Neuroborreliosis (LNB) poses a significant diagnostic challenge in paediatrics due to its non-specific clinical presentation and the limited sensitivity of standard tests. Metabolomics offers the possibility of direct insight into the biochemical changes occurring in the central nervous system during infection. The aim of the study was to characterise the metabolic profile of paired serum and cerebrospinal fluid (CSF) in children with LNB to identify candidate immunometabolic signatures associated with the infection. Samples were analysed using NMR spectroscopy and LC-MS/MS. Data gaps were filled using a Random Forest algorithm, followed by univariate and multivariate statistical tests (PLS-DA) and pathway enrichment analysis based on the KEGG database. Profound dysregulation of purine metabolism was demonstrated, manifested by elevated concentrations of hypoxanthine, xanthine and uric acid, which may reflect a combination of non-specific infection-related responses, oxidative stress, and potentially the pathogen’s metabolic demand for host purines. Significant changes were observed in the biosynthesis of aromatic amino acids (particularly L-tyrosine) and in glycerophospholipid metabolism, suggesting damage to the blood-brain barrier. In CSF, alterations in choline and glutamate levels were identified as candidate metabolic signatures potentially associated with neuroinflammation and excitotoxicity. Metabolomic analysis enables precise mapping of the biochemical response in paediatric LNB. The identified abnormalities in purine metabolism, neuroinflammation and cell membrane integrity represent a promising starting point for future biomarker discovery research.
Post-stroke cognitive impairment (PSCI) is a common long-term complication of acute ischemic stroke often accompanied by metabolic disorder, yet early metabolomic predictors remain poorly characterized. This study aimed to identify acute-phase serum metabolomic signatures associated with PSCI and develop a prediction model for early PSCI risk stratification. In this prospective study, 130 acute ischemic stroke patients were enrolled. Serum samples collected within 24 h of stroke onset were subjected to untargeted metabolomic profiling. Cognitive status was assessed at 3 months after stroke. KEGG pathway and MECNA analyses were performed to assess pathway-level and metabolite-metabolite correlation patterns. Bootstrap-LASSO stability selection and logistic regression were used to develop a prediction model, which was evaluated by stratified 10-fold cross-validation. Calibration curves and decision curve analysis assessed model performance. A total of 51 candidate differential metabolites were identified between PSCI and Non-PSCI patients, involving purine/caffeine-related metabolism, bile acid metabolism, lipid and fatty acid metabolism, and amino acid-related metabolism. MECNA identified 15,120 differential metabolite-metabolite correlation pairs, suggesting distinct acute-phase serum correlation patterns in patients who later developed PSCI. Six metabolites were retained in the final model: 6-Hydroxymellein, 21-Deoxycortisol, Inosine, 2-Hydroxy-3-methylbutyric acid, Isoleucyl-Arginine, and Propylparaben. The metabolite-only model achieved an AUC of 0.774 (95
Pathogenic mitochondrial DNA (mtDNA) mutations contribute to a broad spectrum of both common and rare metabolic diseases. However, clinical presentation is highly variable and only partially explained by the proportion of mutant mtDNA or heteroplasmy. With the relationship between mutation burden and clinical manifestation poorly defined, controlled models are required to uncover underlying mechanisms. Here, we explore the metabolic consequences of increasing heteroplasmy in a well-characterised mouse model harbouring a pathogenic mtDNA deletion. Untargeted urinary metabolomics reveals distinct mutation load-dependent metabolic shifts with some metabolites declining early on, while others exhibit threshold-like increases beyond 60
Bisphenol A (BPA) is a widely distributed endocrine-disrupting chemical with documented metabolic effects in experimental models. Although hepatic transcriptional alterations following BPA exposure have been reported, the extent to which these changes translate into functional metabolic remodeling remains unclear. This study aimed to characterize global hepatic metabolomic alterations following chronic exposure to BPA at the lowest observed adverse effect level (LOAEL) in mice and to determine whether the affected metabolic networks overlap with pathways implicated in non-alcoholic fatty liver disease (NAFLD) progression and early events associated with hepatocarcinogenic susceptibility. Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics was performed on liver samples from BPA-exposed (n = 8) and control (n = 6) mice. Differential metabolite analysis and pathway enrichment analysis were conducted to identify significantly altered metabolites and metabolic pathways. BPA exposure induced marked hepatic metabolic remodeling involving polyunsaturated fatty acids, arachidonic acid-derived eicosanoids, lysophospholipids, retinoid metabolism, and phase II detoxification pathways. Dysregulation of omega-3 and omega-6 fatty acids and altered prostaglandin and thromboxane derivatives indicated disruption of inflammatory lipid mediator balance. Changes in retinol- and retinoic acid-related metabolites suggested impaired differentiation-associated signaling, while increased sulfated and glucuronidated metabolites reflected enhanced xenobiotic metabolism. Pathway enrichment analysis highlighted biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, and retinol metabolism as significantly affected pathways. Chronic BPA exposure at a LOAEL dose induces coordinated hepatic metabolic reprogramming characterized by pro-inflammatory lipid remodeling, disruption of retinoid signaling, and activation of detoxification mechanisms. These alterations may represent metabolic features associated with pathways relevant to NAFLD progression and hepatocarcinogenic susceptibility.
Tuberculous meningitis (TBM), caused by Mycobacterium tuberculosis, is the most severe form of extrapulmonary tuberculosis and isassociated with high morbidity and mortality, particularly when diagnosis is delayed. Improved understanding of the metabolic alterations associated withTBM may support the development of novel diagnostic biomarkers and provide insights into disease pathophysiology. In this study, we applied anuntargeted two-dimensional gas chromatography–time-of-flight mass spectrometry (GC×GC-TOFMS) metabolomics approach to formalin-fixed, paraffin-embedded (FFPE) postmortem human brain tissue from 41 TBM cases and 36 tissue sections from 6 non-TBM control cases. Metabolomics data wereprocessed, normalized, and analyzed using multivariate and univariate statistical approaches, including principal component analysis (PCA) and partialleast squares–discriminant analysis (PLS-DA), with variable importance in projection (VIP) scores. These results were further correlated with patient clinical data. Distinct metabolic profiles were observed between TBM and control tissues. Several metabolites were significantly reduced in TBM samples, particularly within the alkane and alkene classes, with additional decreases observed in metabolites associated with alcohols, fatty acids, lipids, carbohydrates, and amino acids. These metabolic alterations suggest substantial perturbations, primarily in the host lysine degradation pathway (linked to the kynurenine pathway), in TBM-affected brain tissue. Collectively, these findings provide insight into the metabolic landscape of terminalTBM and suggest potential metabolic pathways that may contribute to disease pathophysiology. Further investigation of these metabolic signatures in accessible patient tissue and biofluids may support the development of biomarkers and inform future therapeutic strategies for TBM.
Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA. This study enrolled 195 patients with HUA and 98 healthy controls. Global metabolomics and proteomics profiling were performed to characterize molecular alterations underlying HUA. Based on the biological relevance of the shared dysregulated pathways, a pathway correlation network was constructed to elucidate the pathological mechanisms driving HUA initiation and progression. Furthermore, diagnostic biomarkers for HUA were identified using machine learning algorithms, and were validated with an external cohort. HUA patients exhibited distinct metabolic and proteomic profiles compared with healthy controls. Integrated multi-omics pathway analysis revealed that peroxisome proliferators-activated receptor signaling pathway, arachidonic acid metabolism, purine metabolism, pyrimidine metabolism and sphingolipid signaling pathway were significantly dysregulated in HUA. Among them, arachidonic acid metabolism was identified as a hub pathway involved in HUA progression. Furthermore, a metabolite panel consisting of cysteine-S-sulfate, glycerophosphocholine and 4-hydroxyphenylpyruvic acid was screened by machine learning and validated in an independent cohort, which showed slightly higher diagnostic performance for HUA than UA. This study reveals the core metabolic and protein regulatory networks of HUA, and identifies a novel serum metabolite panel for the diagnosis of HUA. These findings provide new insights for improved clinical diagnosis and management.
Ischemic stroke (IS) is a major cause of mortality and disability globally, with challenges in early diagnosis and prognosis prediction. Dysregulated lipid metabolism is key to IS pathophysiology, but comprehensive profiling of lipid changes during disease progression remains limited. This study enrolled 223 IS patients and 57 healthy controls. Plasma lipid profiles were analyzed using broad-coverage targeted lipidomics by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Differential lipids were identified through orthogonal partial least squares discriminant analysis (OPLS-DA) with univariate analysis, and their changes across acute, subacute, convalescent, and chronic phases were examined by clustering analysis. Machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM), were used to screen diagnostic and prognostic biomarkers, followed by logistic regression models and receiver operating characteristic (ROC) curve evaluation. From 607 identified lipids, 54 showed differential abundance between IS patients and healthy controls, grouped into four clusters. For diagnosis, six lipids—LPG(18:0), PE(O-16:0/18:2), TG(52:2/FA16:0), PE(O-16:0/22:6), PE(O-16:0/20:3), and PE(O-18:0/18:2)— achieved an area under the curve (AUC) of 0.984. For prognosis, six lipids—PE(O-16:0/22:5), PE(O-18:0/22:5), SM(d18:1/14:0), PG(18:0/18:1), PE(O-16:0/20:3), and LPI(16:0)—achieved an AUC of 0.925. This study characterizes lipid metabolism changes across IS stages reconstructed from cross-sectional data of different patient groups and establishes two six-lipid panels for diagnosis and prognosis. These findings provide insights into lipid metabolism evolution following stroke and offer candidate biomarker panels for IS management.
Langerhans cell histiocytosis (LCH) is an inflammatory and neoplastic disorder. The levels of metabolites in the plasma of children with LCH have not been studied and may be related to disease progression. We committed to find novel pre-diagnostic metabolites in the plasma of LCH children with posterior pituitary involvement (PI). Non-targeted metabolomics sequencing was used to detect specific and pre-diagnostic metabolites in the plasma of children with LCH. Plasma samples from 56 children with LCH and 27 healthy volunteers were enrolled. Plasma samples of children with LCH were divided into three groups: children have no PI or central nervous system-risk (CNS-risk) bone lesions (NPC group), children with CNS-risk bone lesions but without PI (CNS-risk group), and children with PI (PI group). The N-acetylneuraminic acid, lipoamide, L-Glutathione oxidized and indole-3-propionic acids were potential pre-diagnostic metabolites for LCH children with PI in comparison with the healthy volunteers. Metabolites in the plasma of LCH children with PI enriched specific signaling pathways including arachidonic acid metabolism, ferroptosis, etc. Through targeted metabolomics sequencing, we verified that specific metabolites were existed in the stratified involvements of patients with LCH, such as N-acetylneuraminic acid and Vitamin A, which may be related with alteration of blood-brain barrier permeability and LCH disease progression. Through the joint multi-omics analyses, we discovered that the peripheral dendritic cells may have ferroptosis phenomena, which was regulated by osteopontin secreted in the plasma of patients with LCH. Analyses and identification the differential metabolites in the plasma of children with LCH may significantly improve the early diagnosis and stratified treatment of children with pituitary invovlement.
INTRODUCTION: Most patients with amyotrophic lateral sclerosis (ALS), a fatal motor neuron disease, experience painful muscle cramps. Our recent pilot trial of the Japanese Kampo medicine TJ-68 suggested its efficacy in improving muscle cramps in patients with ALS. OBJECTIVES: This study analyzed plasma metabolomic changes to identify the underlying mechanisms of muscle cramps in ALS and the effects of TJ-68. METHODS: Plasma was obtained from 11 participants with ALS in the repeated crossover trial at five time points (baseline, two placebo phases, and two TJ-68 phases). Metabolites were analyzed using mass spectrometry. Linear mixed-effects models were applied to identify metabolite changes associated with muscle cramps, determine the effects of TJ-68 on metabolites, and predict which participants would respond to TJ-68. RESULTS: Higher glutamine/glutamate, arginine, and leucine levels were associated with more severe muscle cramps. TJ-68 treatment increased tryptophan and aconitate levels but reduced serotonin and acetylcarnitine levels. Long-chain acylcarnitine levels were correlated with muscle cramp severity, and their levels tended to decrease with treatment. Uric acid, β-aminoisobutyric acid, α-aminoadipic acid, and acetylcholine emerged as predictors of the efficacy of TJ-68. CONCLUSION: This study identified the metabolite profile of muscle cramps in ALS and the changes in metabolite levels after TJ-68 treatment. Several baseline metabolites were associated with the prediction of the response to muscle cramps following TJ-68 treatment. Uric acid might be particularly useful because of its easy measurement in standard assays. Our study affirms the value of metabolomic technology for future pharmacotherapy and studies in ALS.
Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.
Background Osteoporosis (OP) is a prevalent metabolic bone disorder and a major public health concern characterized by reduced bone mass and bone microstructural deterioration. Early identification of osteoporosis and implementation of preventive interventions remain critical for reducing fracture risk and disease burden. Identifying plasma biomarkers reflecting metabolic alterations related to OP may facilitate early detection and risk assessment.Methods Untargeted metabolomics and lipidomics profiling based on ultra-performance liquid chromatography-mass spectrometry (UHPLC-MS) was performed in a discovery cohort comprising 75 patients with OP and 140 healthy controls. Differential features were identified using multivariate statistical analysis (PCA, OPLS-DA), and FDR-adjusted univariate analysis. Multivariable logistic regression and LASSO-regularized logistic regression was employed, with age and sex incorporated as mandatory covariates to identify independent lipid predictors. A Random Forest (RF) model was further evaluated in an independent validation cohort consisting of 20 OP patients and 42 healthy controls.Result Sixty-one differential metabolites were identified, primarily enriched in lipid metabolism pathways. Further targeted lipidomics identified four diagnostic lipid biomarkers, including LPA(16:0), LPI(16:0), LPI(18:0), and LPI(20:0). Following covariate adjustment for age and sex, key lipid species remained independently associated with OP. The RF-based diagnostic model maintained robust performance in the validation cohort, yielding an AUC of 0.916 (95% CI: 0.842-0.990), with high sensitivity and specificity.Conclusions LPA(16:0), LPI(16:0), LPI(18:0), and LPI(20:0) in plasma were negatively correlated with the T value of bone mineral density. These associations persisted after stringent adjustment for age and sex, suggesting that lysophospholipid dysregulation is an independent metabolic hallmark of OP. The multi-metabolites model based on four biomarkers showed promising predictive performance for OP and may provide a potential tool for early risk assessment.
Background Doxorubicin (DOX)-based chemotherapy has improved survival outcomes in breast cancer patients but is often limited by doxorubicin-induced cardiotoxicity (DIC). Currently, no validated biomarkers can predict early DIC. Identifying novel biomarkers is essential for detecting patients at higher risk and enable timely interventions before irreversible cardiac injury occurs. Methods Twenty-seven breast cancer patients treated with DOX-containing chemotherapy were stratified by change in left ventricular ejection fraction (LVEF): 19 patients who maintained normal cardiac function (normal, decline < 10%) and 8 who developed cardiotoxicity (abnormal, decline > 10%). Plasma samples were collected at baseline and after chemotherapy for untargeted metabolomic profiling. Both baseline and pre-post designs were employed to capture static and dynamic metabolic alterations associated with DIC. Stepwise logistic regression was used to filter non-informative metabolites, and predictive performance was further validated using Random Forest modeling. Results A well-marked separation of plasma metabolomic profiles was observed between normal and abnormal cardiotoxicity groups at baseline (T0). Statistical analysis identified 100 significant metabolites at baseline (T0) and 78 metabolites after the first cycle of chemotherapy (T0-T1), with 10 metabolites common to both time-points: 3-phosphoglycerate, 2-hydroxyphenylacetate, inosine, taurine, suberate (C8-DC), sebacate (C10-DC), sphingadienine, oxindolylalanine. Machine learning models identified key metabolites (e.g., sebacate [C10-DC], 2-hydroxyhippurate, orotate, picolinate, and suberate [C8-DC]) as candidate predictors of cardiotoxicity, achieving moderate discriminatory performance in cross-validation, with higher specificity than sensitivity, indicating limited detection of abnormal cases. Conclusions Metabolomic profiling shows potential for early detection of DIC in breast cancer patients, supporting personalized interventions to prevent irreversible cardiac damage.
Alterations in metabolic pathways are a hallmark of cancer and play a pivotal role in breast cancer development and progression. The inherent metabolic heterogeneity of breast cancer contributes to differences in therapeutic response and patients’ prognosis. Clinical metabolomics has emerged as a promising approach for identifying metabolic biomarkers that reflect tumor biology, treatment-related changes after diagnosis, and patients’ outcomes. This review summarizes the metabolomic profiles of breast cancer patients, using various biological materials and analytical methods, to assess their potential role as biomarkers for monitoring therapeutic response, adverse treatment effects, tracking disease progression, and predicting prognosis. Metabolomic shifts generate unique signatures with promising potential as biomarkers for evaluating treatment response, monitoring therapeutic adverse effects, disease progression, and predicting clinical outcomes in breast cancer patients. Biological matrices, such as serum, plasma, and tumor tissue, were commonly used in both untargeted and targeted metabolomics approaches. Liquid chromatography-mass spectrometry is the most commonly used analytical method in clinical metabolomics studies. Altered metabolites were identified and linked to metabolic pathways, particularly amino acids, glucose, and fatty acids metabolism. When integrated with genomic and transcriptomic data, these metabolic fingerprints offer a multidimensional perspective on disease trajectory, thereby enhancing patient stratification and informing personalized therapeutic strategies.
Polycythaemia vera (PV) is a clonal myeloproliferative neoplasm driven by activating mutations in the JAK2 gene and is associated with an increased risk of thromboembolic events. Secondary polycythaemia (SP) comprises non-neoplastic conditions characterised by reactive erythrocytosis, most commonly caused by hypoxia or dysregulated erythropoietin signalling. Despite fundamentally different pathophysiological mechanisms, PV and SP often share overlapping haematological features, underscoring the need for additional disease-specific biomarkers. Metabolomics provides a functional readout of systemic metabolic remodelling and may help distinguish clonal from reactive erythrocytosis and identify potential diagnostic biomarkers. For the first time, targeted LC–QQQ–based serum metabolomics was performed using the MxP® Quant 500 kit. The study included 76 participants: 33 with PV, 22 with SP, and 21 controls, matched for biochemical and anthropometric parameters. Statistical analyses comprised univariate testing (Mann–Whitney U test with Benjamini–Hochberg correction), pathway enrichment analysis, and ROC analysis with support vector machine (SVM) modelling, all performed in MetaboAnalyst Version 6.0. Compared with the control group, PV was associated with significant alterations in 242 metabolites and 152 predefined metabolite sums and ratios, mainly involving amino acid metabolism and lipid remodelling. In SP, fewer metabolic changes were observed, affecting 81 metabolites and metabolite sums and ratios. In a direct comparison between PV and SP, 29 discriminant metabolites were identified, primarily carboxylic acids and derivatives, as well as phosphatidylcholines, with changes ranging from − 79.4
The pathogenic mitochondrial gene variant m.3243A>G disrupts oxidative phosphorylation and is associated with insulin resistance, both of which may be linked to unfavorable lipid metabolism. However, the metabolic alterations in m.3243A>G carriers, including what differentiates those with and without diabetes, remain incompletely understood. To investigate metabolomic profiles in fasting serum and urine samples from m.3243A>G carriers compared to healthy controls. Metabolomic profiling of serum and urine samples using nuclear magnetic resonance-based metabolomics in m.3243A>G carriers (n = 28) was compared to healthy controls matched for age and sex. Additionally, profiles from m.3243A>G carriers with diabetes (n = 16) were compared with carriers without diabetes (n = 12) to identify potential metabolites associated with the presence of diabetes. Twenty-five metabolites in serum and 16 in urine were identified as metabolites separating m.3243A>G carriers from healthy controls. The m.3243A>G carriers presented with increased triglycerides across lipoprotein particles and altered very-low-density lipoprotein concentrations and composition. In addition, there were alterations in metabolites from a number of metabolic pathways, including glycolysis, the tricarboxylic acid cycle, glutathione, one-carbon, and nucleotide metabolism. A three metabolite-urine signature (uracil, hypoxanthine, and 1-methylnicotinamide) demonstrated discriminating potential between m.3243A>G carriers and controls in exploratory machine learning analyses (area under the curve values 0.94–0.99 and cross-validation prediction of 0.81–0.93). Among m.3243A>G carriers, branched-chain amino acids were higher in individuals with diabetes compared with carriers without diabetes. Dysregulated lipoprotein metabolism represents a significant metabolic fingerprint of m.3243A>G carriers. Furthermore, higher levels of branched-chain amino acids may be associated with the presence of diabetes.
Extended computer gaming is characterized by prolonged sedentary activity, disrupted sleep, emotional stress, and unrestricted intake of energy-dense foods and beverages. This study explored short-term metabolic responses and recovery following prolonged gaming in healthy young men. Nine healthy male participants (mean age 25.8 ± 2.6 years) took part in a controlled local area network (LAN) gaming event consisting of two 18-hour gaming sessions separated by a 6-hour sleep period. Serum samples were collected at multiple time points during the intervention and at a 5-day follow-up. Metabolomic profiling was performed using proton nuclear magnetic resonance (1H-NMR)-based metabolomics. Temporal variation in serum metabolomic profiles was observed during prolonged gaming, particularly in lipid-related measures, including very low-density lipoprotein (VLDL) subclasses. Exploratory multivariate analyses showed separation between early and later time points during the intervention. At five days following the gaming sessions, metabolomic profiles showed substantial overlap with baseline. Metabolites that varied during the gaming period generally returned toward baseline concentrations, although inter-individual variability was observed. Prolonged computer gaming was associated with transient alterations in serum metabolomic profiles in healthy young men, most notably in lipid-related measures. Metabolomic profiles at five days showed convergence toward baseline following a single gaming episode. These findings provide time-resolved insight into metabolomic variation under a real-world behavioral exposure. Further studies are required to determine whether repeated exposure to similar conditions is associated with cumulative metabolic changes over time.
IntroductionThe analysis of metabolic profiles using high resolution mass spectrometry (MS) data provides deep insights into biological processes. In metabolomics, MS analysis generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major bottlenecks in the metabolomics field is the identification of MS features, which is a prerequisite for any biochemical interpretation. By identifying similarities and differences within a metabolite family (mFam), evaluating MS features at the metabolite family level can help assigning functional roles to individual MS features. These data can help interpreting metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is important to have good quality, reliable, and comprehensive spectral libraries.ObjectiveWe initiated a global effort to collect high-resolution MS/MS spectra of metabolites from labs working in different fields, including metabolomics of animals, microorganisms, and plants. The mFam-MS/MS collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites.ResultsThe mFam collaboration used a standardized metadata template and has developed a globally curated MS/MS spectral library of 7,872 spectra with 2,126 unique metabolites. This library was compiled from 47 datasets contributed by 25 laboratories measured on 12 instrument types, including QTOF, Orbitrap, and Ion Mobility-QTOF systems. It comprises 4,646 spectra in positive mode and 3,226 in negative mode. This standardized resource significantly enhances metabolite identification capabilities, supports the development of machine learning-based annotation tools, and accelerates the discovery of novel metabolites. All spectra are available under the collective contributor label mFam in the MassBank system, including the web interface and the 2025.10 data release available at GitHub and Zenodo.
BackgroundControlled-environment cultivation of medicinal cannabis (Cannabis sativa L.) typically optimizes light conditions to enhance the biosynthesis of pharmaceutically important metabolites like cannabinoids. Such experimental strategies may also influence other specialized metabolites like terpenoids, flavonoids, alkaloids, among others. Previous untargeted metabolomics studies testing short-wavelength conditions like UV and blue light have shown that terpenoids and prenylated flavonoids in cannabis leaves respond differentially. However, since metabolomic studies in cannabis have so far mostly focused on floral cannabinoids, a comprehensive untargeted study into cannabis' floral metabolome response to short wavelengths is currently lacking.ObjectivesOur study investigates the impact of short-wavelength usage on cannabis specialized metabolism, and in particular the influence of UVB, UVA, and blue light on the cannabis floral flavonoid metabolome and associated glycosylation moieties.MethodsCannabis plants were grown under a white background light and exposed to supplemental UVB, UVA, or blue light during the generative phase of the cultivation cycle. Treatments were compared to a reference white background light without UV or blue light. Metabolites from floral tissue were extracted and analysed via ultra-performance liquid chromatography-tandem mass spectrometry. A comparative metabolomics workflow was designed and used to characterize the floral flavonoid metabolome and associated glycosylation moieties.ResultsOur results demonstrate how short wavelengths differentially affect the metabolism of natural product compound classes including polyketides and phenylpropanoids/shikimates. Blue light induced flavonoids similarly to how UVB did, while both UVA and blue light specifically induced flavanones accumulation. UVB showed the strongest regulatory effect on flavonoids production and glycosylation patterns.ConclusionsUVB reshapes the cannabis floral flavonoid metabolome by selectively stimulating the accumulation and structural modification of flavonoids. Therefore, UVB represents a potential horticultural strategy to enhance flavonoid-related aspects of medicinal cannabis inflorescence phytochemical quality, without affecting cannabinoid levels.
High risk human papillomavirus (HPV) infection and genome integration with pronounced expression of the viral E6/E7 oncogenes is the major cause of cervical cancer. Emerging evidence suggests that HPV reprograms host metabolism to support viral persistence and cellular transformation. However, global HPV oncogene-induced lipidomic reprogramming remains poorly understood, particularly at early stages of HPV-induced transformation. We sought to define the regulation of lipid metabolism in squamous epithelia of transgenic mice expressing the HPV16 oncogene E6 alone or in conjunction with E7. Untargeted lipidomics was used to identify novel lipid biomarkers in the skin and female reproductive tract (FRT) of HPV16 E6 and E6/E7 transgenic compared to wild-type (WT) mice. To investigate enzymatic dysregulation of lipids by HPV oncogene expression, we employed Lipid Network Explorer (LINEX2), which analyzes lipidomics data through lipid enrichment analysis. We also used the Global Natural Product Social Molecular Networking (GNPS) platform to enhance lipid identification, exploring molecular networking to improve feature annotation. Our lipidomic analysis produced several new observations. First, E6 expression caused a consistent alteration of glycerophospholipids, with particularly significant substrate-product shifts in the phosphatidylcholine (PC) to lysophosphatidylcholine (LPC) pathway in the skin. Second, E6/E7 expression caused a dysregulation of glucosylceramide (GlcCer) biosynthesis. Third, both E6/E7 expressing skin and FRT tissues exhibited a redox imbalance and increased levels of oxidized lipids, including oxylipins and several oxidized PCs. These findings suggest that HPV oncoproteins drive lipid reprogramming, potentially contributing to early HPV-related tumorigenesis. These findings provide new insights into HPV‑induced lipid reprogramming and establish a framework for future studies examining the functional and clinical relevance of lipid alterations in HPV‑associated cancers.
