bims-mebolo 2026-05-24 papers

bims-mebolo

Biomed News

on Metabolomics

Issue of 2026–05–24
fourteen papers selected by
Daniel Méndez Rodríguez, Vbi-Ugent

Comparison of feature-based molecular networking strategies for untargeted metabolite identification in endophytic fungal samples from Lespedeza bicolor Turcz.
Glucuronidation metabolomic fingerprinting to map host-microbe metabolism.
A Systematic Evidence Map of FAIR Compliance in Open-Access Metabolomics Research: Results from a Pilot Study.
Ensuring Traceability in Nontargeted HRMS Annotation Workflows: An Urgent Call for Editorial Standards.
Molecular Structural Features and Tandem Mass Spectral Characteristics of Flavonoids From the Analysis of the LIPID MAPS Database.
A MassQL-Based Framework for Rule-Guided MS/MS Class-Level Retrieval and Analog Discovery of Cannabinoids.
Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.
Benchmarking untargeted metabolomics data quality with allopurinol-induced perturbations.
Lignonaut: designing diverse combinatorial libraries for the exploration and annotation of lignin oligomer spaces.
An LC-MS-Based Molecular Networking Proof-of-Concept for Revealing Ecological Functional Dynamics in Nylsvley Ramsar Wetland Waters.
MetaboVariation 2.0: Multivariate analysis for identifying metabolite variation at the individual level.
Serum Metabolomic Profiling Reveals Distinct Signatures and Candidate Biomarkers in Chronic Kidney Disease.
A comprehensive evaluation of solid-phase extraction strategies for targeted and untargeted liquid chromatography - mass spectrometry metabolomic analysis.
Identification of antigonorrhoeal phytochemical lead compounds using a metabolomics-guided approach.

J Pharm Biomed Anal. 2026 May 19. pii: S0731-7085(26)00241-4. [Epub ahead of print]279 117573

Comparison of feature-based molecular networking strategies for untargeted metabolite identification in endophytic fungal samples from Lespedeza bicolor Turcz.

Yuhang Liu, Qixian Zheng, Qing Liu, Yiwen Tao, Hongbo Huang, Bo Ding.

  Feature-Based Molecular Networking (FBMN) is a robust strategy for the structural elucidation of natural products; however, inconsistent workflows across software platforms and ionization modes hinder its standardized application. This study systematically evaluated six FBMN construction approaches using LC-MS/MS data acquired from secondary metabolites produced by Lespedeza bicolor-associated endophytic fungi. We compared the MZmine and GNPS (Global Natural Products Social Molecular Networking, https://gnps.ucsd.edu) platforms across positive and negative ionization modes, as well as across different data integration strategies. The results indicated that platform selection and ionization polarity significantly influenced network topology and annotation efficiency. The MZmine-based data-level merged network exhibited superior performance in node density and overall metabolite coverage. Conversely, the GNPS-based network-level merging strategy was more effective in grouping unknown metabolites and facilitating structural interpretation. Positive ionization consistently yielded a higher number of annotations and better annotation accuracy than negative mode, while dual-polarity integration significantly enhanced network connectivity. Specifically, GNPS network-level merging strengthened spectral-similarity linkages, whereas MZmine data-level merging maximized the representation of chemical diversity. These findings demonstrate that a combinatorial approach, integrating dual ionization modes and complementary FBMN platforms, is essential for optimizing metabolite identification in complex natural product matrices.

Keywords:  Feature-based molecular networking; Ion mode; Merged network; Metabolite annotation; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.jpba.2026.117573
Nat Commun. 2026 May 20.

Glucuronidation metabolomic fingerprinting to map host-microbe metabolism.

Nina R Boyle, Josh J Sekela, Mingxun Wang, Helena Mannochio-Russo, Jeong Joo Pyo, Min Soo Kim, Shuchang Tian, Imhoi Koo, Elliot S Friedman, Ceylan Tanes, Mallappa Anitha, Yuan Tian, Ethan W Morgan, Iain A Murray, Joseph P Zackular, Kyle Bittinger, James D Lewis, Gary H Perdew, Gary D Wu, Babette S Zemel, Pieter C Dorrestein, Jordan E Bisanz, Matthew R Redinbo, Andrew D Patterson.

Glucuronidation is an important detoxification pathway that operates in balance with gastrointestinal microbial β-glucuronidase (GUS) activity, which can regenerate bioactive metabolites from their glucuronidated forms. How this host-microbe interaction shapes the distribution and pool of glucuronidated metabolites (i.e., the glucuronidome) remains poorly understood. In this study, we employed pattern-filtering data science approaches in conjunction with untargeted LC-MS/MS metabolomics to map the glucuronidome in urine, serum, and colon/fecal samples from gnotobiotic and conventional mice, and in humans. We find that microbial colonization and GUS activity compress the colonic glucuronidome and expand urinary glucuronidome diversity, revealing a compartmental redistribution of glucuronidated metabolites. Reverse metabolomics of known glucuronidated chemicals and glucuronidation pattern filtering searches in public metabolomics datasets exposed the diversity of glucuronidated metabolites in human and mouse ecosystems. In summary, we present a glucuronidation fingerprint resource that provides broader access to and analysis of the glucuronidome. Together, this work establishes a scalable analytical framework and provides mechanistic insight into how microbial activity reshapes systemic glucuronidation, with implications for drug metabolism, diet-microbe interactions, and biomarker discovery.

DOI: https://doi.org/10.1038/s41467-026-73398-1
Stud Health Technol Inform. 2026 May 21. 336 1128-1132

A Systematic Evidence Map of FAIR Compliance in Open-Access Metabolomics Research: Results from a Pilot Study.

Charlie Harrison, Tulsi Suchak, Kris Elomaa, Reyer Zwiggelaar, Matt Spick.

  Metabolomics is a powerful tool for precision health and biomarker discovery, yet the field faces persistent transparency and reproducibility challenges. The FAIR (Findable, Accessible, Interoperable, Reusable) Guiding Principles were established in 2016 with the aim of improving scientific data management, but compliance in metabolomics remains inconsistent. Here, we briefly describe our protocol for a Systematic Evidence Map (SEM) to assess data sharing practices in open-access metabolomics publications. We will also assess the accessibility of unpublished datasets claimed to be available on request, to improve understanding of the different incentives and disincentives around data sharing. We will draw from open-access records indexed in PubMed, PMC and DOAJ published between 2013 and 2024, spanning before and after the publication of the FAIR guiding principles. Articles will be screened for relevance and assessed based on their data availability statements and repository use. We will investigate which factors are associated with improved compliance, by funder, journal, and subject area as well as over time. The results of our pilot study of 1106 publications are also presented; we find that while the number of studies including a data availability statement increased from 9% in 2014 to 85% in 2024, 'Available on request' became the most common statement. Just 14% of studies in our pilot made their data available in a repository, suggesting that more work must be done to encourage FAIR compliance.

Keywords:  FAIR; data availability; metabolomics; metascience; reproducibility

DOI:  https://doi.org/10.3233/SHTI260374
Anal Chem. 2026 May 21.

Ensuring Traceability in Nontargeted HRMS Annotation Workflows: An Urgent Call for Editorial Standards.

Francisco José Díaz-Galiano, Yann Guitton, María Murcia-Morales, Gaud Dervilly, Bruno Le Bizec.

Untargeted high-resolution mass spectrometry (HRMS) is widely used in metabolomics, exposomics, and chemical monitoring. However, compound annotation, a central element for interpreting untargeted data, is frequently reported without sufficient information to allow independent evaluation. This communication examines current annotation practices in untargeted HRMS studies and remarks the increasing lack of standardized reporting of metadata, structural identifiers, and confidence criteria. Annotations are often reduced to compound names and exact masses, sometimes relegated to Supporting Information or omitted entirely, despite their central role in data interpretation. Although several community initiatives and guidelines have proposed reporting recommendations and identification confidence frameworks, their application and enforcement remain inconsistent. As a result, annotation traceability is often insufficient to support reproducibility, interstudy comparability, or long-term data reuse. These limitations affect downstream applications, including meta-analyses, automated data mining, and regulatory-relevant fields such as food safety and exposure assessment. This article argues that improving annotation traceability is essential for the scientific robustness of untargeted HRMS workflows and emphasizes the role of journals, reviewers, and authors in ensuring that annotation information remains verifiable, reusable, and scientifically accountable.

DOI: https://doi.org/10.1021/acs.analchem.6c00439
Chem Biodivers. 2026 May;23(5): e03379

Molecular Structural Features and Tandem Mass Spectral Characteristics of Flavonoids From the Analysis of the LIPID MAPS Database.

Usha Perumal, Varatharajan Sabareesh.

  Flavonoids comprise an important class of phytochemicals that have not only been proven to be good antioxidants, but also have been shown to have anticancer and neuroprotective activities, of late. Therefore, these compounds find immense applications in food, pharmaceutical and nutraceutical industries. Though these are prevalently of plant origin, some recent evidences reveal their occurrence in bacteria and fungi as well. Thus, identification and characterization of flavonoids is essential, for which tandem mass spectrometry (MS/MS) plays a significant role, whereby diagnostic fragment ions observed from MS/MS experiments are useful in lucidly distinguishing one subclass from another. In order to understand the extent of diversity of flavonoids, we surveyed the database of LIPID MAPS consortium (www.lipidmaps.org), wherein we found more than 6000 flavonoids within the molecular mass range, 200-800 Daltons (Da). To simplify the complexity of such a large dataset, we classified these compounds based on intact molecular mass as well as degree of glycosylation. In doing so, we found ∼3400 aglycones and ~2700 glycosides (glycosylated flavonoids). The number of O-glycosylated flavonoids are greater than the C-glycosylated flavonoids. Further, we highlight the utility of MS/MS, by reviewing some key fragmentation pathways that aid in discriminating subtle variations in the flavonoids' molecular structures, thereby facilitating to identify different subclass types. Altogether, this review clarifies that the LIPID MAPS database is not just solely limited for analyzing well-known lipids such as fatty acyls, glycerolipids, etc., but also is absolutely suitable for investigating 'flavonoids' as well, including mass spectral data analysis.

Keywords:  Database; Flavonoids; Glycosides; Glycosylated flavonoids; MS/MS

DOI:  https://doi.org/10.1002/cbdv.202503379
Rapid Commun Mass Spectrom. 2026 Aug 30. 40(16): e70112

A MassQL-Based Framework for Rule-Guided MS/MS Class-Level Retrieval and Analog Discovery of Cannabinoids.

Julia Maia Galvão de Queiroz, Fernanda Oliveira Chagas, Valdir Florêncio Veiga-Júnior, Monica Costa Padilha, Ricardo M Borges.

   RATIONALE: Cannabinoids comprise a chemically diverse group of meroterpenoids whose extensive isomerism, variable side-chain length, and frequent oxidative or rearranged derivatives lead to strongly overlapping yet characteristic MS/MS fragmentation patterns. In untargeted LC-MS/MS datasets, this combination of structural diversity and spectral similarity complicates annotation, particularly when reference spectra are sparse or unavailable. Library-based approaches, therefore, recover only a limited fraction of the cannabinoid-related chemical space that is routinely observed in experimental data.
METHODS: In this work, we apply MassQL to encode established cannabinoid fragmentation chemistry into rule-based queries. The resulting compendium covers major cannabinoid subclasses, including neutral and acidic cannabinoids, varinic analogs (C3 side-chain cannabinoids), and structurally modified derivatives, using combinations of diagnostic fragment ions, neutral loss patterns, adducts, and fragment co-occurrence logic. Importantly, class-level retrieval does not depend on complete or unambiguous precursor m/z information and can be driven solely by MS/MS evidence.
RESULTS: Application of this framework to a publicly available untargeted LC-MS/MS dataset demonstrates that rule-based querying can recover known cannabinoids while highlighting additional features that share consistent cannabinoid-like fragmentation patterns. These features include putative analogs, transformation products, and derivatized forms that are not represented in current spectral libraries. At the same time, certain known features, such as in-source dehydrated ions, may be under-recovered depending on query design, illustrating current methodological limitations.
CONCLUSIONS: This study demonstrates the feasibility and interpretability of chemically informed, rule-based MS/MS querying for cannabinoid discovery. Rather than replacing spectral library matching, MassQL-based class-level retrieval provides complementary hypothesis-generating evidence capable of expanding detectable cannabinoid chemical space beyond currently available reference spectra. The results also highlight the importance of polarity-aware fragmentation curation for reliable query-driven metabolomics workflows. MassQL class-level matches should be viewed as chemically informed hypotheses that complement, rather than replace, spectral library identification, while providing a basis for future systematic validation and benchmarking.

Keywords:  MS/MS fragmentation; MassQL; analog discovery; cannabinoid analysis; neutral loss patterns; rule‐based class‐level retrieval; tandem mass spectrometry

DOI:  https://doi.org/10.1002/rcm.70112
Anal Chem. 2026 May 20.

Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.

Thomas Rix, Caroline Jane Sands, Alma Villaseñor, Ana Gradillas, Coral Barbas, Elena Chekmeneva, Elizabeth J Want, Timothy M D Ebbels.

Liquid chromatography-mass spectrometry (LC-MS) untargeted analysis enables comprehensive lipid profiling of biological samples. However, system-level interpretation is often limited by the large number of unannotated features. Assigning features to lipid classes provides a higher-level, yet informative, overview that complements detailed structural analysis and supports biological interpretation at the class level. Recent advances in the systematic prediction of chemical class using tandem mass spectrometry (MS2) help address this; however, a substantial proportion of features in untargeted LC-MS data sets are typically characterized only at the MS1 level. Here, we present a workflow to systematically predict the lipid class from MS1-only data in untargeted LC-MS, without requiring prior annotations or MS2. Motivated by previous research showing that Gaussian graphical models (GGMs) estimated from feature intensities can encode the lipid class structure, our method, GgmLipidClassifier (GLC), combines conventional accurate-mass database searching with a GGM-derived network structure in a unified scoring framework to predict lipid class according to the LIPID MAPS Structure Database (LMSD) ontology. Across three human serum and plasma data sets, GLC achieved overall accuracies of 82-90% at the LMSD main class-level and 72-86% at the lipid subclass level, with improved accuracy and reduced uncertainty compared to closest-m/z matching. GLC provides class predictions for most detected features and also generates prediction quality scores to support downstream interpretation. Applied to serum samples from an Alzheimer's disease study, lipid class enrichment based on GLC predictions was highly consistent with class enrichment derived from ground-truth lipid annotations. Importantly, GLC extended coverage to classes missing from the annotation set, revealing biologically plausible associations with Alzheimer's disease, including cholesterol and derivatives, vitamin D3 and derivatives, and plasmalogen glycerophosphoethanolamines. Overall, GLC provides robust lipid class predictions from MS1-only data, generating lipid class assignments for most detected features and complementing conventional analysis to support broader system-level interpretation.

DOI: https://doi.org/10.1021/acs.analchem.5c08067
Metabolomics. 2026 May 16. pii: 74. [Epub ahead of print]22(3):

Benchmarking untargeted metabolomics data quality with allopurinol-induced perturbations.

Terje Vasskog, Pia J Heinsvig, Ekaterina Sharashova, Terkel Hansen, Torbjørn N Myhre, Marie Mardal.

INTRODUCTION: We present a simple test to assess whether a metabolomics dataset is fit-for-purpose. Current qualitycontrol approaches do not directly evaluate the ability to recover biologically meaningful perturbations.
OBJECTIVES: To evaluate whether known drug-induced metabolic perturbations can serve as internal benchmarks fordataset quality.
METHODS: In a study (the TROMBOLOME study, unrelated to allopurinol therapy), 1,000 serum samples were analyzedwith one targeted and two untargeted metabo lomics panels. Samples were classified as allopurinol-positive (N=19)using detection of allopurinol analytical targets. Endogenous metabolite markers of allopurinol therapy wereevaluated based on hypotheses derived from the literature. Statistical evaluation was performed using Mann-Whitney U-tests.
RESULTS: The hypothesis of upregulation was supported for xanthine, orotate, and orotidine (p < 0.0001) inallopurinol-positive cases (N = 19). These findings demonstrate repro ducibility of well-characterized metabolicperturbations within the dataset.
CONCLUSION: In the absence of external quality assessment schemes for untargeted metabolomics, such benchmarkscould provide a practical way to evaluate whether datasets are suitable for downstream biological interpretation.The proposed targeted exposomics approach complements traditional QC metrics by assessing biologicalrecoverability.

DOI: https://doi.org/10.1007/s11306-026-02457-x
J Cheminform. 2026 May 21.

Lignonaut: designing diverse combinatorial libraries for the exploration and annotation of lignin oligomer spaces.

Mynta Norberg, Margareta Sandahl, Peter Spégel.

  Lignins are a polymeric, renewable resource with remarkable structural diversity. Research is being conducted into the valorisation of lignins into value-added products. The absence of experimental libraries, in particular for process-modified oligomers, hampers analytical feedback to these valorisation efforts. Stochastic methods for generating libraries in-silico have been proposed, but were not designed for use with popular techniques such as high-resolution mass spectrometry. To resolve this, we developed Lignonaut, which is a toolkit for designing lignin libraries through virtual combinatorial synthesis. To ensure high interpretability we also developed new, diversity-oriented nomenclature for lignin oligomers, upon which an efficient SMILES translation algorithm could be built. Libraries of up to 107 oligomers could be generated, in linear time, and at a rate of 106 per minute. Scientific contributionLignonaut applies virtual combinatorial synthesis to exhaustively map lignin chemical spaces for e.g. high-resolution mass spectrometry. It is fast and can account for the high degree of isomerism in lignin oligomers, which were major limitations of previous stochastic approaches.

Keywords:  Feature; HRMS; Library; Spectrometry; Synthesis; Virtual

DOI:  https://doi.org/10.1186/s13321-026-01202-9
Bull Environ Contam Toxicol. 2026 May 18. pii: 105. [Epub ahead of print]116(6):

An LC-MS-Based Molecular Networking Proof-of-Concept for Revealing Ecological Functional Dynamics in Nylsvley Ramsar Wetland Waters.

Florence Mazwi Murungweni, Nakisani Babra Moyo, Farai Dondofema, Ntakadzeni Edwin Madala.

  Wetlands are unique ecological niches that support highly specialized ecosystems, yet their functioning is strongly influenced by surrounding human activities. Increasing settlement around Nylsvley, a Ramsar-designated wetland has resulted in observable ecological changes. This study presents a comprehensive chemical profiling of water from two sites; Jacana Hide Site (JHS) and Site 3 (S3) sampled during dry and wet seasons. Untargeted LC-MS combined with molecular networking revealed clear chemical differences between sites, with JHS showing greater chemical variability than S3. Many detected compounds could not be annotated due to current database limitations. Among the identified compounds, several anthropogenic contaminants were detected, including plastic-associated fatty acid amides (erucamide and oleamide), phthalates and a UV-filter 2-hydroxy-4-methoxybenzophenone, which may pose ecological risks to aquatic organisms. In addition, alkaloids commonly associated with tobacco and opium use were also observed, suggesting potential wastewater-derived inputs into the wetland system. Molecular networking also showed a notable shift in the chemical composition of the investigated sites before and after rainfall. Overall, this work serves as a proof of concept, providing an initial assessment of the chemical status of the Nylsvley wetland and highlighting potential toxicological risks associated with emerging contaminants.

Keywords:  Ecosystem; Mass spectrometry; Molecular networking; Organic pollutants; Wetlands

DOI:  https://doi.org/10.1007/s00128-026-04258-3
PLoS One. 2026 ;21(5): e0343973

MetaboVariation 2.0: Multivariate analysis for identifying metabolite variation at the individual level.

Shubbham Gupta, Isobel Claire Gormley, Lorraine Brennan.

Variations in individuals' metabolic profiles are the result of their genetic makeup and environmental and lifestyle factors. To address the challenge of identifying these intra-individual variations at the individual level, we introduce "MetaboVariation 2.0", a multivariate Bayesian generalised linear model designed to flag individuals with intra-individual variations in metabolite levels across repeated measurements. MetaboVariation 2.0 builds upon the previous univariate MetaboVariation approach by incorporating dependencies between metabolites, offering a more comprehensive assessment of individual metabolic variations. While simultaneously considering all metabolites, MetaboVariation 2.0 flags an individual when their observed metabolite levels deviate from their individual-level posterior predictive interval at a time point. A series of simulation studies were conducted to evaluate the performance of MetaboVariation 2.0. In addition it was applied to a metabolomics data set. The efficacy of this approach was validated through a series of simulation studies. These simulations demonstrated that the multivariate model outperformed its predecessor, particularly in scenarios where the dependencies between the metabolites were positive. The model showed lower mean absolute differences between correlation matrices of metabolite levels from replicate datasets and the original simulated data, indicating improved accuracy in capturing the metabolic dependencies. In addition, analysis of plasma metabolite levels from 164 individuals with 20 metabolites measured across four time points was performed to detect individuals with intra-individual variations. MetaboVariation 2.0 revealed intra-individual variations in 15.2% of the individuals, with 20% or more of their metabolites showing variations beyond their 97.5% posterior predictive intervals in at least one time point. In conclusion, MetaboVariation 2.0 accounts for the inherent dependencies between different metabolites, offering a full view of an individual's metabolic profile which is an important advancement for assessment of individual-level metabolite variation. A software implementation of this approach is freely available through the "MetaboVariation" R package, promoting its accessibility and use in broader metabolomics research.

DOI: https://doi.org/10.1371/journal.pone.0343973
Int J Nephrol Renovasc Dis. 2026 ;19 597353

Serum Metabolomic Profiling Reveals Distinct Signatures and Candidate Biomarkers in Chronic Kidney Disease.

Huimin Jin, Yun Gao, Haochen Zhang, Hanbing Li, Wenqi Wang, Keyi Zhang, Fenghua Zhang.

   Introduction: Chronic kidney disease (CKD) is accompanied by systemic metabolic dysregulation, and metabolomics provides a robust approach for identifying disease-specific metabolic signatures and potential biomarkers. Hypertension may be closely associated with metabolic disturbances in CKD. This study aimed to characterize serum metabolic alterations and dysregulated pathways in CKD, and screen candidate metabolite biomarkers for distinguishing CKD patients from healthy individuals.
Methods: A total of 65 participants (35 CKD patients and 30 healthy controls) were enrolled in this study. Serum metabolic profiling was performed using high-resolution mass spectrometry-based untargeted metabolomics, while targeted analysis of small molecule metabolites was conducted via liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analyses including principal component analysis (PCA) and orthogonal partial least squared-discriminant analysis (OPLS-DA) were applied to identify metabolic alterations between groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to annotate the functional roles of differentially expressed metabolites. Independent t-tests and Pearson correlation analyses were performed to validate the expression and correlation of key metabolites.
Results: A total of 1,426 metabolites were detected in all serum samples, with 1,246 successfully identified by secondary mass spectrometry. Differential analysis revealed 397 significantly altered metabolites (216 up-regulated and 181 down-regulated) between the CKD and control groups. KEGG enrichment analysis indicated that these differential metabolites were mainly involved in phenylalanine metabolism, arginine and proline metabolism, and glutathione metabolism, suggesting systemic metabolic dysfunction in CKD. Targeted analysis of catecholamines showed that serum concentrations of adrenaline and nicotinamide mononucleotide (NMN) were significantly altered in CKD patients compared with healthy controls (P < 0.05), though no significant linear correlation was observed between these two metabolites and CKD progression via Pearson correlation analysis.
Discussion: The identified metabolic pathway dysregulations (amino acid metabolism and redox-related pathways) are core metabolic characteristics of CKD, which are closely associated with renal function impairment, oxidative stress and hypertension in CKD. Adrenaline and NMN may serve as potential candidate biomarkers for CKD, and their abnormal expression may be linked to the activation of the renin-angiotensin system and dysregulation of renal energy metabolism. However, the specific mechanistic roles of these two metabolites in CKD pathophysiology remain to be elucidated.
Conclusion: This study comprehensively characterized serum metabolic alterations in CKD and identified key dysregulated metabolic pathways, as well as adrenaline and NMN as potential candidate biomarkers. These findings enhance the understanding of biochemical dysregulation underlying CKD and provide novel insights for future diagnostic biomarker development and targeted therapeutic exploration for CKD.

Keywords:  catecholamines; chronic kidney disease; hypertension; untargeted metabolomics

DOI:  https://doi.org/10.2147/IJNRD.S597353
Talanta. 2026 May 09. pii: S0039-9140(26)00612-0. [Epub ahead of print]309 129956

A comprehensive evaluation of solid-phase extraction strategies for targeted and untargeted liquid chromatography - mass spectrometry metabolomic analysis.

Eva Cífková, Miroslav Lísa, Zdeňka Neuerová, Ondřej Keresteš.

  Sample preparation remains one of the most critical and challenging steps in liquid chromatography - mass spectrometry (LC-MS) metabolomic analysis, as it directly affects metabolite recovery and analytical accuracy and reliability. In this study, we systematically compared solid-phase extraction (SPE) strategies for serum sample preparation in both targeted and untargeted metabolomic approaches. A total of 71 metabolites with diverse structural and polarity characteristics were analyzed using various SPE formats, including dispersive SPE with multiple sorbents and extraction modes, SPE spin columns, SPE pipette tips, and conventional SPE cartridges. Sorbents within the same extraction mode yielded comparable results. Hydrophilic interaction liquid chromatography sorbents demonstrated the highest performance across a wide range of polarities, whereas reversed-phase sorbents favored moderately polar compounds. Ion-exchange sorbents exhibited limited suitability for broad metabolite coverage due to strong pH dependence but improved recovery of ionizable compounds when combined with other sorbents. While different SPE formats showed similar extraction efficiency, their repeatability varied, with spin columns outperforming conventional cartridges. SPE exhibited mitigation of matrix effects in targeted analysis, particularly for highly polar metabolites, compared to protein precipitation (PPT). Although PPT offered higher efficiency in the untargeted workflow, SPE increased feature coverage by up to 50%. Among commercial products, hydrophilic-lipophilic balanced (HLB) sorbents delivered superior efficiency and repeatability, with HLB-packed spin columns providing the most universal and robust performance for LC-MS metabolomic analysis. The results of this systematic evaluation offer practical guidance for selecting appropriate sample preparation strategies tailored to specific analytical goals and target metabolites.

Keywords:  Mass spectrometry; Metabolomics; Sample preparation; Solid phase extraction; Targeted analysis; Untargeted analysis

DOI:  https://doi.org/10.1016/j.talanta.2026.129956
Folia Microbiol (Praha). 2026 May 20.

Identification of antigonorrhoeal phytochemical lead compounds using a metabolomics-guided approach.

Perfoy Lumu Lumu, Phanankosi Moyo, Alphonse Onana, Patrick Matthew McMillan, Madelien Wooding, Samkelo Malgas, Vinesh Jaichand Maharaj, Sekelwa Cosa.

  Neisseria gonorrhoeae remains a high-priority pathogen according to the World Health Organisation Bacterial Priority Pathogens List. In South Africa, increasing antimicrobial resistance to tetracycline, ciprofloxacin, and penicillin is limiting available treatment options for N. gonorrhoeae infections. This challenge necessitates the exploration of alternative therapeutics, with natural products representing a promising source of novel scaffolds. This study employed a metabolomics-guided approach to tentatively identify antigonorrhoeal compounds from South African medicinal plants. Sixteen crude extracts prepared by ultrasonic extraction, and 112 solid-phase extraction (SPE) fractions, were screened against N. gonorrhoeae ATCC 49981 using broth microdilution. Leaves from Helichrysum odoratissimum (L.) Sweet and leaves and twigs from Terminalia phanerophlebia Engl. & Diels yielded the most active samples, with minimum inhibitory concentrations (MICs) as low as 6.25 µg/mL. SPE fractions from both plants were analysed by Ultra-Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UPLC-HRMS). Data were processed using the Waters UNIFI® platform and analysed by Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) in MetaboAnalyst 6.0. Putative compounds were annotated using in silico tool, including SIRIUS, complemented by MassLynx, and a literature search, enabling tentative annotation of 23 compounds. Of these, 12 were prioritised by the Neisseria Bayesian model (score ≥ 0.5), including kaempferol and carvacrol in H. odoratissimum, and vitexin and isoscopoletin in T. phanerophlebia. Tanimoto similarity analysis revealed low structural similarity to ciprofloxacin (< 0.30), indicating novelty relative to fluoroquinolone scaffolds. These findings provide candidate compounds for further validation and demonstrate that integrating metabolomics with computational prediction can accelerate antigonorrhoeal discovery.

Keywords:   Helichrysum odoratissimum ; Terminalia phanerophlebia ; Antibacterial; Gonorrhoea; Metabolomics

DOI:  https://doi.org/10.1007/s12223-026-01500-7