Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics

Dual LC column characterization for mass spectrometry-based small molecule profiling of human plasma and serum

BACKGROUND

Analyte annotation confidence in untargeted liquid chromatography mass-spectrometry (LC-MS) based chemical analysis can be enhanced by leveraging retention time information. For this, the chromatographic characteristics of the analytical system used should be well characterized. In this study, we measured 604 diverse chemical standards to characterize a dual LC setup consisting of pentabromobenzyl (PBr) and type-C silica hydride (SiH) columns operating in reversed-phase (RP) and aqueous normal-phase (ANP) mode, respectively.

RESULTS

ANP and RP separations individually retained 40 % and 64 % of standards in cLogP range from -6.60 to 8.67 and -3.34 to 12.95, respectively. Using both columns, the coverage increased to 79 % of standards with cLogP range from -6.60 to 12.95 (median cLogP = 1.63). Retention selectivity follows the number of basic nitrogen atoms in the molecule on SiH column and polarity (cLogP) on PBr column. Column repeatability and reproducibility were tested in triplicate using a chemically diverse subset of 108 standards. Repeatability of retention times, peak widths and peak areas was 0.3 %, 14 %, 4 % for SiH column and 0.2 %, 12 %, 4 % for PBr column. Similarly, reproducibility was 15 %, 34 %, 30 % for SiH column and 9 %, 18 % and 34 % for PBr column. Predictive RT models were developed based on experimental RT data, achieving R2 values of 0.92 and 0.96, with mean absolute errors of 0.29 min and 0.27 min for SiH and PBr columns, respectively.

SIGNIFICANCE

As proof of concept, 129 metabolites were annotated in pooled human serum and plasma by matching standard or predicted RT on one or both columns. The RT models and MS2 spectra of standards are openly available, facilitating uptake of this well-characterized chromatographic system to increase confidence in analyte annotation.

Multilayered visual metabolomics analysis framework for enhanced exploration of functional components in wolfberry

Wolfberry, regarded as a nutritious fruit, has garnered significant attention in the food industry due to potential health benefits. However, the tissue-specific distribution and dynamic accumulation patterns of nutritional metabolites such as flavonoids are still unclear. In this study, a novel spatial metabolomics framework was developed, incorporating instrumental optimization, metabolite identification, molecular network analysis, metabolic pathway mapping, and machine learning-based imaging. Using DESI-MSI, this approach enabled rapid, non-destructive, in situ analysis of wolfberry metabolites with enhanced sensitivity and spatial resolution. Detailed insights into chemical and spatial changes during ripening were obtained, with a focus on flavonoids. The visualization of the flavonoid biosynthetic pathway highlighted the impact of C-3 hydroxylation on flavonoid redistribution. Furthermore, a classification model achieved a prediction accuracy exceeding 99 %, consistent with metabolic network analyses. This framework provides a powerful tool for plant metabolomics, facilitating the exploration of functional components and metabolic pathways.

Fragmentation pattern-based nontargeted screening strategy uncovered novel halogenated nucleotides in drinking water

Halogenated nucleotides have been reported as emerging disinfection byproducts in drinking water and the identification of halogenated nucleotides is a challenging but worthwhile endeavor due to their high risk, diverse chemical structures and scarcity of chemical standards. To improve the recognition efficiency of halogenated nucleotides in complex environmental matrices with multiple halogen ions, a fragmentation pattern-based nontargeted screening strategy was developed, which combines halogen isotope features and characteristic product ions to identify halogenated nucleotides within extensive datasets from high-resolution mass spectrometry. The data were preliminary screened based on the halogen isotope features of chemicals with varying halogen atom counts, followed by further identification using shared fragments with m/z values of 78.96, 96.97 and 211.00 Da. We tentatively identified 72 halogenated nucleotides, including 9 brominated, iodinated and mixed halogen-substituted nucleotides that were annotated and reported for the first time. The effectiveness of the nontargeted identification strategy was demonstrated through manual verification of parent and product ions. Additionally, by comparing the Gibbs free energies of reactions (ΔG) of forming chlorinated, brominated and iodinated nucleotides, chlorinated nucleotides were revealed to exhibit the greatest formation potential, followed by brominated and iodinated nucleotides. 8-Chloroadenosine 5'-monophosphate was selected for the cytotoxicity test to evaluate its potential biological hazards and proven to manifest relatively high cytotoxicity. This work provides a new fragmentation pattern-based screening strategy for the effective identification of unknown halogenated nucleotides. This method is anticipated to demonstrate broad applications in the nontargeted identification of halogenated nucleotides in diverse environmental samples.

Metabolomic and Lipidomic Alterations in Patients with Atopic Dermatitis with Dupilumab-Associated Ocular Surface Disease

Atopic dermatitis (AD) is an inflammatory skin disease characterized by chronic pruritic eczema with an estimated prevalence of 10% in adults and 50% of them suffering from moderate-to-severe manifestations. Dupilumab, an IL-4/IL-13 inhibitor, is approved for treating moderate-to-severe AD. However, dupilumab-associated ocular surface disease (DAOSD) emerges in up to 60% of dupilumab-treated patients, constituting a major AD-specific adverse event. DAOSD pathogenesis has not been fully understood yet. To elucidate the metabolic changes occurring after dupilumab treatment in patients with AD, we focused in this prospective single-center cohort study particularly on patients who developed DAOSD. In total, 20 patients with AD underwent dupilumab therapy, with 6 developing DAOSD. Plasma and serum samples were collected at baseline, 4 and 16 weeks after treatment initiation, and during the conjunctivitis episode. In addition, 10 age- and sex-matched healthy controls were sampled solely at baseline. High-resolution mass spectrometry was employed for metabolomic and lipidomic analysis of all blood samples. Targeted metabolomics and lipidomic with multivariate analysis unveiled significant metabolic and lipidic disparities (such as increased activity of benzoic acid, tyrosine and indole metabolism, and others) between AD patients with and those without DAOSD. Metabolomics and lipidomic analysis further deepen our comprehension of DAOSD pathogenesis.

HeuSMA: A Multigradient LC-MS Strategy for Improving Peak Identification in Untargeted Metabolomics

Metabolomics, which involves the comprehensive analysis of small molecules within biological systems, plays a crucial role in elucidating the biochemical underpinnings of physiological processes and disease conditions. However, current coverage of the metabolome remains limited. In this study, we present a heuristic strategy for untargeted metabolomics analysis (HeuSMA) based on multiple chromatographic gradients to enhance the metabolome coverage in untargeted metabolomics. This strategy involves performing LC-MS analysis under multiple gradient conditions on a given sample (e.g., a pooled sample or a quality control sample) to obtain a comprehensive metabolomics data set, followed by constructing a heuristic peak list using a retention index system. Guided by this list, heuristic peak picking in quantitative metabolomics data is achieved. The benchmarking and validation results demonstrate that HeuSMA outperforms existing tools (such as MS-DIAL and MZmine) in terms of metabolite coverage and peak identification accuracy. Additionally, HeuSMA improves the accessibility of MS/MS data, thereby facilitating the metabolite annotation. The effectiveness of the HeuSMA strategy was further demonstrated through its application in serum metabolomics analysis of human hepatocellular carcinoma (HCC). To facilitate the adoption of the HeuSMA strategy, we also developed two user-friendly graphical interface software solutions (HPLG and HP), which automate the analysis process, enabling researchers to efficiently manage data and derive meaningful conclusions (https://github.com/Lacterd/HeuSMA).

Impact of heterologous expression of Cannabis sativa tetraketide synthase on Phaeodactylum tricornutum metabolic profile

BACKGROUND

Pharmaceutical safety is an increasing global priority, particularly as the demand for therapeutic compounds rises alongside population growth. Phytocannabinoids, a class of bioactive polyketide molecules derived from plants, have garnered significant attention due to their interaction with the human endocannabinoid system, offering potential benefits for managing a range of symptoms and conditions. Traditional extraction from cannabis plants poses regulatory, environmental, and yield-related challenges. Consequently, microbial biosynthesis has emerged as a promising biotechnological alternative to produce cannabinoids in a controlled, scalable, and sustainable manner. Developing diatom-based biofactories represent a crucial step in advancing this biotechnology, enabling the efficient production of high-valued compounds such as cannabinoids.

RESULTS

We engineered the diatom Phaeodactylum tricornutum, a unicellular photosynthetic model organism prized for its naturally high lipid content, to produce olivetolic acid (OA), a key metabolic precursor to most cannabinoids. The genes encoding tetraketide synthase and olivetolic acid cyclase from cannabis were cloned onto episomal vectors and introduced using bacterial conjugation in two separate P. tricornutum transconjugant lines to evaluate enzyme activity and OA production in vivo. Both genes were successfully expressed, and the corresponding enzymes accumulated within the transconjugant lines. However, despite testing the cell extracts individually and in combination, OA accumulation was not detected suggesting potential conversion or utilization of OA by endogenous metabolic pathways within the diatoms. To investigate this further, we analyzed the impact of CsTKS expression on the diatom's metabolome, revealing significant alterations that may indicate metabolic flux redirection or novel pathway interactions.

CONCLUSIONS

Our study demonstrates the successful expression of cannabinoid biosynthetic genes in P. tricornutum but highlights challenges in OA accumulation, likely due to endogenous metabolic interactions. These findings underscore the complexity of metabolic engineering in diatoms and suggest the need for further pathway optimization and metabolic flux analysis to achieve efficient cannabinoid biosynthesis. This research contributes to advancing sustainable biotechnological approaches for cannabinoid production.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Untargeted Metabolomic Analysis and Cytotoxicity of Extracts of the Marine Dinoflagellate Amphidinium eilatiense Against Human Cancer Cell Lines

Members of the benthic marine dinoflagellate genus Amphidinium produce a variety of bioactive compounds, exhibiting potent cytotoxicity in cell assays. Crude methanolic extracts from three genetically distinct cultured strains of A. eilatiense J.J. Lee were screened for cytotoxicity against three human breast and four lung cancer cell lines to evaluate potential applications in anticancer therapy. A standard tetrazolium cell viability assay demonstrated that the methanolic crude extract (100 µg mL-1) from strain AeSQ181 reduced cell viability by 20-35% in five cancer cell lines. Further bioassay-guided fractionation of these crude extracts yielded non-polar fractions (FNP-5 and FNP-6) with particularly high cytotoxic activity against lung (H1563) and breast (MDA-MB-231) adenocarcinoma cell lines. Untargeted metabolomic analysis of cytotoxic fractions by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) revealed a much richer chemical diversity profile than previous toxigenicity studies on Amphidinium that exclusively focused on linear and cyclic polyethers and their macrolide analogs as putative cytotoxins. This untargeted metabolomic study showed substantial differences in chemical composition between the biologically active and non-active fractions. Preliminary biological and chemical characterization of these A. eilatiense fractions confirms that this species is a rich source of bioactive natural products with potential applications such as anticancer therapeutics.

Multidimensional-Constrained Suspect Screening of Hydrophobic Contaminants Using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Ion Mobility-Mass Spectrometry

Suspect screening strives to rapidly monitor a large number of substances in a sample using mass spectral libraries. For hydrophobic organic contaminants (HOCs), these libraries are traditionally based on electron ionization mass spectra. However, with the growing use of state-of-the-art mass spectrometers, which often use alternative ionization approaches and separation techniques, new suspect screening workflows and libraries are urgently needed. This study established a new suspect screening library for 1,590 HOCs, including exact mass and a combination of measured and model-predicted values for retention time (RT) and collision cross section (CCS). The accuracy of in silico predictions was assessed using standards for 102 HOCs. Thereafter, using gas chromatography-atmospheric pressure chemical ionization-ion mobility-mass spectrometry, a suspect screening workflow constrained by the full scan mass spectrum of (quasi-)molecular ions (including isotope patterns), RT, CCS, and fragmentation mass spectra, together with a continuous scoring system, was established to reduce false positives and improve identification confidence. Application of the method to fortified and standard reference sediment samples demonstrated true positive rates of 79% and 64%, respectively, with all false positives attributed to suspect isomers. This study offers a new workflow for improved suspect screening of HOCs using multidimensional information and highlights the need to enrich mass spectral databases and extend the applicable chemical space of current in silico tools to hydrophobic substances.

Deciphering the phenotypic, inflammatory, and endocrine disrupting impacts of e-waste plastic-associated chemicals

As the volume of plastic waste from electrical and electronic equipment (WEEE) continues to rise, a significant portion is disposed of in the environment, with only a small fraction being recycled. Both disposal and recycling pose unknown health risks that require immediate attention. Existing knowledge of WEEE plastic toxicity is limited and mostly relies on epidemiological data and association studies, with few insights into the underlying toxicity mechanisms. Therefore, this study aimed to perform comprehensive chemical screening and mechanistic toxicological assessment of WEEE plastic-associated chemicals. Chemical analysis, utilizing suspect screening based on high-resolution mass spectrometry, along with quantitative target chemical analysis, unveiled numerous hazardous compounds including polyaromatic compounds, organophosphate flame retardants, phthalates, benzotriazoles, etc. Toxicity endpoints included perturbation of morphological phenotypes using the Cell Painting assay, inflammatory response, oxidative stress, and endocrine disruption. Results demonstrated that WEEE plastic chemicals altered the phenotypes of the cytoskeleton, endoplasmic reticulum, and mitochondria in a dose-dependent manner. In addition, WEEE chemicals induced inflammatory responses in resting macrophages and altered inflammatory responses in lipopolysaccharide-primed macrophages. Furthermore, WEEE chemicals activated the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, indicating oxidative stress, and the aryl hydrocarbon receptor (AhR). Endocrine disruption was also observed through the activation of estrogenic receptor-α (ER-α) and the induction of anti-androgenic activity. The findings show that WEEE plastic-associated chemicals exert effects in multiple subcellular sites, via different receptors and mechanisms. Thus, an integrated approach employing both chemical and toxicological methods is essential for comprehensive assessment of the toxicity mechanisms and cumulative chemical burden of WEEE plastic-associated chemicals.

Automated Sequential Derivatization for Gas Chromatography-[Orbitrap] Mass Spectrometry-based Metabolite Profiling of Human Blood-based Samples

Many small molecules require derivatization to increase their volatility and to be amenable to gas chromatographic (GC) separation. Derivatization is usually time-consuming, and typical batch-wise procedures increase sample variability. Sequential automation of derivatization via robotic liquid handling enables the overlapping of sample preparation and analysis, maximizing time efficiency and minimizing variability. Herein, a protocol for the fully automated, two-stage derivatization of human blood-based samples in line with GC-[Orbitrap] mass spectrometry (MS)-based metabolomics is described. The protocol delivers a sample-to-sample runtime of 31 min, being suitable for better throughput routine metabolomic analysis. Key features • Direct and rapid methoximation on vial followed by silylation of metabolites in various blood matrices. • Measure ~40 samples per 24 h, identifying > 70 metabolites. • Quantitative reproducibility of routinely measured metabolites with coefficients of variation (CVs) < 30%. • Requires a Thermo ScientificTM TriPlusTM RSH (or comparable) autosampler equipped with incubator/agitator, cooled drawer, and automatic tool change (ATC) station equipped with liquid handling tools. Graphical overview Workflow for profiling metabolites in human blood using automated derivatization.

Unraveling the potential contribution of DHHC2 in cancer biology via untargeted metabolomics

DHHC-mediated protein-S-palmitoylation is recognized as a distinct and reversible lipid modification, playing a pivotal role in the progression and prevention of multiple diseases, including cancer and neurodegenerative disorders. Over the past decade, growing evidence indicated the crucial role of DHHC2 in preventing tumorigenesis by palmitoylation of various protein substrates. However, a comprehensive understanding of the specific impact of DHHC2 on cancer cell metabolic regulation remains unclear. To investigate the metabolic changes by DHHC2, we conducted untargeted metabolomic profiling on the HEK-293T cell line with DHHC2-Knockdown (DHHC2-KD), DHHC2-Overexpression (DHHC2-OE) and empty vector control (Ctrl) conditions via LC-MS/MS-based analysis. Our dataset revealed the identification of a total of 73 metabolites encompassing all the conditions, with only 22 showing significant differences in univariate analysis. Furthermore, we performed pathway analysis with metabolites having VIP ≥ 0.7, P value ≤ 0.05, and fold change (FC) > 2 in DHHC2-OE (upregulated) and FC < 0.5 in DHHC2-OE or FC > 2 in DHHC2-KD condition (downregulated). We unveiled significant expression of the pyrimidine metabolism, urea cycle, and aspartate metabolism due to the abundance of onco-metabolites such as glutamine, uridine, and glutamic acid in the DHHC2-KD condition. However, DHHC2 overexpression resulted in a higher expression of metabolites previously reported to be associated with anti-cancer activity, such as betaine and 5'-methylthioadenosine (5'-MTA). Overall, this study sheds light on the changes mediated by DHHC2 in a cancer cell metabolome and suggests avenues for further investigation into other DHHC isoforms and their metabolic aspects.

Alginate oligosaccharides relieve estrogen-deprived osteosarcopenia by affecting intestinal Th17 differentiation and systemic inflammation through the manipulation of bile acid metabolism

Alginate oligosaccharides (AOS) have gained attention for their capacity to regulate human health as prebiotics. Osteosarcopenia is a progressive disease of the musculoskeletal system and result in heavy burden of patients. Studies suggest that gut microbiota is involved in the pathogenesis of osteosarcopenia, whether AOS can improve the symptoms of osteosarcopenia by modulating gut microbiota remains to be elucidated. In this study, we proved that 200 mg/kg body weight AOS (MW = 4.9 kDa, G/M = 1.88) treatment significantly increased bone mass, boosted muscle function, and promoted gut barrier integrity in ovariectomized (OVX) mice. After AOS treatment, a marked reduction in the proportion of intestinal Th17 subsets and in peripheral levels of relevant inflammatory cytokines was observed compared to the OVX group. 16S rRNA sequencing indicated that AOS treatment could restore the imbalance of gut microbiota caused by estrogen deficiency. Additionally, the impact of AOS on bile acid changes was revealed according to metabolomics. In particular, the Th17 differentiation inhibitor, such as isoLCA, were significantly upregulated after AOS treatment. In conclusion, AOS can alleviate the symptoms of osteoporosis by modulating the relative abundance of gut microbiota and bile acid metabolism, thereby reducing the proportion of intestinal Th17 cells and peripheral Inflammation.

Untargeted Metabolomics Analysis Reveals Differential Accumulation of Flavonoids Between Yellow-Seeded and Black-Seeded Rapeseed Varieties

Rapeseed (Brassica napus) is an important oilseed crop and yellow-seeded and black-seeded varieties have different metabolite profiles, which determines the quality and edibility of their oil. In this study, we performed a non-targeted metabolomics analysis of seeds from four rapeseed varieties at eight developmental stages. This analysis identified 4540 features, of which 366 were annotated as known metabolites. The content of these metabolites was closely related to seed developmental stage, with the critical period for seed metabolite accumulation being between 10 and 20 days after pollination. Through a comparative analysis, we identified 18 differentially abundant flavonoid features between yellow-seeded and black-seeded rapeseed varieties. By combining the flavonoid data with transcriptome data, we constructed a gene regulatory network that may reflect the accumulation of differentially abundant flavonoid features. Finally, we predicted 38 unknown features as being flavonoid features through molecular networking. These results provide valuable metabolomics information for the breeding of yellow-seeded rapeseed varieties.

A guide to reverse metabolomics-a framework for big data discovery strategy

Untargeted metabolomics is evolving into a field of big data science. There is a growing interest within the metabolomics community in mining tandem mass spectrometry (MS/MS)-based data from public repositories. In traditional untargeted metabolomics, samples to address a predefined question are collected and liquid chromatography with MS/MS data are generated. We then identify metabolites associated with a phenotype (for example, disease versus healthy) and elucidate or validate their structural details (for example, molecular formula, structural classification, substructure or complete structural annotation or identification). In reverse metabolomics, we start with MS/MS spectra for known or unknown molecules. These spectra are used as search terms to search public data repositories to discover phenotype-relevant information such as organ/biofluid distribution, disease condition, intervention status (for example, pre- and postintervention), organisms (for example, mammals versus others), geography and any other biologically relevant associations. Here we guide the reader through a four-part process: (1) obtaining the MS/MS spectra of interest (Universal Spectrum Identifier) and (2) Mass Spectrometry Search Tool searches to find the files associated with the MS/MS that are in available databases, (3) using the Reanalysis Data User Interface framework to link the files with their metadata and (4) validating the observations. Parts 1-3 could take from hours to days depending on the method used for collecting MS/MS spectra. For example, we use MS/MS spectra from three small molecules: phenylalanine-cholic acid (a microbially conjugated bile acid), phenylalanine-C4:0 and histidine-C4:0 (two N-acyl amides). We leverage the Global Natural Products Social Molecular Networking-based framework to explore the microbial producers of these molecules and their associations with health conditions and organ distributions in humans and rodents.

Reducing the Bitterness of Rapeseed Protein: Integrating Enzymatic Treatment, Metabolomics, and Sensory Analysis to Elucidate Underlying Mechanisms

Rapeseed products, such as protein concentrates, hold promise for addressing global protein demands, but their application in food products is limited by their bitter and astringent taste. This study investigates the use of β-glucosidase (BG) and laccase (LAC) enzymatic treatment, individually and combined, to enhance the flavor of rapeseed protein concentrate (RPC). Untargeted metabolomics and sensory analysis reveal that LAC reduces the bitter compound kaempferol 3-O-(2‴-O-sinapoyl-β-D-sophoroside) (K3OSS) as well as a general reduction in other phenolic compounds, which correlates with a significant decrease in bitterness and astringency. In contrast, BG treatment elevates the levels of K3OSS and is accompanied by increased bitterness due to the conversion of precursor compounds to K3OSS. In addition, the synergistic use of both enzymes significantly reduces the concentration of K3OSS, resulting in a lower perception of bitterness. The LC-MS analysis of pure reference compounds treated with LAC and BG confirms that BG-mediated treatment facilitates the breakdown of larger kaempferol glycosides into K3OSS, while LAC treatment promotes polyphenol polymerization. Consequently, LAC treatment seems to be an effective strategy to improve the sensory quality of RPC and make it more suitable for human consumption.

Exploring the Chemical Complexity and Sources of Airborne Fine Particulate Matter in East Asia by Nontarget Analysis and Multivariate Modeling

The complex and dynamic nature of airborne fine particulate matter (PM2.5) has hindered understanding of its chemical composition, sources, and toxic effects. In the first steps of a larger study, here, we aimed to elucidate relationships between source regions, ambient conditions, and the chemical composition in water extracts of PM2.5 samples (n = 85) collected over 16 months at an observatory in the Yellow Sea. In each extract, we quantified elements and major ions and profiled the complex mixtures of organic compounds by nontarget mass spectrometry. More than 50,000 nontarget features were detected, and by consensus of in silico tools, we assigned a molecular formula to 13,907 features. Oxygenated compounds were most prominent, followed by mixed nitrogenated/oxygenated compounds, organic sulfates, and sulfonates. Spectral matching enabled identification or structural annotation of 43 substances, and a workflow involving SIRIUS and MS-DIAL software enabled annotation of 74 unknown per- and polyfluoroalkyl substances with primary source regions in China and the Korean Peninsula. Multivariate modeling revealed seasonal variations in chemistry, attributable to the combination of warmer temperatures and maritime source regions in summer and to cooler temperatures and source regions of China in winter.

Approaches for GC-HRMS Screening of Organic Microcontaminants: GC-APCI-IMS-QTOF versus GC-EI-QOrbitrap

This study explores the capabilities of GC-APCI-IMS-QTOF MS and GC-EI-QOrbitrap MS in screening applications and different strategies for wide-scope screening of organic microcontaminants using target suspect and nontarget approaches. On one side, GC-APCI-IMS-QTOF MS excels at preserving molecular information and adds ion mobility separation, facilitating screening through the list of componentized features containing accurate mass, retention time, CCS, and fragmentation data. On the other side, the extensive and robust fragmentation of GC-EI-QOrbitrap MS allows the application of different strategies for target and nontarget approaches using the NIST library spectra. Our findings revealed that GC-EI-QOrbitrap MS is more sensitive in target approaches. Automated workflows for suspect screening in GC-APCI-IMS-QTOF MS minimize false annotations but face challenges with false negatives due to in-source fragmentation and limitations when using in silico fragmentation tools. Conversely, a nontarget approach in GC-EI-QOrbitrap MS can reliably identify unknowns but results in more false annotations in complex matrices. This work highlights the strengths and limitations of each system and guides for their optimal application for wide-scope screening in environmental and food safety applications.

Effect of H2O2 induced oxidative stress on volatile organic compounds in differentiated 3T3-L1 cells

Oxidative stress (OS) refers to the disruption in the balance between free radical generation and antioxidant defenses, leading to potential tissue damage. Reactive oxygen species (ROS) can interact with biological components, triggering processes like protein oxidation, lipid peroxidation, or DNA damage, resulting in the generation of several volatile organic compounds (VOCs). Recently, VOCs provided new insight into cellular metabolism and can serve as potential biomarkers. The objective is to investigate the impact of OS on cell metabolism by analyzing the release or alterations of VOCs in the headspace of differentiated 3T3-L1 adipocytes. An OS model in differentiated 3T3-L1 cell lines was constructed using hydrogen peroxide (H2O2) treatment. The effect of OS on cell metabolism was analyzed by detecting VOCs in the headspace of the cells using solid phase micro extraction (SPME) and gas chromatography-mass spectrometry (GCMS). Our findings indicate that H2O2 concentrations exceeding 300 µM induce significant OS, leading to adipocyte apoptosis, as evidenced by various assays. Of the twenty VOCs identified, ten were upregulated in the cells. VOCs such as diphenyl ether, 1,3,5-trioxane, 5-methyl tridecane, 2-ethyl-1-hexanol, and 2,4-di-tert-butyl phenol emerged as potential biomarkers for OS. This study demonstrates that elevated OS alters VOC profiles in differentiated 3T3-L1 adipocytes, providing insights into the effects of OS on adipose tissue and identifying potential OS biomarkers.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Untargeted metabolite profiling reveals metabolic disorders in livers of rats with mepiquat exposure

Mepiquat is a contaminant produced in thermal-processed food. It can induce spleen and liver injury. However, the mechanism that mepiquat induced hepatotoxicity remains unclear. In this study, a ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS)-based metabolomic analysis of livers in rats was performed to explore metabolic alterations. Our results demonstrated that there were 36 differential metabolites. Eleven major disordered metabolic pathways were found. Network analysis of differential metabolites and metabolic pathways indicated that glutamic acid was a possible key upregulated metabolite. It participated in nine metabolic networks. Glutathione and l-proline may be key downregulated metabolites. They were involved in eight and seven metabolic pathways, respectively. Compared with the control group, serum concentrations of alanine aminotransferase, aspartate aminotransferase, and total bile acid in high dosage group increased significantly. In addition, histopathological analysis showed liver injury in rats with mepiquat exposure. These data were consistent with results of metabolomics. Our results offered new insights for molecular mechanism of liver toxicity induced by mepiquat. PRACTICAL APPLICATION: Mepiquat is a contaminant formed in thermal-processed food. It can induce spleen and liver injury. Our results offered new insights for molecular mechanism of liver toxicity induced by mepiquat. It will provide important information for official limits of mepiquat in foods.

Keeping quality of raw ground beef patties fortified with polyphenols extracted from Acacia mearnsii bark and leaves

Acacia mearnsii byproducts are naturally endowed with a plethora of diverse polyphenols that exhibit antioxidant properties indicating potential application in enhancing oxidative shelf-life of perishable foods. The current study evaluated the oxidative shelf-life of raw ground beef patties fortified with 450 μg/g of polyphenolic extracts from A. mearnsii bark (AMBE) or leaves (AMLE) compared to positive (sodium metabisulphite; SMB) and negative (no extract; CTL) controls for 9 d at 4 °C in a simulated retail display. The AMBE had higher (P ≤ 0.05) contents of proanthocyanidins, and total phenols, flavonoids and tannins, and consequently demonstrated greater (P ≤ 0.05) in vitro antioxidant activity than AMLE. The polyphenolic extracts increased (P ≤ 0.05) antioxidant activity in beef patties compared to the CTL though they were outperformed (P ≤ 0.05) by the SMB. Fortification of beef patties with the polyphenolic extracts, particularly AMBE, delayed colour deterioration and oxidation of myoglobin during retail display relative to the CTL but were less efficient than SMB (P ≤ 0.05). Beef patties fortified with the polyphenolic extracts and SMB had comparable (P > 0.05) peroxide values, TBARS and p-Anisidine values which were all lower (P ≤ 0.05) than those for the CTL patties. The order of protein thiol content in beef patties was as follows: CTL ≥ AMLE ≥ AMBE ≥ SMB (P ≤ 0.05). Findings suggest that A. mearnsii-derived polyphenolic antioxidants, particularly AMBE has great potential to extend oxidative shelf-life of raw beef patties.

The Antioxidant and Anti-Inflammatory Activities of the Methanolic Extract, Fractions, and Isolated Compounds from Eriosema montanum Baker f. (Fabaceae)

Background: Inflammation is a natural body's defense mechanism against harmful stimuli such as pathogens, chemicals, or irradiation. But when the inflammatory response becomes permanent, it can lead to serious health problems. In the present study, the antioxidant and anti-inflammatory potentials of the Eriosema montanum methanolic extract (EMME), as well as its isolated fractions (FA-FJ) and compounds (1-7), were evaluated by using in vitro and cellular models. Methods: The total phenolic and flavonoid contents were determined using, respectively, Folin-Ciocalteu and aluminum chloride colorimetric methods, while 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), 2,2'-diphenyl-1-picrylhy-drazyl (DPPH), and ferric ion reducing antioxidant power (FRAP) were used to determine the antioxidant activity. Thin Layer Chromatography (TLC) and column chromatography (CC) were used to isolate and purify the compounds and their elucidation using their NMR spectroscopic data. Results:EMME had moderate antioxidant and anti-inflammatory activities, while fraction FF showed much higher efficacy with IC50 values of 34.64, 30.60, 16.43, and 77.29 μg/mL against DPPH, ABTS, NO, and 15-LOX inhibitory activities, respectively. The EMME fraction was found to be very rich in flavonoids and phenolic compounds, with 82.11 mgQE/g and 86.77 mgGAE/g of dry extract, respectively. Its LC-MS profiling allowed us to identify genistin (5) as the most concentrated constituent in this plant species, which was further isolated together with six other known compounds, namely, n-hexadecane (1), heptacosanoic acid (2), tricosan-1-ol (3), lupinalbin A (4), d-pinitol (6), and stigmasterol glucoside (7). Given these compounds, genistin (5) showed moderate activity against reactive oxygen species (ROS) and NO production in LPS-stimulated RAW264.7 cells compared to EMME, which suggested a synergy of (5) with other compounds. To the best of our knowledge, compounds (1), (2), and (3) were isolated for the first time from this plant species.

MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API). MolGraph also implements a module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. To validate the GNNs, benchmarking was conducted using the datasets from MoleculeNet, as well as three chromatographic retention time datasets. The benchmarking results demonstrate that the GNNs performed in line with expectations. Additionally, the GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data. MolGraph is available at https://github.com/akensert/molgraph . Installation, tutorials and implementation details can be found at  https://molgraph.readthedocs.io/en/latest/ .

Phenotypic characteristics of adipocyte-like cells generated from C2C12 myoblasts cultured with chicken serum

The aim of this study was to clarify the transcriptional and metabolic characteristics of C2C12 myoblasts cultured in Dulbecco's Modified Eagle Medium (DMEM) containing 20 % chicken serum (CHS) (C2C12-CHS cells) compared with C2C12 myoblasts cultured in DMEM containing 20 % fetal bovine serum (FBS) (C2C12-FBS cells). After 3 days of culture, C2C12-CHS cells showed a marked accumulation of lipid droplets, accompanied by increased expression levels of brown adipocyte-related genes (i.e., Bmp7, Prdm16, Ucp1, Cidea, Pgc1α, Cox7a1, Cox8, and β3-adorenoceptor). Furthermore, stimulation of β3-adorenoceptor by its selective agonist, mirabegron, increased the mRNA expression of Ucp1 and Pgc1α in C2C12-CHS cells. Wide-targeted metabolomic analysis performed by gas chromatography-tandem mass spectrometry revealed that the metabolic profile of C2C12-CHS cells was obviously different to that of C2C12-FBS cells. Additionally, the metabolomic analysis indicated that β3-adrenoceptor stimulation by mirabegron upregulated energy metabolism in C2C12-CHS cells as seen in brown adipocytes. These results suggest that C2C12-CHS cells may differentiate into brown adipocyte-like cells, accompanied by increased functional β3-adrenoceptor.

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.

Hypnea musciformis Seaweed Extract Protected Human Mesenchymal Stem Cells From Oxidative Stress Through NRF2 Activation

Previous studies have shown that Hypnea musciformis seaweed extracts (HMEs) possess antioxidant properties, but the molecular mechanisms accounting for this activity are not known. Thus, the present study investigated the molecular mechanisms through which HME exerted its antioxidant activity in human mesenchymal stem cells (WJ-MSCs). After the isolation of HME, its chemical composition was analyzed with gas chromatography mass spectrometry, indicating that it contained amino acids, organic acids, organic amides, sugar alcohols, saturated fatty acids, hydrogenated diterpene alcohols, and other organic compounds. Afterward, HME was shown in vitro to scavenge DPPH·, ABTS·+, ·OH, and O2 ·- radicals, possess reducing activity, and protect from ROO·-induced DNA strand breakage. Finally, the results showed that HME treatment of WJ-MSCs prevented H2O2-induced oxidative stress by decreasing lipid peroxidation, protein oxidation, reactive oxygen species levels, and DNA damage and by increasing glutathione levels. Moreover, our findings showed for the first time that HME's antioxidant activity in WJ-MSCs was mediated through the activation of NRF2, which upregulated the expression of the antioxidant proteins GCLC, GSR, HMOX1, SOD1, TXN, and GPX1. These results provide new insights into H. musciformis' antioxidant properties, which could help substantially its use as a food supplement or for developing biofunctional foods.

Comprehensive characterization of anthraquinones in Damnacanthus indicus using mass spectrometry molecular networking and metabolomics-based herb discrimination

Damnacanthus indicus is a widely used folk medicine in China, renowned for its various bioactivities. The key active components, anthraquinones, have not been comprehensively profiled due to their complex chemical nature. Establishing a high-throughput strategy to systematically characterize these anthraquinones is essential. Additionally, the cultivation of D. indicus across various provinces results in significant quality differences in the harvested herbs. Thus, developing an effective strategy to distinguish herbs from different regions and identify characteristic chemical markers for quality evaluation and control is crucial. In this study, a strategy based on ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to systematically characterize the chemical composition of D. indicus. Mass spectrometry molecular networking was utilized to rapidly recognize and identify anthraquinones. Principal component analysis (PCA) was applied to cluster the herbs from different habitats, while partial least square discriminant analysis (PLS-DA) was used to screen for chemical markers distinguishing herb origins. The result showed that a total of 112 anthraquinones and 66 non-anthraquinone compounds were identified in D. indicus. The biosynthetic pathways of anthraquinones in this herb were proposed. PCA grouped 15 batches of herbs from different origins into three clusters, corresponding to the climate types of their habitats. PLS-DA identified 27 significant chemical markers that could robustly distinguish the geographical origins of the herbs. This study provides a valuable reference for the quality evaluation and control of D. indicus and offers a scientific basis for the pharmacological research and rational utilization of these medicinal resources.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

Applying infectious clones and untargeted metabolite profiling to characterize citrus tristeza virus-induced stem pitting in citrus

Citrus tristeza virus (CTV) causes economically important stem pitting in sensitive citrus types however the exact mechanisms of stem pitting development in citrus remain unclear. In this study, CTV infectious clones were used to study stem pitting induction in 'Duncan' grapefruit and 'Mexican' lime. A panel of open reading frame (ORF) replacement clones was generated focusing on the CTV ORFs implicated in stem pitting development and pathogenicity, namely p33, p18, p13 and p23. ORF replacements from severe- and mild-pitting CTV isolates were introduced into a mild-pitting infectious clone (genotype T36) to determine if stem pitting could be induced. A broad range of stem pitting outcomes were observed with ORF p18 (from isolate T3-KB) and ORF p23 (from isolate GFMS12-1.3) associated with enhanced stem pitting development. Metabolomic trends underlying the different stem pitting outcomes were further assessed by untargeted metabolite profiling. In each citrus host, the metabolite profiling identified statistically significant compounds that differed between stem pitting groups. These compounds were mainly phenolic acids and phenolic glycosides and are known to function as antioxidant and stress-signaling molecules. These metabolites can serve as targets for future time-course observations to potentially use mass spectrometry profiling to inform CTV management practices.

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.

Regulation of ROS metabolism in macrophage via xanthine oxidase is associated with disease progression in pulmonary tuberculosis

BACKGROUND

Pulmonary tuberculosis (PTB) exacerbation can lead to respiratory failure, multi-organ failure, and symptoms related to central nervous system diseases. The purpose of this study is to screen biomarkers and metabolic pathways that can predict the progression of PTB, and to verify the role of the metabolic enzyme xanthine oxidase (XO) in the progression of PTB.

METHODS

To explore the biomarkers and mechanisms underlying the progression of PTB, plasma metabolomics sequencing was conducted on patients with severe PTB, non-severe PTB, and healthy individuals. Screening differential metabolites and metabolic pathways that can predict the progression of PTB, and verifying the function and mechanism of action of XO through experiments.

RESULTS

The purine metabolism, sphingolipid metabolism, and amino acid metabolism between the three groups differ. In patients with severe PTB, the levels of xanthosine and hypoxanthine are increased, while the levels of D-tryptophan, dihydroceramide and uric acid are decreased. Inhibition of XO activity has been observed to reduce the levels of tumor necrosis factor (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6), as well as to suppress the production of reactive oxygen species (ROS) and the activation of the NF-κB pathway, while also promoting the growth of MTB within cells.

CONCLUSION

D-tryptophan, xanthosine, and dihydroceramide can be utilized as biomarkers for progression of PTB, assisting in the evaluation of disease progression, and XO stands out as a potential therapeutic target for impeding the progression of PTB.

Complementarity of two-dimensional gas chromatography and two-dimensional liquid chromatography for the analysis of depolymerised lignin

Two-dimensional gas chromatography (GC × GC) and two-dimensional liquid chromatography (LC × LC) are nowadays widely used in academia and industry due to their high separation power. However, as far as we know, the complementarity of these two techniques has not yet been thoroughly studied based on the analysis of the same sample. Therefore, this was undertaken here by analysing the liquid fraction obtained after depolymerising a natural waste - lignin - with GC × GC and off-line comprehensive LC × SFC (SFC: supercritical fluid chromatography). Using complementary techniques is also important for lignin valorisation, as thorough structural characterisation of the depolymerised product can aid with developing and improving valorisation processes. For the tentative identification, NIST library was used for GC × GC-MS results and MS-DIAL together with SIRIUS for LC × SFC-MS/MS data. This allowed to study which compounds are detectable with the different 2D methods but also to discuss the limitations of the data analysis processes. The previous knowledge that LC techniques are more suitable than GC × GC for the analysis of larger oligomers and other low volatility compounds was confirmed; however, it was seen that GC × GC enabled the analysis of smaller compounds, such as aliphatic alcohols and saturated compounds. Overall, the study demonstrates the complementarity of the two techniques but also draws attention to the different detectable compound groups and classifications that the two techniques can provide.

Chemical Screening, Identification, and Comparison of Tripterygium Hypoglaucum Hutch Preparations by Ultra-High-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry Combined With Multivariate Statistical Analysis

Colquhounia root tablets (CRT) and Tripterygium hypoglaucum hutch tablets (THHT), two major Tripterygium hypoglaucum hutch (THH) commercial preparations, have been used to treat chronic kidney diseases or rheumatic diseases. However, there have been no reports on the chemical comparison between CRT and THHT, greatly hindering the understanding of their pharmacological difference as well as their rational application in clinical practice. In the present study, ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry combined with automated data analysis by MS-DIAL software and MS-FLO website was employed to systematically screen and characterize the components in CRT and THHT. Multivariate statistical analysis was used to compare the differences between these two preparations. As a result, up to 92 components were tentatively identified, and 17 of them were characterized for the first time in THH preparations. According to the criteria of variable importance in projection (VIP) >1, p < 0.05, and fold change (FC) > 1.2, 46 components could be screened as major differential chemical components. Among them, phenolic acids, organic acids, amino acids, and diterpenoids were higher in CRT, while the sesquiterpene alkaloids were relatively higher in THHT. This study clarified the chemical material basis and the difference between CRT and THHT, providing a valuable reference for quality control and clinical rational use of THH preparations.

A matrix-centered view of mass spectrometry platform innovation for volatilome research

Volatile organic compounds (VOCs) are carbon-containing molecules with high vapor pressure and low water solubility that are released from biotic and abiotic matrices. Because they are in the gaseous phase, these compounds tend to remain undetected when using conventional metabolomic profiling methods. Despite this omission, efforts to profile VOCs can provide useful information related to metabolic status and identify potential signaling pathways or toxicological impacts in natural or engineered environments. Over the past several decades mass spectrometry (MS) platform innovation has instigated new opportunities for VOC detection from previously intractable matrices. In parallel, volatilome research linking VOC profiles to other forms of multi-omic information (DNA, RNA, protein, and other metabolites) has gained prominence in resolving genotype/phenotype relationships at different levels of biological organization. This review explores both on-line and off-line methods used in VOC profiling with MS from different matrices. On-line methods involve direct sample injection into the MS platform without any prior compound separation, while off-line methods involve chromatographic separation prior to sample injection and analyte detection. Attention is given to the technical evolution of platforms needed for increasingly resolved VOC profiles, tracing technical progress over time with particular emphasis on emerging microbiome and diagnostic applications.

Dual column chromatography combined with high-resolution mass spectrometry improves coverage of non-targeted analysis of plant root exudates

BACKGROUND

Within the plant kingdom, there is an exceptional amount of chemical diversity that has yet to be annotated. It is for this reason that non-targeted analysis is of interest for those working in novel natural products. To increase the number and diversity of compounds observable in root exudate extracts, several workflows which differ at three key stages were compared: 1) sample extraction, 2) chromatography, and 3) data preprocessing.

RESULTS

Plants were grown in Hoagland's solution for two weeks, and exudates were initially extracted with water, followed by a 24-h regeneration period with subsequent extraction using methanol. Utilizing the second extraction showed improved results with less ion suppression and reduced retention time shifting compared to the first extraction. A single column method, utilizing a pentafluorophenyl column, paired with high-resolution mass spectrometry ionized and correctly identified 34 mock root exudate compounds, while the dual column method, incorporating a pentafluorophenyl column and a porous graphitic carbon column, retained and identified 43 compounds. In a pooled quality control sample of exudate extracts, the single column method detected 1,444 compounds. While the dual method detected fewer compounds overall (1,050), it revealed a larger number of small polar compounds. Three preprocessing methods (targeted, proprietary, and open source) successfully identified 43, 31, and 38 mock root exudate compounds to confidence level 1, respectively.

SIGNIFICANCE

Enhancing signal strength and analytical method stability involves removing the high ionic strength nutrient solution before sampling root exudate extracts. Despite signal intensity loss, a dual column method enhances compound coverage, particularly for small polar metabolites. Open-source software proves a viable alternative for non-targeted analysis, even surpassing proprietary software in peak picking.

MSBERT: Embedding Tandem Mass Spectra into Chemically Rational Space by Mask Learning and Contrastive Learning

Tandem mass spectrometry (MS/MS) is a powerful technique for chemical analysis in many areas of science. The vast MS/MS spectral data generated in liquid chromatography-mass spectrometry (LC-MS) experiments require efficient analysis and interpretation methods for the following compound identification. In this study, we propose MSBERT based on self-supervised learning strategies to embed MS/MS spectra into reasonable embeddings for efficient compound identification. It adopts the transformer encoder as the backbone for mask learning and uses the same spectra with different masks for contrastive learning. MSBERT is trained on the GNPS data set and tested on the GNPS data set, the MoNA data set, and the MTBLS1572 data set. It exhibits enhanced library matching and analogous compound searching capabilities compared to existing methods. The recalls at 1, 5, and 10 on a GNPS test subset with structures not in the training set are 0.7871, 0.8950, and 0.9080, respectively. The results are better than those of Spec2Vec with 0.6898, 0.8276, and 0.8620, and DreaMS with 0.7158, 0.8327, and 0.8635. The rationality of embeddings is demonstrated by t-SNE visualization, structural similarity, spectra clustering, compound identification, and analogous compound searching. A user-friendly web server is provided for efficient spectral analysis, and the source code for MSBERT is available at https://github.com/zhanghailiangcsu/MSBERT.

Dynamic alterations and ecological implications of rice rhizosphere bacterial communities induced by an insect-transmitted reovirus across space and time

BACKGROUND

Cereal diseases caused by insect-transmitted viruses are challenging to forecast and control because of their intermittent outbreak patterns, which are usually attributed to increased population densities of vector insects due to cereal crop rotations and indiscriminate use of pesticides, and lack of resistance in commercial varieties. Root microbiomes are known to significantly affect plant health, but there are significant knowledge gaps concerning epidemics of cereal virus diseases at the microbiome-wide scale under a variety of environmental and biological factors.

RESULTS

Here, we characterize the diversity and composition of rice (Oryza sativa) root-associated bacterial communities after infection by an insect-transmitted reovirus, rice black-streaked dwarf virus (RBSDV, genus Fijivirus, family Spinareoviridae), by sequencing the bacterial 16S rRNA gene amplified fragments from 1240 samples collected at a consecutive 3-year field experiment. The disease incidences gradually decreased from 2017 to 2019 in both Langfang (LF) and Kaifeng (KF). BRSDV infection significantly impacted the bacterial community in the rice rhizosphere, but this effect was highly susceptible to both the rice-intrinsic and external conditions. A greater correlation between the bacterial community in the rice rhizosphere and those in the root endosphere was found after virus infection, implying a potential relationship between the rice-intrinsic conditions and the rhizosphere bacterial community. The discrepant metabolites in rhizosphere soil were strongly and significantly correlated with the variation of rhizosphere bacterial communities. Glycerophosphates, amino acids, steroid esters, and triterpenoids were the metabolites most closely associated with the bacterial communities, and they mainly linked to the taxa of Proteobacteria, especially Rhodocyclaceae, Burkholderiaceae, and Xanthomonadales. In addition, the greenhouse pot experiments demonstrated that bulk soil microbiota significantly influenced the rhizosphere and endosphere communities and also regulated the RBSDV-mediated variation of rhizosphere bacterial communities.

CONCLUSIONS

Overall, this study reveals unprecedented spatiotemporal dynamics in rhizosphere bacterial communities triggered by RBSDV infection with potential implications for disease intermittent outbreaks. The finding has promising implications for future studies exploring virus-mediated plant-microbiome interactions. Video Abstract.

Identification of three unexpected new psychoactive substances at an Australian drug checking service

Drug checking is a harm reduction measure that provides people with the opportunity to confirm the identity and purity of substances before consumption. The CanTEST Health and Drug Checking Service is Australia's first fixed-site drug checking service, where clients can learn about the contents of the samples they provide while receiving tailored harm reduction and health advice. Three samples were recently presented to the service with the expectation of 4-fluoromethylphenidate (4F-MPH) 1, methoxetamine (MXE) 2 and 3-methylmethcathinone (3-MMC) 3. The identity of all three samples did not meet these expectations and remained unknown on-site, as no high confidence identifications were obtained. However, further analysis by nuclear magnetic resonance spectroscopy, high resolution gas chromatography-electron ionisation-mass spectrometry and liquid chromatography-electrospray ionisation-mass spectrometry at the nearby Australian National University allowed for the structure elucidation of the three samples as 4-fluoro-α-pyrrolidinoisohexanophenone (4F-α-PiHP) 4, 1-(4-fluorobenzyl)-4-methylpiperazine (4F-MBZP) 5 and N-propyl-1,2-diphenylethylamine (propylphenidine) 6, respectively. Given all three samples were not of the expected identity and have not yet been described as new psychoactive substances in the literature, this study presents a full characterisation of each compound. As exemplified by this rapid identification of three unexpected new psychoactive substances, drug checking can be used as an effective method to monitor the unregulated drug market.

Coordinated metabolic adaptation of Arabidopsis thaliana to high light

In plants, exposure to high light irradiation induces various stress responses, which entail complex metabolic rearrangements. To explore these dynamics, we conducted time‐course experiments spanning 2 min to 72 h with Arabidopsis thaliana under high and control light. Comparative metabolomics, transcriptomics, redox proteomics, and stable isotope labeling on leaf rosettes identified a series of synchronous and successive responses that provide a deeper insight into well‐orchestrated mechanisms contributing to high‐light acclimation. We observed transient transcriptome downregulation related to light harvesting and electron flow before the profound remodeling of the photosynthetic apparatus. Throughout the entire time course, redox homeostasis is tightly balanced between downregulation of production and enhanced transformation of NADPH accompanied by redistribution of reducing equivalents across several subcellular compartments. In both light conditions, C4 acids such as malate and fumarate are produced via anaplerosis. In carbon units, their accumulation in vacuoles surpasses plastidic levels of starch and intensifies notably under high light. In parallel, citrate synthesis from pyruvate is significantly hindered diurnally. Isotopic labeling in 2‐oxoglutarate and glutamate suggests a moderate de novo synthesis of C5 acids from a vacuolar citrate reservoir during the light phase while they are largely renewed during the night. In the absence of a diurnal clockwise flow through the tricarboxylic acid (TCA) cycle, increased oxidation of photorespiratory glycine takes over as a source of reductants to fuel mitochondrial ATP production. These findings, along with previous research, contribute to a model integrating redox balance and linking increased carbon assimilation and nitrogen metabolism, especially in the context of an incomplete TCA cycle.

HPLC/ESI-QTOF-MS/MS based untargeted metabolomics authentication of Taxus × media six tissues

INTRODUCTION

Taxus media (Taxus × media Rehder) is renowned for its high paclitaxel content, serving as a major source for industrial paclitaxel production. In addition to paclitaxel, T. media contains a diverse range of metabolites, including flavonoids, alkaloids, and terpenoids, which have been shown to possess antioxidant, antibacterial, anti-inflammatory, and immunomodulatory effects. However, these compounds have not been thoroughly studied as key metabolites in T. media.

OBJECTIVE

The untargeted metabolomics analysis of six T. media tissues provides new insights into the development and utilization of T. media metabolites.

METHOD

The extracts from six tissues of T. media were analyzed and subjected to analysis using high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS/MS) and chemometric techniques.

RESULTS

Using a reliable HPLC-Q-TOF-MS/MS method, we identified 312 compounds in six T. media tissues, including 214 previously unreported in T. media. To identify characteristic compounds across different tissues, 34 metabolites were further screened. KEGG metabolic pathway analysis revealed that these compounds primarily occur in the metabolic pathways of terpene glycosides, flavans, and O-methylated flavonoids.

CONCLUSION

This study initially utilized an HPLC-QTOF-MS/MS-based metabolomics approach to assess the metabolites in different tissues of T. media, providing a basis for their utilization and management.

Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut's quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study

A significant hurdle in untargeted lipid/metabolomics research lies in the absence of reliable, cross-validated spectral libraries, leading to a considerable portion of LC-MS features being labeled as unknowns. Despite continuous advancement in annotation tools and libraries, it is important to safeguard, publish and share acquired data through public repositories. Embracing this trend of data sharing not only promotes efficient resource utilization but also paves the way for future repurposing and in-depth analysis; ultimately advancing our comprehension of Covid-19 and other diseases. In this work, we generated an extensive MS-dataset of 39 Covid-19 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a thorough insight in the lipidome and metabolome, ensuring a repurposable dataset.

Cytotoxic properties, glycolytic effects and high-resolution respirometry mitochondrial activities of Eriocephalus racemosus against MDA-MB 231 triple-negative breast cancer

INTRODUCTION

Triple-negative breast cancer (TNBC) represents a significant global health crisis due to its resistance to conventional therapies and lack of specific molecular targets. This study explored the potential of Eriocephalus racemosus (E. racemosus) as an alternative treatment for TNBC. The cytotoxic properties and high-resolution respirometry mitochondrial activities of E. racemosus against the MDA-MB 231 TNBC cell line were evaluated.

METHODS

Hexane solvent and bioactive fraction extractions of E. racemosus were performed, while mass spectrometry-based metabolite profiling was used to identify the phytochemical constituents of the extracts. The extracts were further tested against MDA-MB 231 TNBC cells to determine their cytotoxicity. The mode of cell death was determined using flow cytometry. The activities of caspases 3, 8, and 9 were assessed using a multiplex activity assay kit. Glycolytic activity and High-resolution respirometry measurements of mitochondrial function in the MDA-MB 231 cell line were conducted using the Seahorse XFp and Oroboros O2K.

RESULTS

Metabolite profiling of E. racemosus plant crude extracts identified the presence of coumarins, flavonoids, sesquiterpenoids, triterpenoids, and unknown compounds. The extracts demonstrated promising cytotoxic activities, with a half maximal inhibitory concentration (IC50) of 12.84 µg/mL for the crude hexane extract and 15.49 µg/mL for the bioactive fraction. Further, the crude hexane and bioactive fraction extracts induced apoptosis in the MDA-MB-231 TNBC cells, like the reference drug cisplatin (17.44%, 17.26% and 20.25%, respectively) compared to untreated cells. Caspase 3 activities confirmed the induction of apoptosis in both cisplatin and the plant crude extracts, while caspase 8 and 9 activities confirmed the activation of both the intrinsic and extrinsic apoptosis pathways. Increased levels of glycolytic activity were observed in the hexane crude extract. High-resolution respiratory measurements showed elevated mitochondrial activities in all mitochondrial states except for complex-IV activity.

CONCLUSION

These findings support further exploration of E. racemosus as a potential therapeutic agent for TNBC, offering a promising avenue for the development of targeted treatments with minimal adverse effects.

Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics

Capillary electrophoresis coupled with tandem mass spectrometry (CE-MS/MS) offers advantages in peak capacity and sensitivity for metabolic profiling owing to the electroosmotic flow-based separation. However, the utilization of data-independent MS/MS acquisition (DIA) is restricted due to the absence of an optimal procedure for analytical chemistry and its related informatics framework. We assessed the mass spectral quality using two DIA techniques, namely, all-ion fragmentation (AIF) and variable DIA (vDIA), to isolate 60-800 Da precursor ions with respect to annotation rates. Our findings indicate that vDIA, coupled with the updated MS-DIAL chromatogram deconvolution algorithm, yields higher spectral matching scores and annotation rates compared to AIF. Additionally, we evaluated a linear migration time (MT) correction method using internal standards to accurately align chromatographic peaks in a data set. Postcorrection, the data set exhibited less than 0.1 min MT drifts, a difference mostly equivalent to that of conventional reverse-phase liquid chromatography techniques. Moreover, we conducted MT prediction for metabolites recorded in mass spectral libraries and metabolite structure databases containing a total of 469,870 compounds, achieving an accuracy of less than 1.5 min root mean squares. Our platform provides a peak annotation platform utilizing MT information, accurate precursor m/z, and the MS/MS spectrum recommended by the metabolomics standards initiative. Applying this procedure, we investigated metabolic alterations in lipopolysaccharide (LPS)-induced macrophages, characterizing 170 metabolites. Furthermore, we assigned metabolite information to unannotated peaks using an in silico structure elucidation tool, MS-FINDER. The results were integrated into the nodes in the molecular spectrum network based on the MS/MS similarity score. Consequently, we identified significantly altered metabolites in the LPS-administration group, where glycinamide ribonucleotide, not present in any spectral libraries, was newly characterized. Additionally, we retrieved metabolites of false-negative hits during the initial spectral annotation procedure. Overall, our study underscores the potential of CE-MS/MS with DIA and computational mass spectrometry techniques for metabolic profiling.

Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a "bioactivity-signature-toxicant" strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

Investigating the Phytochemical Composition, Antioxidant, and Anti-Inflammatory Potentials of Cassinopsis ilicifolia (Hochst.) Kuntze Extract against Some Oxidative Stress and Inflammation Molecular Markers

Oxidative stress is a key factor that activates several transcription factors and mediators involved in the inflammatory pathways responsible for the pathogenesis of many chronic diseases. Targeting the expression of these mediators represents a promising approach to preventing these diseases. Cassinopsis ilicifolia leaf infusion is traditionally used for treating conditions such as inflammation and pain relief. Thus, the present study assessed the antioxidant and anti-inflammatory activities of the hydroethanolic leaf extract of C. ilicifolia using in vitro and cell-based assays. As a result, C. ilicifolia extract exhibited the highest DPPH• and ABTS•+ radical scavenging potential. At the same time, it weakly scavenged the Fe3+-TPTZ radical up to 200 µg/mL, thus suggesting a different antioxidant mechanism triggered during each assay. Additionally, C. ilicifolia extract inhibited NO production and 15-LOX activity with IC50 values of 21.10 µg/mL and 40.28 µg/mL, respectively. Further, C. ilicifolia extract was found to strongly inhibit ROS production in LPS-activated RAW 264.7 cells, and the study of its mechanism of action showed that it exerts its anti-inflammatory effect by downregulating the expression of inflammatory mediators such as IL-1β, TNF-α, and COX-2. Overall, C. ilicifolia extract showed consistent potency in all assays, and the analysis of its phytochemical profile led to the identification of 30 compounds, among which the most abundant were secologanic acid (1), chlorogenic acid (3CQA) (2), monotropein (3), chlorogenic acid (5CQA) (4), geniposidic acid (5), rutin (6), quercetin 3-galactoside (7), astragalin-7-rhamnoside (8), and minecoside (9) that are possibly responsible for its anti-inflammatory and antioxidant activities. Therefore, our findings suggested the potential use of C. ilicifolia as an alternative source for developing plant-based products against oxidative stress and inflammation-related conditions.

Toxicity of Moxifloxacin on the Growth, Photosynthesis, Antioxidant System, and Metabolism of Microcystis aeruginosa at Different Phosphorus Levels

Moxifloxacin (MOX), a widely used novel antibiotic, may pose ecological risks at its actual environmental concentrations, as has been detected in aquatic systems. However, its ecotoxicity to aquatic organisms and regulatory mechanisms of phosphorus in eutrophic aqueous environments are still limited. This study aimed to analyze its physiological and biochemical parameters, including cellular growth, chlorophyll fluorescence, photosynthetic pigments, oxidative stress biomarkers, and metabolomics to elucidate the toxicity induced by environmental concentrations of MOX in Microcystis aeruginosa at different phosphorus levels. The results revealed that the EC50 values of MOX on M. aeruginosa at different phosphorus concentrations were 8.03, 7.84, and 6.91 μg/L, respectively, indicating MOX toxicity was exacerbated with increasing phosphorus levels. High phosphorus intensified the suppression of chlorophyll fluorescence and photosynthetic pigments, while activating the antioxidant enzyme, indicating severe peroxidation damage. Metabolomic analysis showed MOX induced different discriminating metabolites under different phosphorus levels, and perturbed more biological pathways at higher phosphorus concentrations, such as starch and sucrose metabolism, pyrimidine metabolism, and glycerolipid metabolism. This indicates that phosphorus plays an important role in regulating metabolism in M. aeruginosa exposed to MOX. The findings provide valuable information on the mechanisms involved in cyanobacteria responses to antibiotic stress, and offer a theoretical basis for accurately assessing antibiotic toxicity in eutrophic aqueous environments.

Infestation of Rice Striped Stem Borer (Chilo suppressalis) Larvae Induces Emission of Volatile Organic Compounds in Rice and Repels Female Adult Oviposition

Plants regulate the biosynthesis and emission of metabolic compounds to manage herbivorous stresses. In this study, as a destructive pest, the pre-infestation of rice striped stem borer (SSB, Chilo suppressalis) larvae on rice (Oryza sativa) reduced the subsequent SSB female adult oviposition preference. Widely targeted volatilomics and transcriptome sequencing were used to identify released volatile metabolic profiles and differentially expressed genes in SSB-infested and uninfested rice plants. SSB infestation significantly altered the accumulation of 71 volatile organic compounds (VOCs), including 13 terpenoids. A total of 7897 significantly differentially expressed genes were identified, and genes involved in the terpenoid and phenylpropanoid metabolic pathways were highly enriched. Correlation analysis revealed that DEGs in terpenoid metabolism-related pathways were likely involved in the regulation of VOC biosynthesis in SSB-infested rice plants. Furthermore, two terpenoids, (-)-carvone and cedrol, were selected to analyse the behaviour of SSB and predators. Y-tube olfactometer tests demonstrated that both (-)-carvone and cedrol could repel SSB adults at higher concentrations; (-)-carvone could simultaneously attract the natural enemies of SSB, Cotesia chilonis and Trichogramma japonicum, and cedrol could only attract T. japonicum at lower concentrations. These findings provide a better understanding of the response of rice plants to SSB and contribute to the development of new strategies to control herbivorous pests.

Metabolites profiling and cheminformatics bioprospection of selected medicinal plants against the main protease and RNA-dependent RNA polymerase of SARS-CoV-2

Despite the existence of some vaccines, SARS-CoV-2 (S-2) infections persist for various reasons relating to vaccine reluctance, rapid mutation rate, and an absence of specific treatments targeted to the infection. Due to their availability, low cost and low toxicity, research into potentially repurposing phytometabolites as therapeutic alternatives has gained attention. Therefore, this study explored the antiviral potential of metabolites of some medicinal plants [Spondias mombin, Macaranga barteri and Dicerocaryum eriocarpum (Sesame plant)] identified using liquid chromatography-mass spectrometry (LCMS) as possible inhibitory agents against the S-2 main protease (S-2 MP) and RNA-dependent RNA polymerase (RP) using computational approaches. Molecular docking was used to identify the compounds with the best affinities for the selected therapeutics targets. Afterwards, compounds with poor physicochemical characteristics, pharmacokinetics, and drug-likeness were screened out. The top-ranked compounds were further subjected to a 120-ns molecular dynamics (MD) simulation. Only quercetin 3-O-rhamnoside (-48.77 kcal/mol) had higher binding free energy than the reference standard (zafirlukast) (-44.99 kcal/mol) against S-2 MP. Conversely, all the top-ranked compounds (ellagic acid hexoside, spiraeoside, apigenin-4'-glucoside and chrysoeriol 7-glucuronide) except gnetin L (-24.24 kcal/mol) had higher binding free energy (-55.19 kcal/mol, -52.75 kcal/mol, -47.22 kcal/mol and -43.35 kcal/mol) respectively, against S-2 RP relative to the reference standard (-34.79 kcal/mol). The MD simulations study further revealed that the investigated inhibitors are thermodynamically stable and form structurally compatible complexes that impede the regular operation of the respective S-2 therapeutic targets. Although, these S-2 therapeutic candidates are promising, further in vitro and in vivo evaluation is required and highly recommended.Communicated by Ramaswamy H. Sarma.