomic diversity of deep-sea corals in an ecological context by investigating patterns across space and phylogeny.

METHODS

We applied untargeted Liquid Chromatography-Mass Spectrometry to examine the metabolomic diversity of the deep-sea coral, Callogorgia delta, across three sites in the Northern Gulf of Mexico as well as three other deep-sea corals, Stichopathes sp., Leiopathes glaberrima, and Lophelia pertusa, and a shallow-water species, Acropora palmata.

RESULTS

Different coral species exhibited distinct metabolomic fingerprints and differences in metabolomic richness including core ions unique to each species. C. delta was generally least diverse while Lophelia pertusa was most diverse. C. delta from different sites had different metabolomic fingerprints and metabolomic richness at individual and population levels, although no sites exhibited unique core ions. Two core ions unique to C. delta were putatively identified as diterpenes and thus may possess a biologically important function.

CONCLUSION

Deep-sea coral species have distinct metabolomic fingerprints and exhibit high metabolomic diversity at multiple scales which may contribute to their capabilities to respond to both natural and anthropogenic stressors, including climate change.

Biosynthetic Potential of Bioactive Streptomycetes Isolated From Arid Region of the Thar Desert, Rajasthan (India)

Acquisition of Actinobacteria, especially Streptomyces from previously underexplored habitats and the exploration of their biosynthetic potential have gained much attention in the rejuvenated antibiotics search programs. Herein, we isolated some Streptomyces strains, from an arid region of the Great Indian Thar Desert, which possess an ability to produce novel bioactive compounds. Twenty-one morphologically distinctive strains differing in their aerial and substrate mycelium were isolated by employing a stamping method. Among them, 12 strains were identified by a two-level antimicrobial screening method, exerting antimicrobial effects against a panel of indicator strains including methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus species. Based on their potent antimicrobial activity, four isolates were further explored by 16S rRNA gene-based identification, genetic screening, and metabolomic analysis; and it was found that these strains belong to the genus Streptomyces. The selected strains were found to have polyketide synthase and non-ribosomal peptide synthetase systems. In addition, extracellular metabolomic screening revealed that the isolates produced analogs of doxorubicinol, pyrromycin, erythromycin, and 6-13 other putative novel metabolites. These results demonstrate the significance of Streptomyces inhabiting the arid region of Thar Desert, suggesting that similar arid environments can be considered as the reservoirs of novel Streptomyces strains that could have biotechnological significance.

Searching for original natural products by molecular networking: detection, isolation and total synthesis of chloroaustralasines

Four unprecedented chlorinated monoterpenyl quinolones have been isolated using a molecular networking based prioritisation strategy. The bio-inspired total synthesis of chloroaustralasine A involving a chloroperoxydase-mediated hydroxychlorination is described.

Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products.

Omics-based natural product discovery and the lexicon of genome mining

Genome sequencing and the application of omic techniques are driving many important advances in the field of microbial natural products research. Despite these gains, there remain aspects of the natural product discovery pipeline where our knowledge remains poor. These include the extent to which biosynthetic gene clusters are transcriptionally active in native microbes, the temporal dynamics of transcription, translation, and natural product assembly, as well as the relationships between small molecule production and detection. Here we touch on a number of these concepts in the context of continuing efforts to unlock the natural product potential revealed in genome sequence data and discuss nomenclatural issues that warrant consideration as the field moves forward.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Screening and identification of novel biologically active natural compounds

With the advent of very rapid and cheap genome analyses and the linkage of these plus microbial metabolomics to potential compound structures came the realization that there was an immense sea of novel agents to be mined and tested. In addition, it is now recognized that there is significant microbial involvement in many natural products isolated from “nominally non-microbial sources”.

This short review covers the current screening methods that have evolved and one might even be tempted to say “devolved” in light of the realization that target-based screens had problems when the products entered clinical testing, with off-target effects being the major ones. Modern systems include, but are not limited to, screening in cell lines utilizing very modern techniques (a high content screen) that are designed to show interactions within cells when treated with an “agent”. The underlying principle(s) used in such systems dated back to unpublished attempts in the very early 1980s by the pharmaceutical industry to show toxic interactions within animal cells by using automated light microscopy. Though somewhat successful, the technology was not adequate for any significant commercialization. Somewhat later, mammalian cell lines that were “genetically modified” to alter signal transduction cascades, either up or down, and frequently linked to luciferase readouts, were then employed in a 96-well format. In the case of microbes, specific resistance parameters were induced in isogenic cell lines from approximately the mid-1970s. In the latter two cases, comparisons against parent and sibling cell lines were used in order that a rapid determination of potential natural product “hits” could be made. Obviously, all of these assay systems could also be, and were, used for synthetic molecules.

These methods and their results have led to a change in what the term “screening for bioactivity” means. In practice, versions of phenotypic screening are returning, but in a dramatically different scientific environment from the 1970s, as I hope to demonstrate in the short article that follows.

Mass Spectrometry Based Molecular 3D-Cartography of Plant Metabolites

Plants play an essential part in global carbon fixing through photosynthesis and are the primary food and energy source for humans. Understanding them thoroughly is therefore of highest interest for humanity. Advances in DNA and RNA sequencing and in protein and metabolite analysis allow the systematic description of plant composition at the molecular level. With imaging mass spectrometry, we can now add a spatial level, typically in the micrometer-to-centimeter range, to their compositions, essential for a detailed molecular understanding. Here we present an LC-MS based approach for 3D plant imaging, which is scalable and allows the analysis of entire plants. We applied this approach in a case study to pepper and tomato plants. Together with MS/MS spectra library matching and spectral networking, this non-targeted workflow provides the highest sensitivity and selectivity for the molecular annotations and imaging of plants, laying the foundation for studies of plant metabolism and plant-environment interactions.