OBJECTIVES: This study presents a versatile, AI-guided workflow for the targeted isolation and characterization of prenylated flavonoids from Paulownia tomentosa (Thunb.) Steud. (Paulowniaceae). METHODS: The approach integrates established extraction and chromatography-based fractionation protocols with LC-UV-HRMS/MS analysis and supervised machine-learning (ML) custom-trained classification models, which predict prenylated flavonoids from LC-HRMS/MS spectra based on the recently developed Python package AnnoMe (v1.0). RESULTS: The workflow effectively reduced the chemical complexity of plant extracts and enabled efficient prioritization of fractions and compounds for targeted isolation. From the pre-fractionated plant extracts, 2687 features were detected, 42 were identified using reference standards, and 214 were annotated via spectra library matching (public and in-house). Furthermore, ML-trained classifiers predicted 1805 MS/MS spectra as derived from prenylated flavonoids. LC-UV-HRMS/MS data of the most abundant presumed prenyl-flavonoid candidates were manually inspected for coelution and annotated to provide dereplication. Based on this, one putative prenylated (C5) dihydroflavonol (1) and four geranylated (C10) flavanones (2-5) were selected and successfully isolated. Structural elucidation employed UV spectroscopy, HRMS, and 1D as well as 2D NMR spectroscopy. Compounds 1 and 5 were isolated from a natural source for the first time and were named 6-prenyl-4'-O-methyltaxifolin and 3',4'-O-dimethylpaulodiplacone A, respectively. CONCLUSIONS: This study highlights the combination of machine learning with analytical techniques to streamline natural product discovery via MS/MS and AI-guided pre-selection, efficient prioritization, and characterization of prenylated flavonoids, paving the way for a broader application in metabolomics and further exploration of prenylated constituents across diverse plant species.

• Keywords
• bioactive compounds, geranylated flavonoids, prenylated polyphenols, specialized metabolites, untargeted metabolomics,
• Publication type
• Journal Article MeSH

Untargeted high-resolution mass spectrometry is a key tool in clinical metabolomics, natural product discovery and exposomics, with compound identification remaining the major bottleneck. Currently, the standard workflow applies spectral library matching against tandem mass spectrometry (MS2) fragmentation data. Multi-stage fragmentation (MSn) yields more profound insights into substructures, enabling validation of fragmentation pathways; however, the community lacks open MSn reference data of diverse natural products and other chemicals. Here we describe MSnLib, a machine learning-ready open resource of >2 million spectra in MSn trees of 30,008 unique small molecules, built with a high-throughput data acquisition and processing pipeline in the open-source software mzmine.

• Publication type
• Journal Article MeSH

Invisible to human perception, differentiation in chemical traits such as insects cuticular hydrocarbons (CHCs) might contribute to speciation. The species-rich mountain butterfly genus Erebia represents a well-established model for studying speciation because closely related taxa form stable secondary contact zones. However, to which degree these taxa would also differ in their chemical composition of the cuticle has remained unexplored. We compared CHCs of males and females from four locally sympatric or parapatric sister taxa pairs with varying levels of gene flow. Rarely hybridizing taxa pairs (E. cassioides-E. tyndarus, E. euryale-E. ligea) exhibited significant CHC differentiation at both interspecific and intersexual levels. Conversely, taxa pairs with no prior contact (E. melampus-E. sudetica) or frequent ongoing hybridization in their contact zones (E. euryale adyte-E. e. isarica) showed limited CHC differentiation. Our findings suggest that differentiation in CHC profiles scales with among-species gene flow. Although it remains unclear whether CHCs are involved in mate recognition in Erebia, the observed differentiation could play a role in reproductive isolation, particularly under environmental changes that promote novel interspecific interactions. Future research should explore the role of CHC divergence across hybrid zone gradients and pinpoint the genomic regions underlying CHC synthesis and perception.

• Keywords
• CHCs, Lepidoptera, contact pheromones, secondary contact, semiochemicals,
• Publication type
• Journal Article MeSH

Metabolite identification in non-targeted mass spectrometry-based metabolomics remains a major challenge due to limited spectral library coverage and difficulties in predicting metabolite fragmentation patterns. Here, we introduce Multiplexed Chemical Metabolomics (MCheM), which employs orthogonal post-column derivatization reactions integrated into a unified mass spectrometry data framework. MCheM generates orthogonal structural information that substantially improves metabolite annotation through in silico spectrum matching and open-modification searches, offering a powerful new toolbox for the structure elucidation of unknown metabolites at scale.

• MeSH
• Metabolome * MeSH
• Metabolomics * methods   MeSH
• Tandem Mass Spectrometry * methods   MeSH
• Publication type
• Journal Article MeSH

Mass spectrometry (MS) has changed our understanding of health, disease, and the environment through untargeted analyses where entire molecular classes are investigated. These techniques generate huge amounts of data which when processed by statistical tools can identify important molecular features or biomarkers. The complexities of these samples are not compatible with direct introduction to the MS system and require a high-resolution separation step, typically low flow liquid chromatography (LC), prior to MS. LC columns that can produce adequate linear velocities at these low flow rates are small in volume making their results susceptible to resolution loss in extra-column volumes. Here, we investigate the implications of the extra-column effects in five LC-MS systems with triple quadrupole and orbitrap mass analyzers. The extra-column volume of these systems in their standard configuration ranged from 26.4 to 78.1 μL which we reduced to 9.57 to 18.7 μL by optimizing the fluidics. The effects of this volume reduction were assessed by studying a hydrolyzed protein sample in a proteomics environment where the intensity of the largest MS peak was improved by 1.8-3.8×. Additionally, the number of molecular features detected in the protein sample improved by up to 7.5×. The relationship between extra-column volumetric variance and flow rate shows that broadening will become much larger for MS detectors at higher flow rates, unlike a traditional small volume UV detector. The methods, applications, and theoretical insights in this work can be used to improve the mass spectrometric results of any LC-MS system.

• Keywords
• LC-MS, band broadening, extra-column effects, instrumentation, liquid chromatography, omics, proteomics,
• Publication type
• Journal Article MeSH

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

• MeSH
• Data Mining * methods   MeSH
• Mass Spectrometry * methods   MeSH
• Humans MeSH
• Metabolomics * methods   MeSH
• Programming Languages * MeSH
• Software * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Roots from the Aconitum (Wolf's-Bane) and Delphinium (Larkspur) genera have been widely used in traditional medicine owing to the abundance of bioactive diterpenoid alkaloids that they produce. Despite extensive research on these compounds and their potential medicinal applications, their structural complexity precludes their production through total chemical synthesis, and little progress has been made towards elucidation of their biosynthetic pathways. Here, we report the entry steps in the biosynthesis of the diterpenoid alkaloid atisinium, constituting six enzymes identified from the Siberian Larkspur (Delphinium grandiflorum) and garden monkshood (Aconitum plicatum) through a combination of comparative transcriptomics between tissue types and genera and coexpression analysis. This pathway includes a pair of terpene synthases, three cytochromes P450, and a reductase with little homology to other characterized enzymes. We further demonstrate, through incorporation of isotopically labelled substrates, the preference of the reductase for ethanolamine over ethylamine, and similarly that ethanolamine is the preferred source of nitrogen for the majority of detected diterpenoid alkaloids. Identification of these enzymes and production of a key intermediate in a heterologous host paves the way for biosynthetic production of this group of metabolites with promise for medicinal applications.

• Publication type
• Journal Article MeSH
• Preprint MeSH

Lipid A, a crucial component of lipopolysaccharides (LPS), plays a pivotal role in the pathogenesis of Gram-negative bacteria. Lipid A patterns are recognized by mammals and can induce immunostimulatory effects. However, the outcome of the interaction is highly dependent on the chemical composition of individual lipid A species. The diversity of potential fatty acyl and polar headgroup combinations in this complex saccharolipid presents a significant analytical challenge. Current mass spectrometry (MS)-based lipid A methods are focused on either direct matrix-assisted laser desorption/ionization (MALDI)-MS screening or comprehensive structural elucidation by tandem mass spectrometry (MS/MS) hyphenated with separation techniques. In this study, we developed an alternative workflow for rapid lipid A profiling covering the entire analysis pipeline from sample preparation to data analysis. This workflow is based on microextraction and subsequent MALDI-MS/MS analysis of uropathogenic Escherichia coli utilizing trapped ion mobility spectrometry (TIMS), followed by mzmine data processing. The additional TIMS dimension served for enhanced sensitivity, selectivity, and structural elucidation through mobility-resolved fragmentation via parallel accumulation-serial fragmentation (PASEF) in parallel reaction monitoring (prm)-mode. Furthermore, mzmine enabled automated MS/MS acquisition by adapting the spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) strategy for MALDI spot analysis. It also facilitated robust lipid A annotation through a newly developed extension of the rule-based lipid annotation module, allowing for the custom generation of lipid classes, including specific fragmentation rules. In this study, the first publication of lipid A species' collision cross section (CCS) values is reported, which will enhance high-confidence lipid A annotation in future studies.

• MeSH
• Gram-Negative Bacteria * chemistry   MeSH
• Ion Mobility Spectrometry * methods   MeSH
• Lipid A * analysis   chemistry   MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization * methods   MeSH
• Tandem Mass Spectrometry methods   MeSH
• Uropathogenic Escherichia coli * chemistry   MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Lipid A * MeSH

Plant specialized metabolites play key roles in diverse physiological processes and ecological interactions. Identifying structurally novel metabolites, as well as discovering known compounds in new species, is often crucial for answering broader biological questions. The Piper genus (Piperaceae family) is known for its special phytochemistry and has been extensively studied over the past decades. Here, we investigated the alkaloid diversity of Piper fimbriulatum, a myrmecophytic plant native to Central America, using a metabolomics workflow that combines untargeted LC-MS/MS analysis with a range of recently developed computational tools. Specifically, we leverage open MS/MS spectral libraries and metabolomics data repositories for metabolite annotation, guiding isolation efforts toward structurally new compounds (i.e., dereplication). As a result, we identified several alkaloids belonging to five different classes and isolated one novel seco-benzylisoquinoline alkaloid featuring a linear quaternary amine moiety which we named fimbriulatumine. Notably, many of the identified compounds were never reported in Piperaceae plants. Our findings expand the known alkaloid diversity of this family and demonstrate the value of revisiting well-studied plant families using state-of-the-art computational metabolomics workflows to uncover previously overlooked chemodiversity. To contextualize our findings within a broader biological context, we employed a workflow for automated mining of literature reports of the identified alkaloid scaffolds and mapped the results onto the angiosperm tree of life. By doing so, we highlight the remarkable alkaloid diversity within the Piper genus and provide a framework for generating hypotheses on the biosynthetic evolution of these specialized metabolites. Many of the computational tools and data resources used in this study remain underutilized within the plant science community. This manuscript demonstrates their potential through a practical application and aims to promote broader accessibility to untargeted metabolomics approaches.

• Keywords
• Piper fimbriulatum, Piperaceae, Wikidata, alkaloids, angiosperms, computational metabolomics, mass spectrometry, technical advance,
• MeSH
• Alkaloids * metabolism   chemistry   MeSH
• Chromatography, Liquid MeSH
• Metabolomics * methods   MeSH
• Myrmecophytes MeSH
• Piper * metabolism   chemistry   MeSH
• Tandem Mass Spectrometry MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Alkaloids * MeSH

Fusarium fungi are widespread pathogens of food crops, primarily associated with the formation of mycotoxins. Therefore, effective mitigation strategies for these toxicogenic microorganisms are required. In this study, the potential of pulsed electric field (PEF) as an advanced technology of increasing use in the food processing industry was investigated to minimize the viability of Fusarium pathogens and to characterize the PEF-induced changes at the metabolomic level. Spores of four Fusarium species (Fusarium culmorum, Fusarium graminearum, Fusarium poae, and Fusarium sporotrichioides) were treated with PEF and cultured on potato dextrose agar (PDA) plates. The viability of the Fusarium species was assessed by counting the colony-forming units, and changes in the mycotoxin content and metabolomic fingerprints were evaluated by using UHPLC-HRMS/MS instrumental analysis. For metabolomic data processing and compound identification, the MS-DIAL (v. 4.80)-MS-CleanR-MS-Finder (v. 3.52) software platform was used. As we found out, both fungal viability and the ability to produce mycotoxins significantly decreased after the PEF treatment for all of the species tested. The metabolomes of the treated and untreated fungi showed statistically significant differences, and PEF-associated biomarkers from the classes oxidized fatty acid derivatives, cyclic hexapeptides, macrolides, pyranocoumarins, carbazoles, and guanidines were identified.

• Keywords
• Fusarium, UHPLC-HRMS/MS, biomarkers, food pathogens, metabolomic fingerprinting, mycotoxins, pulsed electric field, spore viability,
• MeSH
• Electricity * MeSH
• Fusarium * metabolism   growth & development   MeSH
• Metabolome * MeSH
• Metabolomics MeSH
• Microbial Viability MeSH
• Mycotoxins * metabolism   MeSH
• Spores, Fungal metabolism   growth & development   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Mycotoxins * MeSH