SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information

In vino veritas: A metabolomics approach for authenticating Provence Rosé wines

Provence rosé wines have gained global popularity, making them vulnerable to fraud. This study aimed to identify specific chemical markers to detect counterfeit Provence rosé wines. An untargeted LC-MS-based metabolomics analysis was performed on a set of 30 wines classified as "Provence," "Non-Provence," and "Provence imitations." Using the Molnotator workflow, 1300 potential metabolites were generated, and five key chemomarkers were selected through a machine learning pipeline. Further targeted analysis and bioinformatics using in silico MS/MS fragmentation systems confidently annotated three specific chemomarkers for "Provence" rosé: acuminoside, tetrahydroxydimethoxyflavone, and 5'-methoxycastavinol. A composite score using a PLS model combining the 3 chemomarkers effectively distinguished authentic wines, with high accuracy (sensitivity 83.3 %, specificity 100 %, accuracy 93.3 %).

The high-resolution molecular portrait of coffee: A gateway to insights into its roasting chemistry and comprehensive authenticity profiles

The direct-infusion of 130 coffee samples into a Fourier-transform ion cyclotron mass spectrometer (FT-ICR-MS) provided an ultra-high resolution perspective on the molecular complexity of coffee: The exceptional resolving power and mass accuracy (± 0.2 ppm) facilitated the annotation of unambiguous molecular formulas to 11,500 mass signals. Utilizing this molecular diversity, we extracted hundreds of compound signals linked to the roasting process through guided Orthogonal Partial Least Squares (OPLS) analysis. Visualizations such as van Krevelen diagrams and Kendrick mass defect analysis provided deeper insights into the intrinsic compositional nature of these compounds and the complex chemistry underlying coffee roasting. Predictive OPLS-DA models established universal molecular profiles for rapid authentication of Coffea arabica versus Coffea canephora (Robusta) coffees. Compositional analysis revealed Robusta specific signals, indicative of tryptophan-conjugates of hydroxycinnamic acids. Complementary LC-ToF-MS2 confirmed their compound class, building blocks and structures. Their water-soluble nature allows for application across raw and roasted beans, as well as in ready-made coffee products.

An integrated 3-M workflow for accelerated annotation of natural products: Flavonoids in Daemonorops draco as a case study

Efficient annotation and dereplication of metabolites, particularly those from resource-endangered plants lacking reference standards, is crucial for natural products development. Advanced techniques like high resolution mass spectrometry (LC-HRMS) have significantly enhanced metabolite characterization. However, challenges such as redundant spectral data, limited reference databases, and inferior dereplication capacity hinder its broad applicability. In this study, we propose an integrated annotation strategy utilizing various computational tools, including mass defect filters (MDF), molecular fingerprints, and molecular networks (3-M strategy). We demonstrate this approach using Daemonorops draco (D. draco), a renowned yet resource-endangered natural product rich in functional flavonoids. By applying pre-defined flavonoids MDF windows, the MS1 peaks reduced by 85 % (from 10,043 to 1,585) in positive mode. Subsequent de novo molecular formula annotation and molecular fingerprint-based structure elucidation were automatically performed using the SIRIUS machine learning platform. Additionally, two complementary cluster tools were incorporated, including feature-based molecular network (FBMN) and t-distributed stochastic neighbor embedding (t-SNE) molecular network, to efficiently dereplicate metabolites and discover novel flavonoids in D. draco. Totally, 108 flavonoids (containing flavones, flavanes, flavanones, chalcones, chalcanes, dihydrochalcones, anthocyanins, homoisoflavanes, homoisoflavanones, and isoflavones), 18 flavone derivatives, and 54 flavone oligomers were identified. Among them, 25 compounds were firstly reported in D. draco. This 3-M workflow shed light on the composition of D. draco and validate the effectiveness of our approach, which facilitated the rapid annotation and screening of subclass metabolites in complex natural products.

Modular, Scalable, and Customizable LC-HRMS for Exposomics

In this chapter, we describe a multi-purpose, reversed-phase liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflow for acquiring high-quality, non-targeted exposomics data utilizing data-dependent acquisition (DDA) combined with the use of toxicant inclusion lists for semi-targeted analysis. In addition, we describe expected retention times for >160 highly diverse xenobiotics in human plasma and serum samples. The method described is intended to serve as a generic LC-HRMS exposomics workflow for research and educational purposes. Moreover, it may be employed as a primer, allowing for further adaptations according to specialized research needs, e.g., by including reference and/or internal standards, by expanding to data-independent acquisition (DIA), or by modifying the list of compounds prioritized in fragmentation experiments (MS2).

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Effect-directed analysis of genotoxicants in food packaging based on HPTLC fractionation, bioassays, and toxicity prediction with machine learning

Many chemicals in food packaging can leach as complex mixtures to food, potentially including substances hazardous to consumer health. Detecting and identifying all of the leachable chemicals are impractical with current analytical instrumentation and data processing methods. Therefore, our work aims to expand the analytical toolset for prioritizing and identifying chemical hazards in food packaging. We used a high-performance thin-layer chromatography (HPTLC)-based bioassay to detect genotoxic fractions in paperboard packaging. These fractions were then processed with non-targeted liquid chromatography high-resolution mass spectrometry (LC-HRMS/MS) and machine learning-based toxicity prediction (MLinvitroTox). The HPTLC bioassay detected four genotoxic zones in extracts of the paperboard. One-dimensional HPTLC separation and targeted fraction collection reduced the number of chemical features extracted from paperboard and detected with LC-HRMS by at least 98% (from 1695-2693 to 14-50). The entire process was successful for spiked genotoxic chemicals, which were correctly prioritized in the fractionation and non-target analysis workflow. The native chemical with the strongest genotoxicity signal was identified with a suspect list as 5-chloro-2-methyl-4-isothiazolin-3-one and confirmed with LC-HRMS/MS and HPTLC bioassay. Toward identification of the remaining unknown genotoxicants, two-dimensional HPTLC further reduced the number of chemical features. Genotoxicity predictions with MLinvitroTox based on molecular fingerprints of the unknown signals derived from their MS2 fragmentation spectra helped prioritize two chemical features and suggested candidate structures. This work demonstrates strategies for using HPTLC, HRMS, and toxicity prediction to help identify toxicants in food packaging.

Deciphering the complexity of the chemicals in food packaging materials using molecular networks

Chemicals from packaging materials might be transferred into food resulting in consumer exposure. Identifying these migrated chemicals is highly challenging and crucial to perform their safety assessment, usually starting by the understanding of the chemical composition of the packaging material itself. This study explores the use of the Molecular Networking (MN) approach to support identification of the extracted chemicals. Two formulations of bioplastics were analyzed using Liquid Chromatography hyphenated to High-Resolution Mass Spectrometry. Data processing and interpretation using a conventional manual method was performed as a point of comparison to understand the power of MN. Interestingly, only the MN approach facilitated the identification of unknown chemicals belonging to a novel oligomer series containing the azelaic acid monomer. The MN approach provided a faster visualization of chemical families in addition to the highlight of unrelated chemicals enabling to prioritize chemicals for further investigation improving the safety assessment of packaging materials.

Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?

The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.

Effect of lead on photosynthetic pigments, antioxidant responses, metabolomics, thalli morphology and cell ultrastructure of Iridaea cordata (Rhodophyta) from Antarctica

Over the past decades, the concern about lead pollution in marine environments has increased due to its remarkable toxicity, even at low concentrations. Lead is one of the significant contaminants arising from human activities in Antarctica. However, its effects on polar photosynthetic organisms are poorly known. This work aims to evaluate the effects of two different environmental concentrations of lead (10 μg/L and 50 μg/L) on pigment content, antioxidant enzyme activities (catalase, superoxide dismutase, ascorbate peroxidase and glutathione-S-transferase), metabolome, thalli morphology and cell ultrastructure of the red seaweed Iridaea cordata (Turner) Bory from Terra Nova Bay (Ross Sea, Antarctica). The results highlighted that lead exposure decreased phycocyanin and phycoerythrin content, starting from 10 μg/L, while induced carotenoid accumulation at 50 μg/L. Catalase, ascorbate peroxidase, and superoxide dismutase activities generally increased after lead exposure and distinct biochemical features were identified in the control and treatment groups. Further lead-related effects on cell ultrastructure comprised floridean starch accumulation and plastoglobuli formation. Overall, our results suggested that the enhanced formation of reactive oxygen species in response to lead altered the photosynthetic pigment pattern, antioxidant defenses, metabolome and ultrastructure of I. cordata.

Integrated workflows using metabolomics, genome mining, and biological evaluation reveal oxepine‑sulfur-containing anti-cryptococcal diketopiperazine produced by the endophyte Penicillium setosum

Cryptococcosis is a fungal infection for which treatment relies on old antifungal agents usually leading to drawbacks such as high toxicity and mainly low efficiency since drug resistance of microorganisms is strongly widespread. The discovery of new antifungal agents is urgent and investigations about underexplored Natural Product (NP) can yield the necessary outcomes to guide the discovery of new prototypes to anti-cryptococcal molecules development. In this scenario, an integrated strategy involving metabolomic data analysis, biological assessement and genome mining of P. setosum CMLD 18, revealed the biosynthesis of bis(methyl-sulfanyl) oxepine-containing diketopiperazine derivative, the bisdethiobis(methylthio)acetylaranotine (1) by the endophyte. The molecule showed a minimum inhibitory concentration (MIC) value of 0.125 mM when tested against C. neoformans. Evidence about the corresponding biosynthetic gene cluster (BGC) responsible for the biosynthesis of (1) in P. setosum were found. Moreover, other putative analogues of (1) were also detected, suggesting this microorganism may represent an important source of likely anti-cryptococcal molecules to be further investigated.

Discovery of a Chimeric Polyketide Family as Cancer Immunogenic Chemotherapeutic Leads

Discovery of cancer immunogenic chemotherapeutics represents an emerging, highly promising direction for cancer treatment that uses a chemical drug to achieve the efficacy of both chemotherapy and immunotherapy. Herein, we report a high-throughput screening platform and the subsequent discovery of a new class of cancer immunogenic chemotherapeutic leads. Our platform integrates informatics-based activity metabolomics for the rapid identification of microbial natural products with both novel structures and potent activities. Additionally, we demonstrate the use of microcrystal electron diffraction (MicroED) for direct structure elucidation of lead compounds from partially purified mixtures. Using this strategy to screen geographically and phylogenetically diverse microbial metabolites against pseudomyxoma peritonei, a rare and severe cancer, we discovered a new class of leads, aspercyclicins. The aspercyclicins feature an unprecedented tightly packed polycyclic polyketide scaffold that comprises continuous fused, bridged, and spiro rings. The biogenesis of aspercyclicins involves two distinct biosynthetic pathways, leading to formation of chimeric compounds that cannot be predicted by bottom-up approaches mining natural product biosynthetic genes. With comparable potency to some clinically used anticancer drugs, aspercyclicins are active against multiple cancer cell types by inducing immunogenic cell death (ICD), including the release of damage-associated molecular patterns and subsequent phagocytosis of cancer cells. The broad-spectrum ICD-inducing activity of aspercyclicins, combined with their low toxicity to normal cells, represents a new class of potential cancer immunogenic chemotherapeutics and, particularly, the first drug lead for pseudomyxoma peritonei treatment.

Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions

INTRODUCTION

Tree bacterial diseases are a threat in forestry due to their increasing incidence and severity. Understanding tree defence mechanisms requires evaluating metabolic changes arising during infection. Metabolite extraction affects the chemical diversity of the samples and, therefore, the biological relevance of the data. Metabolite extraction has been standardized for several biological models. However, little information is available regarding how it influences wood extract's chemical diversity.

OBJECTIVES

This study aimed to develop a methodological approach to obtain extracts from different tree species with the highest reproducibility and chemical diversity possible, to ensure proper coverage of the trees' metabolome.

METHODS

A full factorial design was used to evaluate the effect of solvent type, extraction temperature and number of extraction cycles on the metabolic profile, chemical diversity and antibacterial activity of four tree species.

RESULTS

Solvent, temperature and their interaction significantly affected the extracts' chemical diversity, while the number of extraction cycles positively correlated with yield and antibacterial activity. Although 60% of the features were recovered in all the tested conditions, differences in the presence and abundance of specific chemical classes per tree were observed, including organooxygen compounds, prenol lipids, carboxylic acids, and flavonoids.

CONCLUSIONS

Each tree species has a unique metabolic profile, which means that no single protocol is universally effective. Extraction at 50 °C for three cycles using 80% methanol or chloroform/methanol/water showed the best results and is suggested for studying wood metabolome. These observations highlight the need to tailor extraction protocols to each tree species to ensure comprehensive metabolome coverage for metabolic profiling.

Passive accumulation of alkaloids in inconspicuously colored frogs refines the evolutionary paradigm of acquired chemical defenses

Understanding the origins of novel, complex phenotypes is a major goal in evolutionary biology. Poison frogs of the family Dendrobatidae have evolved the novel ability to acquire alkaloids from their diet for chemical defense at least three times. However, taxon sampling for alkaloids has been biased towards colorful species, without similar attention paid to inconspicuous ones that are often assumed to be undefended. As a result, our understanding of how chemical defense evolved in this group is incomplete. Here, we provide new data showing that, in contrast to previous studies, species from each undefended poison frog clade have measurable yet low amounts of alkaloids. We confirm that undefended dendrobatids regularly consume mites and ants, which are known sources of alkaloids. Thus, our data suggest that diet is insufficient to explain the defended phenotype. Our data support the existence of a phenotypic intermediate between toxin consumption and sequestration - passive accumulation - that differs from sequestration in that it involves no derived forms of transport and storage mechanisms yet results in low levels of toxin accumulation. We discuss the concept of passive accumulation and its potential role in the origin of chemical defenses in poison frogs and other toxin-sequestering organisms. In light of ideas from pharmacokinetics, we incorporate new and old data from poison frogs into an evolutionary model that could help explain the origins of acquired chemical defenses in animals and provide insight into the molecular processes that govern the fate of ingested toxins.

Development and thorough evaluation of a multi-omics sample preparation workflow for comprehensive LC-MS/MS-based metabolomics, lipidomics and proteomics datasets

The importance of sample preparation selection if often overlooked particularly for untargeted multi-omics approaches that gained popularity in recent years. To minimize issues with sample heterogeneity and additional freeze-thaw cycles during sample splitting, multiple -omics datasets (e.g. metabolomics, lipidomics and proteomics) should ideally be generated from the same set of samples. For sample extraction, commonly biphasic organic solvent systems are used that require extensive multi-step protocols. Individual studies have recently also started to investigate monophasic (all-in-one) extraction procedures. The aim of the current study was to develop and systematically compare ten different mono- and biphasic extraction solvent mixtures for their potential to aid in the most comprehensive metabolomics, lipidomics and proteomics datasets. As the focus was on human postmortem tissue samples (muscle and liver tissue), four tissue homogenization parameters were also evaluated. Untargeted liquid chromatography mass spectrometry-based metabolomics, lipidomic and proteomics methods were utilized along with 1D sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and bicinchoninic acid (BCA) assay results. Optimal homogenization was found to be achieved by bead-homogenizing 20 mg of muscle or liver tissue with 200 μL (1:10 ratio) Water:Methanol (1:2) using 3 × 30 s pulses. The supernatant of the homogenate was further extracted. Comprehensive ranking, taking nine different processing parameters into account, showed that the monophasic extraction solvents, overall, showed better scores compared to the biphasic solvent systems, despite their recommendation for one or all of the -omics extractions. The optimal extraction solvent was found to be Methanol:Acetone (9:1), resulting in the most comprehensive metabolomics, lipidomics and proteomics datasets, showing the potential to be automated, hence, allowing for high-throughput analysis of samples and opening the door for comprehensive multi-omics results from routine clinical cases in the future.

Kiwifruit spatiotemporal multiomics networks uncover key tissue-specific regulatory processes throughout the life cycle

Kiwifruit (Actinidia chinensis), a recently commercialized horticultural crop, is rich in various nutrient compounds. However, the regulatory networks controlling the dynamic changes in key metabolites among different tissues remain largely unknown. Here, high-resolution spatiotemporal datasets obtained by ultraperformance liquid chromatography-tandem mass spectrometry methodology and RNA-seq were employed to investigate the dynamic changes in the metabolic and transcriptional landscape of major kiwifruit tissues across different developmental stages, including from fruit skin, outer pericarp, inner pericarp, and fruit core. Kiwifruit spatiotemporal regulatory networks (KSRN) were constructed by integrating the 1,243 identified metabolites and co-expressed genes into 10 different clusters and 11 modules based on their biological functions. These networks allowed the generation of a global map for the major metabolic and transcriptional changes occurring throughout the life cycle of different kiwifruit tissues and discovery of the underlying regulatory networks. KSRN predictions confirmed previously established regulatory networks, including the spatiotemporal accumulation of anthocyanin and ascorbic acid (AsA). More importantly, the networks led to the functional characterization of three transcription factors: an A. chinensis ethylene response factor 1, which negatively controls sugar accumulation and ethylene production by perceiving the ripening signal, a basic-leucine zipper 60 (AcbZIP60) transcription factor, which is involved in the biosynthesis of AsA as part of the L-galactose pathway, and a transcription factor related to apetala 2.4 (RAP2.4), which directly activates the expression of the kiwi fruit aroma terpene synthase gene AcTPS1b. Our findings provide insights into spatiotemporal changes in kiwifruit metabolism and generate a valuable resource for the study of metabolic regulatory processes in kiwifruit as well as other fruits.

Sensitivity shift of the meta-metabolome and photosynthesis to the chemical stress in periphyton between months along one year and a half period: Case study of a terbuthylazine exposure

Despite the knowledge of the effects of contaminants on periphyton, information is limited about their natural fluctuations in sensitivity to chemical stress between various months. In particular, the molecular and biochemical mechanisms associated with sensitivity of photosynthesis and its fluctuations remain poorly described. To tackle this lack of knowledge, meta-metabolomics offers a comprehensive picture of the sensitive molecular response preceding the physiological impact. This study aimed to describe changes in the sensitivity of periphyton to chemical stress at different months over one year and a half period, at both the physiological and molecular levels by measuring photosynthetic yield and meta-metabolome responses (targeted and untargeted approaches). Periphyton was colonized for four weeks and then exposed to a range of terbuthylazine concentrations (0.3-30 μg L-1) under controlled conditions for 4 h. Sensitivity was assessed by determining the benchmark doses for the meta-metabolome and photosynthesis, along with the cumulative distribution of aggregated metabolomics signals. The results showed a strong sensitivity shift in the meta-metabolome compared to a smaller shift in photosynthetic yield at different months. This study also confirmed the high sensitivity of the meta-metabolome, as most signals responded before photosynthesis. The annotation highlighted the discrepancies in the molecular response to TBA between the months in terms of metabolite classes (e.g. amino acids, alkaloids, and lipids), their sensitivity, and trends in common classes across months, and correlation to photosynthesis inhibition, notably oxylipins. Overall, this study highlights that the molecular response of the periphyton to chemical stress, and thus toxicity pathways, may differ between the months but can still lead to similar physiological responses.

A scaffold protein manages the biosynthesis of steroidal defense metabolites in plants

Solanaceae plants produce two major classes of valuable sterol-derived natural products-steroidal glycoalkaloids and steroidal saponins-from a common cholesterol precursor. Attempts to heterologously produce these molecules have consistently failed, although the genes responsible for each biosynthetic step have been identified. Here we identify a cellulose synthase-like protein, an unexpected biosynthetic component that interacts with the early pathway enzymes, enabling steroidal scaffolds production in plants. Moreover, knockout of this gene in black nightshade, Solanum nigrum, resulted in plants lacking both steroidal alkaloids and saponins. Unexpectedly, these knockout plants also revealed that steroidal saponins deter serious agricultural insect pests. This discovery provides the missing link to engineer these high-value steroidal molecules and also pinpoints the ecological role for steroidal saponins.

The influence of pH on the liquid-phase transformation of phenolic compounds driven by nitrite photolysis: Implications for characteristics, products and cytotoxicity

The aqueous-phase conversion of phenolic compounds (PhCs) driven by nitrite photolysis has been recognized as a significant source of secondary brown carbon (BrC). However, the influence of pH on the conversion kinetics and product distribution of PhCs remains unclear. In this study, three representative PhCs with varying functional groups were selected to examine their aqueous-phase conversion kinetics in the presence of nitrite under different pH conditions and simulated sunlight conditions. The results indicate that as the pH increases, the decay rates of PhCs decrease, following first-order reaction kinetics. These varying decay rates also suggest that different substituents on the benzene ring significantly impact the reactivity of PhCs. The molecular composition of the products is pH-dependent, with 4-nitrocatechol (4NC) emerging as the primary reaction product. A range of conversion products were detected across different pH values: nitrification dominated at low pH, while hydroxylation products increased with rising pH, and polymerization products appeared prominently at high pH. Due to the electron-withdrawing effect of the nitro group on the benzene ring, fewer products formed from 4-nitrophenol were observed, and the visible absorption spectrum also showed a decreasing trend as the reaction progressed across various pH conditions. Toxicity assays on human non-small cell lung cancer cells (A549) revealed that the toxicity of the reaction products decreased with increasing pH. Correspondingly, the accumulation of reactive oxygen species (ROS) and apoptosis rates in cells also declined. This may be due to the fact that at lower pH levels, nitrophenols (NPs), which tend to promote ROS accumulation and cell death, dominate the product mix. This study provides valuable insights into the toxicological properties of secondary organic aerosols (SOA) formed from the photo-oxidation products of PhCs under different pH conditions. These findings contribute to a deeper understanding of the environmental and health impacts of SOA in atmospheric chemistry.

MWFormer: Estimation of Molecular Weights from Electron Ionization Mass Spectra for Improved Library Searching

Molecular weight (MW) is a crucial property to improve the accuracy of multidimensional compound identification. In this study, we have developed MWFormer, a novel method that predicts MWs solely from spectra of electron ionization mass spectrometry (EI-MS) based on a Transformer encoder. MWFormer achieves a mean absolute error (MAE) of 6.38 Da, which is only one-sixth of the MAE by the peak interpretation method (PIM) on the test set. The MWFormer-predicted MW with superior accuracy can be used to eliminate false positive molecules in multidimensional compound identification. The results show that the MW filter improves the recall@3 metric by nearly 4% points compared with solely spectrum matching results. Moreover, MWFormer can be combined with retention indices (RIs) to achieve GC-EI-MS 3D compound identification to improve the recall@3 metric by nearly 7% points, compared with the results of spectrum matching alone. Besides, a user-friendly web service is provided to predict MWs in single mode or batch mode. All code, data, and models are available at https://github.com/zhanghailiangcsu/MWFormer.

Cytotoxicity, Antiadipogenic, Low-Density Lipoprotein Oxidation Inhibitory Activities, and Acute Toxicity Study of Psychotria densinervia Hydroethanolic Leaf and Bark Extracts

Background: Obesity is increasingly taking an important stage as a cause of death worldwide, and interventions with a good cost-effectiveness ratio are needed. Psychotria densinervia is one of these natural products with health benefits. Objective. The present study evaluated the cytotoxicity, antiadipogenic, low-density lipoprotein (LDL), oxidation inhibitory activities, and acute toxicity of Psychotria densinervia hydroethanolic leaf and bark extracts. Methods: The cytotoxicity evaluation of the extracts (62.5, 125, 250, and 500 μg/mL) using the MTT assay and the antiadipogenic activity (25, 50, 100, and 200 μg/mL) using oleic acid were carried out in SW-872 cells. Copper sulfate (CuSO4)-induced oxidation was used in the evaluation of the effect of extracts (0.25, 0.5, and 1 mg/mL) against LDL oxidation. The oral acute toxicity evaluation of a single dose of 2000 mg/kg of the extracts was performed in Wistar albino rats weighing 127 ± 2 g. Results: The leaf and bark extracts did not show any sign of cytotoxicity at the tested concentrations. The best antiadipogenic activity was observed by the standard orlistat (38.45 ± 1.70 μg/mL), followed by the leaf extract (IC50: 41.47 ± 0.50 μg/mL) and the least the bark extract (IC50: 107.50 ± 0.90 μg/mL). At a concentration of 1 mg/mL, the leaf extract presented an oxidation lag time of 130 min, which was higher and better than that of the bark extract (120 min). Quercetin (standard) presented an oxidation lag time longer than 3 h. The oral acute toxicity evaluation did not show any signs of toxicity indicating that the LD50 was greater than 2000 mg/kg. Conclusion: Based on the results obtained, the P. densinervia hydroethanolic leaf extract possesses a better antioxidant and antiadipogenic activities than the bark extract.

Metabolomic profiles of stony coral species from the Dry Tortugas National Park display inter- and intraspecies variation

Coral reefs are experiencing unprecedented loss in coral cover due to increased incidence of disease and bleaching events. Thus, understanding mechanisms of disease susceptibility and resilience, which vary by species, is important. In this regard, untargeted metabolomics serves as an important hypothesis-building tool enabling the delineation of molecular factors underlying disease susceptibility or resilience. In this study, we characterize metabolomes of four species of visually healthy stony corals, including Meandrina meandrites, Orbicella faveolata, Colpophyllia natans, and Montastraea cavernosa, collected at least a year before stony coral tissue loss disease reached the Dry Tortugas, Florida, and demonstrate that both symbiont and host-derived biochemical pathways vary by species. Metabolomes of Meandrina meandrites displayed minimal intraspecies variability and the highest biological activity against coral pathogens when compared to other species in this study. The application of advanced metabolite annotation methods enabled the delineation of several pathways underlying interspecies variability. Specifically, endosymbiont-derived vitamin E family compounds, betaine lipids, and host-derived acylcarnitines were among the top predictors of interspecies variability. Since several metabolite features that contributed to inter- and intraspecies variation are synthesized by the endosymbiotic Symbiodiniaceae, which could be a major source of these compounds in corals, our data will guide further investigations into these Symbiodiniaceae-derived pathways.

IMPORTANCE

Previous research profiling gene expression, proteins, and metabolites produced during thermal stress have reported the importance of endosymbiont-derived pathways in coral bleaching resistance. However, our understanding of interspecies variation in these pathways among healthy corals and their role in diseases is limited. We surveyed the metabolomes of four species of healthy corals with differing susceptibilities to the devastating stony coral tissue loss disease and applied advanced annotation approaches in untargeted metabolomics to determine the interspecies variation in host and endosymbiont-derived pathways. Using this approach, we propose the survey of immune markers such as vitamin E family compounds, acylcarnitines, and other metabolites to infer their role in resilience to coral diseases. As time-resolved multi-omics datasets are generated for disease-impacted corals, our approach and findings will be valuable in providing insight into the mechanisms of disease resistance.

Molecular characterization of major oxidative potential active species in ambient PM2.5: Emissions from biomass burning and ship exhaust

Ambient fine particulate matter (PM2.5) can catalyze the generation of reactive oxygen species in vivo, causing hazardous effects on human health. Molecular-level analysis of major oxidative potential (OP) active species is still limited. In this study, we used non-targeted high-resolution mass spectrometry to analyze the water-soluble organic components of ambient PM2.5 samples in winter and summer. Chemical components and back trajectory analysis revealed significant impacts of biomass burning and ship emissions on PM2.5 in winter and summer, respectively. Significance Analysis of the Microarray method and correlation analyses were combined to identify OP (OPDTT and OPOH) active species in characteristic organic compounds emitted from ship and biomass combustion emissions and to explore possible mechanisms. The results showed that the characteristic compounds emitted from ship were mainly organic amine compounds and contained more sulfur-containing components, while the characteristic compounds emitted from biomass burning were mainly oxygen-containing aromatic compounds of CHO and CHON groups. The high toxicity of summer PM2.5 might derive from reduced organic nitrogen compounds (C6H14N2O3S, C6H12N2O3S, C10H9N3O, C6H9N5O3S, and C6H14N4O) emission from ship sources. These reduced organic nitrogen compounds can form complexes with metals, affecting their solubility and reactivity in aerosols. Phenolic hydroxyl compounds were the main contributors to the PM2.5 OP from biomass burning in winter. Semiquinone radicals produced by oxidation of phenolic compounds can further promote the generation of reactive oxygen species through Fenton-like reactions. Our studies based on ambient PM2.5 samples further deepened the understanding of the molecular level of organic compounds emitted from ships and biomass burning, and their association with OP.

Untargeted metabolomics analyses to identify a new sweet compound released during post-fermentation maceration of wine

The sensory quality of a wine is mainly based on its aroma and flavor. Sweetness contributes in the gustatory balance of red wines. The investigation of compounds involved in this flavor was based on empirical observations, such as the increase in wine sweetness during yeast autolysis, concomitant to post-fermentation maceration in red winemaking. An untargeted metabolomics approach using UHPLC-HRMS has been developed to discover a new sweet molecule released during this stage. Among several markers highlighted, one compound was selected to be isolated by various separative techniques. It was unambiguously identified by NMR as N6-succinyladenosine and is reported for the first time in wine at an average concentration of 3.16 mg/L in 85 red wines. Furthermore, sensory analysis has highlighted its sweetness. In addition to discovering a new sweet compound in wine, this study proposes new tools for studying taste-active compounds in natural matrices.

Simultaneous targeted and non-targeted analysis of contaminants in fertilizers in Quebec, Canada

In this study, an LC-MS based analytical method was developed and validated for the simultaneous targeted analysis (14 bisphenols and 14 plasticizers) and suspect screening of other plastic-related contaminants in various types of fertilizers. The ultrasound-assisted extraction method showed overall satisfactory performances, achieving a median absolute recovery of 85 % for the target compounds in different types of fertilizers. The method was applied to sixteen different types of fertilizers, including fertilizing residual materials (n = 8 types), one cattle manure, and seven mineral fertilizers collected in Quebec, Canada in 2022 and 2023. Relatively higher levels of the targeted bisphenols and plasticizers were detected in some fertilizing residual materials, such as municipal biosolids and deinking residues. 4-Hydroxyphenyl 4-isoprooxyphenylsulfone (D-8) and bis(2-ethylhexyl) phthalate (DEHP) were dominant contaminants in these matrixes, with concentrations up to 35.6 and 64.7 μg g-1 dw, respectively. A non-targeted workflow was successfully applied to municipal biosolids and deinking residues, and >30 contaminants were identified across multiple chemical families at level 1 identification confidence, with most of them previously unreported in various types of fertilizers. For example, new color developers, N-(2-((Phenylcarbamoyl)amino)phenyl)benzenesulfonamide (NKK-1304) and 2,4-bis(phenylsulfonyl)phenol (DBSP), were reported in deinking residues. This work illustrates the complexity of the contaminant mixtures in fertilizers such as municipal biosolids and deinking residues.

Mass-Guided Single-Cell MALDI Imaging of Low-Mass Metabolites Reveals Cellular Activation Markers

Single-cell MALDI mass spectrometry imaging (MSI) of lipids and metabolites >200 Da has recently come to the forefront of biomedical research and chemical biology. However, cell-targeting and metabolome-preserving methods for analysis of low mass, hydrophilic metabolites (<200 Da) in large cell populations are lacking. Here, the PRISM-MS (PRescan Imaging for Small Molecule - Mass Spectrometry) mass-guided MSI workflow is presented, which enables space-efficient single cell lipid and metabolite analysis. In conjunction with giant unilamellar vesicles (GUVs) as MSI ground truth for cell-sized objects and Monte Carlo reference-based consensus clustering for data-dependent identification of cell subpopulations, PRISM-MS enables MSI and on-cell MS2-based identification of low-mass metabolites like amino acids or Krebs cycle intermediates involved in stimulus-dependent cell activation. The utility of PRISM-MS is demonstrated through the characterization of complex metabolome changes in lipopolysaccharide (LPS)-stimulated microglial cells and human-induced pluripotent stem cell-derived microglia. Translation of single cell results to endogenous microglia in organotypic hippocampal slice cultures indicates that LPS-activation involves changes of the itaconate-to-taurine ratio and alterations in neuron-to-glia glutamine-glutamate shuttling. The data suggests that PRISM-MS can serve as a standard method in single cell metabolomics, given its capability to characterize larger cell populations and low-mass metabolites.

Image Analysis and Untargeted Metabolomics Reveal Potential Phytotoxins from Fusarium venenatum Against Major Parasitic Weed Phelipanche ramosa (L.) Pomel

Branched broomrape (Phelipanche ramosa (L.) Pomel), an obligate parasitic weed with a wide host range, is known for its devasting effects on many crops worldwide. Soil fungi, notably Fusarium sp., are described as pathogenic to broomrape, while the hypothesis of the phytotoxicity of fusaric acid produced by F. verticillioides for parasitic weeds of the genus Orobanche has been proposed. Using image analysis and untargeted metabolomics, this study investigated fungal metabolites phytotoxic for P. ramosa and produced by the F. venenatum MIAE02836 strain, isolated from symptomatic broomrapes and identified as a promising candidate for broomrape biocontrol. Phytotoxicity tests of crude extracts from the fungus alone or in interaction with broomrape on P. ramosa microcalli and quantification of necrosis by image analysis confirmed the phytotoxic potential of F. venenatum MIAE02836 metabolites towards the early developmental stages of P. ramosa. Data analysis of a non-targeted metabolomics approach revealed numerous metabolites produced by F. venenatum MIAE02836. Four of them, accumulated during interaction with the parasitic plant, are known for their phytotoxic potential: maculosin, cyclo(Leu-Phe), phenylalanyl-D-histidine and anguidine. These results suggest that combining image acquisition of the microcalli screening test and untargeted metabolomic approach is an interesting and relevant method to characterize phytotoxic fungal metabolites.

Predicting Tandem Mass Spectra of Small Molecules Using Graph Embedding of Precursor-Product Ion Pair Graph

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.

Source-specific health effects of internally exposed organics in urban PM2.5 based on human serum albumin adductome analysis

Once inhaled, organic compounds in ambient PM2.5 permeate the bloodstream, resulting in internal exposure. The intricate composition of these internalized organic molecules complicates the processes of source attribution and toxicity assessment. A systematic framework to assess the health impacts of water-soluble organic molecules (WSOMs) originating from diverse sources is still undeveloped. This study aims to comprehensively analyze the source-specific health effects of internalized organics in urban PM2.5 through human serum albumin (HSA) non-covalent adductomes with WSOMs. Using high-resolution mass spectrometry, surface plasmon resonance, and machine learning, we mapped HSA-WSOM interactions, uncovering WSOM's potential internal exposure through its HSA adductome. The study identified eight distinct sources of internalized WSOMs, primarily from biogenic emissions, gasoline exhaust, and biomass combustion. Notably, WSOMs from these sources exhibited a predominant interaction with HSA residues ARG257, LEU238, and TRP150, substantially altering the functional dynamics of fatty acid binding site two and the hydrophobic cavity via hydrogen bonding and hydrophobic interactions. The primary health impacts of internalized WSOMs were identified as neurotoxicity and respiratory toxicity. WSOMs originating from biogenic sources and ocean emissions were mainly responsible for neurotoxic effects, whereas those from biomass burning and gasoline exhaust predominantly caused respiratory toxicity. Using the HSA adductome framework, our study identifies source-specific profiles and health effects of internally exposed WSOMs in urban PM2.5, emphasizing the importance of targeted mitigation strategies.

Determination of Flavonoid Glycoside Isomers Using Vision Transformer and Tandem Mass Spectrometry

A vision transformer (ViT)-based deep neural network was applied to classify the flavonoid glycoside isomers by analyzing electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra. Our model successfully classified the flavonoid isomers with various substitution patterns (3-O, 6-C, 7-O, 8-C, 4'-O) and multiple glycosides, achieving over 80% accuracy during training. In addition, the experimental spectra from flavonoid glycoside standards were acquired with different adducts, and our model showed robust performance regardless of the experimental conditions. As a result, the vision transformer-based computer vision model is promising for analyzing mass spectrometry data.

Dual nematode infection in Brassica nigra affects shoot metabolome and aphid survival in distinct contrast to single-species infection

Previous studies showed that aphid performance was compromised on Brassica nigra infected by root-lesion nematodes (Pratylenchus penetrans, Pp), but less, or positively influenced by root-knot nematode (Meloidogyne spp.) infection. These experiments were on single-species nematode infections, but roots can be infected naturally with several nematode species simultaneously. We performed greenhouse assays to assess the effects of single [Meloidogyne incognita (Mi) or Pp] and concurrent (MP) nematode infections on aphid performance. Using targeted and untargeted profiling of leaf and phloem metabolomes, we examined how single and concurrent nematode infections affect shoot metabolomes, and elucidated the possible consequences for aphid performance. We found that the metabolic response to double-infection is different from that to single-species infections. Moreover, Mi and Pp infections triggered discrete changes in B. nigra leaf and phloem metabolic profiles. Both Pp and MP infections reduced aphid survival, suggesting that the biological effect could primarily be dominated by Pp-induced changes. This concurred with increased indole glucosinolates and hydroxycinnamic acid levels in the leaves, in particular the putative involvement of salicylic acid-2-O-β-d-glucoside. This study provides evidence that concurrent infection by different nematode species, as is common in natural environments, is associated with distinct changes in aboveground plant metabolomes, which are linked to differences in the survival of an aboveground herbivore.

MR1 presents vitamin B6-related compounds for recognition by MR1-reactive T cells

The major histocompatibility complex class I related protein (MR1) presents microbially derived vitamin B2 precursors to mucosal-associated invariant T (MAIT) cells. MR1 can also present other metabolites to activate MR1-restricted T cells expressing more diverse T cell receptors (TCRs), some with anti-tumor reactivity. However, knowledge of the range of the antigen(s) that can activate diverse MR1-reactive T cells remains incomplete. Here, we identify pyridoxal (vitamin B6) as a naturally presented MR1 ligand using unbiased mass spectrometry analyses of MR1-bound metabolites. Pyridoxal, and the related compound, pyridoxal 5-phosphate bound to MR1 and enabled cell surface upregulation of wild type MR1*01 and MR1 expressing the Arg9His polymorphism associated with the MR1*04 allotype in a manner dependent on Lys43-mediated Schiff-base formation. Crystal structures of MR1*01 in complex with pyridoxal and pyridoxal 5-phosphate showed how these ligands were accommodated within the A-pocket of MR1. T cell lines transduced with the 7.G5 TCR, which has reported "pan-cancer" specificity, were specifically activated by pyridoxal presented by antigen-presenting cells expressing MR1*01 and MR1 allotypes bearing the less common Arg9His polymorphism. 7.G5 T cells also recognized, to a lesser extent, pyridoxal 5-phosphate and, importantly, recognition of both vitamers was blocked by an anti-MR1 antibody. 7.G5 TCR reactivity toward pyridoxal was enhanced when presented by the Arg9His polymorphism-bearing MR1 allotypes. Vitamin B6, and vitamers thereof, have been associated with various cancers, and here we describe a link between this ligand, MR1, and its allomorphs, and the pan-cancer 7.G5 TCR. This work identifies an MR1 ligand that can activate a diverse MR1-restricted TCR.

A simplified liquid chromatography-mass spectrometry methodology to probe the shikimate and aromatic amino acid biosynthetic pathways in plants

Plants direct substantial amounts of carbon toward the biosynthesis of aromatic amino acids (AAAs), particularly phenylalanine to produce lignin and other phenylpropanoids. Yet, we have a limited understanding of how plants regulate AAA metabolism, partially because of a scarcity of robust analytical methods. Here, we established a simplified workflow for simultaneous quantification of AAAs and their pathway intermediates from plant tissues, based on extraction at two alternative pH and analysis by Zwitterionic hydrophilic interaction liquid chromatography coupled to mass spectrometry. This workflow was then used to analyze metabolic responses to elevated or reduced carbon flow through the shikimate pathway in plants. Increased flow upon expression of a feedback-insensitive isoform of the first shikimate pathway enzyme elevated all AAAs and pathway intermediates, especially arogenate, the last common precursor within the post-chorismate pathway of tyrosine and phenylalanine biosynthesis. Additional overexpression of an arogenate dehydrogenase enzyme increased tyrosine levels and depleted phenylalanine and arogenate pools; however, the upstream shikimate pathway intermediates remained accumulated at high levels. Glyphosate treatment, which restricts carbon flow through the shikimate pathway by inhibiting its penultimate step, led to a predictable accumulation of shikimate and other precursors upstream of its target enzyme but also caused an unexpected accumulation of downstream metabolites, including arogenate. These findings highlight that the shikimate pathway and the downstream post-chorismate AAA pathways function as independently regulated modules in plants. The method developed here paves the way for a deeper understanding of the shikimate and AAA biosynthetic pathways in plants.

Neighbourhood effects on herbivory damage and chemical profiles in short-rotation coppice willows and their hybrids

Short rotation coppices (SRCs) represent an important source of biomass. Since they are grown in various mixtures, SRCs represent an excellent opportunity for assessing the effects of local plant neighbourhoods on their performance. We used a common garden experiment consisting of plots that varied in genotype diversity of SRC willows to test for the effects of chemical traits of individual plants and chemical variation in the plots where they grew on insect herbivory. We also explored whether the composition of willows planted in a plot affected their chemistry. To do this, we performed untargeted metabolomics and quantified various chemical traits related to the total set of metabolites we detected, flavonoids, and salicinoids in four willow genotypes. We measured the leaf herbivory that the plants suffered. The genotypes differed in most chemical traits, yet we found only limited effects of individual traits on herbivory damage. Instead, herbivory damage was positively correlated with structural variation in salicinoids in a plot. When analysing the effects of plot chemical variation on herbivory damage separately for each genotype, we found both positive and negative correlations between the two, suggesting both associational resistance and susceptibility. Finally, we also observed a significant effect of the interaction between genotype and plot composition on structural variation in plant chemistry. Overall, our results suggest that high chemical variation in mixed willow SRCs does not necessarily lower the herbivory damage, possibly due to spillover effects of insect herbivores among genotypes. Our results also show that different genotypes respond differently to plot composition in terms of herbivory damage and chemical composition, which may affect their suitability for growing in mixed stands.

Cytotoxic, antioxidant, and enzyme inhibitory activities of Centaurea stapfiana extracts and their HPLC-ESI-QTOF-MS profiles: Insights into an unexplored Centaurea species

The members of the genus Centaurea have a great interest in pharmaceutical and nutraceutical fields due to their biological potential. Based on this information, we aimed to evaluate the biological properties (antioxidant, enzyme inhibition and cytotoxicity) and chemical profile of the extract of Centaurea stapfiana, an unstudied species. The highest total phenolic content was found in the ethanol/water extract with 32.17 mg GAE/g. A total of 102 of them were identified by HPLC-ESI-QTOF-MS analysis. These compounds were mainly hydroxybenzoic acid and hydroxycinnamic acid as well as flavonoids. In the antioxidant tests, the ethanol/water extract had the best free radical scavenging and reducing ability. However, in the enzyme inhibition test, the ethanol extract was the most active. The extracts were also tested on two tumour cell lines (RAW 264.7 and HepG2) and one non-tumour cell line (S17). The ethanol extract showed the promising effect on HepG2 (cell viability: 28.6 % at 50 g/ml). Furthermore, we examined the interactions between the compounds and enzymatic and cellular targets. A good interaction was found between quercetin-3-xylosyl-(1- > 6)-glucoside and iNOS. In summary, our results suggest that C. stapfiana can be considered as a versatile raw material for the development of health-promoting applications in the pharmaceutical and cosmeceutical fields.

Gut physiology and environment explain variations in human gut microbiome composition and metabolism

The human gut microbiome is highly personal. However, the contribution of gut physiology and environment to variations in the gut microbiome remains understudied. Here we performed an observational trial using multi-omics to profile microbiome composition and metabolism in 61 healthy adults for 9 consecutive days. We assessed day-to-day changes in gut environmental factors and measured whole-gut and segmental intestinal transit time and pH using a wireless motility capsule in a subset of 50 individuals. We observed substantial daily fluctuations, with intra-individual variations in gut microbiome and metabolism associated with changes in stool moisture and faecal pH, and inter-individual variations accounted for by whole-gut and segmental transit times and pH. Metabolites derived from microbial carbohydrate fermentation correlated negatively with the gut passage time and pH, while proteolytic metabolites and breath methane showed a positive correlation. Finally, we identified associations between segmental transit time/pH and coffee-, diet-, host- and microbial-derived metabolites. Our work suggests that gut physiology and environment are key to understanding the individuality of the human gut microbial composition and metabolism.

Emerging trends in plant natural products biosynthesis: a chemical perspective

Biosynthetic pathways are multistep processes transforming simple substrates into more complex structures. Over the past two decades, our understanding of these pathways, especially for specialized plant metabolites, has significantly increased. This surge is due to numerous scientific advancements such as next-generation sequencing, improved analytical platforms, and metabolite-transcript networks. The uprising of data sharing through public databases has also fostered collaboration and knowledge dissemination. Growing concerns about the supply of therapeutic natural products and their environmental impact have led to exploring sustainable alternatives like heterologous expression, which requires extensive knowledge of these pathways. Herein, we review emerging approaches in biosynthetic pathway elucidations and their prospects for their efficient integration.

Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is a potent analytical technique utilized for identifying natural products from complex sources. However, due to the structural diversity, annotating LC-MS/MS data of natural products efficiently remains challenging, hindering the discovery process of novel active structures. Here, we introduce MassKG, an algorithm that combines a knowledge-based fragmentation strategy and a deep learning-based molecule generation model to aid in rapid dereplication and the discovery of novel NP structures. Specifically, MassKG has compiled 407,720 known NP structures and, based on this, generated 266,353 new structures using chemical language models for the discovery of potential novel compounds. Furthermore, MassKG demonstrates exceptional performance in spectra annotation compared to state-of-the-art algorithms. To enhance usability, MassKG has been implemented as a web server for annotating tandem mass spectral data (MS/MS, MS2) with a user-friendly interface, automatic reporting, and fragment tree visualization. Lastly, the interpretive capability of MassKG is comprehensively validated through composition analysis and MS annotation of Panax notoginseng, Ginkgo biloba, Codonopsis pilosula, and Astragalus membranaceus. MassKG is now accessible at https://xomics.com.cn/masskg.

Antitrypanosomal activity of Crossopteryx febrifuga and phytochemical profiling using LC-MS/MS analysis coupled to molecular network and SIRIUS

Crossopteryx febrifuga (Rubiaceae) is a plant widely used in traditional African medicine to treat tryapnosomiasis. The aim of our study was to evaluate the antitrypanosomal activity of C. febrifuga extracts and to identify the compounds responsible for this activity. We prepared 4 extracts by successive maceration of plant leaf powder in n-hexane, dichloromethane, methanol and water. The antitrypanosomal activity of the extracts was assessed on Trypanosoma brucei brucei and their selectivity on Leishmania mexicana mexicana and human non cancer WI38 fibroblast cells. The dichloromethane extract, the most antitrypanosomal (IC50 of 9.3 ± 0.8 μg/mL) was fractionated on an Open Column Chromatography to give 14 fractions. Fractions 6-9 were the most active with an IC50 ranging from 1.3 to 2.1 μg/mL. All fractions were analyzed by UPLC-ZenoTOFHRMS, followed by manual dereplication of metabolites detected in the most active fractions. Manual dereplication was aided by the Molecular Network (MN) and SIRIUS. Metabolic profiling of fractions 6-9 has enabled us to identify 33 compounds, most of which were reported for the first time in C. febrifuga. These include buddlenol C (6), naringenin (7), maslinic acid (22), corosolic acid or isomer (24), asperphenamate (25), hydroxyoctadecenoic acid (29), sumaresinolic acid or isomer I and II (30 and 31), glycyrrhetinic acid (32) and oleanolic acid (36). The bioactivity-based approach identified maslinic (22), corosolic (24), and oleanolic (36) acids as linked to the antitrypanosomal activity. The data obtained support the traditional use of C. febrifuga in the traditional treatment of trypanosomiasis. Further studies are required to verify the activity observed in vivo.

Insights into the modulatory effects of host-gut microbial xanthine co-metabolism on high-fat diet-fed mice

Gut microbiota-mediated endobiotic and xenobiotic metabolism play crucial roles in disease progression, and drug therapy/toxicity. Our recent study suggested that gut microbiota-mediated xanthine metabolism is correlated with resistance to high-fat diet (HFD)-induced obesity. Here, we explored the role of host-gut microbial xanthine co-metabolism in the prevention and treatment of HFD-induced obesity by orally administration of Bifidobacterium longum, xanthine, and a xanthine oxidase inhibitor (topiroxostat). The findings indicate that xanthine exhibits a significantly protective effect against HFD-induced obesity. While B. longum, xanthine, and topiroxostat did not alleviate the dysbiosis of the weight and glucose metabolism of HFD-induced obesity (DIO) and obesity resistance (DIR) mice. 16S rRNA sequencing analyses revealed that treatments with B. longum significantly altered gut microbiota composition in HFD-fed and DIO mice. Microbial interaction network analysis revealed several Bacteroidetes species, such as Amulumruptor caecigallinarius and Muribaculum intestinale, as keystone taxa that were notably enriched by B. longum. Untargeted metabolomics analysis implied that xanthine might serve as a crucial molecule in regulating body weight, exerting a preventive effect on HFD-induced obesity. This study offers new perspectives on the influence of host-gut microbial xanthine co-metabolism on HFD-fed mice and emphasizes the promising role of xanthine in promoting weight loss.

An automatic LC-MS/MS data analysis workflow for herbal compound annotation with AutoAnnotatoR: A case study of ten botanical origins of Fritillaria species

BACKGROUND

Despite the widespread implementation of analytical hardware capable of recording large-scale datasets for botanical natural products, the data processing procedures for compound annotation remain a bothersome obstacle that demand a tremendous amount of time and expert knowledge.

METHODS

Herein, an automatic LC-MS/MS data analysis workflow with AutoAnnotatoR was introduced for the compound annotation of plant derived natural products, which has the merits of great efficiency, high accuracy, saving time and simplified process. This procedure enabled automatic matching of MS2 data with characteristic fragment ions, as well as MS1 data with compound libraries, which improves the accuracy of structural elucidation. Notably, the optimization of collision energy for each target ion was successfully performed for the first time, facilitating the acquisition of comprehensive fragmentation information.

RESULTS

The automatic analysis workflow with AutoAnnotatoR was successfully applied for the annotation of alkaloids from 10 botanical origins of Fritillaria species. Consequently, a total of 2684 chemical constituents were tentatively characterized, with 23 components being unambiguously validated by reference standards and 2434 being probable novel chemicals.

CONCLUSION

The entire data analysis procedure takes only a few hours, vastly improving analysis speed while assuring high accuracy. This method provides a powerful tool for the rapid and precise annotation of complex natural products. The workflow is publicly accessible on Github as an open-source R package called AutoAnnotatoR (https://github.com/anyaling2022/AutoAnnotatoR).

Non-targeted analysis and suspect screening of organic contaminants in temperate snowfall using liquid chromatography high-resolution mass spectrometry

Contaminants released into the atmosphere that undergo regional and long-range transport can deposit back to Earth through snowfall. When snow melts, these contaminants re-enter the environment, sometimes far from their original emission sources. Here we present the first comprehensive characterization of organic contaminants in snow from North America. Fresh snowfall samples were collected in the central United States over a three-year period and measured by liquid chromatography high-resolution mass spectrometry for suspect screening and non-targeted analysis. The resulting data set was screened against experimental MS/MS libraries and underwent supplemental in silico MS/MS analysis. In total, 91 possible compounds were tentatively identified in snow, and 17 were successfully confirmed and semi-quantified with reference standards. These contaminants were mostly anthropogenic in origin and included six herbicides, three insect repellants, one insecticide metabolite, and one fungicide. The most prominent compounds present in all samples were N-cyclohexylformamide (known contaminant in tire leachate), DEET (insect repellent), and dimethyl phthalate (plasticizer), with median deposition fluxes of 4032, 284, and 262 ng m-2, respectively. Three additional compounds were detected in 100% of samples: coumarin (phytochemical and fragrance additive), 5-methylbenzotriazole (antifreeze component), and quinoline (heterocyclic aromatic). The Peto-Peto test revealed statistically significant differences in deposition fluxes for these six contaminants (p < 0.05), with weak but statistically significant positive associations between coumarin and DEET and between coumarin and quinoline according to a Kendall's tau correlation analysis. These findings demonstrate the utility of in silico analysis to complement MS/MS matching with experimental databases. Even so, thousands of unidentified features remained in the data set, highlighting the limitations of current strategies in non-targeted analysis of environmental samples.

Different metabolite profiles across Penicillium roqueforti populations associated with ecological niche specialisation and domestication

Fungi are known to produce many chemically diversified metabolites, yet their ecological roles are not always fully understood. The blue cheese fungus Penicillium roqueforti thrives in different ecological niches and is known to produce a wide range of metabolites, including mycotoxins. Three P. roqueforti populations have been domesticated for cheese production and two populations thrive in other anthropized environments, i.e., food, lumber and silage. In this study, we looked for differences in targeted and untargeted metabolite production profiles between populations using HPLC-HR-Q-TOF and UHPLC-Q-TOF-HR-MS/MS. The non-cheese populations produced several fatty acids and different terpenoids, lacking in cheese strains. The Termignon cheese population displayed intermediate metabolite profiles between cheese and non-cheese populations, as previously shown for other traits. The non-Roquefort cheese population with the strongest domestication syndrome, produced the lowest quantities of measured metabolites, including mycophenolic acid (MPA), andrastin A and PR toxin. Its inability to produce MPA was due to a deletion in the mpaC gene, while a premature stop codon in ORF 11 of the PR toxin gene cluster explained PR toxin absence and the accumulation of its intermediates, i.e., eremofortins A and B. In the Roquefort population, we detected no PR toxin nor eremofortins A or B, but found no indel or frameshift mutation, suggesting downregulation. The hypotoxigenic trait of domesticated cheese populations can be hypothesized to be linked to the loss of this ability through trait degeneration and/or the selection of low toxin producers. It may also be due to the fact that populations from other anthropized environments maintained high metabolite diversity as the bioactivities of these compounds are likely important in these ecological niches.

Targeted discovery of polyketides with antioxidant activity through integrated omics and cocultivation strategies

Fungi generate a diverse array of bioactive compounds with significant pharmaceutical applications. However, the chemical diversity of natural products in fungi remains largely unexplored. Here, we present a paradigm for specifically discovering diverse and bioactive compounds from fungi by integrating genome mining with building block molecular network and coculture analysis. Through pangenome and sequence similarity network analysis, we identified a rare type I polyketide enzyme from Penicillium sp. ZJUT-34. Subsequent building block molecular network and coculture strategy led to the identification and isolation of a pair of novel polyketides, (±)-peniphenone E [(±)-1], three known polyketides (2-4), and three precursor compounds (5-7) from a combined culture of Penicillium sp. ZJUT-34 and Penicillium sp. ZJUT23. Their structures were established through extensive spectroscopic analysis, including NMR and HRESIMS. Chiral HPLC separation of compound 1 yielded a pair of enantiomers (+)-1 and (-)-1, with their absolute configurations determined using calculated ECD methods. Compound (±)-1 is notable for its unprecedented structure, featuring a unique 2-methyl-hexenyl-3-one moiety fused with a polyketide clavatol core. We proposed a hypothetical biosynthetic pathway for (±)-1. Furthermore, compounds 2, 5, and 6 exhibited strong antioxidant activity, whereas (-)-1, (+)-1, 3, and four exhibited moderate antioxidant activity compared to the positive control, ascorbic acid. Our research demonstrates a pioneering strategy for uncovering novel polyketides by merging genome mining, metabolomics, and cocultivation methods. This approach addresses the challenge of discovering natural compounds produced by rare biosynthetic enzymes that are often silent under conventional conditions due to gene regulation.IMPORTANCEPolyketides, particularly those with complex structures, are crucial in drug development and synthesis. This study introduces a novel approach to discover new polyketides by integrating genomics, metabolomics, and cocultivation strategies. By combining genome mining, building block molecular networks, and coculturing techniques, we identified and isolated a unique polyketide, (±)-peniphenone E, along with three known polyketides and three precursor compounds from Penicillium sp. ZJUT-34 and Penicillium sp. ZJUT23. This approach highlights the potential of using combined strategies to explore fungal chemical diversity and discover novel bioactive compounds. The successful identification of (±)-peniphenone E, with its distinctive structure, demonstrates the effectiveness of this integrated method in enhancing natural product discovery and underscores the value of innovative approaches in natural product research.

Chemical Investigation of the Mediterranean Sponge Crambe crambe by UHPLC-HRMS/MS via Manual and Computational Dereplication Approaches

The CH2Cl2-MeOH extract of the Mediterranean sponge Crambe crambe was investigated via UHPLC-HRMS/MS employing manual dereplication and in silico mass spectrometry tools. A deconvolution approach was implemented for the extensive metabolic characterization of the sample, resulting in the annotation of 53 compounds. The analysis of data-dependent HRMS/MS scans was conducted to establish fragmentation patterns characteristic of each crambescin A, B, and C sub-families. Among the 39 compounds identified from these groups, 22 analogues were reported for the first time including 4 new homologous series that differed by the ratio of methylene units in the upper (n + 2) and lower (m + 2) alkyl side chains. More specifically, crambescins presenting m = 5 or 6 and n = 5 (compounds 7, 11, 22 and 24) as well as m = 5 or 6 and n = 4 (compounds 5, 6, 8, 9, 12 and 14) were characterized. Additionally, four new features, potentially corresponding to new crambescidin analogues (compounds 13, 15, 35, and 39), were also reported. The identity of the dereplicated features was further validated by studying crambescins' spectral similarities through a feature-based molecular networking approach. Overall, this study suggests UHPLC-HRMS/MS-through the integration of manual and computational dereplication approaches-as a valuable tool for the investigation and high-throughput characterization of the C. crambe metabolome.

Integrated multi-omics analyses reveal microbial community resilience to fluctuating low oxygen in the East China sea

Climate change and eutrophication are accelerating ocean deoxygenation, leading to a global decline in oxygen levels. The East China Sea, frequently experiencing deoxygenation events, harbors diverse microbial communities. However, the response of these communities to the changing deoxygenation dynamics remains poorly understood. Here, we explored the composition and function of microbial communities inhabiting seawaters of the Changjiang Estuary and offshore areas. Our findings suggested that neutral processes significantly influenced the assembly of these communities. The overall bacterial composition demonstrated remarkable high stability across the oxygen gradient. Salinity exhibited a significantly stronger correlation with bacterial community structure than dissolved oxygen. Both metagenomics and metaproteomics revealed that all of the samples exhibited similar functional community structures. Heterotrophic metabolism dominated these sites, as evidenced by a diverse array of transporters and metabolic enzymes for organic matter uptake and utilization, which constituted a significant portion of the expressed proteins. O2 was the primary electron acceptor in bacteria even under hypoxic conditions, evidenced by expression of low- and high-affinity cytochrome oxidases. Proteins associated with anaerobic processes, such as dissimilatory sulfite reductases, were virtually undetectable. Untargeted liquid chromatography with tandem mass spectrometry analysis of seawater samples revealed a diverse range of dissolved organic matter (DOM) components in amino acids, lipids, organic acids, peptides, and carbohydrates, potentially fueling dominant taxa growth. Despite fluctuations in the abundance of specific genera, the remarkable similarity in community structure, function, and DOM suggests that this ecosystem possesses robust adaptive mechanisms that buffer against abrupt changes, even below the well-defined hypoxic threshold in marine ecosystem.

Bokeelamides: Lipopeptides from Bacteria Associated with Marine Egg Masses

Moon snails (family: Naticidae) lay egg masses that are rich in bacterial species distinct from the surrounding environment. We hypothesized that this microbiome chemically defends the moon snail eggs from predation and pathogens. Herein, we report the discovery of bokeelamides, new lipopeptides from the egg mass-associated bacterium, Ectopseudomonas khazarica, which were discovered using mass spectrometry (MS)-based metabolomics. The structures of the bokeelamides were elucidated using two-dimensional (2D) nuclear magnetic resonance (NMR), tandem MS, Marfey's, and genomic analyses.

Longitudinal analyses of infants' microbiome and metabolome reveal microbes and metabolites with seemingly coordinated dynamics

Population studies have shown that the infant's microbiome and metabolome undergo significant changes in early childhood. However, no previous study has investigated how diverse these changes are across subjects and whether the subject-specific dynamics of some microbes correlate with the over-time dynamics of specific metabolites. Using mixed-effects models, and data from the ABC study, we investigated the early childhood dynamics of fecal microbiome and metabolome and identified 83 amplicon sequence variants (ASVs) and 753 metabolites with seemingly coordinated trajectories. Enrichment analysis of these microbes and molecules revealed eight ASV families and 23 metabolite groups involving 1032 ASV-metabolite pairs with their presence-absence changing in a coordinated fashion. Members of the Lachnospiraceae (464/1032) and metabolites related to cholestane steroids (309/1032) dominated proportional shifts within the fecal microbiome and metabolome as infants aged.

Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

Data Processing of Product Ion Spectra: Methods to Control False Discovery Rate in Compound Search Results for Untargeted Metabolomics

Several database search methods have been employed in untargeted metabolomics utilizing high-resolution mass spectrometry to comprehensively annotate acquired product ion spectra. Recent technical advancements in in silico analyses have facilitated the sorting of the degree of coincidence between a query product ion spectrum, and the molecular structures in the database. However, certain search results may be false positives, necessitating a method for controlling the false discovery rate (FDR). This study proposes 4 simple methods for controlling the FDR in compound search results. Instead of preparing a decoy compound database, a decoy spectral dataset was created from the measured product-ion spectral dataset (target). Target and decoy product ion spectra were searched against an identical compound database to obtain target and decoy hits. FDR was estimated based on the number of target and decoy hits. In this study, 3 decoy generation methods, polarity switching, mirroring, and spectral sampling, were compared. Additionally, the second-rank method was examined using second-ranked hits in the target search results as decoy hits. The performances of these 4 methods were evaluated by annotating product ion spectra from the MassBank database using the SIRIUS 5 CSI:FingerID scoring method. The results indicate that the FDRs estimated using the second-rank method were the closest to the true FDR of 0.05. Using this method, a compound search was performed on 4 human metabolomic data-dependent acquisition datasets with an FDR of 0.05. The FDR-controlled compound search successfully identified several compounds not present in the Human Metabolome Database.