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

New N-acylated aminoalkanoic acids from tea roots derived biocontrol agent Clonostachys rosea 15020

Four new N-acylated aminoalkanoic acids, namely clonoroseins E-H (1-4), together with three previously identified analogs, clonoroseins A, B, and D (5-7), were identified from the endophytic fungus Clonostachys rosea strain 15020 (CR15020), using Feature-based Molecular Networking (FBMN). The elucidation of their chemical structures, including their absolute configurations, was achieved through spectroscopic analysis combined with quantum chemical calculations. Bioinformatics analyses suggested that an iterative type I HR-PKS (CrsE) generates the polyketide side chain of these clonoroseins. Furthermore, a downstream adenylate-forming enzyme of the PKS (CrsD) was suspected to function as an amide synthetase. CrsD potentially facilitates the transformation of the polyketide moiety into an acyl-AMP intermediate, followed by nucleophilic substitution with either β-alanine or γ-aminobutyric acid to produce amide derivatives. These findings significantly expand our understanding of PKS-related products originating from C. rosea and also underscore the powerful application of FBMN analytical methods in characterization of new compounds.

A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery

The ENPKG framework organizes large heterogeneous metabolomics data sets as a knowledge graph, offering exciting opportunities for drug discovery and chemodiversity characterization.

IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication

Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Recent advances in mass spectrometry-based computational metabolomics

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".

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.

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.

Open data and algorithms for open science in AI-driven molecular informatics

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

MS2Query: reliable and scalable MS2 mass spectra-based analogue search

Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.

Chemometric-guided chemical marker selection: A case study of the heat-clearing herb Scrophularia ningpoensis

The selection of medicinal plants' chemical markers focuses on bioactivity as the primary goal, followed by the nature of secondary metabolites, their stability, and availability. However, herbal medicines are valued for their complex and holistic pharmacological effects. A correct chemical marker can be carefully selected by a systematic clarification of their chemical-biological relationships. In the current study, the multi-informative molecular networking (MIMN) approach was employed to construct the anti-inflammatory metabolomic pattern of a heat-clearing herb, Scrophularia ningpoensis Hemsl. (S. ningpoensis). The MIMN molecular families characterized by cinnamic acid glycosides showed a higher bioactivity score compared with the other two major chemical classes (iridoid glycosides and iridoid-cinnamic acid glycosides). The Global Natural Product Social Molecular Networking (GNPS) and Reaxys database were used to assist in the putative annotation of eighteen metabolites from the bioactive and non-bioactive molecular families. The anti-inflammatory validation step was based on the detection of reactive oxygen species (ROS) generation by activated human neutrophils. All compounds from the bioactive MIMN molecular families dose-dependently inhibited the total ROS generation promoted by fMLF (IC₅₀: 0.04-0.42 μM), while the compounds from non-bioactive MIMN clusters did not show any significant anti-inflammatory effect. The ROS-dependent anti-inflammatory activity of these cinnamic acid glycosides was attributed to their oxygen radical scavenging ability. The most abundant cinnamic acid glycoside, angoroside C (IC₅₀: 0.34 μM) was suggested to be selected as a chemical marker for S. ningpoensis. In this study, the MIMN platform was applied to assist in the chemical marker selection of S. ningpoensis. The correct selection of markers will aid in the compilation and revision of herbal monographs and pharmacopeias resulting in the precise analysis and classification of medicinal plants on a scientific basis.

Marine natural products

Covering: January to December 2021This review covers the literature published in 2021 for marine natural products (MNPs), with 736 citations (724 for the period January to December 2021) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1425 in 416 papers for 2021), together with the relevant biological activities, source organisms and country of origin. Pertinent reviews, biosynthetic studies, first syntheses, and syntheses that led to the revision of structures or stereochemistries, have been included. An analysis of the number of authors, their affiliations, domestic and international collection locations, focus of MNP studies, citation metrics and journal choices is discussed.

Untargeted Metabolomics for Integrative Taxonomy: Metabolomics, DNA Marker-Based Sequencing, and Phenotype Bioimaging

Integrative taxonomy is a fundamental part of biodiversity and combines traditional morphology with additional methods such as DNA sequencing or biochemistry. Here, we aim to establish untargeted metabolomics for use in chemotaxonomy. We used three thallose liverwort species Riccia glauca, R. sorocarpa, and R. warnstorfii (order Marchantiales, Ricciaceae) with Lunularia cruciata (order Marchantiales, Lunulariacea) as an outgroup. Liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) were integrated with DNA marker-based sequencing of the trnL-trnF region and high-resolution bioimaging. Our untargeted chemotaxonomy methodology enables us to distinguish taxa based on chemophenetic markers at different levels of complexity: (1) molecules, (2) compound classes, (3) compound superclasses, and (4) molecular descriptors. For the investigated Riccia species, we identified 71 chemophenetic markers at the molecular level, a characteristic composition in 21 compound classes, and 21 molecular descriptors largely indicating electron state, presence of chemical motifs, and hydrogen bonds. Our untargeted approach revealed many chemophenetic markers at different complexity levels that can provide more mechanistic insight into phylogenetic delimitation of species within a clade than genetic-based methods coupled with traditional morphology-based information. However, analytical and bioinformatics analysis methods still need to be better integrated to link the chemophenetic information at multiple scales.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

Advances in Mass Spectrometry-Metabolomics Based Approaches

Highly selective and sensitive analytical techniques are necessary for microbial metabolomics due to the complexity of the microbial sample matrix. Hence, mass spectrometry (MS) has been successfully applied in microbial metabolomics due to its high precision, versatility, sensitivity, and wide dynamic range. The different analytical tools using MS have been employed in microbial metabolomics investigations and can contribute to the discovery or accelerate the search for bioactive substances. The coupling with chromatographic and electrophoretic separation techniques has resulted in more efficient technologies for the analysis of microbial compounds occurring in trace levels. This book chapter describes the current advances in the application of mass spectrometry-based metabolomics in the search for new biologically active agents from microbial sources; the development of new approaches for in silico annotation of natural products; the different technologies employing mass spectrometry imaging to deliver more comprehensive analysis and elucidate the metabolome involved in ecological interactions as they enable visualization of the spatial dispersion of small molecules. We also describe other ambient ionization techniques applied to the fingerprint of microbial natural products and modern techniques such as ion mobility mass spectrometry used to microbial metabolomic analyses and the dereplication of natural microbial products through MS.

MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools

BACKGROUND

Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

AIM OF REVIEW

We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

A Cyclohexapeptide and Its Rare Glycosides from Marine Sponge-Derived Streptomyces sp. OUCMDZ-4539

Pyridapeptide A (1), a cyclohexapeptide containing hexahydropyridazine-3-carboxylic acid (HPDA), 5-hydroxytetrahydropyridazine-3-carboxylic acid (γ-OH-TPDA), and (2S,3R,4E,6E)-2-amino-3-hydroxy-8-methylnona-4,6-dienoic acid residues, and its four glycopeptides, pyridapeptides B-E (2-5, respectively), were isolated from the fermentation broth of the marine sponge-derived Streptomyces sp. OUCMDZ-4539. Their structures were determined on the basis of spectroscopic analysis and chemical methods. Pyridapeptides B-E have one or more 2,3,6-trideoxyhexose sugar units glycosylated at the γ-OH-TPDA residue. The biosynthetic pathways were proposed on the basis of gene cluster analysis. Compounds 4 and 5, containing four sugar groups, displayed significant antiproliferative activity against five human cancer cell lines (PC9, MKN45, HepG2, HCT-116, and K562).

Mass spectrometry data on specialized metabolome of medicinal plants used in East Asian traditional medicine

Traditional East Asian medicine not only serves as a potential source of drug discovery, but also plays an important role in the healthcare systems of Korea, China, and Japan. Tandem mass spectrometry (MS/MS)-based untargeted metabolomics is a key methodology for high-throughput analysis of the complex chemical compositions of medicinal plants used in traditional East Asian medicine. This Data Descriptor documents the deposition to a public repository of a re-analyzable raw LC-MS/MS dataset of 337 medicinal plants listed in the Korean Pharmacopeia, in addition to a reference spectral library of 223 phytochemicals isolated from medicinal plants. Enhanced by recently developed repository-level data analysis pipelines, this information can serve as a reference dataset for MS/MS-based untargeted metabolomic analysis of plant specialized metabolites.

Incorporating LC-MS/MS Analysis and the Dereplication of Natural Product Samples into an Upper-Division Undergraduate Laboratory Course

Growth in the biomedical and biotechnology sectors requires a highly trained and highly skilled workforce to answer the next great scientific questions. Undergraduate laboratory courses incorporating hands-on training based in authentic research position soon-to-be graduates to learn in environments that mirror that of academic, industrial, and government laboratories. Mass spectrometry is one of the most broadly applied analyses carried out in the biomedical and pharmaceutical sciences and thus it is essential that upper-division students gain hands-on experience in techniques and analytical workflows in mass spectrometry. Our pre-course assessments identified weaknesses in student experience and knowledge in the fundamentals of mass spectrometry, supporting that it was a necessary area for improvement. We incorporated a laboratory experiment focused on tandem mass spectrometry and database searching into a preexisting mini-semester project devoted to identifying metabolites from medicinal plants. Implementation of the experiment allowed students to make more confident metabolite identifications, introduced them to a cutting-edge database analysis platform (GNPS: Global Natural Products Social Molecular Networking), and increased student experience and knowledge of mass spectrometry in addition to the principle of dereplication of samples derived from nature.

Study of the effects of Au@ZIF-8 on metabolism in mouse RAW 264.7 macrophages

Mass spectrometry-based metabolomics plays a vital role in discovering new markers and revealing the unpredictable biological effects of external stimuli. However, the current metabolomics research on materials is still in its infancy, and in-depth research on possible toxic mechanisms is lacking. In this study, a nanocomposite of gold nanoparticles (AuNPs)-zeolite-imidazole framework-8 (ZIF-8) (Au@ZIF-8) was designed to investigate its effects on metabolism in mouse RAW 264.7 macrophages. The successful synthesis of Au@ZIF-8 was confirmed by transmission electron microscopy (TEM) and elemental analysis. The changes in the metabolic activity of mouse RAW 264.7 macrophages at different concentrations of Au@ZIF-8 and different treatment times were investigated, and their influence on the morphological changes and behavior of RAW 264.7 cells was discussed. In addition, ultrahigh-performance liquid chromatography quadrupole-orbital high-resolution mass spectrometry (UHPLC/Q-Orbitrap HRMS) was used to study the metabolic effects of Au@ZIF-8 on RAW 264.7 cells, and the results showed different metabolites being expressed at different reaction times. After 4, 8 and 24 h of treatment, the differential expression of 14, 16, and 16 metabolites, respectively, was detected. Twenty-five candidate key metabolites were identified from the results of the expression patterns and metabolic pathways. These metabolites are related to glutamine metabolism, the tricarboxylic acid cycle and glycolytic metabolic pathways, which may provide insight into the treatment of diseases caused and progressed by glutamine metabolism. This study also indicates the effectiveness of high-resolution LC-MS in revealing the nanotoxicity mechanism of Au@ZIF-8.

Genomic and Chemical Decryption of the Bacteroidetes Phylum for Its Potential to Biosynthesize Natural Products

With progress in genome sequencing and data sharing, 1,000s of bacterial genomes are publicly available. Genome mining-using bioinformatics tools in terms of biosynthetic gene cluster (BGC) identification, analysis, and rating-has become a key technology to explore the capabilities for natural product (NP) biosynthesis. Comprehensively, analyzing the genetic potential of the phylum Bacteroidetes revealed Chitinophaga as the most talented genus in terms of BGC abundance and diversity. Guided by the computational predictions, we conducted a metabolomics and bioactivity driven NP discovery program on 25 Chitinophaga strains. High numbers of strain-specific metabolite buckets confirmed the upfront predicted biosynthetic potential and revealed a tremendous uncharted chemical space. Mining this data set, we isolated the new iron chelating nonribosomally synthesized cyclic tetradeca- and pentadecalipodepsipeptide antibiotics chitinopeptins with activity against Candida, produced by C. eiseniae DSM 22224 and C. flava KCTC 62435, respectively. IMPORTANCE The development of pipelines for anti-infectives to be applied in plant, animal, and human health management are dried up. However, the resistance development against compounds in use calls for new lead structures. To fill this gap and to enhance the probability of success for the discovery of new bioactive natural products, microbial taxa currently underinvestigated must be mined. This study investigates the potential within the bacterial phylum Bacteroidetes. A combination of omics-technologies revealed taxonomical hot spots for specialized metabolites. Genome- and metabolome-based analyses showed that the phylum covers a new chemical space compared with classic natural product producers. Members of the Bacteroidetes may thus present a promising bioresource for future screening and isolation campaigns.

Automatic MS/MS Data Mining Strategy for Discovering Target Natural Products: A Case of Lindenane Sesquiterpenoids

Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used method for discovering natural products (NPs); however, automatic MS/MS data mining for the discovery of NPs remains a challenge. In this work, LindenaneExtractor, a program based on characteristic MS/MS ions of lindenane sesquiterpenoids (LSs) was developed to automatically extract the LSs features for target LS discovery in plant extracts. To build this program, fragmentation mechanisms of characteristic ions of LSs were elucidated and confirmed by quantum chemical calculation and deuterium-labeled compounds. Subsequently, the information of characteristic ions was integrated and coded to develop LindenaneExtractor, which was further examined by standards and several public databases. Finally, the target LS features in Sarcandra hainanensis extract were automatically extracted by LindenaneExtractor and visualized by feature-based molecular networking and two-dimensional (2D) retention time-m/z plot, leading to the discovery of 96 target LSs in total, 37 of these compounds were potentially new NPs and one was confirmed by further isolation. This work proposed a new strategy for target NP analysis and discovery based on automatic MS/MS data mining, which could significantly improve the efficiency and accuracy of NP discovery.

MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets

In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis.

NMR Characterization of Lignans

Lignans are particularly interesting secondary metabolites belonging to the phenyl-propanoid biosynthetic pathway. From the structural point of view, these molecules could belong to the aryltetralin, arylnaphtalene, or dibenzylbutyrolactone molecular skeleton. Lignans are present in different tissues of plants but are mainly accumulated in seeds. Extracts from plant tissues could be characterized by using the NMR-based approach, which provides a profile of aromatic molecules and detailed structural information for their elucidation. In order to improve the production of these secondary metabolites, elicitors could effectively stimulate lignan production. Several plant species are considered in this review with a particular focus on Linum species, well recognized as the main producer of lignans.

Allopeptimicins: unique antibacterial metabolites generated by hybrid PKS-NRPS, with original self-defense mechanism in Actinoallomurus

In the search for structurally novel metabolites with antibacterial activity, innovative approaches must be implemented to increase the probability of discovering novel chemistry from microbial sources. Here we report on the application of metabolomic tools to the genus Actinoallomurus, a poorly explored member of the Actinobacteria. From examining extracts derived from 88 isolates belonging to this genus, we identified a family of cyclodepsipeptides acylated with a C₂₀ polyketide chain, which we named allopeptimicins. These molecules possess unusual structural features, including several double bonds in the amino-polyketide chain and four non-proteinogenic amino acids in the octapeptide. Remarkably, allopeptimicins are produced as a complex of active and inactive congeners, the latter carrying a sulfate group on the polyketide amine. This modification is also a mechanism of self-protection in the producer strain. The structural uniqueness of allopeptimicins is reflected in a biosynthetic gene cluster showing a mosaic structure, with dedicated gene cassettes devoted to formation of specialized precursors and modular assembly lines related to those from different pathways.

Untargeted metabolomic analysis of Actinoallomurus spp. unveiled an unprecedented acylated cyclodepsipeptide with unusual features and potent antibacterial activity.