bims-mebolo 2026-05-17 papers

bims-mebolo

Biomed News

on Metabolomics

Issue of 2026–05–17
twelve papers selected by
Daniel Méndez Rodríguez, Vbi-Ugent

Data-Driven Filter for Detector Oscillation Artifacts in Time-of-Flight Mass Spectrometry.
Metabolomics data of root and stem tissues in five-year-old Sophora flavescens.
An Overview of Metabolomic Approaches to Polyphenol Profiling for Nutraceutical Development.
An Exploratory LC-HRMS Metabolomics Study of Culture Medium-Dependent Metabolic Variation and Bioactivity in Ten Fungal Strains.
Practical guide for marine exo-metabolomic sample preparation.
Application of Ambient Ionization Mass Spectrometry to the Analysis of Cannabis.
LanthMS: A Computational Tool for the Structure Elucidation of Lanthipeptides from Tandem Mass Spectrometry Data.
Reliable Molecular Retrieval from Mass Spectra Using Conformal Prediction.
Chemical language models for natural product discovery.
Scanning central carbon metabolism: a HILIC-HR-TOF-MS metabolome method.
Untargeted Metabolomics Reveals Metabolic Reprogramming Associated with Variable Temperature Stratification During Dormancy Release in Zanthoxylum armatum Seeds.
Mass Spectrometry-based Lactylome and Confirmation of Lactylated Proteins.

Anal Chem. 2026 May 12.

Data-Driven Filter for Detector Oscillation Artifacts in Time-of-Flight Mass Spectrometry.

Kas J Houthuijs, Karl J Jobst, Frederic Béen.

Time-of-flight mass spectrometry (TOF-MS) is widely used for complex mixture analysis due to its ability to detect thousands of chemical compounds in a single sample. However, high-intensity ions can saturate the detector and generate artifacts, commonly referred to as "detector ringing". These artifacts resemble true MS features and often persist after componentization, complicating data interpretation and feature prioritization. Here, we introduce a data-driven filtering approach that exploits the time-domain origin of detector ringing to remove such artifacts. Because ion flight times scale with m/z, oscillations in the detector response manifest as artifactual features with constant delays in m/z, providing a universal detection criterion independent of ion source conditions and chromatographic or ion mobility separation. The workflow was evaluated on GC-, LC-, and ion mobility-hyphenated TOF-MS datasets from multiple instrumental platforms, identifying and removing up to 5.3% of features. Comparison across instruments revealed that Δm/z spacings are not only constant but also instrument-specific and polarity-independent, suggesting potential applications in data forensics and traceability. To assess broader relevance, the filter was applied to the MassBank and NIST DART-MS Forensics fragmentation mass spectral libraries. Although the overall incidence was low relative to library size (1.7%), over 1000 mass spectra were provisionally identified as containing ringing artifacts. Clustering these spectra by their Δm/z effectively partitioned the data by TOF-MS platform, revealing distinct groupings across libraries and contributors. By specifically targeting detector-derived artifacts, this workflow reduces false positives and improves feature prioritization and compound identification, enhancing the robustness of TOF-MS-based chemical analyses.

DOI: https://doi.org/10.1021/acs.analchem.6c00762
Data Brief. 2026 Jun;66 112735

Metabolomics data of root and stem tissues in five-year-old Sophora flavescens.

Ning Zhang, Juan-Fang Li, Si-Chen Zhao, Xiao-Yu Wang, Min Li, Fei-Li Xu, Xiao-Qing Li.

  This dataset provides full metabolomic data for root and stem samples of Sophora flavescens, a commonly used Chinese medicine. Metabolites were extracted separately from root and stem samples and subjected to high-resolution LC-MS in positive and negative modes. Raw spectral data were used for peak detection, alignment, normalization, and metabolite annotation using established metabolomic pipelines and public databases. Principal component analysis and partial least-squares discriminant analysis were used to obtain tissue-specific metabolic profiles. Differential metabolite analysis was conducted to determine the metabolites that were more enriched and depleted in roots and stems, and pathway enrichment analysis was performed to determine the metabolic pathways related to these metabolites. The dataset contains raw LC-MS files, processed feature tables, annotated metabolite lists, and statistical analysis results in a standard format. In these data, there are important materials for studying the tissue-specific metabolism different of S. flavescens, as well as for conducting research on the different distribution of medicine materials and studying biosynthesis routes and quality identification of medicinal plants. The same will be helpful for comparative metabolomics, integrative multi-omics, and natural products.

Keywords:  LC-MS; Medicinal plants; Metabolomics; Sophora flavescens; Tissue-specific metabolism

DOI:  https://doi.org/10.1016/j.dib.2026.112735
Molecules. 2026 Apr 28. pii: 1468. [Epub ahead of print]31(9):

An Overview of Metabolomic Approaches to Polyphenol Profiling for Nutraceutical Development.

Temitope Oluwaferanmi Egbeniyi, Julius Dongsogo, Titilayo Oluwayemisi Bamidele, Alberta N A Aryee.

  Secondary plant metabolites such as polyphenols (flavonoids, phenolic acids, stilbenes, and lignans) are valued for their numerous benefits and commonly associated with antioxidants, anti-inflammatory, anticancer, neuroprotective, and antidiabetic effects. Comprehensive profiling facilitates their identification and quantification, with metabolomics emerging as an increasingly valuable tool. This current work provides an overview of recent application of metabolomics for investigating polyphenols with nutraceutical potential. It also highlights the influence of plant species and environmental stressors (both biotics and abiotic) inducing metabolic shifts that promote the production and accumulation of these bioactive compounds (BACs). While various analytical tools including mass spectrometry (MS) coupled with liquid chromatography (LC-MS) or gas chromatography (GC-MS), as well as nuclear magnetic resonance (NMR) spectroscopy have been utilized to identify the diverse group of polyphenol metabolites, LC-MS has been predominantly used due to its superior sensitivity and wider metabolite coverage, with flavonoids being the main compounds identified. The integration of bioinformatic tools and pathway enrichment analysis in metabolomics is providing expansive insight into the pool of polyphenols, and their bio-functional interpretation and metabolic variations beyond the narrow scope of chromatographic separation alone. This overview also identifies limitations of current methods and suggests directions for future research, aimed at facilitating the development of nutraceuticals.

Keywords:  data analysis; environmental stressors; metabolomics; nutraceuticals; phytochemicals

DOI:  https://doi.org/10.3390/molecules31091468
Int J Mol Sci. 2026 Apr 27. pii: 3866. [Epub ahead of print]27(9):

An Exploratory LC-HRMS Metabolomics Study of Culture Medium-Dependent Metabolic Variation and Bioactivity in Ten Fungal Strains.

Ria Desai, Gagan Preet, Rishi V Astakala, Adriana Romero-Otero, Pilar Sanchez, Thomas A Mackenzie, Thomas O Larsen, Rainer Ebel, Marcel Jaspars.

  Fungi represent a prolific source of structurally diverse secondary metabolites, yet the extent to which culture conditions reshape the metabolic profile and functional bioactivity remains incompletely understood. In this exploratory study, ten fungal strains belonging to genera Penicillium and Aspergillus were cultivated in Yeast Extract Sucrose (YES) and Czapek Yeast Autolysate (CYA) media and analysed using untargeted LC-HRMS metabolomics. The objective of this study was to evaluate how culture medium influences metabolic profiles and to investigate medium-dependent metabolic variation and its relation to cytotoxic, antibacterial, and antifungal activities. Global metabolic profiling revealed moderate but statistically significant medium-associated metabolite variation, with discriminant metabolites predominantly enriched under CYA conditions. Putative structural annotation suggested patterns consistent with differential regulation of isoprenoid-derived sterols, terpenoids, alkaloid-like metabolites, and aromatic polyketides. While antimicrobial activities displayed a heterogeneous, strain-dependent pattern with limited correlation to individual metabolites, cytotoxic activity co-varied with metabolite composition in OPLS regression modelling. Sterols and terpenoid-related features emerged as major contributors to cytotoxicity. Given the absence of biological replication and the limited sample size inherent to this pilot study, all findings should be considered hypothesis-generating and interpreted within an exploratory framework. These results suggest that nutrient composition influences biosynthetic pathway activation while functional outcomes remain strongly dependent on strain-specific metabolic capacity. This work provides a systematic framework and targeted hypothesis for future investigations into condition-dependent fungal chemical diversity in natural product discovery.

Keywords:  OPLS regression; cytotoxic activity; fungal secondary metabolism; metabolite-bioactivity association; molecular networking; untargeted metabolomics

DOI:  https://doi.org/10.3390/ijms27093866
ISME J. 2026 May 12. pii: wrag115. [Epub ahead of print]

Practical guide for marine exo-metabolomic sample preparation.

Catherine C Bannon, Jana K Geuer, Lennart Stock, Bruna Y P Imai, Manuel Liebeke.

  Metabolomic strategies are being increasingly applied in marine systems, leading to unprecedented insights into the ocean and its inhabitants. In particular, exo-metabolites, small molecules produced by marine organisms and released into the environment, have received recognition as factors that influence marine microbial communities, global nutrient cycles, and ecosystem function. Studying exo-metabolites in the marine system is challenging due to the low metabolite concentrations, high salt content, and substantial chemical diversity, all of which can interfere with metabolite detection and therefore necessitate specialized, often tedious sample preparation. Advantageously, new marine metabolomic methods have recently emerged that improve the coverage and detection of key metabolite classes. Here, we offer a practical guide for selecting suitable extraction methods based on specific research needs alongside a systematic review of five recently published methods designed for marine exo-metabolomics. Specifically, these workflows enable quantification of various primary metabolites using liquid and gas chromatography coupled to mass spectrometry. The selection process is guided by several key questions that consider both experimental design and the specific research questions. Additionally, we highlight both the practical constraints and aspects of sample handling for each method. This guide aims to support researchers in effectively choosing a method that aligns with their study goals and logistical capabilities, while also highlighting the opportunities for innovation in the field moving forward.

Keywords:  dissolved organic material; environmental metabolomics; seawater

DOI:  https://doi.org/10.1093/ismejo/wrag115
Annu Rev Anal Chem (Palo Alto Calif). 2026 May;19(1): 25-47

Application of Ambient Ionization Mass Spectrometry to the Analysis of Cannabis.

B Garosi, R A Musah.

  To circumvent multiple challenges associated with Cannabis analysis by conventional methods such as gas chromatography (GC), liquid chromatography (LC), and hyphenated techniques such as GC and LC mass spectrometry (MS), there is increasing interest in the application of ambient ionization mass spectrometry (AIMS) for its chemical characterization, as this approach can in principle address several of the issues associated with interrogation of Cannabis-derived complex matrix samples. Among the advantages that these methods confer are rapid analysis times; limited or no need for sample pretreatment steps; detection of a range of compound classes in a single analysis, including cannabinoids, terpenes, flavonoids, and pesticides; avoidance of nuanced method development tailored to particular analyte classes; and the ability to analyze samples in their native forms. This review highlights the progress thus far in the nascent area of application of AIMS approaches to Cannabis and Cannabis-derived materials, and the further developments required in order for AIMS methods to be more widely adopted for routine analysis.

Keywords:  Cannabis; ambient ionization; cannabinoids; high-throughput analysis; mass spectrometry

DOI:  https://doi.org/10.1146/annurev-anchem-080524-100042
Protein Pept Lett. 2026 ;33(2): 439-453

LanthMS: A Computational Tool for the Structure Elucidation of Lanthipeptides from Tandem Mass Spectrometry Data.

Lingyun Zhao, Wenya Zhao, Yujing Li, Yang Zhang, Yana Wang, Feiyan Zhang, Yingxue Feng, Liping Zhang, Hongwei Liu.

   INTRODUCTION/OBJECTIVE: Lanthipeptides are a class of ribosomally synthesized peptides with intricate ring structures, whose structural elucidation poses significant challenges. This study aimed to develop a computational tool named LanthMS to efficiently and accurately determine the topology of lanthipeptides directly from tandem Mass Spectrometry (MS/MS) data, thereby overcoming the limitations of conventional approaches in deciphering their dehydration and cyclization modifications.
METHODS: This study developed the specialized software LanthMS. The software exhaustively enumerates all possible lanthipeptide structures derived from given peptide sequences and assigns multidimensional scores by comprehensively comparing theoretical spectra against experimental MS/MS data, thereby predicting the most probable structures.
RESULTS: Using this approach, two novel lanthipeptides, amyA and amyC, were identified, from the Bacillus amyloliquefaciens WS-8 strain.
DISCUSSION: The LanthMS tool developed and validated in this study provides an automated solution for the structural elucidation of lanthipeptides. It not only significantly reduces the difficulty and subjectivity of manual interpretation but also deeply integrates computational structural prediction with experimental mass spectrometry data. This establishes a key technological framework for accelerating the discovery of lanthipeptides with novel activities and guiding their rational engineering.
CONCLUSION: As a specialized in silico prediction tool, LanthMS substantially reduces the burden of manual interpretation, enhances the efficiency and accuracy of structural confirmation, and serves as a powerful engine for rapidly exploiting and engineering lanthipeptides with novel activities.

Keywords:  Antimicrobial peptides; Bacillus amyloliquefaciens; bioinformatics; computational prediction; dehydration; posttranslational modifications

DOI:  https://doi.org/10.2174/0109298665461951260413052350
J Chem Inf Model. 2026 May 11.

Reliable Molecular Retrieval from Mass Spectra Using Conformal Prediction.

Morteza Rakhshaninejad, Gaetan De Waele, Mira Jürgens, Willem Waegeman.

A key task in the computational analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data is identifying the molecular structure underlying a measured spectrum. A common approach ranks candidate molecules retrieved from chemical databases using predicted fingerprint similarities, yet standard metrics such as top-k accuracy summarize performance only at the data set level and provide no spectrum-specific reliability statement. In this work, we apply conformal prediction to candidate-based molecular retrieval to construct spectrum-specific prediction sets that contain the true molecule with a user-specified probability. We evaluate marginal and conditional conformal prediction across three experimental scenarios representing in-distribution, partially shifted, and fully out-of-distribution settings on the MassSpecGym benchmark. When calibration and test data are aligned, conformal prediction attains the target coverage with small candidate sets for most spectra. Under distribution shift, prediction sets become larger as rankings grow more ambiguous, although candidates can still be reduced when calibration remains representative. Conditional conformal prediction improves subgroup reliability across spectra of different difficulty, with the best gains obtained using confidence-based grouping. Overall, conformal prediction turns candidate rankings into reliable, spectrum-specific candidate sets with an explicit reliability-efficiency trade-off.

DOI: https://doi.org/10.1021/acs.jcim.6c00727
Nat Prod Rep. 2026 May 12.

Chemical language models for natural product discovery.

Koh Sakano, Kairi Furui, Apakorn Kengkanna, Yuta Kikuchi, Masahito Ohue.

Covering: up to 2026Natural products are an important source of medicines, yet their discovery can be a slow and laborious process. The recent development of chemical language models (CLMs), which process string-based molecular representations, is reshaping the field of natural product science. This review provides an overview of the role of CLMs in natural product drug discovery, tracing their evolution from early neural networks to modern large-scale Transformers. We describe how these models accelerate discovery timelines by predicting bioactivity, biosynthetic pathways, and spectral data. Furthermore, we cover their use in proposing novel, natural-product-like scaffolds that expand the computationally explored chemical space. The review also addresses persistent challenges, including the limited availability of natural product data and the need for model interpretability. Finally, we discuss future directions, outlining the current status and prospects for CLM-enabled natural product science.

DOI: https://doi.org/10.1039/d6np00002a
Metabolomics. 2026 May 13. pii: 71. [Epub ahead of print]22(3):

Scanning central carbon metabolism: a HILIC-HR-TOF-MS metabolome method.

Victoria Pozo Garcia, Valentina Ferro, Jolene Rier, Sofia Moco.

   INTRODUCTION: Central carbon metabolism (CCM) is the primary metabolic hub of the cell, governing energy production and providing precursors essential for a myriad of biosynthetic pathways. Developing analytical tools that can identify and quantify intermediates of these metabolic reactions is crucial for studying cell metabolism in biomedical and biotechnological applications.
OBJECTIVE: This study proposes a liquid chromatography (LC)-high-resolution (HR) mass spectrometry (MS) method, covering the CCM of mammalian cell systems.
METHODS: Cells were extracted using a one-step liquid extraction, recovering the hydrophilic metabolites. A stable isotope dilution approach was employed, utilizing a U-13C-yeast internal standard (IS). A LC-HRMS metabolomics method using hydrophilic interaction liquid chromatography (HILIC) coupled to a Zeno-time-of-flight (ZenoTOF) MS was implemented for metabolite semi-quantification.
RESULTS: A total of 82 CCM metabolites is reported, of which 77 were confirmed with authentic standards, and for 63 , linearity ranges were obtained. IS normalization enhanced overall robustness, from sample preparation to metabolite semi-quantification. To study the effects on CCM by 5 chemical inhibitors (2-deoxy-D-glucose, etomoxir, UK-5099, rotenone, and 3-nitropropionic acid), our HILIC-HR-TOF-MS method was used. The approach proved efficient in capturing altered metabolite concentrations, within implicated metabolic reactions, as a consequence of inhibitor exposure.
CONCLUSION: Our HILIC-HR-TOF-MS metabolome method is efficient in mapping changes in metabolic intermediates of the CCM in mammalian cells. This approach holds potential for analysing a variety of biological samples across a range of applications, from drug development to biomedicine.

Keywords:  Central carbon metabolism; HILIC; LC–MS; Metabolomics

DOI:  https://doi.org/10.1007/s11306-026-02434-4
Biology (Basel). 2026 Apr 23. pii: 666. [Epub ahead of print]15(9):

Untargeted Metabolomics Reveals Metabolic Reprogramming Associated with Variable Temperature Stratification During Dormancy Release in Zanthoxylum armatum Seeds.

Manyi Fu, Fengjuan Zhou, Chang Liu, Jihong Xiao, Yushan Zheng.

  The deep physiological dormancy of Zanthoxylum armatum DC. seeds severely limits its seedling propagation efficiency. Variable temperature stratification is an effective treatment for promoting dormancy release; however, the metabolic basis underlying this process remains poorly understood. In this study, we utilized a UPLC-MS/MS-based untargeted metabolomics approach, coupled with multivariate statistical analyses (PCA and OPLS-DA), to profile metabolic changes in Z. armatum seeds subjected to variable temperature stratification in a moist sand substrate (15 °C in the dark for 10 days, followed by 4 °C for 20 days). A total of 3687 metabolic features were detected, among which 33 structurally annotated differential metabolites were retained for biological interpretation, including 8 upregulated and 25 downregulated metabolites. Pathway enrichment analysis indicated that α-linolenic acid metabolism and linoleic acid metabolism were markedly altered after stratification. In particular, 9-(S)-HPOTE, colneleate, jasmonic acid (JA), and JA-ACC were significantly reduced, suggesting that attenuation of JA-related oxylipin metabolism may be associated with dormancy release in Z. armatum seeds. In addition, coordinated changes in phenylpropanoid- and cutin/wax-related metabolites implied remodeling of seed-coat-associated metabolism, whereas the accumulation of branched-chain amino acids and the alteration of sulfur- and purine-related metabolites suggested reorganization of metabolic reserves during the transition from dormancy to germination. Overall, these results provide metabolomic evidence that variable temperature stratification is associated with extensive metabolic reprogramming in Z. armatum seeds and highlight JA-related lipid metabolism as a candidate pathway involved in dormancy release.

Keywords:  Zanthoxylum armatum DC.; seed dormancy release; untargeted metabolomics; variable temperature stratification

DOI:  https://doi.org/10.3390/biology15090666
J Vis Exp. 2026 Apr 24.

Mass Spectrometry-based Lactylome and Confirmation of Lactylated Proteins.

Maude Hamilton, Camille Bazin, Dominique Lévesque, François-Michel Boisvert, Véronique Giroux.

First identified in 2019, lactylation, a new post-translational modification (PTM) on lysine residues, has since been shown to be of interest in multiple pathologies and physiological contexts. It possesses two isomers (L- and D-lactylation) and is known to alter complex formation, cellular localization, or stability of target proteins. This protocol describes a method for analyzing lactylation from cell culture to confirmation of potential lactylated targets, using the TE11 esophageal squamous cell carcinoma cell line as an example. The following protocol will provide details on the identification of lactylated proteins through L-lactyllysine (KL-LA)-specific immunoprecipitation (IP) of peptides for mass spectrometry (MS) analysis: (1) protein extraction, (2) protein reduction, alkylation and digestion, (3) protein purification and peptides concentration, (4) IP using KL-LA beads and (5) concentration of KL-LA peptides. Following these steps, analyses of the MS raw data will be performed to identify lactylated sites, and confirmation of these putative lactylated proteins by IP will be described. Using this method, a range of 100 to 500 peptides can be identified, and lactylated proteins of interest can be confirmed. To conclude, studying lactylated proteins has the potential to enhance our understanding of PTM-driven cell signaling in both normal and disease conditions.

DOI: https://doi.org/10.3791/68597