bims-mebolo Biomed News
on Metabolomics
Issue of 2026–05–31
forty-four papers selected by
Daniel Méndez Rodríguez, Vbi-Ugent
  1. Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry.
  2. MS2DECIDE: Harnessing the complementarity of mass spectrometry annotation tools for prioritizing novel natural products.
  3. Assessing the metabolomics "dark matter" by a detectable khipu model.
  4. Bioactivity-guided metabolomics reveals discriminant cytotoxic signatures in Siparuna guianensis.
  5. An LC-MS untargeted metabolomic comparison between three blood microsampling devices, whole blood, and plasma.
  6. Geographic Influence on Secondary Metabolite Profiles in Leaves of the Endemic Agathosma betulina (P.J. Bergius) Pillans. in the Western Cape Province, South Africa.
  7. A decision-driven framework for the mass spectrometry analysis of previously uncharacterized protein modifications.
  8. High-Throughput LC-MS/MS Quantification of Eighteen Cannabinoids in Hemp Flowers with Baseline Separation of Structural Isomers.
  9. MSIDAT: an automated platform for improved metabolite annotation in mass spectrometry imaging via mass shift evaluation and customized databases.
  10. A time-efficient cyclic ion mobility spectrometry-mass spectrometry method for the separation and detection of ergot alkaloids.
  11. UPLC-Q-TOF-MS/MS-linked bioactivity-guided elucidation of tissue-specific chemical diversity in Tamarix austromongolica.
  12. Identification of biomarkers for organic milk authenticity using combined untargeted and targeted metabolomics.
  13. Temperature-associated metabolic responses of Aspergillus cristatus revealed by untargeted LC-MS metabolomics.
  14. Comparative analysis of the dental plaque metabolome in tooth stain and caries within primary dentition.
  15. Identification of salivary lipid biomarkers for noninvasive diagnosis of reflux esophagitis via UHPLC-MS-based lipidomics.
  16. From Metabolomics to Function: Ranking Plant Stem Cell Metabolomes for Use in Health and Cosmetics.
  17. Serum metabolomic profiling reveals LysoPC/PC depletion as a potential biomarker to detect avian reoviral infection in neonatal broiler chickens.
  18. Chemical Components and Hypouricemic Activity Monitoring of Astragali radix-Huaier During Fermentation Processing Using High-Resolution Mass Spectrometry Combined with Untargeted Metabolomics.
  19. Integration of multidimensional data acquisition and data processing strategy for comprehensive characterization of chemical constituents in Qingre Sanjie capsule by UPLC-TWIMS-QTOF-MS and molecular networking.
  20. Untargeted metabolomic dataset of leaves from twenty-four accessions of wild and cultivated tomato plants.
  21. MS-MINT: An Open-Source Data Analysis Software for Large-Scale Metabolomics Studies.
  22. Chronic Nitrous Oxide Exposure Disrupts Metabolism in Mice: A Plasma Untargeted Metabolomics Study.
  23. Characterization of Metabolite and Lipid Compositions in Lycopene-Enriched Egg Yolk Using Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry.
  24. A Novel HILIC-UPLC-DAD-MS/MS Method for the Analysis of Mycosporine-Like Amino Acids and Their Quantification in Diverse Algae.
  25. Low-Temperature HILIC Provides Enhanced Separations and Stability for LC-MS-Based Metabolomics.
  26. Dataset on the differential metabolite composition of ripe and green fruit coats of Solanum mauritianum.
  27. Chemical Profiling and Scaffold-Based Drug-Discovery Analysis of Bioactive Compounds from Ceratonia siliqua L. with Computational and Biological Validation.
  28. Untargeted metabolomics of mature sorghum [Sorghum bicolor (L.) Moench] grain reveals metabolites associated with antimicrobial activity against Clostridium perfringens.
  29. Integrated metabolomics and transcriptomics analysis of flavonoids in different parts of Houttuynia cordata.
  30. Analysis of Chemical Components in Tangerine Peel Flower Tea by Ultra-Performance Liquid Chromatography-Q-Exactive Orbitrap Tandem Mass Spectrometry and High-Performance Liquid Chromatography-Photodiode Array.
  31. An Untargeted Liquid Chromatography Mass Spectrometry Method for Identifying and Quantifying Saponins in Pulses.
  32. Metabolomics analysis reveals kernel quality variation among main yellowhorn (Xanthoceras sorbifolium Bunge) cultivars.
  33. A lipidomics roadmap: from basic research to societal challenges.
  34. Recent developments in analytical techniques for monitoring anabolic-androgenic steroids in biological and environmental matrices: challenges and future opportunities.
  35. Untargeted Metabolomics Reveals Region-Specific Metabolic Signatures and Discriminative Markers in Goji Berry (Lycium barbarum L.).
  36. Physicochemical Characterization and Metabolites Identification of the Synthetic Cannabinoid MDMB-5'Br-PINACA Using In Silico and In Vitro Approaches.
  37. Widely targeted metabolomics reveals organ-specific metabolite accumulation and functional differentiation of leaves, pulp and seeds in wild jujube (Ziziphus jujuba var. spinosa).
  38. Development of a Sensitive UPLC-MS/MS Method for the Simultaneous Quantification of Mycotoxins in Wheat Products and Human Urine.
  39. Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.
  40. A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.
  41. Seasonal and Organ-Specific Variations of Alkaloids in Buxus obtusifolia (Mildbr.) Hutch: A Multivariate LC/MS Study.
  42. Integrating untargeted metabolomics and deep learning approaches to identify specific metabolic signatures and new mechanisms in unstable plaques.
  43. Activity-guided substructure prioritization accelerates discovery of gut microbiota-derived immune-regulating metabolites.
  44. AnnoMe: user-defined classification of HR-MS/MS spectra for natural product discovery.
  1. bioRxiv. 2026 May 11. pii: 2026.05.06.723373. [Epub ahead of print]
    Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry.
    Xianghu Wang, Gwendolyn Kiler, Daniela Herrera-Rosero, Mohammed Reza Shahneh, Michael Strobel, Christian Geibel, Yasin El Abiead, Vanessa V Phelan, Daniel Petras, Mingxun Wang.
      Tandem mass spectrometry (MS/MS) fragments molecules into smaller pieces, generating spectra composed of m/z values and intensities that encode structural information for molecular annotation. With increasing mass spectrometry data acquisition speeds, manual annotation from MS/MS lags far behind data generation and remains a bottleneck in metabolite annotation. Current computational methods, such as molecular networking, address this challenge by organizing similar structures into families of related compounds. However, they generally provide only similarity scores, offering weak actionable insights for structural annotation. To address this limitation, we present the Molecular Transformation Graph Edit Measure (MT-GEM), a distance metric that quantifies discrete structural transformations between molecules through graph edge removals that approximate structural modifications. Building on this metric, we developed an ensemble machine learning architecture, the Spectrum Transformation Edit Predictor (STEP), that builds upon TransExION and DREAMS to predict MT-GEM distances from MS/MS spectra. STEP achieves an average precision of 48.4% for identifying single structural transformations between MS/MS pairs, representing more than a tenfold improvement over state-of-the-art similarity metrics, including spectral entropy similarity (3.8%) and modified cosine (2.5%). On experimental human gut microbial community data, STEP identifies 3 times more single-transformation metabolite pairs than feature-based molecular networking at equivalent precision. In a discovery application, STEP highlights one drug metabolite and two new natural product analogs missed by modified cosine in feature-based molecular networking. By providing discrete transformation predictions rather than continuous similarity scores, MT-GEM and STEP enable hypothesis-driven metabolite annotation with testable structural modifications, which we envision will accelerate discovery of new molecules from MS/MS metabolomics datasets.
    DOI:  https://doi.org/10.64898/2026.05.06.723373
  2. Methods Enzymol. 2026 ;pii: S0076-6879(26)00075-3. [Epub ahead of print]730 169-176
    MS2DECIDE: Harnessing the complementarity of mass spectrometry annotation tools for prioritizing novel natural products.
    Yassine Mejri, Olivier Cailloux, Pierre Le Pogam, Meltem Öztürk-Escoffier, Mehdi A Beniddir.
      Natural products (NPs) are valuable sources for drug development, as they are often associated with potent bioactivities. Therefore, approaches that enable the rapid discovery of novel NPs by mining their presence in crude extracts metabolomics data while providing insights into their novelty are highly sought after by chemists. Mass spectrometry-based NPs targeted discovery often relies on a complicated decision-making process involving tedious comparison of exact masses data and tandem mass spectra-based annotation tools output against various databases. To address this bottleneck, we developed MS2DECIDE, for which a tutorial workflow for the targeted discovery of novel NPs is outlined in this chapter.
    Keywords:  Decision-making; Dereplication; Mass spectrometry; Multiannotation; Natural products; Prioritization
    DOI:  https://doi.org/10.1016/bs.mie.2026.03.002
  3. Metabolomics. 2026 May 24. pii: 79. [Epub ahead of print]22(3):
    Assessing the metabolomics "dark matter" by a detectable khipu model.
    Yuanye Chi, Joshua M Mitchell, Shujian Zheng, Maheshwor Thapa, Shuzhao Li.
      Background There is no consensus on how to interpret the large number of unknown features in untargeted metabolomics, which are sometimes referred as the "dark matter". Are these features real compounds or artifacts? Understanding this problem is critical to the annotation and interpretation of metabolomics data and future development of the field. Methods We propose a "detectable khipu" model here, to show that compounds exhibit ion group patterns that depend on their abundance. We apply this model to a systematic analysis of 61 representative public datasets from blood LC-MS metabolomics, the most common data type in biomedical studies. Results The results indicate that majority of abundant features have identifiable ion patterns, and in-source fragments contribute to less than 10% of features. Each dataset detects 1 ~ 2,000 high confidence compounds, over half of which are unknown. Conclusion The major knowledge gap in LC-MS metabolomics is therefore not the methods of grouping ions or counting fragments, but the identification of unknown compounds.
    DOI:  https://doi.org/10.1007/s11306-026-02456-y
  4. Metabolomics. 2026 May 29. pii: 88. [Epub ahead of print]22(3):
    Bioactivity-guided metabolomics reveals discriminant cytotoxic signatures in Siparuna guianensis.
    Salva Asghar, Giovanni B Bevilaqua, Gracimerio J Guarneire, Samira C C Ludgero, Sandra B R Castro, Alessandra P Carli, Caio César S Alves, Boniek Gontijo, Danielle F Dias, Marisi G Soares, Nerilson M Lima.
       INTRODUCTION: Natural products remain a privileged source of structurally diverse bioactive compounds with potential for the development of safer and more selective anticancer agents.
    OBJECTIVES: In this study, a bioactivity-guided untargeted metabolomics approach was applied to investigate the cytotoxic chemical space of Siparuna guianensis.
    METHODS: The hydroethanolic leaf extract and solvent-partitioned fractions (hexane, ethyl acetate, butanol, and aqueous) were evaluated for cytotoxic activity against MCF-7, 4T1, and MDA-MB-231 breast cancer cell lines, followed by metabolomic profiling using HPLC-HRMS.
    RESULTS: Cytotoxicity was predominantly associated with low- and intermediate-polarity fractions, which were classified as active and subsequently compared with inactive samples using chemometric methods. Structural annotation supported by spectral libraries enabled MSI level 2-3 annotation of 60 metabolites. Alkaloids and flavonoids were proportionally enriched in cytotoxic fractions despite the overall dominance of terpenoids. Multivariate and univariate statistical analyses demonstrated clear metabolic discrimination between active and inactive groups. Integration of VIP scores, Volcano analysis, and ROC-based prioritization identified isoquinoline and aporphine alkaloids, together with glycosylated flavonoids, as the principal contributors to cytotoxicity. The alkaloid norglaucine emerged as a key discriminant feature in the VIP-Volcano intersection, consistent with its previously reported cytotoxic activity against multiple tumor cell lines. Consensus discriminant ions included m/z 330.170 and 296.12, both showing high discriminative potential.
    CONCLUSIONS: This study suggests a strong association between metabolomic composition and cytotoxic activity in S. guianensis, highlighting isoquinoline-derived scaffolds as promising candidates for future isolation and mechanistic investigation while demonstrating the power of metabolomics-guided strategies for natural product discovery.
    Keywords:  Aporphine alkaloids; Bioactivity-guided fractionation; Cancer; High-resolution mass spectrometry (HRMS); Untargeted metabolomics
    DOI:  https://doi.org/10.1007/s11306-026-02461-1
  5. Metabolomics. 2026 May 29. pii: 89. [Epub ahead of print]22(3):
    An LC-MS untargeted metabolomic comparison between three blood microsampling devices, whole blood, and plasma.
    Dennisse Avella, Soile Turunen, Glykeria Avgerinou, Anatoli Petridou, Vassilis Mougios, Iman Zarei, Seppo Auriola, Kati Hanhineva, Olli Kärkkäinen.
       INTRODUCTION: Blood microsampling (BµS) devices collect less than 100 µL of blood, offering a less invasive and more cost-effective alternative to venipuncture. However, its metabolomic comparability to conventional samples remains unclear, and standardized BµS metabolomic workflows are lacking.
    OBJECTIVES: This study evaluated the impact of using three BµS devices (Mitra®, Capitainer®, and Whatman™ 903) on the metabolomic interpretation of human biomonitoring samples. We compared them to conventional samples (plasma and whole blood) and evaluated the interplay of different analytical conditions.
    METHODS: Venous blood from 10 adults (5 males, 5 females) was sampled onto the three devices. First, three agitation conditions (ultrasound, shaker, and homogenizer) were evaluated at three blood concentrations (1.5%, 5.5%, and 11%). The optimized method was then used to compare the metabolite profiles between BµS devices, whole blood, and plasma. Reverse-phase and hydrophilic-interaction chromatography, in positive and negative ionization modes, were combined for liquid chromatography-mass spectrometry (LC-MS) analysis.
    RESULTS: All agitation conditions and concentrations proved suitable for BµS untargeted metabolomics. Combining different analytical modes and fragmentation ranges proved helpful for maximizing metabolite coverage. BµS-derived metabolite profiles aligned more closely with whole blood than plasma. Some metabolites were more characteristic of a sample type, whereas others were common across sample types. All sample types enabled sex-based differentiation, with metabolites such as amino acids, lipids, and acylcarnitines driving the separation.
    CONCLUSIONS: These findings enhance our understanding of BµS metabolite coverage and highlight its potential in human biomonitoring. The choice of device depends on the application and the metabolites of interest, offering flexibility for clinical use and research.
    Keywords:  Blood microsampling (BµS); Capitainer® ; Dried blood spots (DBS); Liquid chromatography–mass spectrometry (LC-MS); Metabolomics; Volumetric absorptive microsampling (VAMS)
    DOI:  https://doi.org/10.1007/s11306-026-02424-6
  6. Int J Mol Sci. 2026 May 16. pii: 4486. [Epub ahead of print]27(10):
    Geographic Influence on Secondary Metabolite Profiles in Leaves of the Endemic Agathosma betulina (P.J. Bergius) Pillans. in the Western Cape Province, South Africa.
    Nompumelelo H Mnisi, Rotondwa P Gunununu, Luvolwethu Dukashe, Manaka J Makgato, Azwimbavhi R Mulidzi, Callistus Bvenura, Ngwatshipane M Mashabela.
      The role of medicinal plants in primary healthcare and livelihoods around the world is both ancient and well-documented. Agathosma betulina (P.J. Bergius) Pillans, commonly known as 'buchu', has long been utilised in traditional medicine as a household remedy for various ailments and is also valued for its essential oils in the cosmetics and pharmaceutical industries. This study aimed to profile and quantify the secondary metabolites in buchu using ultra-performance liquid chromatography quadrupole time-of-flight combined with mass spectrometry (UPLC-QTOF-MS) techniques, whereby plant material from three distinct locations in the Western Cape Province, Groot Winterhoek, Citrusdal, and Cederberg, was collected. A total of 32 maker compounds were identified from buchu leaves. The results revealed a significant location-dependent variation in the accumulation of multiple classes of phytochemicals, including phenolic acids, flavonoids, saponins, terpenoids, oligosaccharides, vitamins, and steroids. Citrusdal samples had the most bioactive compounds compared to the Cederberg and Groot Winterhoek. Citrusdal had the highest flavonoid levels, while Cederberg samples were the richest in phenolic acids and Groot Winterhoek was dominant in iridoid glycoside levels. Principal component analysis (PCA) revealed distinct clusters corresponding to the three different regions, confirming chemical differences. Elucidating the distribution of secondary metabolites in this species may provide new information for possible medicinal and pharmacological uses, such as the creation of novel and enhanced organic medications and food products. These results will aid in selecting a buchu chemotype with optimal attributes for the intended therapeutic application, helping to protect wild populations from over-exploitation through cultivation.
    Keywords:  Agathosma betulina; flavonoids; medicinal plant; phenolic acids; secondary metabolites
    DOI:  https://doi.org/10.3390/ijms27104486
  7. STAR Protoc. 2026 May 28. pii: S2666-1667(26)00220-0. [Epub ahead of print]7(2): 104567
    A decision-driven framework for the mass spectrometry analysis of previously uncharacterized protein modifications.
    Yiying Zhu.
      Identification of unknown protein modifications remains challenging when the modification chemistry or site is not defined in advance. Conventional workflows often rely on predefined modification lists or enrichment strategies that assume prior knowledge of modification type and may therefore bias discovery toward annotated post-translational modifications (PTMs). This primer outlines a decision-driven analytical framework for investigating previously uncharacterized modifications using bottom-up (liquid chromatography-tandem mass spectrometry) LC-MS/MS that emphasizes chemistry-informed hypothesis generation, iterative refinement of candidate modification search space, integration of experimental controls, and targeted data interpretation. Rather than presenting a single prescriptive workflow, the guide highlights key decision points in experimental design, acquisition strategy, and database search configuration that influence confident identification and residue-level localization. The framework is broadly applicable to drug-induced covalent adducts, chemically introduced modifications, as well as endogenous modifications arising across diverse experimental and biological contexts.
    Keywords:  Chemistry; Mass Spectrometry; Proteomics
    DOI:  https://doi.org/10.1016/j.xpro.2026.104567
  8. Molecules. 2026 May 16. pii: 1684. [Epub ahead of print]31(10):
    High-Throughput LC-MS/MS Quantification of Eighteen Cannabinoids in Hemp Flowers with Baseline Separation of Structural Isomers.
    Na Liu, Maggie Schoener, Naima Jannath Rimi, Md Imon Hossain, Supraja Regunathan, Robert Powers, Liguo Song.
      Following the passage of the Agriculture Improvement Act of 2018, demand for accurate cannabinoid quantification in hemp flowers has increased to ensure regulatory compliance. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a triple-quadrupole mass spectrometer provides high sensitivity and selectivity and is well suited for this purpose; however, a review of the literature indicates that many published LC-MS/MS methods target only a limited number of cannabinoids, and reliable differentiation of structural isomers remains challenging. In this study, an LC-MS/MS method was developed for the simultaneous quantification of eighteen cannabinoids in hemp flowers. Baseline chromatographic separation of structural isomers enabled reliable differentiation of compounds with highly similar fragmentation patterns and allowed the use of the most sensitive multiple reaction monitoring (MRM) transitions for quantification. Both positive and negative ionization modes were employed to achieve optimal sensitivity using dynamic polarity switching within a single analytical run. Following validation in accordance with ISO/IEC 17025, the method was applied to a proficiency test hemp sample and six commercial hemp samples, demonstrating excellent time efficiency (11 min for 18 cannabinoids) and an exceptionally wide calibration range (8-5000 ng/mL, corresponding to 0.032-20% (w/w) for all cannabinoids).
    Keywords:  MRM; cannabinoids; hemp flowers; liquid chromatography; quantification; triple-quadrupole mass spectrometer
    DOI:  https://doi.org/10.3390/molecules31101684
  9. Anal Bioanal Chem. 2026 May 27.
    MSIDAT: an automated platform for improved metabolite annotation in mass spectrometry imaging via mass shift evaluation and customized databases.
    Ying Zhu, Qianyu Wang, Haimei Zhao, Hanchuan Guo, Jian Zhong, Xuezhi Li, Bin Wu, Songlin Yu, Ling Qiu.
      Spatially resolved metabolomics based on mass spectrometry imaging (MSI) enables in situ characterization of tissue-specific metabolic functions by mapping the spatial distribution of metabolites. However, accurate metabolite annotation and automated analysis of large-scale MSI data remain challenging, mainly due to mass-to-charge ratio (m/z) shifts and dependence on generic databases. To address these challenges, we developed the MSI Data Analysis Tool (MSIDAT), an automated and user-friendly MSI data processing platform. By integrating liquid chromatography-tandem mass spectrometry (LC-MS/MS)-assisted metabolite identification, customized metabolite ion databases can be constructed to improve the specificity and reliability of metabolite annotation. In addition, m/z shifts in MSI data were systematically evaluated using endogenous reference ions by calculating the relative mass error between theoretical and measured m/z values, enabling adaptive mass tolerance correction. Based on this strategy, mass error-informed metabolite matching and putative annotation were achieved. Furthermore, MSIDAT provides flexible parameter settings, modular workflows, and open-source accessibility, facilitating efficient and reproducible MSI data analysis. The performance of the platform was demonstrated in a clinical cohort of rectal cancer patients, in which hundreds of metabolites were putatively annotated and spatial alterations in tumor-associated metabolites were observed, suggesting fatty acid-related metabolic alterations. Overall, this study presents a robust and versatile analytical platform for improving metabolite annotation in MSI, thereby enhancing data mining efficiency and supporting spatial metabolomics-driven biomarker discovery and clinical applications.
    Keywords:   m/z shift evaluation; Data processing tool; Mass spectrometry imaging; Metabolite database; Spatially resolved metabolomics
    DOI:  https://doi.org/10.1007/s00216-026-06576-1
  10. Anal Chim Acta. 2026 Aug 01. pii: S0003-2670(26)00510-6. [Epub ahead of print]1409 345560
    A time-efficient cyclic ion mobility spectrometry-mass spectrometry method for the separation and detection of ergot alkaloids.
    Laura Carbonell-Rozas, Ane Arrizabalaga-Larranaga, Laura Righetti.
       BACKGROUND: The diversity of ergot alkaloids (EAs) isomers poses significant challenges for traditional analytical methodologies. By adding an orthogonal rapid gas-phase separation step, Ion mobility mass spectrometry (IM-MS) enables faster workflows and higher throughput without sacrificing data quality. However, its application in this context remains limited. We hypothesize that high resolution IM-MS can shorten chromatographic analysis times for natural compounds while maintaining or even enhancing separation performance.
    RESULTS: In this study, we developed a high-throughput LC-cyclic IMS (cIMS) methodology for the analysis of 28 EAs including 9 isomeric pairs, which comprises epimers, conformational isomers and constitutional isomers, within 4 min by leveraging the additional dimension of separation provided by the cyclic ion mobility with high-resolution mass spectrometry. A 60% reduction in analysis time has been shown using LC-cIM-MS compared to LC-MS while keeping selectivity of the method. Ion mobility was particularly effective for the separation of small (<300 Da) and more polar conformational and constitutional isomers with peak-to-peak resolution, outperforming chromatographic separation.
    SIGNIFICANCE: These results highlight the importance of complementary separation dimensions for the analysis of complex mixtures of natural compounds. The method developed in study allows to separate currently regulated EAs epimers within 4-min LC-cIM-MS run.
    Keywords:  CCS; Ergot alkaloids; Food safety; cyclic ion mobility
    DOI:  https://doi.org/10.1016/j.aca.2026.345560
  11. J Chromatogr A. 2026 May 27. pii: S0021-9673(26)00470-X. [Epub ahead of print]1783 467141
    UPLC-Q-TOF-MS/MS-linked bioactivity-guided elucidation of tissue-specific chemical diversity in Tamarix austromongolica.
    Jianjin Guo, Zhou Xu, Jing Gao, Yan Guo, Chi-Tang Ho, Naisheng Bai.
      Tamarix austromongolica (TA), an endemic salt-tolerant tree species in northwestern China, remains largely unexplored in terms of its phytochemical composition. In this study, an untargeted metabolomics approach based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS), combined with multivariate statistical analysis, was employed for the first time to systematically characterize the metabolite profiles of six TA parts, namely roots, barks, stems, flowers, seeds, and leaves. A total of 105 metabolites belonging to 20 structural classes were identified, among which flavonoids accounted for 33% of the total metabolite pool. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) revealed significant tissue-specific distribution patterns of metabolomes across the six parts, with flavonoids identified as the core chemical markers driving inter-tissue differentiation. Parallel in vitro anti-inflammatory screening of extracts from the six parts demonstrated that the leaf extract (TAL) possessed the highest safety margin and potent NO inhibitory activity. Network pharmacology and molecular docking analyses indicated that three major flavonoids in TAL-Tricin, Quercetin-3-O-β-d-glucuronide (Q3GA), and Diosmetin-could form high-affinity complexes with MAPK3, a key target in the PI3K-Akt/NF-κB signaling pathway, and molecular dynamics simulations further validated the binding stability of these complexes. This study provides systematic analytical method support and a scientific basis for the phytochemical characterization, bioactive part screening, and quality marker discovery of TA.
    Keywords:  Anti-inflammatory activity; Flavonoids; Molecular docking; Multivariate statistical analysis; Tamarix austromongolica; UPLC-Q-TOF-MS/MS; Untargeted metabolomics
    DOI:  https://doi.org/10.1016/j.chroma.2026.467141
  12. Food Res Int. 2026 Aug 31. pii: S0963-9969(26)01142-7. [Epub ahead of print]238 119459
    Identification of biomarkers for organic milk authenticity using combined untargeted and targeted metabolomics.
    Min Kang, Ran Suo, Jianfeng Sun.
      Organic milk, particularly desert organic milk, commands a market premium that drives economically motivated adulteration. Current authentication lacks mechanistically validated biomarkers. Here, UHPLC-Q-Exactive Orbitrap-based untargeted metabolomics combined with chemometrics and Random Forest analysis was used to compare traditional milk with milk produced under traditional organic and desert organic systems. Fifteen differential metabolites were prioritized, and organic milk showed higher abundances of dehydroascorbic acid, ascorbic acid, fumarate, 2-oxoglutarate, adenine, adenosine, pyrimidine, 5'-S-methyl-5'-thioadenosine, and glycolic acid than traditional milk. Metabolic pathway analysis revealed significant enrichment of six metabolic pathways, including purine and one‑carbon metabolism, reflecting the modulation of the core bovine metabolic network by organic feeding practices. This shift manifested as pronounced nucleoside enrichment. Consequently, adenosine was established as a robust biomarker, unambiguously confirmed via mass spectrometry fragmentation patterns. Targeted quantification by UHPLC-Q-Exactive Orbitrap revealed a definitive gradient: traditional milk < desert organic milk < traditional organic milk. Crucially, mixture models demonstrated that a 50% traditional milk addition drops adenosine entirely below all organic baselines, explicitly including desert organic milk. This mechanistic, quantitative framework provides a high-fidelity tool to safeguard premium dairy supply chains.
    Keywords:  Adenosine; Authenticity; Holstein; Metabolomics
    DOI:  https://doi.org/10.1016/j.foodres.2026.119459
  13. World J Microbiol Biotechnol. 2026 May 27. pii: 304. [Epub ahead of print]42(6):
    Temperature-associated metabolic responses of Aspergillus cristatus revealed by untargeted LC-MS metabolomics.
    Lingqing Lu, Rongrong Zhang, Wenli Jie, Hang Zhou, Yi Yang, Yunxue He, Mingzheng Ren, Lihong Zhou.
      Temperature is a key environmental factor affecting fungal growth, reproduction, and metabolism. In this study, the industrially important fungus Aspergillus cristatus was investigated by integrating physiological phenotyping with liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics to systematically characterize its morphological traits, antioxidant activity, and untargeted metabolic profiles at 20 °C, 25 °C, 30 °C, and 35 °C. The results showed that approximately 30 °C was the most favorable temperature for the growth of Aspergillus cristatus. Metabolomic analysis indicated that Aspergillus cristatus exhibited a pattern of metabolic redirection under different temperature conditions, reflecting a multilayered and coordinately changing metabolic mode associated with temperature adaptation. At low temperatures (20 °C and 25 °C), unsaturated lysophospholipids, such as PC O-18:2, accumulated substantially, suggesting their involvement in membrane adaptation, whereas high temperature was associated with changes in specific phospholipid molecules. In terms of oxidative stress defense, flavonoid- and phenolic-related metabolites showed more prominent changes under low and optimal temperatures, whereas glutathione-related metabolites increased significantly at high temperature, indicating a greater reliance on glutathione-associated antioxidant defense. Regarding energy and osmotic balance, reserve carbohydrates accumulated at the optimal temperature, consistent with coordination between growth and stress resistance, whereas high-temperature stress was associated with a shift toward sugar alcohol accumulation and a metabolic pattern consistent with metabolic deceleration. This study presents the metabolic changes of Aspergillus cristatus under different temperature conditions from a systems-level metabolic perspective and suggests that Aspergillus cristatus may adapt to environmental change by coordinating growth-related metabolism with stress defense-related metabolism. In addition, these findings may provide a reference for future studies on fermentation regulation and stress resistance in Aspergillus cristatus.
    Keywords:   Aspergillus cristatus ; Adaptation strategy; Antioxidant defense; Metabolic remodeling; Temperature stress
    DOI:  https://doi.org/10.1007/s11274-026-05019-4
  14. BMC Oral Health. 2026 May 29.
    Comparative analysis of the dental plaque metabolome in tooth stain and caries within primary dentition.
    Li Zhang, Ying Chen, Jing Xie, Guicong Ding, Guo-Bing Wang, Dali Zheng, Youguang Lu.
       BACKGROUND: Dental black tooth stain (BTS) has been epidemiologically associated with a lower prevalence of dental caries, yet the underlying metabolic mechanisms remain poorly understood. This study aimed to characterize the metabolic profiles of dental plaque to explore the association between tooth stain and low caries risk.
    METHODS: Dental plaque samples were collected from three groups of children: caries-free with BTS (CF-BTS), caries-free without BTS (CF), and severe early childhood caries without BTS (SECC). Metabolomic profiling was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Multivariate statistical analyses, including PCA and OPLS-DA, were employed to identify differential metabolites (DMs). Metabolic pathway enrichment and metabolite source tracing were conducted using KEGG and MetOrigin databases.
    RESULTS: PCA showed excellent analytical stability, and OPLS-DA revealed distinct metabolomic profiles among groups. A total of 1,069 molecular features were detected, with 658 metabolites identified. Lipids and lipid-like molecules (39.97%) constituted the most abundant class. Venn analysis identified core differential metabolites, including irganox 259 (up-regulated in CF-BTS), Δ17-6-keto-PGF1α (up-regulated in CF-BTS), and 6-phosphogluconic acid (down-regulated in CF-BTS). MetOrigin analysis classified metabolites into host (n = 19), microbial (n = 98), and co-metabolic (n = 173) sources. KEGG enrichment indicated significant alterations in ABC transporters, pyrimidine metabolism, and neuroactive ligand-receptor interactions in BTS-associated plaque.
    CONCLUSION: Dental plaque associated with BTS exhibits a distinct metabolomic signature characterized by enhanced antioxidant capacity. These findings provide critical functional insights into the caries-protective mechanism of BTS and identify promising metabolic targets for the development of novel anti-caries strategies.
    Keywords:  Black tooth stain; Dental caries; Dental plaque; Metabolomics
    DOI:  https://doi.org/10.1186/s12903-026-08401-8
  15. Metabolomics. 2026 May 24. pii: 78. [Epub ahead of print]22(3):
    Identification of salivary lipid biomarkers for noninvasive diagnosis of reflux esophagitis via UHPLC-MS-based lipidomics.
    Ying Wang, Ying Zhou, Jian Gao, Lin Zhang, Jing Liu, Lele Zhang, Tongcan Cui, Jingran Zhang, Xiangqin Ou, Xiumei Gao, Lifeng Han, Hai Li.
       INTRODUCTION: Reflux esophagitis (RE) is a common upper gastrointestinal disorder, and its diagnosis currently relies primarily on invasive endoscopic examination. The lack of reliable non-invasive biomarkers substantially limits early detection and large-scale screening. Saliva represents a promising biofluid for metabolomics research, as it can reflect metabolic alterations associated with upper gastrointestinal pathology.
    OBJECTIVES: This study aimed to identify potential salivary lipid biomarkers associated with RE, and to develop a non-invasive diagnostic model using metabolomics and lipidomics.
    METHODS: Saliva samples from patients clinically diagnosed with RE and healthy controls were analyzed. The analysis included a discovery cohort (n = 144) and an independent validation cohort (n = 146). Differential metabolites were screened using the untargeted metabolomics approach of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS), and then quantitative verification was conducted using targeted lipidomics. Multivariate statistical analysis, random forest algorithms, and receiver operating characteristic (ROC) analysis were applied.
    RESULTS: Untargeted metabolomics revealed significant metabolic differences between RE patients and healthy controls, with marked enrichment of sphingolipid and glycerophospholipid metabolism. Targeted lipidomics identified six consistently dysregulated salivary lipids: DAG (18:1_18:2), S-1-P, PE (P-16:0_18:1), DAG (16:0_18:2), DAG (18:1_18:1), and DAG (16:0_18:1). A multimetabolite model based on these lipids effectively distinguished RE patients from healthy controls, achieving an AUC of 99.45% in the discovery cohort and 97.17% in the validation cohort.
    CONCLUSION: This study identified a salivary lipid signature associated with RE and supports the potential of this lipidomic approach as a non-invasive method to distinguish RE from healthy controls.
    Keywords:  Biomarkers; Lipidomics; Metabolomics; Reflux esophagitis; Saliva
    DOI:  https://doi.org/10.1007/s11306-026-02435-3
  16. Biomolecules. 2026 May 07. pii: 695. [Epub ahead of print]16(5):
    From Metabolomics to Function: Ranking Plant Stem Cell Metabolomes for Use in Health and Cosmetics.
    Assaf Zemach, Mikhail R Plaza, Bong Seop Lee, Leo Little Dog, Efrain Santiago-Rodriguez, Dior Simmons, Melissa Palomares, Dodanim Talavera-Adame, Nathan Newman.
      Plants produce diverse metabolites with potential benefits for human health. However, the metabolomes of plant callus cultures-cell cultures analogous to stem cells-remain poorly characterized in terms of their functional relevance. This study aimed to systematically profile and functionally annotate metabolites from diverse plant callus cultures to better understand their potential biological activities and applications. We profiled the metabolomes of six plant calli: Acacia concinna (Shikakai), Daucus carota (carrot), Hibiscus sabdariffa (hibiscus), Linum usitatissimum (flax), Ocimum sanctum (tulsi), and the Nicotiana tabacum Bright-Yellow 2 (BY-2) cell line. To facilitate functional interpretation, we developed Metabolite2Function (M2F), a pipeline that annotates metabolites with biological functions using scientific literature and large language modeling. Untargeted metabolomics identified 177 metabolites, revealing clustering patterns independent of genetic relationships, culture age, or growth rate. Tulsi and carrot calli exhibited enrichment in metabolites relative to the tobacco reference line, whereas flax and hibiscus were comparatively depleted. Most metabolites varied across at least four calli, and 10% were unique to a single species. Using M2F, we annotated 87 metabolites with beneficial activities, including antioxidant, anti-glycation, anti-inflammatory, and anti-senescence functions, as well as skin-related effects such as collagen production and brightening. Notably, antioxidant and anti-senescence metabolite levels correlated with corresponding biological activities in human cells. Plant callus cultures generate distinct and functionally diverse bioactive metabolomes. M2F provides a scalable framework for systematic functional annotation relevant to human health and cosmetic applications.
    Keywords:  GPT; anti-glycation; anti-senescence; artificial intelligence; large language models; metabolite ontology; metabolomics; plant callus; plant stem cells
    DOI:  https://doi.org/10.3390/biom16050695
  17. Front Cell Infect Microbiol. 2026 ;16 1750590
    Serum metabolomic profiling reveals LysoPC/PC depletion as a potential biomarker to detect avian reoviral infection in neonatal broiler chickens.
    Asha Ranaraja, Iresha Subhasinghe, Noor Ahmad Shaik, Babajan Banaganapalli, Shelly Popowich, Lisanework E Ayalew, Rupasri Mandal, David S Wishart, Suresh Tikoo, Susantha Gomis.
       Introduction: Avian reovirus (ARV) infection causes viral arthritis in broiler chickens, leading to lameness and substantial economic loss in poultry production. Current conventional diagnostic methods detect infection only after clinical signs appear, limiting the ability to prevent disease progression. Hence, this study utilized serum metabolomics to identify early biomarkers of ARV infection prior to tissue damage.
    Methods: A total of 150 serum samples were collected from ARV-challenged broilers at 24, 48, and 72 h post-infection, along with uninfected controls. The samples underwent liquid chromatography-tandem mass spectrometry (LC-MS/ MS) profiling, followed by preprocessing through standardization, log2 transformation, and batch correction. Differential metabolite expressions were assessed using limma, and pathway enrichment was performed by using hypergeometric testing. Logistic regression (LR) and receiver operating characteristic (AUC) analyses identified metabolites that were consistently altered across all timepoints.
    Results: Among 587 metabolites detected, 371, 96, and 402 were differentially expressed at 24, 48, and 72 h, respectively. Our findings have shown that lipid biomarkers like lysophosphatidylcholine (LysoPC) a C14:0, LysoPC a C20:4, and phosphatidylcholine (PC) aa C38:5 were consistently decreased and demonstrated strong single-analyte discrimination, with LysoPC a C14:0 showing nearperfect specificity at 24/72 h (AUC ≈ 1.00). Conversely, the levels of non-lipids like N-acetylputrescine (NAP) and 2-hydroxyglutarate (2-HG) were elevated, exhibiting high AUC (≈0.97-1.00) as sensitive early-screening markers. The pathway analysis revealed significant alterations in arginine-proline, branched-chain amino acid, glycine/serine/threonine, and one-carbon/folate metabolism.
    Discussion: These findings demonstrated that ARV infection induced early metabolic reprogramming within 24 h, with lipid remodeling and polyamine catabolism identified as key early biomarkers. Serum metabolomics thus provides a rapid, non-invasive tool for the early detection and surveillance of ARV infection in poultry flocks.
    Keywords:  LysoPC/PC; avian reovirus; broiler chicken; serum metabolomics; viral pathogenesis
    DOI:  https://doi.org/10.3389/fcimb.2026.1750590
  18. Foods. 2026 May 15. pii: 1758. [Epub ahead of print]15(10):
    Chemical Components and Hypouricemic Activity Monitoring of Astragali radix-Huaier During Fermentation Processing Using High-Resolution Mass Spectrometry Combined with Untargeted Metabolomics.
    Zhicheng Yin, Jie Li, Shuyi Song, Hong Wang, Tianmei Niu, Xiaojie Wang, Shengqian Sun, Jiayu Zhang.
      Recent evidence highlights the therapeutic potential of Astragali radix-Huaier fermentation products for hyperuricemia treatment, although the dynamics of the fermentation process remain poorly understood. This study employed high-resolution mass spectrometry and untargeted metabolomics for real-time monitoring of chemical components and hypouricemic activity throughout fermentation. The results revealed significant alterations in the chemical composition, with distinct sample separations observed on days 7, 14, 21, and 28. A total of 33 differential components were identified, including 20 flavonoids and 13 saponins, eight of which showed notable changes. Polysaccharides and saponins were found to correlate positively with the uric acid-lowering effect. On day 21, the levels of total polysaccharides and cycloastragenol-6-glucoside, a saponin derivative, peaked, coinciding with the highest hypouricemic activity of the Astragalus fungal fermentation products. This study provides the first evidence of dynamic changes in the chemical profile and pharmacological activity of Astragali radix-Huaier during fermentation, paving the way for optimizing fermentation processes and developing medicinal and dietary products based on Astragali radix.
    Keywords:  Astragali radix-Huaier fermentation; bidirectional solid fermentation; lowering uric acid; metabolite identification; metabolomics
    DOI:  https://doi.org/10.3390/foods15101758
  19. J Pharm Biomed Anal. 2026 May 21. pii: S0731-7085(26)00245-1. [Epub ahead of print]279 117577
    Integration of multidimensional data acquisition and data processing strategy for comprehensive characterization of chemical constituents in Qingre Sanjie capsule by UPLC-TWIMS-QTOF-MS and molecular networking.
    Shiyu Zhang, Siyi Ma, Xi Chen, Rong Hu, Jingru Zhang, Yunyu Zhao, Xike Xu, Weidong Zhang, Ji Ye.
      Molecular networking (MN) is widely utilized in the compositional analysis of herbal medicines and complex formulations. While the technique effectively links compounds through shared MS2 fragmentation patterns and structural homology, it is often compromised by dispersed in-source fragment ions and adduct ions, which weaken spectral correlations and introduce redundant nodes. To address these limitations, we propose a novel integrated strategy combining Ion Identity Molecular Networking (IIMN) and Feature-Based Molecular Networking (FBMN), aiming to reduce interference from co-eluted ions and to enhance isomers discrimination, respectively. Evaluation based on precursor ion node counts, edge connectivity, and cluster topology confirmed IIMN and FBMN as complementary methods for identifying non-isomers and isomers, respectively. Importantly, IIMN significantly strengthens network connectivity, reduces data redundancy, and improves annotation reliability across diverse ion species. To demonstrate the applicability of this strategy, we employed Qingre Sanjie Capsule (QSC), a traditional Chinese medicine prescription for treating upper respiratory tract infections and allergic conjunctivitis, as a case study and performed systematic chemical identification using ultra-high performance liquid chromatography coupled with traveling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-TWIMS-QTOF-MS). Two hybrid scan modes, Data-Dependent Acquisition (DDA) and high-definition DDA, were utilized to acquire both precursor and fragment ion data. A total of 143 compounds were unambiguously identified or tentatively characterized, including three novel constituents first reported. This strategy effectively minimizes interference from redundant nodes, increases the capacity for novel compound discovery, and provides a valuable approach for the systematic analysis of complex systems.
    Keywords:  Collision cross section; Feature-based molecular networking; Ion identity molecular networking; Mass spectrometry; Qingre Sanjie Capsule; Traditional Chinese medicine
    DOI:  https://doi.org/10.1016/j.jpba.2026.117577
  20. Data Brief. 2026 Jun;66 112828
    Untargeted metabolomic dataset of leaves from twenty-four accessions of wild and cultivated tomato plants.
    Komla Exonam Amegan, Florent Magot, Alan Kergunteuil, Bernard Caromel, Anne-Violette Lavoir, Romain Larbat.
      Tomato (Solanum lycopersicum var. lycopersicum), one of the most important crops worldwide, has a complex domestication history that began in Latin America, region hosting also fourteen wild relative species and subspecies. Domestication and subsequent breeding efforts have led to the development of the modern cultivated tomato, prized for its agronomic performance and economic value. However, this process also resulted in a substantial erosion of genetic and metabolic diversity, potentially limiting the plant's adaptive capacity and resilience to environmental stresses. Previous comparative studies between domesticated tomato cultivars and wild relative species have underscored the evolutionary shifts in various plant traits associated with biotic stress. Yet, most of these studies relied on a limited number of wild accessions, reflecting a general tendency to underestimate their genetic and metabolic diversity. In this study, we sought to characterize both intra- and inter-specific metabolic diversity in tomato and its wild relatives. Using Liquid Chromatography High Resolution Mass Spectrometry (LC-HRMS) analysis, we profiled the chemical composition of hydro-methanolic leaf extracts from twenty-four accessions representing five Solanum species and subspecies, each with distinct natural histories and domestication levels. This dataset provides a comprehensive overview of leaf metabolic diversity across cultivated and wild tomato species, offering insights into the evolutionary and ecological forces shaping specialized metabolism within the tomato clade. It is available at https://doi.org/10.57745/QM0BOR.
    Keywords:  Interspecific and intraspecific diversity; Metabolite; Solanaceae
    DOI:  https://doi.org/10.1016/j.dib.2026.112828
  21. Anal Chem. 2026 May 26.
    MS-MINT: An Open-Source Data Analysis Software for Large-Scale Metabolomics Studies.
    Mario E Valdés-Tresanco, Mario S Valdés-Tresanco, Soren Wacker, Nicholas I Brodie, Luis F Ponce, Raied Aburashed, Alikhan Mansuri, Ryan A Groves, Annegret Ulke-Lemée, Ian A Lewis.
      Metabolomics has emerged as a mainstream approach for investigating the complex metabolic underpinnings of living systems, and over recent years, it has increasingly been applied to large cohort studies that tax the limits of existing computational tools. Most existing metabolomics software tools are effective at analyzing small data sets but exhibit a number of shortcomings that limit their utility when applied to large studies: they store entire data sets in memory, they use batch-dependent fitting algorithms, and they do not use concrete metrics for peak fitting, which not only results in inconsistent peak-picking results across samples but also complicates the documentation of data analyses. To address this, we developed the mass-spectrometry metabolomics integrator (MS-MINT), a Python application for processing, analyzing, and visualizing large liquid chromatography-mass spectrometry (LC-MS) data sets. To enable reproducible large-scale data processing, MS-MINT uses a region of interest (ROI)-based approach to extract data. We illustrate the function of this new tool by analyzing metabolites present in the media of a large data set (3334 files) of Staphylococcus aureus cultures. We show that MS-MINT accurately reproduces data generated from other software tools in a fraction of the time. In summary, MS-MINT offers a purpose-built software platform to support large-scale metabolomics data analyses. MS-MINT software is freely available at https://www.lewisresearchgroup.org/software.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01083
  22. Metabolites. 2026 May 13. pii: 324. [Epub ahead of print]16(5):
    Chronic Nitrous Oxide Exposure Disrupts Metabolism in Mice: A Plasma Untargeted Metabolomics Study.
    Juan Jia, Fenglin Zhang, Wen Zhang, Congying Liu, Keming Yun, Yujin Wang, Jiangwei Yan.
       BACKGROUND: Nitrous oxide (N2O) is increasingly used as a recreational drug, leading to neurological and systemic toxicities. However, due to the rapid elimination and minimal alteration of nitrogen oxides, the short direct detection window complicates the assessment of N2O exposure.
    METHOD: In this study, we investigated the effects of chronic N2O exposure on plasma metabolites using an untargeted metabolomics approach in a mouse model. C57BL/6 mice were exposed to 90,000 ppm N2O (1 h, twice daily for 28 days) or room air. Plasma samples were analyzed via UHPLC -Triple TOF -MS. Orthogonal partial least squares discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) curves were used to identify differential metabolites.
    RESULT: A total of 35 differential metabolites were identified. Eight metabolites with an area under the curve (AUC) > 0.90 were selected as candidate biomarkers, including up-regulated SOPC and PC(16:0/16:0) (suggesting disrupted phospholipid remodeling and membrane integrity), and down-regulated DL-tryptophan, creatine, ectoine, indole, His-Ser, and Ile-Pro. Pathway enrichment analysis revealed significant alterations in glycine, serine and threonine metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; protein digestion and absorption; and tryptophan metabolism.
    CONCLUSIONS: Our data indicate that chronic N2O exposure disrupts multiple amino acid-related metabolic pathways (e.g., tryptophan-kynurenine pathway) and phospholipid homeostasis. The identified metabolite changes, along with vitamin B12, homocysteine, and methylmalonic acid, may constitute a specific metabolic fingerprint for N2O exposure. These findings help reveal the intrinsic mechanistic links underlying metabolic disorders induced by N2O exposure.
    Keywords:  biomarkers; metabolic pathways; nitrous oxide (N2O); toxicity; untargeted metabolomics
    DOI:  https://doi.org/10.3390/metabo16050324
  23. Foods. 2026 May 09. pii: 1653. [Epub ahead of print]15(10):
    Characterization of Metabolite and Lipid Compositions in Lycopene-Enriched Egg Yolk Using Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry.
    Xianyu You, Jishi Wang, Zeying He, Xiaoxue Yu, Xin Zhao, Liuan Li, Chaoqi Ren.
      This study established an untargeted metabolomic approach based on ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to investigate differences in metabolites and lipid composition of lycopene-enriched egg yolk at different enrichment levels and conventional egg yolks. Principal component analysis and hierarchical clustering revealed clear unsupervised discrimination and separation among the control group and two treatment groups. Metabolomic analysis identified 14 differential metabolites, including amino acids, which were associated with 13 metabolic pathways such as cysteine and methionine metabolism. Lipidomic analysis revealed 48 significantly altered lipids, including phospholipids and glycerides. The results demonstrated that lycopene supplementation significantly altered the metabolic and lipid profiles of egg yolks. Specifically, lycopene enrichment upregulated phospholipid synthesis and increased the levels of antioxidant-related metabolites. This study confirms that untargeted metabolomics and lipidomics can effectively identify potential biomarkers in egg yolks with varying lycopene enrichment levels, offering new insights and a scientific basis for nutritional research and metabolic mechanism analysis of functional eggs.
    Keywords:  UPLC-QTOF-MS; lipidomics; lycopene-enriched egg yolk; untargeted metabolomics
    DOI:  https://doi.org/10.3390/foods15101653
  24. Electrophoresis. 2026 May 23.
    A Novel HILIC-UPLC-DAD-MS/MS Method for the Analysis of Mycosporine-Like Amino Acids and Their Quantification in Diverse Algae.
    Armin Oberosler, Anastasiia Fedorova, Ignacio Zegri, Fabian Hammerle, Michael Zwerger, Markus Ganzera.
      Mycosporine-like amino acids (MAAs) are photoprotective compounds primarily produced by marine organisms, especially red algae. Chemically, MAAs and their precursors are low-molecular-weight natural products that absorb ultraviolet radiation in the range of 270-360 nm without generating free radicals. Owing to these properties, MAAs have attracted considerable interest for potential pharmaceutical and cosmetic applications as natural sunscreen agents. In this study, the first hydrophilic interaction liquid chromatography-ultra-performance liquid chromatography (HILIC-UPLC) method, coupled with both diode array detector (DAD) and tandem mass spectrometry (MS/MS), was developed for the determination of 11 MAAs and 2 MAA precursors in various algal species. As column, a YMC-Triart Diol-HILIC (particle size: 1.9 µm) was used, and the separation of the MAAs realized in under 5 min. The validation of the method was carried out following International Council for Harmonisation (ICH) guidelines, demonstrating linearity, selectivity, precision, and accuracy. The analysis of extracts prepared from well-known species, such as Porphyra sp. and Chondrus crispus, confirmed practical applicability. Demonstrating feasibility of MS/MS-based quantification of co-eluting compounds, the method represents an appealing alternative that surpasses established procedures not only in terms of analysis time but also by providing a substantially increased informative value.
    Keywords:  hydrophilic interaction liquid chromatography (HILIC); marine organisms; mycosporine‐like amino acids (MAAs); ultra‐performance liquid chromatography (UPLC)
    DOI:  https://doi.org/10.1002/elps.70110
  25. J Proteome Res. 2026 May 27.
    Low-Temperature HILIC Provides Enhanced Separations and Stability for LC-MS-Based Metabolomics.
    Yifan Liu, Madison L Jastrab, Michael Xiao, Miriam Lisci, Bindu Y Srinivasu, Taysir K Bader, Alexis A Jourdain, Thomas E Wales, Owen S Skinner.
      Liquid chromatography-mass spectrometry is a potent and robust tool for studying metabolism. However, conventional workflows can suffer from poor peak shapes, limited pressure tolerance, coelution of polar metabolites, and unstable retention times. Here, we describe the development of a more stable HILIC method for LC-MS metabolomics of human plasma and cell extracts, optimizing a zwitterionic HILIC (Z-HILIC) column for improved untargeted performance. We found that using high-pH ammonium bicarbonate with 90% acetonitrile in mobile phase B (ABC B) can greatly improve peak shapes of select metabolites when compared to 100% acetonitrile (ACN B), but at the cost of poor retention time stability. We therefore focused on optimizing chromatography for the ACN B method and observed that cooling the column to 5 °C substantially enhanced peak shape for the BEH-bound Z-HILIC and amide columns but had little effect on the polymeric ZIC-pHILIC column. The low-temperature method with the Z-HILIC column (LT-ZHILIC) enables high-resolution separation of 471 metabolite library standards and from both cellular extracts and human plasma and demonstrates robust stability over 100 consecutive injections and multiple days. Application of the untargeted LT-ZHILIC method to characterize the metabolic consequences of glutamine and pyruvate deficiency in human cells revealed a striking change in nucleotide phosphates─a perturbation that was not observed in the ZIC-pHILIC analysis of the same samples likely due to inadequate elution profiles. In sum, the LT-ZHILIC workflow offers a robust platform to advance untargeted metabolomics by improving metabolite coverage, resolution, and retention time stability, making it a promising technique for providing novel insights into cellular metabolic rewiring and the human plasma metabolome.
    Keywords:  HILIC; metabolomics; nucleotides; separation
    DOI:  https://doi.org/10.1021/acs.jproteome.5c01216
  26. Data Brief. 2026 Jun;66 112829
    Dataset on the differential metabolite composition of ripe and green fruit coats of Solanum mauritianum.
    Abraham Goodness Ogofure, Ezekiel Green.
      This data article presents a comprehensive comparative metabolomic dataset characterizing the differential secondary metabolite profiles of ripe and green fruit coats of Solanum mauritianum Scop., an invasive species with significant ethnopharmacological relevance. The dataset complements the research article published by Ogofure et al [1] in Scientific Reports, which focused on ripe fruit components and identified promising antibacterial and anticancer properties. The current dataset addresses a critical research gap by providing untargeted LC-QTOF-MS/MS metabolomic profiling data comparing ripe and green fruit coats, revealing maturation-dependent biochemical transformations. A total of 35 secondary metabolites were putatively annotated (MSI Level 2) across both maturation stages, with detailed information on retention times, accurate mass measurements, and MS/MS fragmentation patterns. The ripe fruit coat exhibited 17 unique metabolites, while the green fruit coat contained 7 unique metabolites, with 11 metabolites shared between both stages. This dataset provides valuable insights into the ontogenic variation in phytochemical composition and offers a foundation for understanding the biosynthetic pathways active during fruit maturation.
    Keywords:  Comparative metabolomics; Fruit maturation; LC-QTOF-MS/MS; Phytochemistry; Secondary metabolites; Solanum mauritianum
    DOI:  https://doi.org/10.1016/j.dib.2026.112829
  27. J Chem Inf Model. 2026 May 28.
    Chemical Profiling and Scaffold-Based Drug-Discovery Analysis of Bioactive Compounds from Ceratonia siliqua L. with Computational and Biological Validation.
    Deli-Bright N T Oku, Garland Kgosi More, Yannick Nuapia, Ramakwala Christinah Chokwe.
      The scaffold concept is central in medicinal chemistry and drug design to generate, analyze, and capture the core structural frameworks that define bioactive compounds. Natural products and their underlying molecular scaffolds have long provided many of the biologically active ingredients in modern medicines and continue to inspire new therapeutic agents. However, linking these core structural frameworks to observed biological activity remains a key challenge in natural-product research. Here, this study integrates a computational-experimental approach combining scaffold-based drug analysis, chemical profiling, and computational and biological validation to identify bioactive motifs in Ceratonia siliqua L pods. A total of 253 identified compounds from the gas chromatography-mass spectrometry (GC-MS) profiling were clustered and grouped into antioxidant, antimicrobial, and cytotoxic activity sets, after which their Bemis-Murcko scaffolds were extracted using RDKit. The most common scaffolds were ranked and visualized to give a clear picture of the prevalent structural patterns throughout the data sets. Experimental assays validated the computational dominant scaffold predictions, revealing that the antioxidant activity was associated with phenolic and terpenoid scaffolds, the antimicrobial response was aligned with monoterpenoid and cyclohexane/cyclohexene-based scaffolds, and the cytotoxic trends were consistent with small heterocycles and imide-containing scaffolds. To further bridge the gap between the identified scaffolds and their corresponding biological activity, molecular docking was performed against key biological targets, including KEAP1, Staphylococcus aureus DHFR, and EGFR. The docking results demonstrated favorable binding interactions and identified key ligand-protein interactions, supporting the potential contribution of the GC-MS-identified compounds to the observed biological activities of the plant extract. These scaffold-activity relationships demonstrate that recurring structural motifs are associated with the biological effects observed in C. siliqua L., highlighting the plant as a promising source of pharmacologically relevant scaffolds for drug discovery.
    DOI:  https://doi.org/10.1021/acs.jcim.6c00748
  28. Front Plant Sci. 2026 ;17 1821654
    Untargeted metabolomics of mature sorghum [Sorghum bicolor (L.) Moench] grain reveals metabolites associated with antimicrobial activity against Clostridium perfringens.
    María Antonella Conti, Carolina Ballén-Taborda, Corey D Broeckling, Richard Elmer Boyles.
      The poultry industry is facing challenges with antibiotic-resistant bacteria, particularly Clostridium perfringens, the causal agent of necrotic enteritis. Sorghum grain, well-known for its high phenolic content that can provide consumers with health benefits, offers a great opportunity for poultry feed. While condensed tannins in sorghum grain have an antinutritional effect by binding proteins and decreasing nutrient absorption in the poultry gut, their concentration is genetically controlled, and tannin-free sorghums exist. This study analyzed a non-tannin sorghum recombinant inbred line (RIL) population consisting of 189 individuals along with the two parents using LC-MS untargeted metabolomics to investigate its antimicrobial (AM) activity against C. perfringens measured by 1) a minimum inhibitory concentration (MIC) analysis with phenol extract of the grain, and 2) a qPCR-based approach to measure bacterium inhibition with in vitro enzymatically digested grain. Results showed that RILs differing in AM activity exhibited distinct metabolic profiles, which varied across methods. The qPCR assay classification revealed a greater number of pathways enriched, with alpha-linolenic acid metabolism being the most significant. Meanwhile, flavonoid biosynthesis was the most enriched pathway for the MIC-based AM trait. Metabolite selection analysis revealed that 13 metabolites were most relevant in separating high and low AM RILs, including two metabolites (C0153b and C0560) tentatively identified as Fusarium products, a naturally occurring pathogen in the tested environment. Only two of the 13 metabolites exhibited higher abundance in the high AM lines, C0540, a hydroxycinnamic acid, and C0916, a glycerophosphate. QTL mapping revealed that five metabolites (C0153b, C0560, C0540, C0916, and C0024) identified a major-effect QTL on chromosome 1 at 25.6 Mb, co-localizing with a previously reported QTL for the qPCR-based AM trait. Relative abundance for each of these five metabolites also mapped to a secondary, and in some cases, a tertiary QTL, indicating oligogenic control of metabolite biosynthesis that is associated with inhibition of C. perfringens. This work provides insight into the metabolic and genetic basis of AM activity in sorghum that can be leveraged to develop sorghum-based feed additives to improve poultry health using natural compounds.
    Keywords:  Fusarium; QTL mapping; antimicrobial metabolites; non-tannin sorghum; qPCR; untargeted metabolomics
    DOI:  https://doi.org/10.3389/fpls.2026.1821654
  29. BMC Genomics. 2026 May 26.
    Integrated metabolomics and transcriptomics analysis of flavonoids in different parts of Houttuynia cordata.
    Jingjian Liu, Juncheng Lin, Huimin Liu, Changhu Ke, Fangling Ren, Lun Wu, Pengju Wang, Fang Peng, Tingwen Ming, Zhihao Li.
       BACKGROUND: Houttuynia cordata (H. cordata) is an important plant used for both medicinal and edible purposes. However, the metabolic profile and biosynthetic pathways of its flavonoids are still poorly understood. Therefore, metabolomics and transcriptomics were employed to analyze the roots, stems, and leaves of H. cordata, aiming to clarify this issue.
    RESULTS: A total of 471 metabolites were identified, with flavonoids being the predominant class. Key flavonoids, including quercetin, kaempferol, and rutin, showed the highest relative abundance in leaves, subsequently confirmed by targeted absolute quantification. Transcriptomic analysis revealed a large number of differentially expressed genes (DEGs) among tissues. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the phenylpropanoid and flavonoid biosynthesis pathways were significantly enriched among the DEGs. The majority of DEGs within these pathways, such as those encoding PAL, 4CL, CHS, and CHI, were found to be up-regulated in the leaves. These genes were further validated by real-time quantitative PCR (RT-qPCR), which showed consistent results with the transcriptomic data.
    CONCLUSIONS: This work provides a comprehensive overview of the metabolite distribution and gene expression in different parts of H. cordata. Our findings highlight the leaf as the primary site for flavonoid accumulation and biosynthesis in H. cordata, laying the foundation for the utilization of candidate genes and future functional characterization of these pathways.
    Keywords:   Houttuynia cordata ; Flavonoid biosynthesis; Metabolomics; Transcriptomics
    DOI:  https://doi.org/10.1186/s12864-026-12961-y
  30. J Sep Sci. 2026 May;49(5): e70449
    Analysis of Chemical Components in Tangerine Peel Flower Tea by Ultra-Performance Liquid Chromatography-Q-Exactive Orbitrap Tandem Mass Spectrometry and High-Performance Liquid Chromatography-Photodiode Array.
    Xiaojing Deng, Yongchang Zeng, Jinji Deng, Mengdie Peng, Baizhong Chen, Jiepei Xu, Yijia Wang, Minyan Wei, Yi Cai, Liming Li, Guodong Zheng.
      Tea has long been consumed in China as a significant part of wellness practices. Tangerine Peel Flower Tea, meticulously developed as a beverage with throat-moistening and liver-protecting functions, is formulated with tangerine peel and licorice as its core ingredients, supplemented with other medicinal and edible herbs such as chrysanthemum and jujube. However, a comprehensive understanding of its chemical composition remains to be elucidated. In this study, the chemical component of Tangerine Peel Flower Tea was comprehensively analyzed using ultra-performance liquid chromatography-Q-Exactive Orbitrap tandem mass spectrometry. A total of 134 components were preliminarily identified through the integration of chromatographic retention time, m/z values, and secondary fragments. These encompassed 80 flavonoids and their glycosides, 16 terpenoids, eight phenylpropanoids, five alkaloids, and 25 other components. Additionally, a high-performance liquid chromatography-photodiode array (HPLC-PDA) was established to enable the concurrent quantification of six main constituents in 20 batches of Tangerine Peel Flower Tea. The results showed that the contents of the six components ranked from high to low as follows: hesperidin (4.164-10.822 mg·g-1), glycyrrhizic acid (1.463-5.953 mg·g-1), nobiletin (0.831-1.637 mg·g-1), tangeretin (0.558-1.160 mg·g-1), chlorogenic acid (0.456-1.037 mg·g-1), and ferulic acid (0.084-0.208 mg·g-1). The established HPLC method demonstrated acceptable feasibility and reliability through methodological validation. This study promotes the quality standardization of Tangerine Peel Flower Tea and supports the further development and utilization of tangerine peel.
    Keywords:  HPLC‐PDA; Tangerine Peel Flower Tea; chemical composition; ultra‐performance liquid chromatography‐Q‐Exactive Orbitrap tandem mass spectrometry
    DOI:  https://doi.org/10.1002/jssc.70449
  31. J Agric Food Chem. 2026 May 26.
    An Untargeted Liquid Chromatography Mass Spectrometry Method for Identifying and Quantifying Saponins in Pulses.
    Antonio Trani, Giuseppe N Mezzapesa, Lea Piscitelli, Ilaria D'Ippolito, Arlinda Kristo, Adriana Colucci, Donato Mondelli.
      Untargeted liquid chromatography mass spectrometry, with negative and positive ionization and data-dependent acquisition, was applied to investigate the type and quantity of saponins present in raw and cooked seeds of five legume species produced in Mediterranean countries. Overall, 15 saponins were detected, and their fragmentation pattern were reported. Three of them were found in all samples, namely, soyasaponins I, αg, and βg. Results show significant differences among species and among different varieties. Soyasaponins βg and I were the most frequent saponins with a concentration range of 15-395 mg/100 g in raw seeds. After soaking and cooking, the majority of saponins reduced their concentration by 50-100%, whereas soyasaponins I and V reported an important concentration increase in beans and cowpeas. Results provide new information on the types and amounts of saponins consumed with pulses in the human diet.
    Keywords:  legumes; pulses; raw seeds; saponins
    DOI:  https://doi.org/10.1021/acs.jafc.6c05390
  32. BMC Plant Biol. 2026 May 28.
    Metabolomics analysis reveals kernel quality variation among main yellowhorn (Xanthoceras sorbifolium Bunge) cultivars.
    Lu Lu, Ruoyao Zhao, Dan Wu, Qiang Liang, Yi Han, Zhigang Bao, Hongcheng Fang, Yongjun Zhao.
      Yellowhorn (Xanthoceras sorbifolium Bunge) is a woody oil species endemic to China. Its oil is predominantly composed of unsaturated fatty acids (UFAs, 85-93%), including the functional component nervonic acid. The kernels were approved as a new food raw material in 2023, highlighting their nutritional and economic value. However, research on differences in kernel nutritional composition and metabolite components among its main cultivars remains insufficient, and no analysis of quality variation among widely cultivated high-yield cultivars from major production regions in China has been conducted. This study compared the main nutrient compositions in kernels from seven yellowhorn cultivars (lines) using gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC), and conducted the first investigation of widely targeted metabolites in kernels from three representative types using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The results showed significant variation in nutrient content across the analyzed samples-LG08 exhibited the highest oil and total fatty acid content, and the highest comprehensive quality among the tested accessions, representing a promising genetic material for high-quality variety breeding. A total of 782 metabolites were identified, with 164-324 showing significant differences. The differential metabolites were significantly enriched in pathways, including ABC transporters and biosynthesis of amino acids, indicating that differences in kernel nutrient accumulation among cultivars may be related to genetic variations in transmembrane transport and primary metabolic regulation. These results provide data supporting the further breeding, processing, and industrial utilization of yellowhorn kernels.
    Keywords:  Cultivar; Kernel; Metabolite components; Nutritional composition; UPLC-MS/MS; Widely targeted metabolomics; Yellowhorn (Xanthoceras sorbifolium Bunge)
    DOI:  https://doi.org/10.1186/s12870-026-09097-2
  33. Nat Commun. 2026 May 28. pii: 4778. [Epub ahead of print]17(1):
    A lipidomics roadmap: from basic research to societal challenges.
    Maria Fedorova, Anne K Bendt, Justine Bertrand-Michel, Oliver Fiehn, Joanna Godzien, Laura Goracci, Florian Gruber, Xue Li Guan, Xianlin Han, Michal Holčapek, Julia Kuligowski, Peter J Meikle, Palina Nepachalovich, Valerie B O'Donnell, Matej Orešič, Sider Penkov, Snježana Petrović, Anton Potapov, Patricia Prabutzki, Laura Righetti, Tatjana Ruskovska, Andrej Shevchenko, Kai Simons, Corinne M Spickett, Olga Vvedenskaya, Jennifer Watts, Markus R Wenk, Michael Witting, Yu Xia, Baoru Yang, Maria Rosário Domingues.
      Lipidomics, a rapidly evolving discipline at the interface of biology and analytical chemistry, seeks to comprehensively characterize the lipid composition of biological systems. Driven by advances in mass spectrometry, chromatography and computational analysis, lipidomics has enabled the high-resolution mapping of lipid networks and their functional dynamics across molecular, cellular and organismal scales. In biomedical research, lipidomics is emerging as a powerful platform for biomarker discovery, enabling early diagnosis, prognosis, and therapeutic monitoring of cancer, metabolic, and neurodegenerative diseases. The field is also reshaping drug discovery by uncovering lipid-mediated pathways, identifying novel therapeutic targets, and refining assessments of drug efficacy and safety. Beyond medicine, lipidomic analyses are redefining food and nutrition science by elucidating how dietary lipids influence metabolic health and disease risk. In parallel, environmental and ecological lipidomics are emerging as powerful frameworks for assessing ecosystem health, tracking the impact of pollutants and exploring the biological consequences of climate change. Such approaches are also informing the discovery of sustainable lipid resources and the development of novel biotechnological and agricultural innovations. With its rapidly expanding analytical repertoire and cross-disciplinary relevance, lipidomics is poised to make substantial contributions to both fundamental biology and applied science. This Perspective aims to synthesise the current state of the field, delineate major analytical and conceptual challenges, and outline future directions for translating lipidomic knowledge into tangible societal and environmental benefits.
    DOI:  https://doi.org/10.1038/s41467-026-73797-4
  34. Bioanalysis. 2026 May 27. 1-14
    Recent developments in analytical techniques for monitoring anabolic-androgenic steroids in biological and environmental matrices: challenges and future opportunities.
    Nouf M Alourfi.
      Anabolic-androgenic steroids (AAS) are widely used in clinical practice but are also frequently misused, necessitating reliable analytical methods for their detection in biological and environmental matrices. This review summarizes recent developments in analytical techniques for AAS determination between 2011 and 2024, with emphasis on chromatographic and mass spectrometric approaches. Relevant literature was identified through searches of major scientific databases, including Scopus, Web of Science, and PubMed, using keywords related to anabolic-androgenic steroids and analytical detection methods; studies were selected based on relevance to methodological advances. Gas chromatography-mass spectrometry (GC-MS) remains a robust platform, with high-resolution GC-HRMS enhancing selectivity and retrospective analysis capabilities. Liquid chromatography-mass spectrometry (LC-MS/MS) provides high sensitivity and throughput for targeted quantification, while LC-HRMS enables broader screening through suspect and non-targeted approaches. Advances in sample preparation, including microextraction and automated workflows, have improved analytical efficiency and reduced matrix effects. Emerging platforms such as ion mobility spectrometry and ambient ionization methods offer rapid screening but remain complementary to established techniques. Overall, modern AAS analysis reflects a shift toward integrated analytical strategies combining targeted and high-resolution approaches to address increasing analytical complexity and evolving regulatory demands.
    Keywords:  Anabolic–androgenic steroids; LC–MS/MS; analytical chemistry; biological matrices; chromatographic techniques; environmental analysis; high-resolution mass spectrometry; mass spectrometry; sample preparation
    DOI:  https://doi.org/10.1080/17576180.2026.2676685
  35. Metabolites. 2026 May 14. pii: 326. [Epub ahead of print]16(5):
    Untargeted Metabolomics Reveals Region-Specific Metabolic Signatures and Discriminative Markers in Goji Berry (Lycium barbarum L.).
    Yan Yan, Wei Ma, Yage Li, Chen Zhang, Fang Li, Tianqing Huang, Beibei Gao, Huihui Meng, Yunfei Hu, Huan Wu.
      Background/Objectives: Goji berry (Lycium barbarum L.), renowned as a typical medicinal and edible plant, is mainly cultivated across four agroclimatic zones in China, including semi-arid, arid, monsoon, and high-altitude regions. Ningxia has long been recognized as the daodi production area for goji berries. However, the metabolic diversity of goji berries from other core cultivation regions and how these differences are shaped by local environments remain poorly understood. Methods: In this study, untargeted metabolomics was employed to comprehensively investigate the metabolic difference in goji across seven production regions. By integrating multivariate analysis with KEGG pathway enrichment (p < 0.05), 49 discriminative markers enriched in 10 key pathways were putatively identified, and their roles in plant stress tolerance were elucidated. In addition, we conducted targeted quantification of key bioactive components and antioxidant capacity. Results: Significant regional differences were revealed. Redundancy analysis further identified rainfall, temperature, and UV radiation as the key climatic drivers of this variation. Conclusions: These findings provide insights into the metabolic adaptation of goji to local environments and serve as a basis for further functional studies.
    Keywords:  discriminative markers; environmental factors; goji berry (Lycium barbarum L.); plant stress resistance; untargeted metabolomics
    DOI:  https://doi.org/10.3390/metabo16050326
  36. Chem Res Toxicol. 2026 May 24.
    Physicochemical Characterization and Metabolites Identification of the Synthetic Cannabinoid MDMB-5'Br-PINACA Using In Silico and In Vitro Approaches.
    Alexandre B Godoi, Natalícia J Antunes, Júlio C C da Silva, Gabriel Cordeiro, Tássia F D Castro, Jose L Costa.
      MDMB-5'Br-PINACA is a recently identified brominated synthetic cannabinoid that was detected in herbal materials seized in Brazil in 2025, raising concerns regarding further potential intoxication cases. In this sense, the evaluation of physicochemical properties and metabolic fate may improve its analytical detectability. Therefore, an integrated in silico and in vitro approach was employed to investigate the physicochemical properties and phase I metabolism of MDMB-5'Br-PINACA. Physicochemical parameters and predicted metabolic pathways were first evaluated using BioTransformer 3.0 and XenoSite, providing complementary insights into likely sites of metabolism. In vitro metabolism was subsequently assessed using pooled human liver microsomes associated with liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) analysis. MS2-based molecular networking (MN) was applied as an exploratory and confirmatory strategy to guide metabolite annotation by clustering structurally related features and prioritizing candidates linked to MDMB-5'Br-PINACA. A total of twenty-seven metabolites were level 2 annotated, encompassing aliphatic and aromatic hydroxylation, sequential alcohol oxidation to ketone, aldehyde, and carboxylic acid derivatives, ester hydrolysis, intramolecular lactone formation, and N-dealkylation with loss of the pentyl side chain. Hydroxylations of the pentyl chain and tert-butyl moiety and secondary oxidative reactions emerged as the predominant pathways under the experimental conditions, in agreement with in silico predictions. However, lactone formation was exclusively revealed by in vitro experiments, demonstrating limitations of current in silico prediction approaches. The integration of computational prediction, LC-HRMS, and MN substantially enhanced metabolite coverage and confidence of structural assignment. These findings provide a detailed metabolic map of MDMB-5'Br-PINACA and underscore the value of combining in silico and in vitro approaches to improve metabolite identification, supporting forensic and clinical investigations of intoxication involving this synthetic cannabinoid.
    DOI:  https://doi.org/10.1021/acs.chemrestox.6c00107
  37. BMC Plant Biol. 2026 May 25.
    Widely targeted metabolomics reveals organ-specific metabolite accumulation and functional differentiation of leaves, pulp and seeds in wild jujube (Ziziphus jujuba var. spinosa).
    Jidong Ju, Jing Xuan, Xiaochen Song, Hui Fang, Yongqing Zhang, LongFei Zhang.
       BACKGROUND: Ziziphus jujuba var. spinosa, commonly known as 'wild jujube,' is a significant medicinal and edible plant. While the value of its seeds and pulps is well established, the potential of its leaves has been largely neglected.
    METHODS: In this study, we determined the contents of basic chemical components-including total flavonoids, total phenolics, and total saponins-in the leaves, pulp, and seeds of Z. jujuba var. spinosa, and we evaluated their antioxidant activities. In addition, widely targeted metabolomics combined with high-performance liquid chromatography (HPLC)-based targeted validation was employed to systematically compare the metabolite profiles.
    RESULTS: The three organs exhibited distinct metabolic functional differentiation. Leaves displayed the highest contents of total flavonoids, total phenolics, and total saponins, along with the strongest in vitro antioxidant activity. The characteristic accumulation of flavonoid glycosides, such as rutin and isoquercitrin, and of triterpene saponins, such as pedunculagin, may be associated with defense functions. Pulp accumulated significant amounts of soluble sugars and organic acids, such as citric acid, and contained moderate levels of flavonoids. Seeds functioned as a protein reservoir and accumulated C-glycosylated flavonoids and dammarane-type triterpene saponins. KEGG pathway enrichment analysis showed that flavonoid biosynthesis, phenylpropanoid biosynthesis, and terpenoid backbone biosynthesis are the core pathways driving this organ-specific metabolic differentiation. This study systematically suggests, for the first time from a metabolomics perspective, the "defense-dispersal-reserve" functional differentiation pattern among the leaves, pulp, and seeds of Z. jujuba var. spinosa. These findings provide a scientific basis for developing the leaf, a nontraditional organ, as a high-value antioxidant resource and lay a theoretical foundation for comprehensive whole-plant utilization.
    Keywords:   Ziziphus jujuba var. spinose ; Flavonoids; Metabolomics; Organ specificity; Resource utilization; Saponins
    DOI:  https://doi.org/10.1186/s12870-026-09088-3
  38. Toxins (Basel). 2026 May 06. pii: 219. [Epub ahead of print]18(5):
    Development of a Sensitive UPLC-MS/MS Method for the Simultaneous Quantification of Mycotoxins in Wheat Products and Human Urine.
    Bin Gao, Jialin Sun, Zechao Xu, Xiaohui Li, Jianxin Ma, Xiaomin Han, Shuo Wang.
      Mycotoxin contamination in wheat products has consistently been a key issue of concern in food safety, and urinary biomonitoring provides an effective approach for assessing internal human exposure. In this study, a sensitive ultra-performance liquid chromatography-tandem mass spectrometry method was developed and validated for the simultaneous determination of 28 mycotoxins in wheat products and human urine. For the two matrices, the extraction solvent, acid concentration, solid-phase extraction cartridge type, and enzymatic hydrolysis parameters were optimized. Under the optimized conditions, all target compounds showed excellent linear relationships within the tested concentration ranges (R2 > 0.99). In wheat products, the spiked recoveries ranged from 70.2% to 120%, the repeatabilities ranged from 1.6% to 9.1%, and the limits of detection and limits of quantification were 0.001~8.3 μg/kg and 0.002~25.0 μg/kg, respectively. In urine, the spiked recoveries ranged from 79.3% to 120%, the repeatabilities ranged from 0.7% to 9.4%, and the limits of detection and limits of quantification were 0.0001~1.0 μg/L and 0.0002~3.0 μg/L, respectively. Analysis of real samples showed that at least seven mycotoxins were detected in wheat product samples, and at least five were detected in urine samples. In wheat products, the detection rates of deoxynivalenol, enniatin B, enniatin A1, enniatin B1, tenuazonic acid, and tentoxin were all 100%, whereas in urine, the detection rate of fumonisin B1 reached 100%, and tenuazonic acid showed the highest mean concentration in both matrices. In conclusion, the developed ultra-performance liquid chromatography-tandem mass spectrometry method is suitable for the simultaneous quantification of 28 mycotoxins in wheat products and human urine, and its preliminary application demonstrates good practical applicability.
    Keywords:  UPLC-MS/MS; human urine; mycotoxins; wheat products
    DOI:  https://doi.org/10.3390/toxins18050219
  39. Sensors (Basel). 2026 May 10. pii: 3001. [Epub ahead of print]26(10):
    Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.
    Gianfranco Picone.
      Metabolomics has emerged as a powerful analytical approach for comprehensive chemical profiling in complex biological and environmental systems. The increasing volume, dimensionality, and complexity of metabolomics data have driven the adoption of machine learning (ML) techniques to enhance chemical detection, classification, and interpretation. This narrative review critically discusses the integration of metabolomics and machine learning for advanced chemical detection, with particular emphasis on analytical workflows, data preprocessing strategies, supervised and unsupervised learning models, and validation approaches. In this context, advanced chemical detection refers to the data-driven identification, classification, and quantification of chemical signatures in complex matrices with improved sensitivity, selectivity, robustness, and interpretability. Current applications across food science, environmental monitoring, clinical diagnostics, and exposomics are discussed, along with key challenges related to data quality, interpretability, and reproducibility. Finally, future perspectives on explainable AI, multimodal data integration, and standardized pipelines are highlighted.
    Keywords:  chemical detection; chemometrics; data-driven analysis; machine learning; metabolomics; pattern recognition
    DOI:  https://doi.org/10.3390/s26103001
  40. Metabolites. 2026 Apr 22. pii: 288. [Epub ahead of print]16(5):
    A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.
    Maria-Christina P Papatheodorou, Panagiotis Vlamos, Marios G Krokidis.
      Lipidomics has emerged as a transformative discipline in biomedical research, providing high-resolution insights into metabolic signaling and disease pathophysiology. The R programming language provides a widely adopted framework for extensible analysis of complex lipidomic datasets due to its robust biostatistical infrastructure. Herein, we present a comprehensive roadmap for lipidomics in R, structured around a standardized analytical lifecycle: from raw data acquisition and preprocessing to structural annotation, statistical modeling and functional interpretation. We critically contextualize and integrate a curated suite of widely adopted R packages (version 4.3.0), including xcms and MSnbase for feature extraction, LipidMS 3.0 for fragmentation-based identification, and lipidr for quality control and normalization. Furthermore, we demonstrate how advanced tools such as mixOmics and clusterProfiler can be integrated to bridge the gap between differential lipid abundance and systems-level biological insights. Particular emphasis is placed on reproducibility, nomenclature standardization and the emerging role of machine learning in biomarker discovery. By synthesizing these resources into a coherent pipeline, this guide provides a structured reference for researchers. Further discussion addresses methodological pitfalls, statistical assumptions and reproducibility constraints that frequently compromise lipidomics studies. Ultimately, this structured approach facilitates systematic tool selection, accelerating the translation of complex lipidomic signatures into reproducible and clinically meaningful discoveries.
    Keywords:  R libraries; data processing; functional analysis; lipid ontology; lipidomics; multi-omics integration
    DOI:  https://doi.org/10.3390/metabo16050288
  41. Plants (Basel). 2026 May 08. pii: 1439. [Epub ahead of print]15(10):
    Seasonal and Organ-Specific Variations of Alkaloids in Buxus obtusifolia (Mildbr.) Hutch: A Multivariate LC/MS Study.
    Justus Wambua Mukavi, Jandirk Sendker, Njogu M Kimani, Leonidah Kerubo Omosa, Thomas J Schmidt.
      Buxus obtusifolia (Mildbr.) Hutch is an evergreen shrub endemic to East Africa and is traditionally used to treat chest ailments. Our recent investigation on the dichloromethane leaf extract of this species yielded several aminosteroid alkaloids, some of which demonstrated promising in vitro antiprotozoal activity warranting more detailed studies on this interesting plant and its bioactive constituents. Given that abiotic factors are known to influence the biosynthesis and accumulation of plant secondary metabolites, this study aimed to investigate seasonal and organ-specific variability in the alkaloid profile of B. obtusifolia to gain insights into the dynamics of their formation and, potentially, obtain hints at the best times to harvest individual alkaloids. Consequently, leaf and twig samples were collected each month from the same population over a period of one year and analyzed using ultra-high-performance liquid chromatography coupled with positive-mode electrospray ionization double quadrupole time-of-flight tandem mass spectrometry (UHPLC-ESI+-QqTOF-MS/MS). The resulting data, after conversion to <retention time: mass/charge ratio> (<tR:m/z>) variables, were analyzed by principal component analysis (PCA) to characterize variations in the metabolite profile. Evaluation of the first three principal components revealed clear differences between leaves and twigs, as well as subtle overall seasonal changes with some distinct dry-season clustering. A volcano plot was used to further analyze the differences between the minor constituents of the two organs. In total, 15 aminosteroid alkaloids were identified as key contributors to these differences. This represents the first seasonal and organ-specific phytochemical variability investigation in B. obtusifolia. Thus, this study offered the first valuable insights into the possible association of some abiotic factors and the phytochemical profile of this plant. Studies including further populations of this species from different locations will have to show whether the present findings allow general conclusions with respect to the investigated compounds' accumulation in response to external factors. Furthermore, the present results represent a basis to delineate the optimal harvest period for targeted isolation of larger quantities of bioactive aminosteroids for further development.
    Keywords:  Buxaceae; Buxus obtusifolia (Mildbr.) Hutch; aminosteroid alkaloids; liquid chromatography–mass spectrometry; multivariate data analysis; principal component analysis; seasonal variation; volcano plot
    DOI:  https://doi.org/10.3390/plants15101439
  42. Front Cardiovasc Med. 2026 ;13 1646067
    Integrating untargeted metabolomics and deep learning approaches to identify specific metabolic signatures and new mechanisms in unstable plaques.
    Jia-Qi Ma, Lu Wang, Xiao-Peng Qu, Yue Zhang, Li-Jia Song, Guo-Dong Gao, Chao Wang, Long-Long Zheng, Qi-Xing Fang, Yan Qu, Liang-Liang Shen, Bei Liu.
       Background: Unstable carotid artery plaques are an important risk factor for ischemic stroke, and their clinical prognosis is poor. The present study to systematically investigate the metabolic changes of carotid plaques and use machine learning methods to identify and screen metabolic biomarkers in unstable carotid plaques for helping diagnosis of stroke risk caused by unstable plaques.
    Method: A non-targeted metabolomics analysis was performed on 67 cases (40 stable and 27 unstable) of carotid artery plaques. Specific metabolic signatures were identified in unstable plaques. Four machine learning algorithms, including random forest (RF), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR), were used to construct feature analysis models for unstable carotid artery plaques and predict the associated metabolic biomarkers.
    Results: A total of 98 metabolites significantly differentially associated with unstable plaques were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the cGMP-PKG signaling pathway, glucagon signaling pathway, central carbon metabolism in cancer, and lipolysis regulation in adipocytes are metabolic pathways significantly associated with unstable plaques. The network diagram of metabolites and metabolic pathways revealed the relationship between 43 metabolites and their corresponding pathways. Furthermore, some metabolites that may serve as biomarkers for unstable plaques were screened.
    Conclusion: Different metabolite patterns associated with unstable plaque tissue were identified and characterized. This study identified some potential metabolic biomarkers significantly associated with unstable carotid artery plaques, which can predict metabolic products and further improve the prediction of stroke risk in unstable plaques.
    Keywords:  biomarkers; carotid artery; machine learning; metabolomics; unstable plaque
    DOI:  https://doi.org/10.3389/fcvm.2026.1646067
  43. Nat Commun. 2026 May 26.
    Activity-guided substructure prioritization accelerates discovery of gut microbiota-derived immune-regulating metabolites.
    Haoduo Zhao, Liang Chi, Zhenfa Zhang, Yun-Chung Hsiao, Chih-Wei Liu, Yifei Yang, R Balfour Sartor, Kun Lu.
      Gut microbiota exert significant influences on host physiology through bioactive molecules such as indole derivatives. The characteristic aromatic heterocycle substructure shared among various indole derivatives is intrinsically associated with the activation of aryl hydrocarbon receptor (AhR), suggesting a potential structure-activity relationship (SAR). Here, we develop a substructure-activity relationship mass spectrometry pipeline aiming at elucidating SAR information encompassed within mass spectrometry data, further applying it to identify the unknown gut microbiome-derived, indole-like derivatives with AhR agonistic potential. A total of 22 metabolites exhibiting activity-related substructures are identified, four of which were previously uncharacterized. Notably, the discovered sulfonated and methylthiolated compounds represent two previously underexplored microbiome-mediated reactions. Subsequent assays validate their AhR agonistic activities and downstream immune regulatory effects in male mice. Overall, this study presents an exploratory framework for MS data mining focusing on activity-guided substructure prioritization, enabling the discovery of bioactive microbiota-derived molecules and distinct microbiome-mediated reactions.
    DOI:  https://doi.org/10.1038/s41467-026-73030-2
  44. Bioinform Adv. 2026 ;6(1): vbag111
    AnnoMe: user-defined classification of HR-MS/MS spectra for natural product discovery.
    Christoph Bueschl, Tomas Rypar, Lenka Molcanova, Juraj Markus, Bernhard Seidl, Maria Doppler, David Ruso, Christina Maisl, Karel Smejkal, Rainer Schuhmacher.
       Summary: Annotation of HR-MS/MS spectra is a complex task that can be tackled either by expert interpretation or machine learning models that rely on large spectral/structural databases for training. Frequently, users want to find novel compounds of a particular substance class they are already familiar with. This requires the classification of detected compounds as "relevant" (i.e. belonging to the compound class of interest) or not (i.e. "other"). For such applications, the python-based AnnoMe software is presented that allows users to classify their experimental HR-MS/MS spectra according to their aims. By leveraging a user-curated dataset of "relevant" and "other" reference HR-MS/MS spectra alongside structure-informed embeddings (MS2DeepScore), the package enables rapid and accurate prediction of "relevant" compounds with custom-trained classification models and a majority vote, facilitating exploration of the complex chemical space inherent to LC-HRMS/MS data. This software is demonstrated by predicting putative prenylated flavonoids for prioritization in natural product discovery.
    Availability and implementation: Code, documentation, and datasets are available at https://github.com/chrboku/AnnoMe and https://zenodo.org/records/16322488.
    DOI:  https://doi.org/10.1093/bioadv/vbag111
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