Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data

Anabolic steroids in livestock production: Background and implications for chemical food safety

The use of steroids in livestock animals is a source of concern for consumers because of the risks associated with the presence of their residues in foodstuffs of animal origin. Technological advances such as mass spectrometry have made it possible to play a fundamental role in controlling such practices, firstly for the discovery of marker metabolites but also for the monitoring of these compounds under the regulatory framework. Current control strategies rely on the monitoring of either the parent drug or its metabolites in various matrices of interest. As some of these steroids also have an endogenous status specific strategies have to be applied for control purposes. This review aims to provide a comprehensive and up-to-date knowledge of analytical strategies, whether targeted or non-targeted, and whether they focus on markers of exposure or effect in the specific context of chemical food safety regarding the use of anabolic steroids in livestock. The role of new approaches in data acquisition (e.g. ion mobility), processing and analysis, (e.g. molecular networking), is also discussed.

Biological activities and metabolomic profiles of extracts from the marine sediment bacterium Nocardiopsis alba DP1B cultivated in different media

The soil bacterium DP1B was isolated from a marine sediment collected off the coast of Randayan Island, Kalimantan Barat, Indonesia and identified based on 16S rDNA as Nocardiopsis alba. The bacterium was cultivated in seven different media (A1, ISP1, ISP2, ISP4, PDB, PC-1, and SCB) with three different solvents [distilled water, 5% NaCl solution, artificial seawater (ASW)] combinations, shaken at 200 rpm, 30 °C, for 7 days. The culture broths were extracted with ethyl acetate and each extract was tested for its antimicrobial activity and brine shrimp lethality, and the chemical diversity was assessed using thin-layer chromatography (TLC), gas chromatography (GC), and liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). The result showed that almost all extracts showed antibacterial but not antifungal activity, whereas their brine shrimp toxicity levels vary from high to low. The best medium/solvent combinations for antibacterial activity and toxicity were PC-1 (in either distilled water, 5% NaCl solution, or ASW) and SCB in ASW. Different chemical diversity profiles were observed on TLC, GC-MS, and LC-MS/MS. Extracts from the PC-1 cultures seem to contain a significant number of cyclic dipeptides, whereas those from the SCB cultures contain sesquiterpenes, indicating that media and solvent compositions can affect the secondary metabolite profiles of DP1B. In addition, untargeted metabolomic analyses using LC-MS/MS showed many molecular ions that did not match with those in the Global Natural Products Social Molecular Networking (GNPS) database, suggesting that DP1B has great potential as a source of new natural products.

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography-Mass Spectrometry

Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC-MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data. Furthermore, orthogonal projection resolution (OPR) was integrated into GCMSFormer to resolve minor components. The GCMSFormer model was trained, validated, and tested using 100,000 augmented data. It achieves 99.88% of the bilingual evaluation understudy (BLEU) value on the test set, significantly higher than the 97.68% BLEU value of the baseline sequence-to-sequence model long short-term memory (LSTM). GCMSFormer was also compared with two nondeep learning resolution tools (MZmine and AMDIS) and two deep learning resolution tools (PARAFAC2 with DL and MSHub/GNPS) on a real plant essential oil GC-MS data set. Their resolution results were compared on evaluation metrics, including the number of compounds resolved, mass spectral match score, correlation coefficient, explained variance, and resolution speed. The results demonstrate that GCMSFormer has better resolution performance, higher automation, and faster resolution speed. In summary, GCMSFormer is an end-to-end, fast, fully automatic, and accurate method for analyzing GC-MS data of complex samples.

Chemical diversity of Brittonodoxa subpinnata, a Brazilian native species of moss

Plants should be probably thought of as the most formidable chemical laboratory that can be exploited for the production of an incredible number of molecules with remarkable structural and chemical diversity that cannot be matched by any synthetic libraries of small molecules. The bryophytes chemistry has been neglected for too long, but in the last ten years, this scenery is changing, with several studies being made using extracts from bryophytes, aimed at the characterization of interesting metabolites, with their metabolome screened. The main objective of this study was to analyze the metabolome of Brittonodoxa subpinnata, a native Brazilian moss species, which occurs in the two Brazilian hotspots. GC-MS and LC-MS2 were performed. All extracts were analyzed using the molecular networking approach. The four extracts of B. subpinnata (polar, non-polar, soluble, and insoluble) resulted in 928 features detected within the established parameters. 189 (20.4%) compounds were annotated, with sugars, fatty acids, flavonoids, and biflavonoids as the major constituents. Sucrose was the sugar with the highest quantity; palmitic acid the major fatty acid but with great presence of very long-chain fatty acids rarely found in higher plants, glycosylated flavonoids were the major flavonoids, and biflavonoids majorly composed by units of flavones and flavanones, exclusively found in the cell wall. Despite the high percentage, this work leaves a significant gap for future works using other structure elucidation techniques, such as NMR.

GC-MS-based Metabolomics Unravels Metabolites across Larval Development and Diapause of a Specialist Insect

Plant-insect interactions are a driving force into ecosystem evolution and community dynamics. Many insect herbivores enter diapause, a developmental arrest stage in anticipation of adverse conditions, to survive and thrive through seasonal changes. Herein, we investigated the roles of medium- to non-polar metabolites during larval development and diapause in a specialist insect herbivore, Chlosyne lacinia, reared on Aldama robusta leaves. Varying metabolites were determined using gas chromatography-mass spectrometry (GC-MS)-based metabolomics. Sesquiterpenes and steroids were the main metabolites putatively identified in A. robusta leaves, whereas C. lacinia caterpillars were characterized by triterpenes, steroids, fatty acids, and long-chain alkanes. We found out that C. lacinia caterpillars biosynthesized most of the identified steroids and fatty acids from plant-derived ingested metabolites, as well as all triterpenes and long-chain alkanes. Steroids, fatty acids, and long-chain alkanes were detected across all C. lacinia instars and in diapausing caterpillars. Sesquiterpenes and triterpenes were also detected across larval development, yet they were not detected in diapausing caterpillars, which suggested that these metabolites were converted to other molecules prior to the diapause stage. Our findings shed light on the chemical content variation across C. lacinia development and diapause, providing insights into the roles of metabolites in plant-insect interactions.

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

The impact of the administration of red ginseng (Panax ginseng) on lipid metabolism and free fatty acid profiles in healthy horses using a molecular networking approach

This study investigated the potential benefits of the administration of red ginseng (RG) on lipid metabolism and the profiles of individual free fatty acids (FFAs) in healthy horses. Eight healthy horses, raised under similar conditions, were randomly divided into two groups, each comprising four horses. The experimental group received powdered RG (600 mg/kg/day) mixed with a carrier, and the control group received only the carrier. The parameters associated with lipid metabolism and probable adverse effects were evaluated in both groups after 3 weeks. The computational molecular networking (MN) approach was applied to analyze the FFA profiles. The results indicated that RG administration significantly reduced blood triglyceride levels in the experimental group. Analysis of the FFAs using MN revealed significant decreases in specific types of FFAs (C12:0, dodecanoic acid; C14:0, myristric acid; C18:1, oleic acid; C18:2, linoleic acid). RG consumption did not produce significant adverse effects on the renal, hepatic, and immune functions. Thus, RG was found to effectively modulate lipid metabolism and the levels of individual FFAs. The application of the MN for the analysis of FFAs represents a novel approach and can be considered for future research.

Annotation of GC-MS Data of Antimicrobial Constituents in the Antarctic Seaweed Phaeurus antarcticus by Molecular Networking

Phaeurus antarcticus is a member of the Desmarestiaceae family endemic to the Antarctic Peninsula. Reports addressing its chemical composition and biological activities are scarce. Herein, bioactive non-polar compounds of P. antarcticus against pathogenic bacteria, Leishmania amazonensis and Neospora caninum parasites were targeted through GC-MS Molecular Networking and multivariate analysis (OPLS-DA). The effects on horseradish peroxidase (HRP) were also evaluated. P. antarcticus exhibited selective bacteriostatic and bactericidal activities against Staphylococcus aureus with MIC and MBC values from 6.25-100 μg mL-1 . Fractions HX-FC and HX-FD were the most active against L. amazonensis with EC50 ranging from 18.5-62.3 μg mL-1 . Additionally, fractions HX-FC and HX-FD showed potent inhibition of N. caninum at EC50 values of 2.8 and 6.3 μg mL-1 , respectively. All fractions inhibited HRP activity, indicating possible interactions with Heme proteins. It was possible to annotate compounds from tree mains clusters, containing terpenoids, steroids, fatty acids, and alcohols by correlating the spectral data of the GC-MS analysis with Molecular Networking and the OPLS-DA results.

Eco-Metabolomics Applied to the Chemical Ecology of Poison Frogs (Dendrobatoidea)

Amphibians are one of the most remarkable sources of unique natural products. Biogenic amines, peptides, bufodienolides, alkaloids, and volatile organic compounds have been characterized in different species. The superfamily Dendrobatoidea represents one of the most enigmatic cases of study in chemical ecology because their skin secretome is composed by a complex mixture (i.e. cocktail) of highly lethal and noxious unique alkaloid structures. While chemical defences from dendrobatoids (families Dendrobatidae and Aromobatidae) have been investigated employing ecological, behavioral, phylogenetic and evolutionary perspectives, studies about the analytical techniques needed to perform the chemical characterization have been neglected for many years. Therefore, our aim is to summarize the current methods applied for the characterization of chemical profiles in dendrobatoids and to illustrate innovative Eco-metabolomics strategies that could be translated to this study model. This approach could be extended to natural products other than alkaloids and implemented for the chemical analysis of different species of dendrobatoids employing both low- and high-resolution mass spectrometers. Here, we overview important biological features to be considered, procedures that could be applied to perform the chemical characterization, steps and tools to perform an Eco-metabolomic analysis, and a final discussion about future perspectives.

Sniffing out safety: canine detection and identification of SARS-CoV-2 infection from armpit sweat

Detection dogs were trained to detect SARS-CoV-2 infection based on armpit sweat odor. Sweat samples were collected using cotton pads under the armpits of negative and positive human patients, confirmed by qPCR, for periods of 15-30 min. Multiple hospitals and organizations throughout Belgium participated in this study. The sweat samples were stored at -20°C prior to being used for training purposes. Six dogs were trained under controlled atmosphere conditions for 2-3 months. After training, a 7-day validation period was conducted to assess the dogs' performances. The detection dogs exhibited an overall sensitivity of 81%, specificity of 98%, and an accuracy of 95%. After validation, training continued for 3 months, during which the dogs' performances remained the same. Gas chromatography/mass spectrometry (GC/MS) analysis revealed a unique sweat scent associated with SARS-CoV-2 positive sweat samples. This scent consisted of a wide variety of volatiles, including breakdown compounds of antiviral fatty acids, skin proteins and neurotransmitters/hormones. An acceptability survey conducted in Belgium demonstrated an overall high acceptability and enthusiasm toward the use of detection dogs for SARS-CoV-2 detection. Compared to qPCR and previous canine studies, the detection dogs have good performances in detecting SARS-CoV-2 infection in humans, using frozen sweat samples from the armpits. As a result, they can be used as an accurate pre-screening tool in various field settings alongside the PCR test.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Metabolic Fingerprinting of Different Sideritis Taxa Infusions and Their Neurogenic Activity

Over the last years, Sideritis extracts were shown to improve memory. However, their potential to promote the generation of new neurons, starting with the neuronal differentiation of neural stem cells, remains unexplored. Therefore, the present study aimed to evaluate the neurogenic effects of different Sideritis infusions in neural stem and precursor cells and their impact on cell viability. Moreover, the metabolic fingerprints were recorded using LC-DAD, LC-HRESIMS, and GC-MS. The neurogenic potential of infusions of the eight Sideritis taxa tested was as potent as the classical neuronal inducer combination of retinoic acid and valproic acid. Further cytotoxicity assays revealed that the IC50 values of the extracts were between 163 and 322 µg/mL. Hierarchical cluster analyses of the metabolic fingerprints unveiled that the two Sideritis taxa with the lowest IC50 values were the most divergent in the analytical techniques used. As the analysis focused on polyphenols, it is reasonable to assume that these compounds are responsible for the effect on the cell viability of SH-SY5Y neuroblastoma cells. This study is the first report on the neurogenic potential of Sideritis taxa and might support the use of Sideritis herbal preparations in the context of neurodegenerative diseases.

Expanding our coverage: Strategies to detect a greater range of metabolites

Whilst the study of metabolites can arguably be traced back several hundred years it began in earnest in the 20th century with studies based on single metabolites or simple metabolic pathways. The advent of metabolomics and in particular the adoption of high-resolution mass spectrometry now means we can faithfully annotate and quantify in excess of 1000 plant metabolites. Whilst this is an impressive leap it falls well short of the estimated number of metabolites in the plant kingdom. This, whilst considerable and important insights have been achieved using commonly utilized approaches, there is a need to improve the coverage of the metabolome. Here, we review three largely complementary strategies (i) methods based on using chemical libraries (ii) methods based on molecular networking and (iii) approaches that link metabolomics and genetic variance. It is our contention that using all three approaches in tandem represents the best approach to tackle this challenge.

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.

Plant and microbial sciences as key drivers in the development of metabolomics research

This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Universal Method for Label-Free Detection of Pathogens and Biomolecules by Surface-Enhanced Raman Spectroscopy Based on Gold Nanoparticles

The detection of biomolecules is the key to basic molecular research, diagnostics, drug screening, and other biomedical applications. However, the existing detection techniques can only detect single classes of biomolecules, which warrant the development of a versatile biomolecule detection platform. Here, we developed a universal method for label-free detection of biomolecules via surface-enhanced Raman spectroscopy (SERS) by using sulfhydryl-modified gold nanoparticles as the substrate. The biomolecules can be adsorbed on the surface of gold nanoparticles cleaned by bromide ions to obtain initially enhanced Raman signals, and the aggregator (calcium ion) was further added to form a "hot spot", which enhanced the biomolecular signal again. Through the "two-step enhancement method", we were able to obtain fingerprints of DNA, RNA, amino acids, peptides, proteins, viruses, bacteria, and lipid molecules. This low-toxic, highly sensitive, and widely applicable technique has potential applications in biomedical research, clinical testing, and disease diagnosis and lays the foundation for the development of SERS technology in various fields.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

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

Databases and Tools to Investigate Protein-Metabolite Interactions

Protein-metabolite interactions (PMIs) are directly responsible for the regulation of numerous processes. From the direct regulation of enzymes to complex developmental processes intermediated by hormones, PMIs are central to understanding the molecular mechanisms of important physiological phenomena. Still, proving such interactions experimentally has proven an arduous task. We discuss here some of the current technologies contributing to expand our knowledge on PMIs, with particular emphasis on platforms and databases to explore the highly heterogenous nature of characterized PMIs, which is likely to be an essential resource on the development of new computational approaches to predict and validate interactions based on large-scale PMI screenings.

Enhancing untargeted metabolomics using metadata-based source annotation

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Volatile Compounds in Actinomycete Communities: A New Tool for Biosynthetic Gene Cluster Activation, Cooperative Growth Promotion, and Drug Discovery

The increasing appearance of multiresistant pathogens, as well as emerging diseases, has highlighted the need for new strategies to discover natural compounds that can be used as therapeutic alternatives, especially in the genus Streptomyces, which is one of the largest producers of bioactive metabolites. In recent years, the study of volatile compounds (VOCs) has raised interest because of the variety of their biological properties in addition to their involvement in cell communication. In this work, we analyze the implications of VOCs as mediating molecules capable of inducing the activation of biosynthetic pathways of bioactive compounds in surrounding Actinomycetes. For this purpose, several strains of Streptomyces were co-cultured in chamber devices that allowed VOC exchange while avoiding physical contact. In several of those strains, secondary metabolism was activated by VOCs emitted by companion strains, resulting in increased antibiotic production and synthesis of new VOCs. This study shows a novel strategy to exploit the metabolic potential of Actinomycetes as well as emphasizes the importance of studying the interactions between different microorganisms sharing the same ecological niche.

Analysis of Streptomyces Volatilomes Using Global Molecular Networking Reveals the Presence of Metabolites with Diverse Biological Activities

Streptomyces species produce a wide variety of specialized metabolites, some of which are used for communication or competition for resources in their natural environments. In addition, many natural products used in medicine and industry are derived from Streptomyces, and there has been interest in their capacity to produce volatile organic compounds (VOCs) for different industrial and agricultural applications. Recently, a machine-learning workflow called MSHub/GNPS was developed, which enables auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data, molecular networking, and library search capabilities, but it has not been applied to Streptomyces volatilomes. In this study, 131 Streptomyces isolates from the island of Newfoundland were phylogenetically typed, and 37 were selected based on their phylogeny and growth characteristics for VOC analysis using both a user-guided (conventional) and an MSHub/GNPS-based approach. More VOCs were annotated by MSHub/GNPS than by the conventional method. The number of unknown VOCs detected by the two methods was higher than those annotated, suggesting that many novel compounds remain to be identified. The molecular network generated by GNPS can be used to guide the annotation of such unknown VOCs in future studies. However, the number of overlapping VOCs annotated by the two methods is relatively small, suggesting that a combination of analysis methods might be required for robust volatilome analysis. More than half of the VOCs annotated with high confidence by the two approaches are plant-associated, many with reported bioactivities such as insect behavior modulation. Details regarding the properties and reported functions of such VOCs are described. IMPORTANCE This study represents the first detailed analysis of Streptomyces volatilomes using MSHub/GNPS, which in combination with a routinely used conventional method led to many annotations. More VOCs could be annotated using MSHub/GNPS as compared to the conventional method, many of which have known antimicrobial, anticancer, and insect behavior-modulating activities. The identification of numerous plant-associated VOCs by both approaches in the current study suggests that their production could be a more widespread phenomenon by members of the genus, highlighting opportunities for their large-scale production using Streptomyces. Plant-associated VOCs with antimicrobial activities, such as 1-octen-3-ol, octanol, and phenylethyl alcohol, have potential applications as fumigants. Furthermore, many of the annotated VOCs are reported to influence insect behavior, alluding to a possible explanation for their production based on the functions of other recently described Streptomyces VOCs in dispersal and nutrient acquisition.

The Chemistry of Green and Roasted Coffee by Selectable 1D/2D Gas Chromatography Mass Spectrometry with Spectral Deconvolution

Gas chromatography/mass spectrometry (GC/MS) is a long-standing technique for the analysis of volatile organic compounds (VOCs). When coupled with the Ion Analytics software, GC/MS provides unmatched selectivity in the analysis of complex mixtures and it reduces the reliance on high-resolution chromatography to obtain clean mass spectra. Here, we present an application of spectral deconvolution, with mass spectral subtraction, to identify a wide array of VOCs in green and roasted coffees. Automated sequential, two-dimensional GC-GC/MS of a roasted coffee sample produced the retention index and spectrum of 750 compounds. These initial analytes served as targets for subsequent coffee analysis by GC/MS. The workflow resulted in the quantitation of 511 compounds detected in two different green and roasted coffees. Of these, over 100 compounds serve as candidate differentiators of coffee quality, AAA vs. AA, as designated by the Coopedota cooperative in Costa Rica. Of these, 72 compounds survive the roasting process and can be used to discriminate green coffee quality after roasting.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Past accomplishments and future challenges of the multi-omics characterization of leaf growth

The advent of omics technologies has revolutionized biology and advanced our understanding of all biological processes, including major developmental transitions in plants and animals. Here, we review the vast knowledge accumulated concerning leaf growth in terms of transcriptional regulation before turning our attention to the historically less well-characterized alterations at the protein and metabolite level. We will then discuss how the advent of biochemical methods coupled with metabolomics and proteomics can provide insight into the protein-protein and protein-metabolite interactome of the growing leaves. We finally highlight the substantial challenges in detection, spatial resolution, integration, and functional validation of the omics results, focusing on metabolomics as a prerequisite for a comprehensive understanding of small-molecule regulation of plant growth.

Towards Unbiased Evaluation of Ionization Performance in LC-HRMS Metabolomics Method Development

As metabolomics increasingly finds its way from basic science into applied and regulatory environments, analytical demands on nontargeted mass spectrometric detection methods continue to rise. In addition to improved chemical comprehensiveness, current developments aim at enhanced robustness and repeatability to allow long-term, inter-study, and meta-analyses. Comprehensive metabolomics relies on electrospray ionization (ESI) as the most versatile ionization technique, and recent liquid chromatography-high resolution mass spectrometry (LC-HRMS) instrumentation continues to overcome technical limitations that have hindered the adoption of ESI for applications in the past. Still, developing and standardizing nontargeted ESI methods and instrumental setups remains costly in terms of time and required chemicals, as large panels of metabolite standards are needed to reflect biochemical diversity. In this paper, we investigated in how far a nontargeted pilot experiment, consisting only of a few measurements of a test sample dilution series and comprehensive statistical analysis, can replace conventional targeted evaluation procedures. To examine this potential, two instrumental ESI ion source setups were compared, reflecting a common scenario in practical method development. Two types of feature evaluations were performed, (a) summary statistics solely involving feature intensity values, and (b) analyses additionally including chemical interpretation. Results were compared in detail to a targeted evaluation of a large metabolite standard panel. We reflect on the advantages and shortcomings of both strategies in the context of current harmonization initiatives in the metabolomics field.

Mammalian gut metabolomes mirror microbiome composition and host phylogeny

In the past decade, studies on the mammalian gut microbiome have revealed that different animal species have distinct gut microbial compositions. The functional ramifications of this variation in microbial composition remain unclear: do these taxonomic differences indicate microbial adaptations to host-specific functionality, or are these diverse microbial communities essentially functionally redundant, as has been indicated by previous metagenomics studies? Here, we examine the metabolic content of mammalian gut microbiomes as a direct window into ecosystem function, using an untargeted metabolomics platform to analyze 101 fecal samples from a range of 25 exotic mammalian species in collaboration with a zoological center. We find that mammalian metabolomes are chemically diverse and strongly linked to microbiome composition, and that metabolome composition is further correlated to the phylogeny of the mammalian host. Specific metabolites enriched in different animal species included modified and degraded host and dietary compounds such as bile acids and triterpenoids, as well as fermentation products such as lactate and short-chain fatty acids. Our results suggest that differences in microbial taxonomic composition are indeed translated to host-specific metabolism, indicating that taxonomically distant microbiomes are more functionally diverse than redundant.

Comparative bioactivity evaluation and chemical profiling of different parts of the medicinal plant Glycyrrhiza uralensis

Glycyrrhiza uralensis is a popular medicinal plant worldwide. Its roots and rhizomes are used as the traditional Chinese medicine Gan-Cao. However, little is known on medicinal potential and chemistry of the other parts of the plant. In this work, the biological activities and chemical components of the roots, stems, leaves, and seeds of G. uralensis were investigated comparatively. The four parts exhibited different but noticeable biological activities. The chemicals in the four parts were globally characterized by liquid chromatography coupled with mass spectrometry (LC/MS) on a Thermo Vanquish UHPLC system connected to a Q-Exactive quadrupole Orbitrap mass spectrometer. By integrating molecular networking, compound spectral matching, MS2LDA-based substructure recognition, and reference standards comparison, a total of 1301 compounds were rapidly characterized. Three flavonoid C-glycosides were purified and their structures were identified by NMR spectroscopic analysis. Orthogonal partial least squares-discriminate analysis (OPLS-DA) further revealed 196 differential chemicals for the four parts. This work will promote the medicinal resource utilization of G. uralensis.

Mass spectrometry-based metabolomics in microbiome investigations

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

Combination of GC-MS Molecular Networking and Larvicidal Effect against Aedes aegypti for the Discovery of Bioactive Substances in Commercial Essential Oils

Dengue is a neglected disease, present mainly in tropical countries, with more than 5.2 million cases reported in 2019. Vector control remains the most effective protective measure against dengue and other arboviruses. Synthetic insecticides based on organophosphates, pyrethroids, carbamates, neonicotinoids and oxadiazines are unattractive due to their high degree of toxicity to humans, animals and the environment. Conversely, natural-product-based larvicides/insecticides, such as essential oils, present high efficiency, low environmental toxicity and can be easily scaled up for industrial processes. However, essential oils are highly complex and require modern analytical and computational approaches to streamline the identification of bioactive substances. This study combined the GC-MS spectral similarity network approach with larvicidal assays as a new strategy for the discovery of potential bioactive substances in complex biological samples, enabling the systematic and simultaneous annotation of substances in 20 essential oils through LC50 larvicidal assays. This strategy allowed rapid intuitive discovery of distribution patterns between families and metabolic classes in clusters, and the prediction of larvicidal properties of acyclic monoterpene derivatives, including citral, neral, citronellal and citronellol, and their acetate forms (LC50 < 50 µg/mL).

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Use of metabolomics in refining the effect of an organic food intervention on biomarkers of exposure to pesticides and biomarkers of oxidative damage in primary school children in Cyprus: A cluster-randomized cross-over trial

BACKGROUND

Exposure to pesticides has been associated with oxidative stress in animals and humans. Previously, we showed that an organic food intervention reduced pesticide exposure and oxidative damage (OD) biomarkers over time; however associated metabolic changes are not fully understood yet.

OBJECTIVES

We assessed perturbations of the urine metabolome in response to an organic food intervention for children and its association with pesticides biomarkers [3-phenoxybenzoic acid (3-PBA) and 6-chloronicotinic acid (6-CN)]. We also evaluated the molecular signatures of metabolites associated with biomarkers of OD (8-iso-PGF2a and 8-OHdG) and related biological pathways.

METHODS

We used data from the ORGANIKO LIFE + trial (NCT02998203), a cluster-randomized cross-over trial conducted among primary school children in Cyprus. Participants (n = 149) were asked to follow an organic food intervention for 40 days and their usual food habits for another 40 days, providing up to six first morning urine samples (>850 samples in total). Untargeted GC-MS metabolomics analysis was performed. Metabolites with RSD ≤ 20% and D-ratio ≤ 50% were retained for analysis. Associations were examined using mixed-effect regression models and corrected for false-discovery rate of 0.05. Pathway analysis followed.

RESULTS

Following strict quality checks, 156 features remained out of a total of 610. D-glucose was associated with the organic food intervention (β = -0.23, 95% CI: -0.37,-0.10), aminomalonic acid showed a time-dependent increase during the intervention period (β_int = 0.012; 95% CI:0.002, 0.022) and was associated with the two OD biomarkers (β = -0.27, 95% CI:-0.34,-0.20 for 8-iso-PGF2a and β = 0.19, 95% CI:0.11,0.28 for 8-OHdG) and uric acid with 8-OHdG (β = 0.19, 95% CI:0.11,0.26). Metabolites were involved in pathways such as the starch and sucrose metabolism and pentose and glucuronate interconversions.

DISCUSSION

This is the first metabolomics study providing evidence of differential expression of metabolites by an organic food intervention, corroborating the reduction in biomarkers of OD. Further mechanistic evidence is warranted to better understand the biological plausibility of an organic food treatment on children's health outcomes.

The reliance of phytohormone biosynthesis on primary metabolite precursors

There is some debate as to whether phytohormone metabolites should be classified as primary or secondary metabolites. Phytohormones have profound effects on growth - a typical trait of primary metabolites - yet several of them are formed from secondary metabolite precursors. This is further exacerbated by the blurred distinction between primary and secondary metabolism. What is clearer, however, is that phytohormones display distinctive regulatory mechanisms from other metabolites. Moreover, by contrast to microbial and mammalian systems, the majority of plant metabolite receptors characterized to date are hormone receptors. Here, we provide an overview of the metabolic links between primary metabolism and phytohormone biosynthesis in an attempt to complement recent reviews covering the signaling crosstalk between elements of core metabolism and the phytohormones. In doing so, we cover the biosynthesis of both the classical metabolic phytohormones namely auxins, salicylic acid, jasmonate, ethylene, cytokinins, brassinosteroids, gibberellins and abscisic acid as well as recently described plant growth regulators which have been proposed as novel phytohormones namely strigolactones blumenols, zaxinone and β-cyclocitral as well as melatonin. For each hormone, we describe the primary metabolite precursors which fuel its synthesis, act as conjugates or in the case of 2-oxoglutarate act more directly as a co-substrate in the biosynthesis of gibberellin, auxin and salicylic acid. Furthermore, several amino acids operate as hormone conjugates, such as jasmonate-conjugates. In reviewing the biosynthesis of all the phytohormones simultaneously, the exceptional intricacy of the biochemical interplay that underpins their interaction emerges.

Molecular networking in infectious disease models

Small molecule metabolites are the product of many enzymatic reactions. Metabolomics thus opens a window into enzyme activity and function, integrating effects at the post-translational, proteome, transcriptome and genome level. In addition, small molecules can themselves regulate enzyme activity, expression and function both via substrate availability mechanisms and through allosteric regulation. Metabolites are therefore at the nexus of infectious diseases, regulating nutrient availability to the pathogen, immune responses, tropism, and host disease tolerance and resilience. Analysis of metabolomics data is however complex, particularly in terms of metabolite annotation. An emerging valuable approach to extend metabolite annotations beyond existing compound libraries and to identify infection-induced chemical changes is molecular networking. In this chapter, we discuss the applications of molecular networking in the context of infectious diseases specifically, with a focus on considerations relevant to these biological systems.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

Mass Spectrometry-Based Technology and Workflows for Studying the Chemistry of Fungal Endophyte Derived Bioactive Compounds

Bioactive compounds have gained substantial attention in research and have conferred great advancements in the industrial and pharmacological fields. Highly diverse fungi and their metabolome serve as a big platform to be explored for their diverse bioactive compounds. Omics tools coupled with bioinformatics, statistical, and well-developed algorithm tools have elucidated immense knowledge about fungal endophyte derived bioactive compounds. Further, these compounds are subjected to chromatography-gas chromatography and liquid chromatography (LC), spectroscopy-nuclear magnetic resonance (NMR), and "soft ionization" technique-matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) based analytical techniques for structural characterization. The mass spectrometry (MS)-based approach, being highly sensitive, reproducible, and reliable, produces quick and high-profile identification. Coupling these techniques with MS has resulted in a descriptive account of the identification and quantification of fungal endophyte derived bioactive compounds. This paper emphasizes the workflows of the above-mentioned techniques, their advancement, and future directions to study the unraveled area of chemistry of fungal endophyte-derived bioactive compounds.

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.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

The INCREASE project: Intelligent Collections of food-legume genetic resources for European agrofood systems

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.

MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.

Biosynthesis, evolution and ecology of microbial terpenoids

Covering: through June 2021Terpenoids are the largest class of natural products recognised to date. While mostly known to humans as bioactive plant metabolites and part of essential oils, structurally diverse terpenoids are increasingly reported to be produced by microorganisms. For many of the compounds biological functions are yet unknown, but during the past years significant insights have been obtained for the role of terpenoids in microbial chemical ecology. Their functions include stress alleviation, maintenance of cell membrane integrity, photoprotection, attraction or repulsion of organisms, host growth promotion and defense. In this review we discuss the current knowledge of the biosynthesis and evolution of microbial terpenoids, and their ecological and biological roles in aquatic and terrestrial environments. Perspectives on their biotechnological applications, knowledge gaps and questions for future studies are discussed.

Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery

Microbial specialized metabolites are key mediators in host-microbiome interactions. Most of the chemical space produced by the microbiome currently remains unexplored and uncharacterized. This situation calls for new and improved methods to exploit the growing publicly available genomic and metabolomic data sets and connect the outcomes to structural and functional knowledge inferred from transcriptomics and proteomics experiments. Here, we first describe currently available approaches that support the comprehensive mining of metabolomics and genomics data. Next, we provide our vision on how to move forward toward the automated linking of omics data of specialized metabolites to their structures, biosynthesis pathways, producers, and functions.

First characterization of toxic alkaloids and volatile organic compounds (VOCs) in the cryptic dendrobatid Silverstoneia punctiventris

Background

Poison frogs are known for the outstanding diversity of alkaloid-based chemical defences with promising therapeutic applications. However, current knowledge about chemical defences in Dendrobatoidea superfamily has two sources of bias. First, cryptic, brown-colored species have been neglected in comparison to those conspicuously colored, and second, there has been little interest in characterizing metabolites other than alkaloids mediating defensive functions. In an effort to contribute to fill the gap of knowledge about cryptic species and broadening the spectrum of compounds analyzed we have applied head-space solid phase microextraction coupled to gas chromatography and mass spectrometry (HS-SPME/GC-MS) for extracting amphibian alkaloids and volatile organic compounds (VOCs) from Silverstoneia punctiventris.

Results

Using the skin from 8 specimens in 4 biological replicates we have found 33 different compounds. Twenty of them were classified as VOCs into 15 chemical classes including alkanes, alcohols, carbonyl compounds, methylpyridines, benzothiazoles, N-alkylpyrrolidines, pyrazines, and sesquiterpenoids, some of which were previously reported as repellents, defence compounds or defence pheromones in other organisms, and as sex pheromones in a treefrog. Interestingly, six of the remaining compounds were identified as alkaloids previously reported in other toxic/unpalatable dendrobatid frogs.

Conclusions

This is the first report of alkaloids and VOCs found in the Silverstoneia genus, which has been assumed for decades as non-chemically defended. This study establishes HS-SPME/GC-MS as a new application for a simultaneous approach to amphibian alkaloids and VOCs in poison frogs while opens up new research questions to assess the co-occurrence of both type of compounds and to investigate the evolutionary significance of a defence gradient that includes olfactory avoidance, unpalatability, and toxicity in dendrobatids. In addition, our results show that amphibian alkaloids could have a dual function (olfactory at distance, taste by contact) never explored before neither in Silverstonaeia nor in any other dendrobatid species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12983-021-00420-1.