Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species

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.

Recent advances on cyclodepsipeptides: biologically active compounds for drug research

Cyclodepsipeptides are a large family of peptide-related natural products consisting of hydroxy and amino acids linked by amide and ester bonds. A number of cyclodepsipeptides have been isolated and characterized from fungi and bacteria. Most of them showed antitumor, antifungal, antiviral, antimalarial, and antitrypanosomal properties. Herein, this review summarizes the recent literatures (2010-2022) on the progress of cyclodepsipeptides from fungi and bacteria except for those of marine origin, in order to enrich our knowledge about their structural features and biological sources.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Metabolome-guided genome mining of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

Correlative metabologenomics of 110 fungi reveals metabolite-gene cluster pairs

Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics-metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product-GCF linkages to direct future discovery.

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.

MS/MS molecular networking-guided in-depth profiling of triterpenoid saponins from the fruit of Eleutherococcus senticosus and their neuroprotectivity evaluation

INTRODUCTION

Eleutherococcus senticosus fruit (ESF) is a natural health supplement resource that has been extensively applied as a tonic for the nervous system. The structures and neural bioactivities of triterpenoid saponins (TS), which are the major constituents of ESF, have not been comprehensively analyzed thus far.

OBJECTIVE

We conducted a complete in-depth MS/MS molecular networking (MN)-based targeted analysis of TS from the crude extract of ESF and investigated its neuroprotective value.

METHODS

An MS/MS MN-guided strategy was used to rapidly present a series of precursor ions (PIs) of TS in a compound cluster as TS-targeted information used in the discovery and characterization of TS. In addition, a prepared TS-rich fraction of ESF was assayed for its restraining effects on β-amyloid-induced inhibition of neurite outgrowth.

RESULTS

A total of 87 TS were discovered using a PI tracking strategy, 28 of which were characterized as potentially undescribed structures according to their high-resolution MS values. Furthermore, the TS-rich fraction can significantly reduce β-amyloid-induced damage to neural networks by promoting the outgrowth of neurites and axons.

CONCLUSION

Our findings reveal the richness of TS in ESF and will accelerate their application in the treatment of neurodegenerative diseases.

Segregation of the genus Parahypoxylon (Hypoxylaceae, Xylariales) from Hypoxylon by a polyphasic taxonomic approach

During a mycological survey of the Democratic Republic of the Congo, a fungal specimen that morphologically resembled the American species Hypoxylon papillatum was encountered. A polyphasic approach including morphological and chemotaxonomic together with a multigene phylogenetic study (ITS, LSU, tub2, and rpb2) of Hypoxylon spp. and representatives of related genera revealed that this strain represents a new species of the Hypoxylaceae. However, the multi-locus phylogenetic inference indicated that the new fungus clustered with H. papillatum in a separate clade from the other species of Hypoxylon. Studies by ultrahigh performance liquid chromatography coupled to diode array detection and ion mobility tandem mass spectrometry (UHPLC-DAD-IM-MS/MS) were carried out on the stromatal extracts. In particular, the MS/MS spectra of the major stromatal metabolites of these species indicated the production of hitherto unreported azaphilone pigments with a similar core scaffold to the cohaerin-type metabolites, which are exclusively found in the Hypoxylaceae. Based on these results, the new genus Parahypoxylon is introduced herein. Aside from P. papillatum, the genus also includes P. ruwenzoriensesp. nov., which clustered together with the type species within a basal clade of the Hypoxylaceae together with its sister genus Durotheca.

Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets

Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.

Marine bioactive compounds as antibiofilm agent: a metabolomic approach

The ocean is a treasure trove of both living and nonliving creatures, harboring incredibly diverse group of organisms. A plethora of marine sourced bioactive compounds are discovered over the past few decades, many of which are found to show antibiofilm activity. These are of immense clinical significance since the formation of microbial biofilm is associated with the development of high antibiotic resistance. Biofilms are also responsible to bring about problems associated with industries. In fact, the toilets and wash-basins also show degradation due to development of biofilm on their surfaces. Antimicrobial resistance exhibited by the biofilm can be a potent threat not only for the health care unit along with industries and daily utilities. Various recent studies have shown that the marine members of various kingdom are capable of producing antibiofilm compounds. Many such compounds are with unique structural features and metabolomics approaches are essential to study such large sets of metabolites. Associating holobiome metabolomics with analysis of their chemical attribute may bring new insights on their antibiofilm effect and their applicability as a substitute for conventional antibiotics. The application of computer-aided drug design/discovery (CADD) techniques including neural network approaches and structured-based virtual screening, ligand-based virtual screening in combination with experimental validation techniques may help in the identification of these molecules and evaluation of their drug like properties.

Deciphering the chemical composition of Ganoderma lucidum from different geographical origins by mass spectrometry molecular networking coupled with multivariate analysis

Ganoderma lucidum is a medicinal fungus that has been widely used in China and many Asian countries for thousands of years. This once rare macrofungus has now been artificially cultivated in a number of regions in China. However, detailed knowledge of its composition across different geographical origins is still lacking, as are analytical methods for comprehensive profiling of the diverse phytochemicals contained in G. lucidum. In this work, an on-demand strategy based on high-resolution MS and molecular networking is applied for natural product characterization, which led to the identification of 84 constituents in G. lucidum. Moreover, multivariate analysis, including hierarchical cluster analysis and orthogonal partial least squares-discriminant analysis, was used to analyze the (dis)similarity of the G. lucidum samples collected from the three main production areas (i.e., Jilin, Henan and Shandong Province). The results revealed a significant variation in the chemical composition of samples from different provinces. Marker constituents corresponding to the differentiation were then screened in terms of the variable importance in projection value, P-value and fold change. A total of 24 constituents were identified as geoherbalism markers, such as ganoderenic acid A for Henan, ganolucidic acid B for Jilin and ganodernoid D for Shandong. This proof-of-concept application demonstrates that combining MS molecular networking with meticulous multivariate analysis can provide a sensitive and comprehensive analytical approach for the quality assessment of traditional Chinese medicine ingredients. This study also suggests that the bioactivity and efficacy from different origins should be further evaluated considering the large difference in chemical compositions.

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

Identification of the Biosynthetic Gene Cluster for Pyracrimycin A, an Antibiotic Produced by Streptomyces sp

Actinomycetes make a wealth of complex, structurally diverse natural products, and a key challenge is to link them to their biosynthetic gene clusters and delineate the reactions catalyzed by each of the enzymes. Here, we report the biosynthetic gene cluster for pyracrimycin A, a set of nine genes that includes a core nonribosomal peptide synthase (pymB) that utilizes serine and proline as precursors and a monooxygenase (pymC) that catalyzes Baeyer-Villiger oxidation. The cluster is similar to the one for brabantamide A; however, pyracrimycin A biosynthesis differs in that feeding experiments with isotope-labeled serine and proline suggest that a ring opening reaction takes place and a carbon is lost from serine downstream of the oxidation reaction. Based on these data, we propose a full biosynthesis pathway for pyracrimycin A.

Natural Products from Actinobacteria as a Potential Source of New Therapies Against Colorectal Cancer: A Review

Colorectal cancer (CRC) is a common, and deadly disease. Despite the improved knowledge on CRC heterogeneity and advances in the medical sciences, there is still an urgent need to cope with the challenges and side effects of common treatments for the disease. Natural products (NPs) have always been of interest for the development of new medicines. Actinobacteria are known to be prolific producers of a wide range of bioactive NPs, and scientific evidence highlights their important protective role against CRC. This review is a holistic picture on actinobacter-derived cytotoxic compounds against CRC that provides a good perspective for drug development and design in near future. This review also describes the chemical structure of 232 NPs presenting anti-CRC activity with the being majority of quinones, lactones, alkaloids, peptides, and glycosides. The study reveals that most of these NPs are derived from marine actinobacteria followed by terrestrial and endophytic actinobacteria, respectively. They are predominantly produced by Streptomyces, Micromonospors, Saliniospors and Actinomadura, respectively, in which Streptomyces, as the predominant contributor generating over 76% of compounds exclusively. Besides it provides a valuable snapshot of the chemical structure-activity relationship of compounds, highlighting the presence or absence of some specific atoms and chemical units in the structure of compounds can greatly influence their biological activities. To the best of our knowledge, this is the first comprehensive review on natural actinobacterial compounds affecting different types of CRC. Our study reveals that the high diversity of actinobacterial strains and their NPs derivatives, described here provides a new perspective and direction for the production of new anti-CRC drugs and paves the way to innovation for drugs discovery in the future. The knowledge obtain from this review can help us to understand the pivotal application of actinobacteria in future drugs development.

Genome Mining and Metabolomics Unveil Pseudonochelin: A Siderophore Containing 5-Aminosalicylate from a Marine-Derived Pseudonocardia sp. Bacterium

Pseudonochelin (1), a siderophore from a marine-derived Pseudonocardia sp. bacterium, was discovered using genome mining and metabolomics technologies. A 5-aminosalicylic acid (5-ASA) unit, not previously found in siderophore natural products, was identified in 1. Annotation of a putative psn biosynthetic gene cluster combined with bioinformatics and isotopic enrichment studies enabled us to propose the biosynthesis of 1. Moreover, 1 was found to display in vitro and in vivo antibacterial activity in an iron-dependent fashion.

Exploring Newer Biosynthetic Gene Clusters in Marine Microbial Prospecting

Marine microbes genetically evolved to survive varying salinity, temperature, pH, and other stress factors by producing different bioactive metabolites. These microbial secondary metabolites (SMs) are novel, have high potential, and could be used as lead molecule. Genome sequencing of microbes revealed that they have the capability to produce numerous novel bioactive metabolites than observed under standard in vitro culture conditions. Microbial genome has specific regions responsible for SM assembly, termed biosynthetic gene clusters (BGCs), possessing all the necessary genes to encode different enzymes required to generate SM. In order to augment the microbial chemo diversity and to activate these gene clusters, various tools and techniques are developed. Metagenomics with functional gene expression studies aids in classifying novel peptides and enzymes and also in understanding the biosynthetic pathways. Genome shuffling is a high-throughput screening approach to improve the development of SMs by incorporating genomic recombination. Transcriptionally silent or lower level BGCs can be triggered by artificially knocking promoter of target BGC. Additionally, bioinformatic tools like antiSMASH, ClustScan, NAPDOS, and ClusterFinder are effective in identifying BGCs of existing class for annotation in genomes. This review summarizes the significance of BGCs and the different approaches for detecting and elucidating BGCs from marine microbes.

Uncovering the biodiversity and biosynthetic potentials of rare actinomycetes

Background

Antibiotic resistance is on the rise, and new antibiotic research has slowed in recent years, necessitating the discovery of possibly novel microbial resources capable of producing bioactive compounds. Microbial infections are gaining resistance to existing antibiotics, emphasizing the need for novel medicinal molecules to be discovered as soon as possible. Because the possibilities of isolating undiscovered actinomycetes strains have decreased, the quest for novel products has shifted to rare actinomycetes genera from regular environments or the identification of new species identified in unusual habitats.

Main body of the abstract

The non-streptomyces actinobacteria are known as rare actinomycetes that are extremely difficult to cultivate. Rare actinomycetes are known to produce a variety of secondary metabolites with varying medicinal value. In this review, we reported the diversity of rare actinomycetes in several habitat including soil, plants, aquatic environment, caves, insects and extreme environments. We also reported some isolation methods to easily recover rare Actinobacteria from various sources guided with some procedures to identify the rare Actinobacteria isolates. Finally, we reported the biosynthetic potential of rare actinomycetes and its role in the production of unique secondary metabolites that could be used in medicine, agriculture, and industry. These microbial resources will be of interest to humanity, as antibiotics, insecticides, anticancer, antioxidants, to mention but a few.

Short conclusion

Rare actinomycetes are increasingly being investigated for new medicinal compounds that could help to address existing human health challenges such as newly emerging infectious illnesses, antibiotic resistance, and metabolic disorders. The bioactive secondary metabolites from uncommon actinomycetes are the subject of this review, which focuses on their diversity in different habitats, isolation, identification and biosynthetic potentials.

Optimization of LC-MS2 Data Acquisition Parameters for Molecular Networking Applied to Marine Natural Products

Since the introduction of the online open-source GNPS, molecular networking has quickly become a widely applied tool in the field of natural products chemistry, with applications from dereplication, genome mining, metabolomics, and visualization of chemical space. Studies have shown that data dependent acquisition (DDA) parameters affect molecular network topology but are limited in the number of parameters studied. With an aim to optimize LC-MS² parameters for integrating GNPS-based molecular networking into our marine natural products workflow, a design of experiment (DOE) was used to screen the significance of the effect that eleven parameters have on both Classical Molecular Networking workflow (CLMN) and the new Feature-Based Molecular Networking workflow (FBMN). Our results indicate that four parameters (concentration, run duration, collision energy and number of precursors per cycle) are the most significant data acquisition parameters affecting the network topology. While concentration and the LC duration were found to be the two most important factors to optimize for CLMN, the number of precursors per cycle and collision energy were also very important factors to optimize for FBMN.

An isotopic labeling approach linking natural products with biosynthetic gene clusters

Major advances in genome sequencing and large-scale biosynthetic gene cluster (BGC) analysis have prompted an age of natural product discovery driven by genome mining. Still, connecting molecules to their cognate BGCs is a substantial bottleneck for this approach. We have developed a mass-spectrometry-based parallel stable isotope labeling platform, termed IsoAnalyst, which assists in associating metabolite stable isotope labeling patterns with BGC structure prediction to connect natural products to their corresponding BGCs. Here we show that IsoAnalyst can quickly associate both known metabolites and unknown analytes with BGCs to elucidate the complex chemical phenotypes of these biosynthetic systems. We validate this approach for a range of compound classes, using both the type strain Saccharopolyspora erythraea and an environmentally isolated Micromonospora sp. We further demonstrate the utility of this tool with the discovery of lobosamide D, a new and structurally unique member of the family of lobosamide macrolactams.

Rare actinobacteria isolated from the hypersaline Ojo de Liebre Lagoon as a source of novel bioactive compounds with biotechnological potential

The Ojo de Liebre Lagoon is a Marine Protected Area that lies within a UNESCO World Heritage Site and is a critical habitat for important migratory species such as the grey whale and bird species. Unique hypersaline environments, such as the Ojo de Liebre Lagoon, are underexplored in terms of their bacterial and chemical diversity, representing a potential source for new bioactive compounds with pharmacological properties. Actinobacteria are one of the most diverse and prolific taxonomic bacterial groups in terms of marine bioactive compounds. This study aimed to identify the culturable actinobacterial community inhabiting the Lagoon, as well as to test their potential as new sources of anticancer compounds with pharmacological potential. A selective isolation approach focused on spore-forming bacteria from 40 sediment samples generated a culture collection of 64 strains. The 16S rRNA gene analyses identified three phyla in this study, the Actinobacteria, Firmicutes and Proteobacteria, where the phylum Actinobacteria dominated (57%) the microbial community profiles. Within the Actinobacteria, nine different genera were isolated including the Actinomadura, Micromonospora, Nocardiopsis, Plantactinospora and Streptomyces sp. We observed seasonal differences on actinobacteria recovery. For instance, Micromonospora strains were recovered during the four sampling seasons, while Arthrobacter and Pseudokineococcus were only isolated in February 2018, and Blastococcus, Rhodococcus and Streptomyces were uniquely isolated in June 2018. Ethyl acetate crude extracts derived from actinobacterial cultures were generated and screened for cytotoxic activity against six cancer cell lines. Strains showed promising low percentages of viability on lung (H1299), cervical (SiHa), colon (Caco-2) and liver (HepG2) cancer lines. Molecular networking results suggest many of the metabolites produced by these strains are unknown and they might harbour novel chemistry. Our results showed the Ojo de Liebre Lagoon is a novel source for isolating diverse marine actinobacteria which produce promising bioactive compounds for potential biotechnological use as anticancer agents.

Differential regulation and production of secondary metabolites among isolates of the fungal wheat pathogen Zymoseptoria tritici

The genome of the wheat pathogenic fungus, Zymoseptoria tritici, represents extensive presence-absence variation in gene content. Here, we addressed variation in biosynthetic gene clusters (BGCs) content and biochemical profiles among three isolates. We analysed secondary metabolite properties based on genome, transcriptome and metabolome data. The isolates represent highly distinct genome architecture, but harbor similar repertoire of BGCs. Expression profiles for most BGCs show comparable patterns of regulation among the isolates, suggesting a conserved "biochemical infection program". For all three isolates, we observed a strong up-regulation of a putative abscisic acid (ABA) gene cluster during biotrophic host colonization, indicating that Z. tritici potentially interfere with host defenses by the biosynthesis of this phytohormone. Further, during in vitro growth the isolates show similar metabolomes congruent with the predicted BGC content. We assessed if secondary metabolite production is regulated by histone methylation using a mutant impaired in formation of facultative heterochromatin (H3K27me3). In contrast to other ascomycete fungi, chromatin modifications play a less prominent role in regulation of secondary metabolites. In summary, we show that Z. tritici has a conserved program of secondary metabolite production contrasting the immense variation in effector expression, some of these metabolites might play a key role during host colonization. Importance Zymoseptoria tritici is one of the most devastating pathogens of wheat. So far the molecular determinants of virulence and their regulation are poorly understood. Previous studies have focused on proteinasous virulence factors and their extensive diversity. In this study, we focus on secondary metabolites produced by Z. tritici. Using a comparative framework, we here characterize core and non-core metabolites produced by Z. tritici by combining genome, transcriptome and metabolome datasets. Our findings indicate highly conserved biochemical profiles contrasting genetic and phenotypic diversity of the field isolates investigated here. This discovery has relevance for future crop protection strategies.

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.

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.

The intriguing chemistry and biology of sulfur-containing natural products from marine microorganisms (1987-2020)

Natural products derived from marine microorganisms have received great attention as a potential resource of new compound entities for drug discovery. The unique marine environment brings us a large group of sulfur-containing natural products with abundant biological functionality including antitumor, antibiotic, anti-inflammatory and antiviral activities. We reviewed all the 484 sulfur-containing natural products (non-sulfated) isolated from marine microorganisms, of which 59.9% are thioethers, 29.8% are thiazole/thiazoline-containing compounds and 10.3% are sulfoxides, sulfones, thioesters and many others. A selection of 133 compounds was further discussed on their structure-activity relationships, mechanisms of action, biosynthesis, and druggability. This is the first systematic review on sulfur-containing natural products from marine microorganisms conducted from January 1987, when the first one was reported, to December 2020.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-021-00101-2.

Applications and Restrictions of Integrated Genomic and Metabolomic Screening: An Accelerator for Drug Discovery from Actinomycetes?

Since the golden age of antibiotics in the 1950s and 1960s actinomycetes have been the most prolific source for bioactive natural products. However, the number of discoveries of new bioactive compounds decreases since decades. New procedures (e.g., activating strategies or innovative fermentation techniques) were developed to enhance the productivity of actinomycetes. Nevertheless, compound identification remains challenging among others due to high rediscovery rates. Rapid and cheap genome sequencing as well as the advent of bioinformatical analysis tools for biosynthetic gene cluster identification in combination with mass spectrometry-based molecular networking facilitated the tedious process of dereplication. In recent years several studies have been dedicated to accessing the biosynthetic potential of Actinomyces species, especially streptomycetes, by using integrated genomic and metabolomic screening in order to boost the discovery rate of new antibiotics. This review aims to present the various possible applications of this approach as well as the newly discovered molecules, covering studies between 2014 and 2021. Finally, the effectiveness of this approach with regard to find new bioactive agents from actinomycetes will be evaluated.

Mining genomes to illuminate the specialized chemistry of life

All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.

Biology and applications of co-produced, synergistic antimicrobials from environmental bacteria

Environmental bacteria, such as Streptomyces spp., produce specialized metabolites that are potent antibiotics and therapeutics. Selected specialized antimicrobials are co-produced and function together synergistically. Co-produced antimicrobials comprise multiple chemical classes and are produced by a wide variety of bacteria in different environmental niches, suggesting that their combined functions are ecologically important. Here, we highlight the exquisite mechanisms that underlie the simultaneous production and functional synergy of 16 sets of co-produced antimicrobials. To date, antibiotic and antifungal discovery has focused mainly on single molecules, but we propose that methods to target co-produced antimicrobials could widen the scope and applications of discovery programs.

Mining and unearthing hidden biosynthetic potential

Genetically encoded small molecules (secondary metabolites) play eminent roles in ecological interactions, as pathogenicity factors and as drug leads. Yet, these chemical mediators often evade detection, and the discovery of novel entities is hampered by low production and high rediscovery rates. These limitations may be addressed by genome mining for biosynthetic gene clusters, thereby unveiling cryptic metabolic potential. The development of sophisticated data mining methods and genetic and analytical tools has enabled the discovery of an impressive array of previously overlooked natural products. This review shows the newest developments in the field, highlighting compound discovery from unconventional sources and microbiomes.

Natural products are an important source of bioactive compounds and have versatile applications in different fields, but their discovery is challenging. Here, the authors review the recent developments in genome mining for discovery of natural products, focusing on compounds from unconventional microorganisms and microbiomes.

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.

Bioprospecting of Soil-Derived Actinobacteria Along the Alar-Hotan Desert Highway in the Taklamakan Desert

Taklamakan desert is known as the largest dunefield in China and as the second largest shifting sand desert in the world. Although with long history and glorious culture, the Taklamakan desert remains largely unexplored and numerous microorganisms have not been harvested in culture or taxonomically identified yet. The main objective of this study is to explore the diversity, novelty, and pharmacological potential of the cultivable actinomycetes from soil samples at various sites along the Alar-Hotan desert highway in the Taklamakan desert. A total of 590 actinobacterial strains were recovered by the culture-dependent approach. Phylogenetic analysis based on 16S ribosomal RNA (rRNA) gene sequences unveiled a significant level of actinobacterial diversity with 55 genera distributed in 27 families of 12 orders. Thirty-six strains showed relatively low 16S rRNA similarities (<98.65%) with validly described species, among which four strains had already been characterized as novel taxa by our previous research. One hundred and forty-six actinobacterial isolates were selected as representatives to evaluate the antibacterial activities and mechanism of action by the paper-disk diffusion method and a double fluorescent protein reporter "pDualrep2" system, respectively. A total of 61 isolates exhibited antagonistic activity against the tested "ESKAPE" pathogens, among which seven strains could produce bioactive metabolites either to be able to block translation machinery or to induce SOS-response in the pDualrep2 system. Notably, Saccharothrix sp. 16Sb2-4, harboring a promising antibacterial potential with the mechanism of interfering with protein translation, was analyzed in detail to gain deeper insights into its bioactive metabolites. Through ultra-performance liquid chromatography (UPLC)-quadrupole time-of-flight (QToF)-MS/MS based molecular networking analysis and databases identification, four families of compounds (1-16) were putatively identified. Subsequent bioassay-guided separation resulted in purification of four 16-membered macrolide antibiotics, aldgamycin H (8), aldgamycin K (9), aldgamycin G (10), and swalpamycin B (11), and their structures were elucidated by HR-electrospray ionization source (ESI)-MS and NMR spectroscopy. All compounds 8-11 displayed antibacterial activities by inhibiting protein synthesis in the pDualrep2 system. In conclusion, this work demonstrates that Taklamakan desert is a potentially unique reservoir of versatile actinobacteria, which can be a promising source for discovery of novel species and diverse bioactive compounds.

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.

Planomonospora: A Metabolomics Perspective on an Underexplored Actinobacteria Genus

Despite an excellent track record, microbial drug discovery suffers from high rates of rediscovery. Better workflows for the rapid investigation of complex extracts are needed to increase throughput and to allow early prioritization of samples. In addition, systematic characterization of poorly explored strains is seldomly performed. Here, we report a metabolomic study of 72 isolates belonging to the rare actinomycete genus Planomonospora, using a workflow of commonly used open access tools to investigate its secondary metabolites. The results reveal a correlation of chemical diversity and strain phylogeny, with classes of metabolites exclusive to certain phylogroups. We were able to identify previously reported Planomonospora metabolites, including the ureylene-containing oligopeptide antipain, the thiopeptide siomycin including new congeners, and the ribosomally synthesized peptides sphaericin and lantibiotic 97518. In addition, we found that Planomonospora strains can produce the siderophore desferrioxamine or a salinichelin-like peptide. Analysis of the genomes of three newly sequenced strains led to the detection of 59 gene cluster families, of which three were connected to products found by LC-MS/MS profiling. This study demonstrates the value of metabolomic studies to investigate poorly explored taxa and provides a first picture of the biosynthetic capabilities of the genus Planomonospora.

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.

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

BACKGROUND

Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).

RESULTS

Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.

CONCLUSIONS

BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

BiG-FAM: the biosynthetic gene cluster families database

Computational analysis of biosynthetic gene clusters (BGCs) has revolutionized natural product discovery by enabling the rapid investigation of secondary metabolic potential within microbial genome sequences. Grouping homologous BGCs into Gene Cluster Families (GCFs) facilitates mapping their architectural and taxonomic diversity and provides insights into the novelty of putative BGCs, through dereplication with BGCs of known function. While multiple databases exist for exploring BGCs from publicly available data, no public resources exist that focus on GCF relationships. Here, we present BiG-FAM, a database of 29,955 GCFs capturing the global diversity of 1,225,071 BGCs predicted from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs). The database offers rich functionalities, such as multi-criterion GCF searches, direct links to BGC databases such as antiSMASH-DB, and rapid GCF annotation of user-supplied BGCs from antiSMASH results. BiG-FAM can be accessed online at https://bigfam.bioinformatics.nl.

Expansion of Gamma-Butyrolactone Signaling Molecule Biosynthesis to Phosphotriester Natural Products

Bacterial hormones, such as the iconic gamma-butyrolactone A-factor, are essential signaling molecules that regulate diverse physiological processes, including specialized metabolism. These low molecular weight compounds are common in Streptomyces species and display species-specific structural differences. Recently, unusual gamma-butyrolactone natural products called salinipostins were isolated from the marine actinomycete genus Salinispora based on their antimalarial properties. As the salinipostins possess a rare phosphotriester motif of unknown biosynthetic origin, we set out to explore its construction by the widely conserved 9-gene spt operon in Salinispora species. We show through a series of in vivo and in vitro studies that the spt gene cluster dually encodes the salinipostins and newly identified natural A-factor-like gamma-butyrolactones (Sal-GBLs). Remarkably, homologous biosynthetic gene clusters are widely distributed among many actinomycete genera, including Streptomyces, suggesting the significance of this operon in bacteria.

Integration of LC/MS-based molecular networking and classical phytochemical approach allows in-depth annotation of the metabolome of non-model organisms - The case study of the brown seaweed Taonia atomaria

Untargeted LC-MS based metabolomics is a useful approach in many research areas such as medicine, systems biology, environmental sciences or even ecology. In such an approach, annotation of metabolomes of non-model organisms remains a significant challenge. In this study, an analytical workflow combining a classical phytochemical approach, using the isolation and the full characterization of the chemical structure of natural products, together with the use of MS/MS-based molecular networking with various levels of restrictiveness was developed. This protocol was applied to the marine brown seaweed Taonia atomaria, a cosmopolitan algal species, and allowed to annotate more than 200 metabolites. First, the algal organic crude extracts were fractionated by flash-chromatography and the chemical structure of eight of the main chemical constituents of this alga were fully characterized by means of spectroscopic methods (1D and 2D NMR, HRMS). These compounds were further used as chemical standards. In a second step, the main fractions of the algal extracts were analyzed by UHPLC-MS/MS and the resulting data were uploaded to the Global Natural Products Social Molecular Networking platform (GNPS) to create several molecular networks (MNs). A first MN (MN-1) was built with restrictive parameters and allowed the creation of clusters composed by nodes with highly similar MS/MS spectra. Then, using database hits and chemical standards as "seed" nodes and/or similarity between MS/MS fragmentation pattern, the main clusters were easily annotated as common glycerolipids and phospholipids, much rare lipids -such as acylglycerylhydroxymethyl-N,N,N-trimethyl-ß-alanines or fulvellic acid derivatives- but also new glycerolipids bearing a terpene moiety. Lastly, the use of less and less constrained MNs allowed to further increase the number of annotated metabolites.

Bioactive natural products from the genus Salinospora: a review

Actinomycetes are an important source for bioactive secondary metabolites. Among them, the genus Salinispora is one of the first salt obligatory marine species worldwide and is typically found in various types of substrates in tropical and subtropical marine environments including sediments and marine organisms. This genus produces a wide range of chemical scaffolds and bioactive compounds such as lomaiviticins, cyclomarins, rifamycins, salinaphthoquinones, and salinosporamides. This review arranged Salinispora derived secondary metabolites according to the three species that comprise the genus. Moreover, muta- and semi-synthesis analogs derived from salinosporamide were also described in this review.

Plant Endophytes and Epiphytes: Burgeoning Sources of Known and "Unknown" Cytotoxic and Antibiotic Agents?

In the last 20 or so years, the influence of endophytes and, quite recently, epiphytes of plants upon the compounds found in those plants, which were usually assumed to be phytochemicals produced by the plant for a variety of reasons, often as a defense against predators, is becoming more evident, in particular in the case of antitumor agents originally isolated from plant sources, though antibiotic agents might also be found, particularly from epiphytes. In this review, we started with the first report in 1993 of a taxol-producing endophyte and then expanded the compounds discussed to include camptothecin, the vinca alkaloids, podophyllotoxin, and homoharringtonine from endophytic microbes and then the realization that maytansine is not a plant secondary metabolite at all, and that even such a well-studied plant such as Arabidopsis thaliana has a vast repertoire of potential bioactive agents in its leaf epiphytic bacteria. We have taken data from a variety of sources, including a reasonable history of these discoveries that were not given in recent papers by us, nor in other papers covering this topic. The sources included the Scopus database, but we also performed other searches using bibliographic tools, thus, the majority of the papers referenced are the originals, though we note some very recent papers that have built on previous results. We concluded with a discussion of the more modern techniques that can be utilized to "persuade" endophytes and epiphytes to switch on silent biosynthetic pathways and how current analytical techniques may aid in evaluating such programs. We also comment at times on some findings, particularly in the case of homoharringtonine, where there are repetitious data reports differing by a few years claiming the same endophyte as the producer.

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.

NPS: scoring and evaluating the statistical significance of peptidic natural product-spectrum matches

MOTIVATION

Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved.

RESULTS

Here we present NPS, a statistical learning-based approach for scoring PNP-spectrum matches. We incorporated NPS into two leading PNP discovery tools and benchmarked them on millions of natural product mass spectra. The results demonstrate more than 45% increase in the number of identified spectra and 20% more found PNPs at a false discovery rate of 1%.

AVAILABILITY AND IMPLEMENTATION

NPS is available as a command line tool and as a web application at http://cab.spbu.ru/software/NPS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Custom Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometric Database for Identification of Environmental Isolates of the Genus Burkholderia and Related Genera

Success of discovery programs for microbial natural products is dependent on quick and concise discrimination between isolates from diverse environments. However, laboratory isolation and identification of priority genera using current 16S rRNA PCR-based methods are both challenging and time-consuming. An emerging strategy for rapid isolate discrimination is protein fingerprinting via matrix-assisted laser desorption ionization (MALDI) mass spectrometry. Using our in-house environmental isolate repository, we have created a main spectral (MSP) library for the Bruker Biotyper MALDI mass spectrometer that contains 95 entries, including Burkholderia, Caballeronia, Paraburkholderia, and other environmentally related genera. The library creation required the acquisition of over 2,250 mass spectra, which were manually reviewed for quality control and consolidated into a single reference library using a commercial software platform. We tested the effectiveness of the reference library by analyzing 49 environmental isolate strains using two different sample preparation methods. Overall, this approach correctly identified all strains to the genus level provided that suitable reference spectra were present in the MSP library. In this study, we present a fast, accurate method for taxonomic assignment of environmentally derived bacteria from the order Burkholderiales, providing a valuable alternative to traditional PCR-based methods. The MSP library described in the manuscript is available for use.IMPORTANCE The Gram-negative proteobacterial order Burkholderiales has emerged as a promising source of novel natural products in recent years. This order includes the genus Burkholderia and the newly defined genera Caballeronia and Paraburkholderia However, development of this resource has been hampered by difficulties with rapid and selective isolation of Burkholderiales strains from the environment. Environmental metagenome sequencing has revealed that the potential for natural products is not evenly distributed throughout the microbial world. Thus, large but targeted microbial isolate libraries are needed to effectively explore the chemical potential of natural products. To study these organisms efficiently, methods to quickly identify isolates to the genus level are required. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is already used in clinical settings to reliably identify unknown bacterial pathogens. We have adapted similar methodology using the MALDI Biotyper instrument to rapidly identify environmental isolates of Burkholderia, Caballeronia, and Paraburkholderia for downstream natural product discovery.

C-Methylation of S-adenosyl-L-Methionine Occurs Prior to Cyclopropanation in the Biosynthesis of 1-Amino-2-Methylcyclopropanecarboxylic Acid (Norcoronamic Acid) in a Bacterium

Many pharmacologically important peptides are bacterial or fungal in origin and contain nonproteinogenic amino acid (NPA) building blocks. Recently, it was reported that, in bacteria, a cyclopropane-containing NPA 1-aminocyclopropanecarboxylic acid (ACC) is produced from the L-methionine moiety of S-adenosyl-L-methionine (SAM) by non-canonical ACC-forming enzymes. On the other hand, it has been suggested that a monomethylated ACC analogue, 2-methyl-ACC (MeACC), is derived from L-valine. Therefore, we have investigated the MeACC biosynthesis by identifying a gene cluster containing bacterial MeACC synthase genes. In this gene cluster, we identified two genes, orf29 and orf30, which encode a cobalamin (B12)-dependent radical SAM methyltransferase and a bacterial ACC synthase, respectively, and were found to be involved in the MeACC biosynthesis. In vitro analysis using their recombinant enzymes (rOrf29 and rOrf30) further revealed that the ACC structure of MeACC was derived from the L-methionine moiety of SAM, rather than L-valine. In addition, rOrf29 was found to catalyze the C-methylation of the L-methionine moiety of SAM. The resulting methylated derivative of SAM was then converted into MeACC by rOrf30. Thus, we demonstrate that C-methylation of SAM occurs prior to cyclopropanation in the biosynthesis of a bacterial MeACC (norcoronamic acid).

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.

Biologically active marine natural products and their molecular targets discovered using a chemical genetics approach

Covering: 2000 to 2019The discovery of new natural products that have some combination of unprecedented chemical structures, biological activities of therapeutic interest for urgent medical needs, and new molecular targets provides the fuel that sustains the vitality of natural products chemistry research. Unfortunately, finding these important new compounds is neither routine or trivial and a major challenge is finding effective discovery paradigms. This review presents examples that illustrate the effectiveness of a chemical genetics approach to marine natural product (MNP) discovery that intertwines compound discovery, molecular target identification, and phenotypic response/biological activity. The examples include MNPs that have complex unprecedented structures, new or understudied molecular targets, and potent biological activities of therapeutic interest. A variety of methods to identify molecular targets are also featured.

Genome mining as a biotechnological tool for the discovery of novel marine natural products

Compared to terrestrial environments, the oceans harbor a variety of environments, creating higher biodiversity, which gives marine natural products a high occurrence of significant biology and novel chemistry. However, traditional bioassay-guided isolation and purification strategies are severely limiting the discovery of additional novel natural products from the ocean. With an increasing number of marine microorganisms being sequenced, genome mining is gradually becoming a powerful tool to retrieve novel marine natural products. In this review, we have summarized genome mining approaches used to analyze key enzymes of biosynthetic pathways and predict the chemical structure of new gene clusters by introducing successful stories that used genome mining strategy to identify new marine-derived compounds. Furthermore, we also put forward challenges for genome mining techniques and their proposed solutions. The detailed analysis of the genome mining strategy will help researchers to understand this novel technique and its application. With the development of a genome sequence, genome mining strategies will be applied more widely, which will drive rapid development in the field of marine natural product development.

New tetrodotoxin analogs in Brazilian pufferfishes tissues and microbiome

While tetrodotoxin (TTX) is commonly found in pufferfish tissues, it is unclear if bacterial symbionts isolated from pufferfish tissues can produce TTX. In this investigation, UPLC qTOF-MS/MS analysis of tissue extracts obtained from Sphoeroides spengleri and Canthigaster figuereidoi identified TTX in their composition, indicating their consumption is unsafe. UPLC qTOF-MS/MS analysis coupled with Molecular Networking indicated new TTX analogs (methyl-TTX, TTX-acetate, hydroxypropyl-TTX and glycerol-TTX). Bacterial extracts from sixteen strains revealed a compound with a [M+H]⁺ ion at m/z 320.1088, identical to TTX. However, TTX itself was not detected in these cultures by UPLC-MS/MS. Neurotoxicity of Vibrio A665 purified fraction 2 (with precursor [M+H]⁺ ion at m/z 320.1088) was significant in human neural stem cells (hNSCs), but the Na_v blockage activity was not confirmed by the veratridine/ouabain essays, indicating a possible difference in the mechanism of action between the bacterium A665 purified fraction 2 and TTX. Vibrios symbionts of pufferfish point out involving in the production of TTX precursors.

Current status and contemporary approaches to the discovery of antitumor agents from higher plants

Higher plant constituents have afforded clinically available anticancer drugs. These include both chemically unmodified small molecules and their synthetic derivatives currently used or those in clinical trials as antineoplastic agents, and an updated summary is provided. In addition, botanical dietary supplements, exemplified by mangosteen and noni constituents, are also covered as potential cancer chemotherapeutic agents. Approaches to metabolite purification, rapid dereplication, and biological evaluation including analytical hyphenated techniques, molecular networking, and advanced cellular and animal models are discussed. Further, enhanced and targeted drug delivery systems for phytochemicals, including micelles, nanoparticles and antibody drug conjugates (ADCs) are described herein.