Book of Abstract for the second Italian Metabolomics Network Meeting

The 2nd Meeting of the Italian Metabolomics Network Firenze Campus Novoli 15th – 16th December 2025 Scientific Committee Andrea Armirotti Luigi Atzori Michele Chierotti Daniel Cicero Federica Dal Bello Pietro Franceschi Alice Passoni Giuseppe Pieraccini Antonio Randazzo Sara Tortorella Paola Turano Local Organising Committee Veronica Ghini Giuseppe Pieraccini Paola Turano MONDAY, DECEMBER 15TH, 2025 9:30 – 10:45 Registration 10:45 – 11:00 Welcome & Introduction (GIDRM, IMaSS, IMN) SESSION 1 - Chair: P. Turano, A. Armirotti 11:00 – 11:40 Plenary I: G. Theodoridis “LC-MS Metabolomics. Constraints, Perspectives and Potential for Biomarker Discovery and Clinical Application” 11:40 – 12:00 A. Noto “Metabolomic Signatures in Toddlers and Adolescents with Autism Spectrum Disorder: Insights from Ten Years of the UNICA Experience” 12:00 – 12:20 G. Solarino “Untargeted Fingerprinting in Bipolar Disorder: Chemometric Insights from the BORDER project” 12:20 – 12:40 G. Petrella “Urinary Metabolomics Reveals Inflamation, Immune Inhibition, and Gut Dysbiosis as Predictors of Bladder Cancer Recurrence” 12:40 – 13:00 A. Vignoli “Studying Alzheimer’s Disease Through an Integrative Serum Metabolomic and Lipoproteomic NMR-Based Approach: the Medea Study” 13:00 – 13:20 A. Gallo “Exploring the Urinary Metabolomic Fingerprint of Human Cytomegalovirus: a 1H-NMR Study on Congenitally Infected Newborns” 13:20 – 14:20 LUNCH AND NETWORKING SESSION 2 - Chair: P. Franceschi, D.O. Cicero 14:20 – 14:40 E. Bossi “Mass Spectrometry-Based Workflow Optimization for Combined Metabolomics and Lipidomics Analysis from Blood Microsamples” 14:40 – 15:00 A. Ottas “Transferable Machine Learning Models for Cardiovascular Disease Prediction through NMR Metabolomics data and Optimal Transport” 15:00 – 15:20 L. Brunelli “Plasma gut-microbiota-derived metabolites trajectories to uncover the link between gut-microbiota dysbiosis and frailty in older adults” 15:20 – 15:40 N. Iaccarino “From Transcriptome to Metabolome: Uncovering the Cellular Impact of G-Quadruplex Ligands in Cancer Cells” 15:40 – 16:00 L. Tenori “Deriving Three One-Dimensional NMR Spectra from a Single Spectrum” 16:00 – 17:00 COFFEE BREAK AND POSTER SESSION (ALL POSTERS) SESSION 3 - Chair: A. Randazzo, G. Pieraccini 17:00 – 17:15 Sponsorship lecture: Bruker Mass Spec G. Calza, “Beyond Isomer Separation | Trapped Ion Mobility Spectrometry (TIMS) Boosts Annotation Confidence in 4D-Metabolomics & 4D Lipidomics” 17:15 – 17:35 M. Spada “Metabolomics Reveals Metabolic Adaptations Following Glutamine Metabolism Impairment In Colorectal Cancer Cells” 17:35 – 17:55 S. Serrao “From Primary Tumor to Circulating Tumor Cells and Metastasis: Tracing Metabolic Reprogramming in Lung Cancer” 17:55 – 18:15 G. Picone “Influence of IMTA-RAS and Probiotics on the Molecular Profile of Solea Senegalensis: a 1H-NMR Metabolomics Approach” 18:15 – 18:35 G. Meoni “Integrating NMRand MS-Based approaches to characterize coffee micro-lots: elemental, polyphenolic, and metabolomic fingerprints” TUESDAY, DECEMBER 16TH, 2025 SESSION 4 - Chair: L. Atzori, F. Dal Bello 9:00 – 9:20 S. Zampieri “Metabolomics Overcomes Age Bias in Non-Invasive Fibrosis Staging: the GP Index” 9:20 – 9:40 V. Balloni “Combined Tissue and Liquid Biopsies Reveal the Metabolic Interactions Among Blood, Liver, and Adipose Tissue in Bariatric Surgery of Morbid Obesity” 9:40 – 10:00 M. Nobile “Exercise-Induced Metabolic Adaptations During Cardiac Rehabilitation: a Longitudinal Metabolomic Investigation on DBS” 10:00 – 10:20 V. Righi “NMR Based Metabolomics to Investigate Molecular Mechanisms in ADLD Neurodegenerative Disorder” 10:20 – 10:40 M. Gallo “Saliva Metabolomics as an Emerging Tool for Advanced Diagnostics: the Leukoplakia Paradigm” 10:40 – 10:55 Sponsorship lecture: Sciex M. Armelao “Quantitative and Qualitative Analysis of Oxylipins Using Highresolution Mass Spectrometry” 10:55 – 11:15 COFFEE BREAK AND POSTER SESSION SESSION 5 - Chair: A. Passoni, M.R. Chierotti 11:15 – 11:35 G. Boschetti “Metabolomics and Cystic Fibrosis Drugs: Exposure of Dams to Tezacaftor During Pregnancy and Breastfeeding Induces Molecular Alterations in Mice Brain” 11:35 – 11:55 C. Marino “The Metabolomic Dysfunctions of Spinal Muscular Atrophy: a Journey from Animal Models to Human CSF” The 2nd Meeting of the Italian Metabolomics Network 4 11:55 – 12:15 C. Guerrini “Unravelling Urinary Metabolic Alterations Induced by Thirdhand Smoke Exposure: from Untargeted Analysis in Animal Models to Targeted LCMS Validation in Children” 12:15 – 12:30 Sponsorship lecture: Bruker Biospin M. Zani “Building the Ecosystem for Standardized and Automated NMR Metabolomics” 12:30 – 13:10 Plenary II: O. Millet “Integrative ¹H-NMR and Machine Learning Framework for Large-Scale Serum Metabolomics and Disease Classification” 13:10 – 14:20 LUNCH AND NETWORKING 14:20 – 15:20 Meeting IMN The 2nd Meeting of the Italian Metabolomics Network 5 Invited Lectures The 2nd Meeting of the Italian Metabolomics Network LC-MS METABOLOMICS. CONSTRAINTS, PERSPECTIVES AND POTENTIAL FOR BIOMARKER DISCOVERY AND CLINICAL APPLICATION G. Theodoridis1,2, H. Gika1,3 1Biomic_CIRI, Aristotle University Thessaloniki, Thermi, Greece 2Department Chemistry, Aristotle University Thessaloniki, Greece 3Department Medicine, Aristotle University Thessaloniki, Greece E-mail: [email protected] Metabolomics show strong growth as the field can offer new insights in disease/health/wellness biochemistry. Liquid Chromatography Mass Spectrometry (LC-MS) takes the largest part of the metabolomics market due to its agility, performance, sensitivity, fit for the analysis of biological samples, direct applicability, large numbers of instruments and practitioners. LC-MS proves superior in the discovery, development of new biomarkers and their application in disease, nutrition, wellness, exposure or safety assessment. Recently LC-MS is also entering clinical chemistry practice, however its application in the clinic is not problem free. Translational aspects and application of research findings to the clinical practice, proceeds slowly. As metabolomics evolve as a technology protocols differ between laboratories. These differences hinder harmonization and may result in problems in replication of findings and the adoption of biomarkers. We discuss the constraints that slow the finalization and uptake of LC-MS metabolomics biomarker development. To illustrate the strong perspective of metabolomics, examples of biomarker discovery will be presented with focus on larger initiatives where metabolic profiles are associated with genetic, gut-microbiome data, clinical and anthropometric data to identify associations of omics profiles with diet or cardiac health. In Corlipid project, blood samples from 1500 cases of coronary disease were analysed by untargeted UPLC-TOF-MS lipidomics, untargeted metabolomics, and new targeted methods to quantify ceramides, carnitines (UPLC-MS/MS), and fatty acids (GC-MS). A machine learning algorithm selected 17 parameters (8 metabolites) to predict the coronary angiography results (syntax score the golden standard for quantifying the complexity of coronary artery disease). In Codiet blood and urine samples from four European cohorts were analysed by six UPLC-MS methods (combining targeted and untargeted modes) to map the metabolome and link the obtained profiles with nutrition. The presentation will emphasize on the needs and the benefits of the development of new analytical methods to effectively map the metabolome. To better illustrate this, we report the development of a new method for the quantitation of aromatic aminoacids and sulphated metabolites. These molecules are important markers in inflammation, cancer and other disorders, however the lack of commercially available standards hinders their quantitation in biological samples. We synthesized 14 sulphated metabolites; after preparative LC purification, NMR and MS verification, we developed a new UPLCMS/ MS that allowed for the quantitation of 30 metabolites in urine, and the subsequent application in biomarker studies and the analysis of clinical samples. Acknowledgements The authors acknowledge funding from the Horizon Europe Project 101079370 — BiACEM WIDERA Twinning “Biomic_AUTh, Center of Excellence in Metabolomics research”. 7 The 2nd Meeting of the Italian Metabolomics Network INTEGRATIVE 1H-NMR AND MACHINE LEARNING FRAMEWORK FOR LARGE-SCALE SERUM METABOLOMICS AND DISEASE CLASSIFICATION A. Ibañez de Opakua and O. Millet Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Biziaia Technology Park, Bld. 800, 48160 Derio, spain E-mail: [email protected] Nuclear Magnetic Resonance (NMR) spectroscopy provides a powerful and reproducible platform for metabolic profiling, enabling non-invasive insights into human health and disease. In this study, we present an integrated computational framework that combines ¹H-NMR spectral analysis with machine learning models to extract clinically relevant information from large-scale serum metabolomics data. From over 30,000 serum samples, 2D J-resolved spectra were used to quantify 51 metabolites with minimized signal overlap, while 1D NOESY spectra were leveraged to estimate 25 clinical parameters through supervised regression models. These derived biochemical and clinical features were then used to train a multiclass disease classifier based on eXtreme Gradient Boosting (XGBoost), designed to distinguish nine health categories encompassing seven disease conditions and two age-defined healthy groups. The model achieved an overall accuracy of 0.79, with area-under-curve (AUC) values between 0.91 and 1.00 across classes, demonstrating high sensitivity and specificity. Misclassifications were primarily observed between physiologically related groups, such as older adults and individuals with metabolic syndrome or long COVID, reflecting underlying metabolic similarities. Feature importance analyses using SHAP values highlighted key metabolites and clinical markers associated with systemic inflammation, lipid metabolism, and energy balance as major drivers of disease discrimination. This framework underscores the potential of NMR-based machine learning for scalable, interpretable, and non-invasive health assessment, supporting precision diagnostics through individualized metabolic phenotyping. 8 Oral Presentations The 2nd Meeting of the Italian Metabolomics Network MASS SPECTROMETRY-BASED WORKFLOW OPTIMIZATION FOR COMBINED METABOLOMICS AND LIPIDOMICS ANALYSIS FROM BLOOD MICROSAMPLES E. Bossi1*, M. Nobile1, S. Serrao1, P. Reveglia2, A. Ferrara2, V. Ramundi3, C. Prehn3, G. Corso2, M. Witting3, G. Paglia1 1University of Milano-Bicocca, Department of Medicine and Surgery, Via Follereau 3, 20854, Vedano al Lambro, Italy. 2University of Foggia, Department of Clinical and Experimental Medicine, Viale Pinto, 71122, Foggia, Italy. 3Helmholtz Zentrum München, German Research Center for Environmental Health, Metabolomics and Proteomics Core Facility, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. Blood microsampling has emerged as a promising alternative to conventional venipuncture for metabolomics studies, granting advantages such as minimal invasiveness, ease of collection, suitability for multiple sampling, and optimal use in longitudinal designs [1]. This study aimed to optimize a liquid chromatography-mass spectrometry-based workflow enabling both untargeted metabolomics and lipidomics analyses from a single dried blood spot (DBS). In parallel, extraction optimization was performed for targeted metabolomics on DBS (Whatman) using TMIC MEGA kits. For the untargeted protocol optimization three commercially available microsampling devices—Capitainer and Whatman (whole blood) and Telimmune (plasma)—were evaluated. Among five extraction solutions tested, pure methanol provided the best compromise for simultaneous extraction of polar metabolites and lipids. Based on these results, a two-step consecutive extraction protocol was developed, using methanol followed by water to enhance the recovery of more polar metabolite classes and improve metabolome coverage. Short-term stability of polar metabolites and lipids was also evaluated at room temperature (RT) for up to five days. Capitainer showed the best results, preserving the stability of all evaluated classes of compounds for up to five days at RT. Regarding targeted protocol optimization, modifications to the original extraction protocol for panels A and B increased metabolite coverage in DBS with the highest improvement observed for Panel B. Overall, this work suggests that methanol extraction enables integrated metabolomics and lipidomics analysis from a single spot, and that a two-step approach can further enhance polar metabolite coverage. Targeted workflow optimization also improved metabolite coverage in DBS, emphasizing the importance of tailoring device selection and extraction protocols to study aims, matrices and analytical scope. References 1. Bossi, E. et al. (2025) ”Pre-analytic assessment of dried blood and dried plasma spots: integration in mass spectrometry–based metabolomics and lipidomics workflow”, Analytical and Bioanalytical Chemistry, 417(9), pp.1791-1805. doi:10.1007/s00216-025-05760-z. 16 The 2nd Meeting of the Italian Metabolomics Network TRANSFERABLE MACHINE LEARNING MODELS FOR CARDIOVASCULAR DISEASE PREDICTION THROUGH NMR METABOLOMICS DATA AND OPTIMAL TRANSPORT A. Ottas*1, L. Goldoni2, A. Armirotti2, , S. Decherchi3 1Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia, 2Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy, 3Fondazione Istituto Italiano di Tecnologia, Data Science & Computation Facility, Genoa, Italy Machine learning and, more in general artificial intelligence methods, represent powerful tools to address clinical questions related to the discovery of biomarkers and the prediction of disease onset through omics data. Nevertheless, these modeling attempts are often plagued by the non-transferability issue across different patients cohorts. This is due to several factors such as different approaches in metabolite absolute quantification, human and instrumental factors and the biological sample stability, which ultimately lead to out-of-domain data distributions which invalidate the underlying assumptions of the learnt models. Here, we investigate this issue for metabolomics data and cardiovascular diseases and propose a protocol, and a software tool, based on the Optimal Transport theory, which is able to calibrate and normalize data such that the learnt models become more transferable on new data. Results show that the learnt models improve systematically their accuracy after the proposed calibration procedure (Figure 1). Figure 1: Adapting the domains of two omics measurements different in time and with different experimental apparatus. 17 The 2nd Meeting of the Italian Metabolomics Network PLASMA GUT-MICROBIOTA-DERIVED METABOLITES TRAJECTORIES TO UNCOVER THE LINK BETWEEN GUT-MICROBIOTA DYSBIOSIS AND FRAILTY IN OLDER ADULTS M. Tiddia1, A. Multari1, A. Morabito2, A. Davin3, A. Guaita3,L. Brunelli1* 1Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCC, Via Mario Negri 2, 20156 Milano, Italy 2Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano, Italy 3Fondazione Golgi Cenci, Corso S. Martino, 10, 20081 Abbiategrasso, Italy Frailty is a geriatric syndrome characterized by a decline in physiological function and increased vulnerability to stressors. Although closely associated with aging, frailty differs between individuals of the same age [1]. Increasing evidence supports a link between frailty development and gut microbiota dysbiosis, wherein altered microbial composition and reduced diversity contribute to frailty progression through enhanced intestinal permeability and systemic low-grade inflammation [2]. This study aimed to build a comprehensive in-house database of microbial-derived metabolites to investigate their association with frailty. Monoisotopic masses of gut microbiota-derived metabolites were retrieved from the Human Metabolome Database (HMDB) and Microbial Metabolites Database (MiMeDB), yielding over 20,000 candidate compounds. After deduplication, the database included monoisotopic mass values, common adducts (+Na, +K), HMDB IDs, and molecular class annotations. The database was applied to plasma metabolomic profiles of 942 older adults (726 non-frail, 216 frail), defined by the Frailty Index, from the Invecchiamento Cerebrale in Abbiategrasso (InveCe.Ab) cohort. Plasma metabolites were analyzed using flow-injection analysis high-resolution mass spectrometry (FIA-HRMS) and annotated with the EASY-FIA tool, combined with an in-house database [3]. Compound identities were confirmed using MS/MS fragmentation spectra from public databases or in silico predictions. 307 metabolites were annotated to be linked to microbial metabolism. Four of these differed significantly (Wilcoxon Mann-Whitney test) between frail and non-frail older adults. Frail individuals exhibited increased plasma levels of indoxyl sulfate, caproic acid, and 6-deoxy-6-sulfo-D-fructose, alongside decreased levels of rhamnose. The increase in metabolites associated with increased cardiovascular risk, bone disorders, and endothelial dysfunction, along with the decrease of potential beneficial metabolite in frail adults, highlights the critical role of altered microbial metabolism in frailty pathophysiology. References 1. Clegg, A., Young, J., Iliffe, S., Rikkert, M. O., and Rockwood, K. (2013). Frailty in elderly people. The lancet, 381(9868), 752-762. doi: 10.1016/S0140-6736(12)62167-9 2. Escudero-Bautista S., et al. Geriatrics. 2024 Aug 31;9(5):110. doi: 10.3390/geriatrics9050110. 3. Morabito, A., et al. Metabolites. 2022 Dec 21;13(1):13. doi: 10.3390/metabo13010013. 18 The 2nd Meeting of the Italian Metabolomics Network FROM TRANSCRIPTOME TO METABOLOME: UNCOVERING THE CELLULAR IMPACT OF G-QUADRUPLEX LIGANDS IN CANCER CELLS N. Iaccarino1*, F. Romano1, C. Persico1, A. Barra1, I. Aiello1, G. Pinto2, A. Amoresano2, A. Di Porzio1, A. Randazzo1 1University of Naples Federico II, Department of Pharmacy, Via D. Montesano 49, 80131, Naples, Italy. 2University of Naples Federico II, Department of Chemical Sciences, Via Cinthis 26, 80126, Naples, Italy. G-quadruplexes (G4s) are non-canonical DNA structures increasingly recognized as therapeutic targets in cancer. Although numerous ligands have been developed to stabilize G4s, the global cellular and metabolic consequences of this interaction remain largely unknown. Here, we applied an integrated multi-omics approach, combining transcriptomics, proteomics, and NMR-based metabolomics, to characterize the biological effects of three representative G4 ligands (berberine, pyridostatin (PDS), and RHPS4) in human cervical adenocarcinoma (HeLa) cells. Our results revealed that PDS profoundly reprograms cellular metabolism, suppressing glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle, leading to reduced ATP, NADPH, and glutathione levels. This energetic and redox collapse was accompanied by accumulation of amino acids such as glutamine and valine, suggesting adaptive anaplerotic responses. In contrast, RHPS4 selectively impaired mitochondrial bioenergetics, consistent with enhanced mitochondrial G4 formation, while berberine exerted minimal effects. Multi-omics data fusion confirmed a coordinated downregulation of key metabolic enzymes (PKM, IDH1, FASN, G6PD) and ribosomal proteins, linking G4 stabilization to both metabolic and translational suppression. Collectively, our multi-omics analysis unveiled the main cellular circuitries that turned out to be perturbed by the investigated G4 binders offering a multi-omics perspective on how G4-binding molecules elicit their anti-tumor activity and potentially guiding the design of more effective G4-directed therapies. References 1. Romano, F. et al. (2025) ”Unveiling the Biological Effects of DNA G-Quadruplex Ligands through Multi-Omics Data Integration”. International Journal of Biological Macromolecules, 313(May), p. 144325. DOI: 10.1016/j.ijbiomac.2025.144325. 19 The 2nd Meeting of the Italian Metabolomics Network DERIVING THREE ONE-DIMENSIONAL NMR SPECTRA FROM A SINGLE SPECTRUM A. Vignoli1,2, S. Cacciatore3,L. Tenori1,2* 1University of Florence, Department of Chemistry “Ugo Schiff”, Via della Lastruccia 3-13, 50019 Sesto Fiorentino, Italy 2University of Florence, Magnetic Resonance Center (CERM), Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Italy 3International Centre for Genetic Engineering and Biotechnology, Bioinformatics Unit, Anzio Road, Cape Town, 7925, South Africa Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for analyzing complex mixtures due to its ability to manage matrix complexity, provide detailed molecular insights, and preserve sample integrity. Various NMR experiments, such as NOESY, CPMG, diffusion-edited, and J-resolved spectroscopy (JRES), offer complementary insights into biofluids like serum and plasma. For instance, CPMG selectively detects small molecules, diffusion-edited emphasizes signals from macromolecules, and NOESY captures both, and JRES is particularly useful for signal assignment. However, acquiring multiple NMR spectra can be resource-intensive and time-consuming, especially for high-throughput studies. Here, we present a simple and efficient strategy to computationally derive CPMG, diffusion-edited, and projected JRES (pJRES) spectra from a single NOESY acquisition using Partial Least Squares (PLS) regression. Serum samples were used as a case study. We used serum NMR data from a total of 1842 individuals enrolled from 18 recruitment centers. The1H-NMR spectra for all samples were recorded using a Bruker 600 MHz spectrometer operating at 600.13 MHz. The dataset, comprising serum spectra of samples collected in 17 different recruitment centers, was divided into training (80%) and validation (20%) sets. Furthermore, the spectra of 232 samples from one independent recruitment center were used as the independent test set. Predictive models were created applying PLS regression with 1D NOESY spectra used as independent variables for the prediction of CPMG, diffusion-edited, and pJRES spectra, these latter used as dependent variables. Experimental and predicted spectra were compared in regions with signal intensities at least three times above the noise level. Evaluation metrics included the median relative error (MRE%), root mean square error (RMSE), coefficient of determination (R2), and ratio of performance to deviation (RPD). In the independent test set, MRE% values of 6%, 4%, and 13% were achieved for predicted CPMG, diffusion-edited, and pJRES spectra, respectively. 20 The 2nd Meeting of the Italian Metabolomics Network METABOLOMICS REVEALS METABOLIC ADAPTATIONS FOLLOWING GLUTAMINE METABOLISM IMPAIRMENT IN COLORECTAL CANCER CELLS M. Spada1*, C. Piras1, V. P. Leoni1, M. Casula2, A. Noto1, P. Caria1and L. Atzori1 1University of Cagliari, Department of Biomedical Science, Cittadella Universitaria, SS 554, km 4.5, 09042 Monserrato, Italy. 2University of Cagliari, Department of Life and Environmental Sciences, Cittadella Universitaria, SS 554, km 4.5, 09042 Monserrato, Italy. Cancer cells rewire their metabolism to fulfil the high bio-energetic demand due to their high proliferative rate [1]. Some cancer cells become addicted to alternative bioenergetic sources, as glutamine (Gln) [2], including colorectal cancer (CRC) cells. In this scenario, metabolomics may represent a successful approach to investigate the metabolic alterations presented by cancer cells, to highlight potential therapeutic targets, and to investigate the possible mechanisms of resistance to antitumoral therapies. The present study aims to evaluate the metabolic effects induced by Gln deprivation or by the pharmacological inhibition of glutaminase-1 with CB-839, the first enzyme in Gln metabolism. Three CRC cell lines (HCT116, HT29, and SW480) were deprived of glutamine or treated with different concentrations of CB-839 (2.5-20 µM). Cell viability was assessed by MTT assay (48 and 96 h). The metabolomic profile was explored with GC-MS and 1H-NMR with an untargeted approach, while targeted evaluation of Glutaminolysis and the Krebs’cycle was performed with GC-MS/MS analysis. Data underwent Multivariate and Univariate statistical analysis. Gln deprivation induced a marked cell viability reduction in all the studied cell lines, while the drug CB-839 induced a cytotoxic effect predominantly in HT29 cells and a less pronounced cell viability decrease in HCT116 cells, while SW480 cells were resistant to the treatment. Metabolomic analysis showed a strong perturbation of the energetic pathways (glycolysis and TCA cycle), the amino acid pool (alanine, leucine, serine), and the antioxidant reserves, in the form of glutathione, following glutamine deprivation in all cell lines. Moreover, metabolomic investigation highlighted that CB-839 treatment induced alterations or adaptations in both the sensitive HT29 cells and the drug-resistant SW480 cells, impacting sugar content, especially glucose, the amino acid pool (alanine, aspartate, phenylalanine), GSH content, TCA cycle intermediates (succinic acid and fumarate), ATP levels, and GABA amount, which is used for TCA anaplerosis. Furthermore, metabolomic analysis also revealed differences between respondent and resistant CRC cells, as fructose, galactose, citric acid, NAD+, lysine, threonine, and leucine levels, suggesting interesting insights for exploring possible resistance mechanisms. In conclusion, metabolomics offers important support to the research in the oncological field, allowing for the identification of potential therapeutic targets, promising drug combinations, and the investigation of possible mechanisms of resistance to antitumoral compounds. References 1. Hanahan, D. et Weinberg, R.A.(2011) ”Hallmarks of cancer: the next generation”, Cell, 144(5):646-74. doi: 10.1016/j.cell.2011.02.013. 2. Jin, J. et al. (2023) ”Targeting glutamine metabolism as a therapeutic strategy for cancer”, Exp Mol Med, 55(4):706-715. doi: 10.1038/s12276-023-00971-9. 3. Spada, M. et al. (2023) ”Glutamine Starvation Affects Cell Cycle, Oxidative Homeostasis and Metabolism in Colorectal Cancer Cells”, Antioxidants (Basel), 2(3):683. doi: 10.3390/antiox12030683. 21 The 2nd Meeting of the Italian Metabolomics Network BEYOND ISOMER SEPARATION: TRAPPED ION MOBILITY SPECTROMETRY (TIMS) BOOSTS ANNOTATION CONFIDENCE IN 4D-METABOLOMICS AND 4D-LIPIDOMICS G. Calza Bruker Italy Key challenges in high-throughput as well as low-input metabolomics are related to instrument robustness, selective identification and quantitation in complex matrix, and streamlined data processing. In every single run, many chemically diverse compounds, whose concentrations can cover several orders of magnitude, must be selectively captured. Ideally, they are reliably identified and quantified despite the presence of interferences. In our contribution, we will demonstrate that trapped ion mobility spectrometry (TIMS), which adds a fourth dimension of selectivity to LC-MS/MS workflows, can provide increased profiling depth and annotation confidence beyond traditional (3D) omics approaches. Mobility-based ion sorting minimizes chemical interference in MS and MS/MS. Cleaner, less chimeric data lends themselves to automated annotation and provide a basis for machine-learning approaches in data exploration. The synchronization of TIMS with downstream QTOF ion handling can be customized to build dedicated 4D methods enabling high coverage profiling of certain classes of biosamples, sensitive quantitation of individual compounds, or hybrid approaches. With this contribution, we will introduce the recently launched timsMetabo™, a timsTOF platform designed to delivery excellent 4D performance for small molecule bioanalysis. The new system combines speed and robustness, essentials for high-throughput approaches, with the required sensitivity and flexibility for low-input metabolomics and lipidomics. 4D workflows are enabled by dedicated software tools for data processing and annotation. Specifically, the ion mobility-derived information is utilized by the MetaboScape® software package to identify metabolic features, increase annotation confidence and enable easy data review. In addition, we present tools for real-time quality control monitoring as well as system suitability testing for small molecules workflows. 22 The 2nd Meeting of the Italian Metabolomics Network FROM PRIMARY TUMOR TO CIRCULATING TUMOR CELLS AND METASTASIS: TRACING METABOLIC REPROGRAMMING IN LUNG CANCER S. Serrao1*, B. Gerosa2, M. Nobile1, E. Bossi1, G. Bertolini2, G. Paglia1 1University of Milano Bicocca, Department of Medicine and Surgery, Via Raoul Follereau 3, 20854, Vedano al Lambro (MB), Italy 2Fondazione IRCCS Istituto Nazionale dei Tumori, Unit of Epigenomics and Biomarkers of Solid Tumors, Via Venezian 1, 20133, Milano, Italy Lung cancer is the leading cause of cancer-related mortality worldwide, mainly due to its high incidence and late diagnosis. Circulating tumor cells (CTCs), which are released from primary tumors and can be detected in the bloodstream, play a central role in metastatic cascade, representing mobile mediators of metastatic spread from the primary tumor and distant metastases [1]. Despite their prognostic value, CTCs remain poorly characterized, particularly in terms of their metabolic properties. This work aims to investigate metabolic reprogramming, fundamental for tumor progression, survival, and invasion, that occur during the transition from primary tumor (PT) cells to CTCs and finally to metastatic cells (Met) in non-small cell lung cancer (NSCLC). To do this, cell lines PT, CTCs, and Met were isolated from patient-derived xenografts, expanded in vitro, and analyzed with mass spectrometry-based metabolomics/lipidomics. The results revealed significantly metabolic reprogramming across these disease stages. During the transition from PT to CTC, we observed alterations in purine metabolism, glycolysis, branched-chain amino acid catabolism, and fatty acid β-oxidation, along with an increased recruitment of storage lipids by CTCs. Conversely, the transition from CTC to Met was characterized by a lipid metabolic switch, with increased levels of both storage and membrane lipids in Met, reflecting structural remodeling to boost invasive capacity. Parallelly, isotope-tracing mass spectrometry experiments with ¹³C-glucose and ¹³C-glutamine were performed to further investigate the pathways involved in the transition. PT cells exhibited active glycolysis, moderate TCA cycle activity, and fatty acid mobilization. CTCs displayed a reduced TCA cycle and increased purine salvage pathway, increasing ATP levels through regeneration from ribose-5-phosphate and adenine. This indicates a rewiring of energy metabolism to sustain CTCs in bloodstream. In contrast, Met cells showed TCA cycle activity, and a metabolic shift toward alanine metabolism, which reflects in increased α-ketoglutarate levels and acetyl-CoA utilization, and therefore elevated β-oxidation. Collectively, our results showed a distinctive metabolic profile of CTCs, highlighting dynamic adaptation in energy production, purine recycling and lipid metabolism, that may support CTC survival in circulation and in turn facilitate metastatic colonization, offering potential targets for therapeutic treatment in lung cancer. References 1. Siegel, R.L. et al. (2022) ”Cancer statistics, 2022”, CA: A Cancer Journal for Clinicians, 72(1), pp. 7–33. doi:10.3322/caac.21708. 23 The 2nd Meeting of the Italian Metabolomics Network INFLUENCE OF IMTA-RAS AND PROBIOTICS ON THE MOLECULAR PROFILE OF SOLEA SENEGALENSIS: A 1H-NMR METABOLOMICS APPROACH G. Picone 1*, C. Palmas2, M. Lastres3, J. Cremades4, J. Pintado5, L. Bruni5, and F. C. Marincola2 1Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Bologna, Italy 2Department of Chemical and Geological Sciences, University of Cagliari, Monserrato, Cagliari, Italy, 3Instituto Galego de Formación en Acuicultura (IGAFA), Pontevedra, Spain, 4Biología Costera (BioCost). Centro de Investigaciones Científicas Avanzadas (CICA), Universidad de A Coruña, A Coruña, Spain 5Instituto de Investigacións Mariñas - Institute of Marine Research (IIM - CSIC), Vigo, Spain Aquaculture plays a pivotal role in addressing the global demand for seafood while fostering environmental sustainability and food security. This sector encompasses the controlled farming of finfish, molluscs, crustaceans, and other aquatic organisms, including algae, polychaetes, and cnidarians, contributing to the conservation of marine resources and reducing the pressure on wild stocks. Sustainable approaches such as recirculating aquaculture systems (RAS) and integrated multi-trophic aquaculture (IMTA) have been shown to minimize ecological impact and improve product quality [1,2]. This study investigates the metabolic effects of IMTA-RAS and probiotics on the flatfish Solea senegalensis. High-resolution ¹H NMR-based metabolomics was employed to characterize the molecular profile of sole muscle across three experimental systems: (i) RAS, (ii) IMTA-RAS incorporating Ulva ohnoi for nutrient biofiltration, and (iii) IMTA-RAS enriched with Phaeobacter sp. 4UAC3, a probiotic bacterium known for its antagonistic activity against fish pathogen. Multivariate statistical analyses of NMR spectra revealed significant differences in metabolite concentrations among the three systems (Figure 1). Figure 1: Graphical abstract illustrating the experimental design and main findings of the study. The results indicate that IMTA-RAS, particularly when combined with Phaeobacter and Ulva, enhances energy metabolism, amino acid turnover, and overall metabolic activity in S. senegalensis. These findings suggest that integrating probiotic microflora and macroalgae within recirculating systems creates a more balanced and health-promoting environment, contributing to both fish welfare and the sustainability of aquaculture practices [3]. 24 The 2nd Meeting of the Italian Metabolomics Network References 1. Khanjani, M.H., Zahedi, S. and Mohammadi, A., 2022. ”Integrated multitrophic aquaculture (IMTA) as an environmentally friendly system for sustainable aquaculture”, Environmental Science and Pollution Research, 29, pp.67513–67531. Available at: https://doi.org/10.1007/s11356-022-22371-8. 2. Pintado, J., Bruni, L., et al., 2023. ”Engineering Ulva-associated bacteria for disease control in fish-algae IMTA-RAS cultures”, Aquaculture Research. Available at: https://doi.org/10.1007/s10811-023-02986-. 3. Marincola, F.C., Palmas, C., Lastres Couto, M.A., et al., 2023. ”Metabolic profile of Senegalese sole (Solea senegalensis) muscle: Effect of fish–macroalgae IMTA-RAS aquaculture”, Molecules, 30(12), p.2518. Available at: https://doi.org/10.3390/molecules30122518. 25 The 2nd Meeting of the Italian Metabolomics Network QUANTITATIVE AND QUALITATIVE ANALYSIS OF OXYLIPINS USING HIGHRESOLUTION MASS SPECTROMETRY M. Armelao Sciex Oxylipins, bioactive lipid mediators derived from polyunsaturated fatty acids, play critical roles in inflammation, vascular function, and immune responses. Their low abundance and structural diversity present analytical challenges, particularly in complex biological matrices. This study demonstrates a robust workflow for the comprehensive profiling of oxylipins using high-resolution mass spectrometry (HRMS) on the SCIEX ZenoTOF 8600 system. The method combines high-throughput chromatographic separation with advanced MS/MS acquisition, leveraging Zeno trap-enabled electron-activated dissociation (EAD) to enhance sensitivity and structural elucidation. A targeted panel of hydroxy-, epoxy-, and dihydroxy-fatty acids was analyzed in human plasma, achieving low limits of detection and quantification. The approach enables simultaneous qualitative and quantitative analysis, facilitating the identification of isomeric species and supporting biomarker discovery in lipidomics. This workflow offers a powerful tool for researchers investigating the role of oxylipins in health and disease, with potential applications in clinical and nutritional studies. 32 The 2nd Meeting of the Italian Metabolomics Network METABOLOMICS AND CYSTIC FIBROSIS DRUGS: EXPOSURE OF DAMS TO TEZACAFTOR DURING PREGNANCY AND BREASTFEEDING INDUCES MOLECULAR ALTERATIONS IN MICE BRAIN A. Squarzoni1,G. Boschetti1, S. Mandrup Bertozzi1, M. Summa1, E. Milandri, R. Mandrioli2, M. Protti, L. Mercolini2, C. Montani3, G. Capodivento, G. Cangemi, N. Pedemonte, T. Bandiera1, F. Benfenati1,3, L. Nobbio3, R. Bertorelli , and A. Armirotti 1Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genova, Italy 2Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy. 3IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genova, Italy 4IRCCS Istituto Giannina Gaslini, Genova, Italy. We demonstrated1 that Tezacaftor inhibits the enzyme (DEGS) that converts dihydroceramides (dHCer) into ceramides, thus producing accumulation of dHCer in cells and tissues. We are now conducting an in-vivo safety study, by administering this drug to mice during pregnancy and breastfeeding. The drug was incorporated as powder into mice food in high-fat diet regimen. Drug and dHCer levels in plasma and tissues, as well as changes in the global lipidome and proteome were measured by UPLC-MS. We here present the results observed in pups sacrificed 10 days after birth. We observed a significant accumulation of dHCer in the brains of pups born from drug-fed dams compared to controls. No accumulation was observed in the sciatic nerve, likely due to much lower levels of ETI compared to the brain: Dihydroceramides levels the brain (left) and sciatic nerve (right) of pups sacrificed at P10 for the ETI arm compared to the control arm. Data (as sum of all dHCer species) is reported as average ± SEM, N=18 and 15 for ETI and control groups respectively (*** p<0.001 two-tailed t-test). We also conducted an untargeted lipidomic survey, which revealed other alterations in lipid metabolism associated with exposure to the drug. We also show that this exposure translates into some physical and behavioral changes, although the animals treated with the drug recover at P28. Our data show that exposure to ETI during pregnancy and breastfeeding is associated with observable molecular changes in the brain lipidome of the pups, which are not likely limited to the inhibition of DEGS. This study is currently ongoing and further data on aging mice are being collected. References 1. Ciobanu et al. (2024) ”Tezacaftor is a direct inhibitor of sphingolipid delta-4 desaturase enzyme (DEGS).”, J.Cystic Fibrosis, 23(6):1167-1172, 10.1016/j.jcf.2024.05.004 33 The 2nd Meeting of the Italian Metabolomics Network THE METABOLOMIC DYSFUNCTIONS OF SPINAL MUSCULAR ATROPHY: A JOURNEY FROM ANIMAL MODELS TO HUMAN CSF C. Marino*1, F. Errico2,3, V. Bassareo4, V. Valsecchi5, T. Nuzzo3,6, A. D’Amico7, M. Carta4, E. Bertini7, G. Pignataro5, G. Raffa8, L. Scatolini8, A.M. D’Ursi1, A. Usiello*3,6 1University of Salerno, Department of Pharmacy, 84084 Fisciano, Salerno, Italy. 2University of Naples “Federico II”, Department of Agricultural Sciences, 80055 Portici, Italy. 3Ceinge Biotecnologie Avanzate, Laboratory of Translational Neuroscience, 80145 Naples, Italy. 4University of Cagliari, Department of Biomedical Sciences, 09042 Monserrato, Italy. 5University of Naples “Federico II”,Division of Pharmacology, Department of Neuroscience, Reproductive and Dentistry Sciences, School of Medicine, 80131 Naples, Italy. 6Università degli Studi della Campania “Luigi Vanvitelli”, Department of Environmental, Biological and Pharmaceutical Science and Technologies, 81100 Caserta, Italy. 7Unit of Neuromuscular and Neurodegenerative Disorders, Bambino Gesù Children’s Hospital IRCCS, 00163 Rome, Italy. 8Sapienza Università di Roma, Department of Biology and Biotechnology, 00185 Rome, IT. Spinal muscular atrophy (SMA) is an infantile neuromuscular disorder with a high mortality rate. It results from the degeneration of motor neurons caused by a homozygous deletion of the gene coding for the SMN (Survival Motor Neuron) protein. The condition develops when all functional copies of smn1 are lost, leaving the smn2 gene as the only source of functional protein [1]. Although the genetic basis of the pathology has been clarified, the metabolic dysregulations that trigger or worsen the pathophysiology of SMA are not well understood. Based on this evidence, this work aims to outline the neurometabolomic profile of SMA using different animal models: from the simplest, represented by Drosophila melanogaster, to the more complex mouse model SMN∆7, and then compare these findings with human biofluid—most indicative of dysregulation in the pathology-namely cerebrospinal fluid (CSF) [3]. The exploration of such models was conducted employing an untargeted metabolomic methodology that integrated Nuclear Magnetic Resonance (NMR) with techniques such as high-performance liquid chromatography (HPLC), quantitative RT-PCR (qrt-PCR), Western blotting (WB), and immunohistochemistry (IHC) [2]. The comprehensive and transversal metabolomic approach adopted elucidated specific metabolic dysregulations involving neuromediators and agents of energy metabolism, which were corroborated across multiple systems, thereby providing significant insights into the pathophysiology of SMA, as well as its diagnostic and therapeutic implications. References 1. Errico, F., Marino, C., Grimaldi, M., Nuzzo, T., et al. (2022) ”Nusinersen Induces Disease-Severity-Specific Neurometabolic Effects in Spinal Muscular Atrophy”, Biomolecules, 12(10). DOI: 10.3390/biom12101431. 2. Valsecchi, V., Errico, F., Bassareo, V., Marino, et al. (2023) ”SMN deficiency perturbs monoamine neurotransmitter metabolism in spinal muscular atrophy”, Communications Biology, 6(1), pp. 1155. DOI: 10.1038/s42003-023-05543-1. 3. Scatolini, L., et al (2025) ”RNase H1 counteracts DNA damage and ameliorates SMN-dependent phenotypes in a Drosophila model of Spinal Muscular Atrophy”, pp. 2025.04.17.649348. DOI: 10.1101/2025.04.17.649348 %J bioRxiv. 34 The 2nd Meeting of the Italian Metabolomics Network UNRAVELLING URINARY METABOLIC ALTERATIONS INDUCED BY THIRDHAND SMOKE EXPOSURE: FROM UNTARGETED ANALYSIS IN ANIMAL MODELS TO TARGETED LC-MS VALIDATION IN CHILDREN C. Guerrini1,4*, C. Merino2, M. Martins-Greenc3, M. Vinaixa4,5, N. Ramirez1,5 1Institut d’Investigació Sanitària Pere Virgili, Paediatric Nutrition and Human Development Research Unit, 43204, Reus, Spain 2Biosfer Teslab, 43204, Reus, Spain 3University of California, Department of Molecular, Cell, and Systems Biology, 92521, Riverside, USA 4University Rovira i Virgili, Department of Electronic Engineering, 43003, Tarragona, Spain 5Centre for Biomedical Research in Diabetes and Associated Metabolic Diseases (CIBERDEM), 28029, Madrid, Spain Tobacco smoke exposure (TSE), including both second-hand (SHS) and thirdhand (THS) smoke, is responsible for over 7 million deaths annually and has a significant impact on the health of vulnerable populations such as children [1]. Thus, it remains a critical public health concern worldwide. SHS results from direct exposure and inhalation of burning tobacco products, whilst THS consists of persistent tobacco smoke residues on surfaces, clothing, and dust, that can react with environmental compounds to form harmful secondary pollutants [2,3,4]. While the harmful effects of SHS are well established, as well as the ubiquity of tobacco smoke, the metabolic and health effects of THS remain underexplored, particularly in young population [4]. In this work, we present a wide-scope, multiplatform approach aimed at broadening chemical coverage in the study of urinary metabolic alterations associated with THS exposure. Urine samples from mice exposed to THS-contaminated environments, mimicking human exposure conditions, were analysed by untargeted metabolomics using gas and liquid chromatography coupled to high-resolution mass spectrometry (GC-HRMS and LC-HRMS/MS) in positive and negative ionisation modes [5,6,7,8]. LC-HRMS/MS analysis revealed 1805 significantly altered metabolic features compared to controls (filters: intensity > 4000, analytical variability, mass error > 5 ppm, FDR-corrected p-value < 0.05, fold change > 1.5). By GC-HRMS, we detected 290 significantly altered compounds (filters: mass error < 5 ppm, spectral match > 85%, RI error < 1%). Across both techniques, we confidently annotated 77 dysregulated metabolites in exposed-mice (between levels 1 and 2 of confidence [9]), belonging to different chemical classes and associated with 17 metabolic pathways. The tryptophan metabolism pathway was particularly affected by THS, with 15 altered metabolites and increased levels of quinolinic acid/kynurenic acid ratios, suggesting THS-induced neurotoxicity, neuroinflammation and behavioural disorders. To further validate these findings and assess the health risks of TSE exposure in children, we developed a targeted LC-MS method to quantify 21 specific urinary biomarkers, including 16 neurotransmitters, 3 tobacco metabolites, and 2 oxidative stress biomarkers. We analysed the urine of 215 children aged 8 to 12 years, with varying levels of tobacco smoke exposure at home. ANOVA analysis revealed statistically significant differences in the levels of all 21 target metabolites between exposed and non-exposed children (SHS and THS) at home and those not exposed (FDR corrected p-values < 0.05). A one-way hierarchical clustering heatmap displayed 17 up-regulated compounds and 4 down-regulated (kynurenic acid, kynurenine, melatonin and xanthurenic acid) in children exposed to tobacco smole, suggesting TSE-induced metabolic dysregulations, particularly in pathways linked to neurological and hormonal function, and potential carcinogenic activation. Our findings highlight the need for advanced policies and public health interventions advocating to minimise tobacco smoke exposure and protect children from long-term health effects. 35 The 2nd Meeting of the Italian Metabolomics Network References 1. World Health Organization Tobacco: Key facts: https://www.who.int/news-room/fact-sheets/detail/tobacco (accessed on Aug 20, 2025) 2. Jacob, P. et al. (2017) ”Thirdhand smoke: new evidence, challenges, and future directions”, Chemical Research in Toxicology, 30(1), pp. 270–294. doi:10.1021/acs.chemrestox.6b00343. 3. Ramirez, N. et al. (2014) ”Exposure to nitrosamines in thirdhand tobacco smoke increases cancer risk in non-smokers”, Environment International, 71, pp. 139–147. doi:10.1016/j.envint.2014.06.012. 4. Díez-Izquierdo, A. et al. (2018) ”Update on thirdhand smoke: a comprehensive systematic review”, Environmental Research, 167, pp. 341–371. doi:10.1016/j.envres.2018.07.020. 5. Adhami, N. et al. (2017) ‘Biomarkers of disease can be detected in mice as early as 4 weeks after initiation of exposure to third-hand smoke levels equivalent to those found in homes of smokers’, Clinical Science, 131(21), pp. 2409–2426. doi:10.1042/CS20171053. 6. Khamis, M.M. et al. (2017) ”Mass spectrometric based approaches in urine metabolomics and biomarker discovery”, Mass Spectrometry Reviews, 36(2), pp. 115–134. doi:10.1002/mas.21455. 7. Chan, E.C.Y. et al. (2011) ”Global urinary metabolic profiling procedures using gas chromatography–mass spectrometry”, Nature Protocols, 6(10), pp. 1483–1499. doi:10.1038/nprot.2011.375. 8. Vinaixa, M. et al. (2012) ”A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data”, Metabolites, 2(4), pp. 775–795. doi:10.3390/metabo2040775. 9. Schymanski, E.L. et al. (2014) ”Identifying small molecules via high resolution mass spectrometry: communicating confidence”, Environmental Science & Technology, 48(4), pp. 2097–2098. doi:10.1021/es5002105. 36 The 2nd Meeting of the Italian Metabolomics Network 3BUILDING THE ECOSYSTEM FOR STANDARDIZED AND AUTOMATED NMR METABOLOMICS M. Zani Global Sales Director NMR Metabolomics at Bruker BBIO E-mail: [email protected] This talk will present the development of a comprehensive ecosystem for standardised and automated quantitation of metabolites by NMR, with a focus on delivering robust, reproducible, and fully exchangeable data across a range of biological samples. Leveraging the IVDr NMR platform at 600 MHz, we built the IVDr methods for 800 MHz, enabling standardized acquisition for plasma and urine—the main targets—as well as CSF, saliva, faeces, follicular fluid, and tissue samples using HR-MAS. Furthermore, the efforts to develop fully automated and validated absolute quantitation of metabolites in serum/plasma on a benchtop NMR system will be shared. The approach is complemented by software tools compatible with any field strength, streamlining metabolite quantitation workflows by NMR. 37 Posters The 2nd Meeting of the Italian Metabolomics Network TARGETING THE PHOSPHATIDYLCHOLINE CYCLE IN TRIPLE-NEGATIVE BREAST CANCER: A 1H-NMR METABOLOMICS APPROACH F. M. Bonanni*, M. E. Pisanu, M. Chirico, I. Ruspantini, R. Canese, D. Pietraforte, E. Iorio Servizio Grandi Strumentazioni e Core Facilities Istituto Superiore di Sanità, Viale regina Elena 299, 00161 Roma Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by pronounced chemoresistance and profound metabolic reprogramming [1-2]. A distinctive feature of TNBC is the dysregulation of phosphatidylcholine (PC) turnover, partly driven by the activity of PC-specific phospholipase C (PC-PLC) [3]. Through high-resolution 1H-NMR spectroscopy, we detected markedly elevated phosphocholine (PCho) levels in MDA-MB-231 cells relative to non-tumorigenic breast epithelial cells (HMEC), confirming aberrant activation of the PC cycle. To investigate the pharmacological modulation of this pathway, we assessed the effects of the PC-PLC inhibitor D609 and the antidiabetic agent metformin, which is under active investigation for anticancer applications and acts, in part, by inhibiting mitochondrial complex I (NADH dehydrogenase). NMR-based metabolomic analyses revealed that both compounds significantly impacted cellular energy metabolism, with metformin enhancing glycolytic flux. Notably, the combination with D609 produced a more pronounced antiproliferative effect and deeper alterations in energy-related metabolites, including ATP and ADP. These findings demonstrate that pharmacological targeting of the phosphatidylcholine metabolic pathway profoundly affects TNBC bioenergetics and metabolism and underscore the value of 1H-NMR spectroscopy as a powerful tool for elucidating drug-induced metabolic reprogramming in cancer cells. References 1. Iorio E., et al. (2016). ”Key Players in Choline Metabolic Reprograming in Triple-Negative Breast Cancer”.Front Oncol. ;6:205. doi:10.3389/fonc.2016.00205 2. Bao R., et al. (2024). ”The role of metabolic reprogramming in immune escape of triple-negative breast cancer”.Frontiers in immunology. vol. 15 1424237. doi:10.3389/fimmu.2024.1424237 3. Abalsamo L., et al. (2012). ”Inhibition of phosphatidylcholine-specific phospholipase C results in loss of mesenchymal traits in metastatic breast cancer cells”. Breast Cancer Res. 14(2):R50. doi: 10.1186/bcr3151. 39 The 2nd Meeting of the Italian Metabolomics Network METABOLOMIC FINGERPRINTS OF OPIOIDS: A COMMON SIGNATURE BEYOND CHEMICAL STRUCTURE S. Cesaroni1*, L. Caccavelli1, C. Amore2, F. Cortese3, S. Bilel4, M. Marti4, C. Montesano5, G. Petrella1, D.O. Cicero1 1University of Rome ”Tor Vergata” Department of Chemical Science and Technology, Via della Ricerca Scientifica 1, 00133, Rome, Italy. 2University of Turin, Department of Chemistry, Via Pietro Giuria 7, 10125, Turin, Italy. 3University of Rome ”Tor Vergata”, Department of Electronic Engineering, Via del Politecnico 1, 00133, Rome, Italy. 4University of Ferrara, Department of Translational Medicine, Section of Legal Medicine and LTTA Centre, Via Ludovico Ariosto 35, 44121, Ferrara, Italy. 5University of Rome ”La Sapienza”, Department of Chemistry, Piazzale Aldo Moro 5, 00185, Rome, Italy. New Psychoactive Substances (NPS) represent a complex and diverse group of drugs of abuse, designed as analogs of controlled compounds or synthesized de novo to mimic traditional illicit drugs [1]. Their continuous emergence on illegal markets, often in chemically uncharacterized forms, poses a growing challenge to conventional identification methods that rely heavily on prior structural knowledge [2]. This study adopts a metabolomics-based approach to investigate the systemic effects of four opioids - morphine, brorphine, etonitazene, and fentanyl - in CD-1 mice. Urine samples, collected after administration of equi-effective doses, were analyzed by 1H-NMR fingerprinting combined with multivariate statistical analysis to identify metabolic signatures reflecting the physiological response to opioid exposure. All compounds induce significant alterations in the urinary metabolic profile, most evident within the first 12 hours post-administration and still detectable at 24 hours. Brorphine exhibits the strongest perturbation, consistent with its pharmacokinetic profile. Importantly, a common metabolic perturbation pattern emerges across these structurally diverse opioids, suggesting potential functional biomarkers of opioid exposure. The alteration involves changes in creatine and 2-oxoglutarate levels, metabolites associated with cellular redox balance and energy metabolism. Despite limitations such as the animal model, limited sample size, and lack of definitive class-specific evidence, this study highlights the potential of metabolomics as a complementary tool in forensic and toxicological research, enabling early detection of novel opioids and reducing reliance on structure-based identification to improve regulatory responsiveness to emerging NPS. References 1. Shafi, A. et al. (2020) ”New psychoactive substances: a review and updates”, Therapeutic Advances in Psychopharmacology. SAGE Publications Ltd. doi:10.1177/2045125320967197. 2. Steuer, A.E., Brockbals, L. and Kraemer, T. (2019) ”Metabolomic strategies in biomarker research-new approach for indirect identification of drug consumption and sample manipulation in clinical and forensic toxicology?”. Frontiers in Chemistry. Frontiers Media S.A. doi:10.3389/fchem.2019.00319. 40 The 2nd Meeting of the Italian Metabolomics Network URINARY METABOLOMICS OF MUSCLE INVASIVE BLADDER CANCER: A STEP TOWARD SAFER PROGNOSIS F. Cortese1*, G. Gasparri2, S. Albisinni3, D. Capozzi3, D. O. Cicero2, G. Petrella2 1University of Rome “Tor Vergata”, Department of Electronic Engineering, Via del Politecnico 1, 00133, Rome, Italy 2University of Rome “Tor Vergata”, Department of Chemical Sciences and Technologies, Via della Ricerca Scientifica 1, 00133, Rome, Italy 3University of Rome “Tor Vergata”, Unit of Urology, Department of Surgical Sciences, Viale Oxford 81, 00133, Rome, Italy E-mail: [email protected]oma2.eu Muscle-invasive bladder cancer (MIBC) is a clinically aggressive malignancy associated with poor outcomes and high metastatic potential. Diagnosis currently depends on transurethral resection of the bladder tumor (TURBT), a procedure vital for histological confirmation and staging. However, TURBT is invasive and has been associated with complications such as tumor cell dissemination, which may adversely impact prognosis and therapeutic decisions [1,2]. These concerns emphasize the urgent need for reliable, non-invasive diagnostic tools for MIBC. This study explored the urinary metabolic profiles of 139 bladder cancer patients from two clinical centers using NMR fingerprinting-based metabolomics to distinguish MIBC from non-muscle-invasive bladder cancer (NMIBC). Multiple linear regression on NMR binning data revealed consistent metabolic alterations, including increased lactate and decreased 3-indoxyl sulfate levels in MIBC patients. Moreover, a significant chemical shift variation was observed for creatinine between the two groups, without a corresponding change in concentration, suggesting its interactions with urinary components such as metal ions. This finding underscores the sensitivity of the NMR fingerprinting approach in capturing subtle spectral differences beyond concentration shifts. Finally, a predictive model was developed using NMR data from patients with known tumor staging and applied to 30 urine samples from patients awaiting histological diagnosis. The model’s classifications were later compared to actual outcomes, achieving 80% accuracy. This highlights the potential of urinary metabolomics for non-invasive diagnosis while providing insights into the biochemical understanding of tumor progression and systemic responses. References 1. Tufano, A., Rosati, D., Moriconi, M., Santarelli, V., Canale, V., Salciccia, S., Sciarra, A., Franco, G., Cantisani, V. and Di Pierro, G.B. (2024) ”Diagnostic accuracy of contrast-enhanced ultrasound (CEUS) in the detection of muscle-invasive bladder cancer: a systematic review and diagnostic meta-analysis”, Current Oncology, 31(2), pp. 818–827. doi: 10.3390/curroncol31020060.2. S. Sun et al., Bladder Cancer 10, e21200009 (2023). 2. Sun, S., Wang, H., Zhang, X. and Chen, G. (2023) ”Transurethral resection of bladder tumor: novel techniques in a new era”, Bladder Cancer, 10, e21200009. doi: 10.14440/bladder.2023.865. 41 The 2nd Meeting of the Italian Metabolomics Network METABOLOMICS-DRIVEN BIOCHEMOMETRIC PROFILING OF GUIERA SENEGALENSIS LEAVES UNCOVERS ANTIBACTERIAL NAPHTHYL DERIVATIVES E. Gargiulo1*°,C. Sirignano1*°, U. Galdiero2, E. Roscetto2, O. Taglialatela-Scafati1and G. Chianese1 1Department of Pharmacy, School of Medicine and Surgery, University of Naples “Federico II”, Via D. Monesano,49, 80131 Naples, Italy 2Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy ° These authors contributed equally to this work Plant-derived natural products, owing to their intrinsic chemical diversity, represent a promising avenue to address multidrug resistance (MDR), one of the most pressing emerging threats to global public health [3]. Staphylococcus epidermidis is an MDR pathogen and a major contributor to hospital-acquired infections, particularly due to its ability to form biofilms, complicating the treatment of device-associated infections and sepsis, thus requiring advanced diagnostics and therapies to mitigate its impact in healthcare settings [1]. In the omics era, state-of-the-art analytical platforms are essential for revitalizing ethnopharmacological traditions to address urgent challenges by accelerating natural products drug discovery pipeline. In this study, we employed a combined approach integrating untargeted LC-HRMS and Molecular-Networking-based metabolomics and NMR-biochemometrics to investigate Guiera senegalensis leaves, traditionally used across African countries to treat infectious and chronic diseases [2]. This methodology enabled both the comprehensive snapshot of G. senegalensis leaves metabolome and the identification of key NMR features associated with the anti-bacterial activity of the extract. NMR-fingerprint driven the chromatographic purification allowing the isolation of 7 naphthyl derivatives, including the new naphthyl-butenone guieranone C, and two unprecedented naphtho-γ-pirones, namely guierapyrone A and B. Isolated metabolites were tested for their anti-bacterial activity against S. epidermidis and oxacillin resistant S. epidermidis unveiling guieranone A as a good candidate for fighting staphylococcal infections. References 1. Ahmed, S. K., et al. (2024). ”Antimicrobial resistance: Impacts, challenges, and future prospects”, Journal of Medicine, Surgery, and Public Health, 2, 100081. https://doi.org/10.1016/j.glmedi.2024.100081 2. Gargiulo, E., et al. (2025). ”A combined metabolomics and biochemometrics approach for the rapid identification of antibacterial naphthyl derivatives from Guiera senegalensis leaves”, Journal of Pharmaceutical and Biomedical Analysis, 265, 117069. https://doi.org/10.1016/j.jpba.2025.117069 3. Woo, S., et al. (2023). ”Recent advances in the discovery of plant-derived antimicrobial natural products to combat antimicrobial resistant pathogens: Insights from 2018–2022”, Natural Product Reports, 40(7), 1271–1290. https://doi.org/10.1039/D2NP00090C 48 The 2nd Meeting of the Italian Metabolomics Network TACKLING BIOTRANSFORMATIONS IN THE EXTREMELY RARE METABOLIC CONDITION ALKAPTONURIA USING NUCLEAR MAGNETIC RESONANCE D. Grasso*, V. Balloni, G. Jacomelli, L. Peruzzi, A. Santucci, A. Bernini University of Siena, Department of Biotechnology, Chemistry and Pharmacy, via Aldo Moro, 2 53100 Siena - Italy. Introduction: In the early 19th century, Dr. Archibald Garrod coined the term ”inborn error of metabolism” to describe Alkaptonuria (AKU), an ultra-rare condition with an incidence of 1 in 1’000’000 also known as ”black bone disease.” Garrod rightly linked the discolouration of connective tissue (ochronotic pigment) to a phenolic compound (alkapton) buildup due to a missing enzyme. Nowadays, we know that alkaptonuria (AKU) is an autosomal recessive disorder caused by mutations in the HGD gene, leading to a deficiency of homogentisate 1,2-dioxygenase. This results in the accumulation of homogentisic acid (HGA), which is excreted in the urine and deposited in tissues, causing ochronosis, joint degeneration, valvular heart disease, and the rupture of ligaments, muscles, and tendons. The metabolic defect occurs in the tyrosine catabolic pathway, where HGA accumulates due to a block in the enzyme-mediated breakdown. nitisinone, a drug used to treat hereditary tyrosinemia, has emerged as a treatment for AKU, leading to the approval of Orfadin®for adult AKU patients. By inhibiting 4-hydroxyphenylpyruvate dioxygenase (HPPD), nitisinone reduces the production of HGA, thereby improving symptoms and preventing further metabolic damage. However, nitisinone treatment elevates plasma tyrosine levels, mirroring tyrosinemia type 2 and potentially causing eye damage. This highlights an urgent need for alternative or adjuvant treatments that can reduce HGA without triggering tyrosinemia. Aim: Assess the impact of nitisinone on the tyrosine metabolism before and after treatment, analysing serum and urine samples of alkaptonuria patients via NMR spectroscopy. Method: Serum and urine samples of 14 AKU patients were collected before and after nitisinone treatment. The samples were prepared and analysed through NMR spectroscopy; 15 principal metabolites of the tyrosine pathway were identified and quantified for both serum and urine samples. Results: As predicted, 4-hydroxyphenylpyruvate and tyrosine concentrations increase upon nitisinone administration. Additionally, 4-hydroxyphenillactate, 4-hydroxyphenilacetate and, partially, tyramine also increase in urine samples, suggesting a good metabolic clearance for these compounds. NMR has emerged as a valuable tool for monitoring the metabolic impact of nitisinone administration, with potential applications in other disorders related to tyrosine catabolism. Study design for the metabolic analysis of AKU patients treated with Orfadin® 49 The 2nd Meeting of the Italian Metabolomics Network METABOLIC PROFILING AND SKIN PENETRATION STUDY OF SERICOSIDE USING HR-MAS NMR AND HPLC-MS TECHNIQUES S. Ianni1*, A. Gambini1,2, V. Piccolo3, R. Di Lorenzo3, A. Mucci2, S. Laneri3, V. Righi1 1Department of Life Quality Studies, University of Bologna, Campus of Rimini, Corso d’Augusto 237, 47921Rimini, Italy 2Department of Geological and Chemical Sciences, University of Modena and Reggio Emilia, via Campi 103, 41125 Modena, Italy 3Department of Pharmacy, RD Cosmetics Laboratory, University of Naples Federico II, Via Montesano 49, 80131 Naples, Italy Sericoside is a naturally occurring bioactive compound derived from Terminalia sericea, a medicinal plant known for its rich array of bioactive components such as saponins, flavonoids, tannins, and triterpenic acids. These constituents provide antioxidant, antimicrobial, and skin-rejuvenating effects. Sericoside is important for enhancing skin health and maintaining the integrity of connective tissues, highlighting its potential applications in dermatology and anti-aging treatments [1]. The objective of this thesis is to investigate the potential of Sericoside conducting in vitro experiments focused on assessing the cytocompatibility and regenerative potential of Sericoside in SH-SY5Y neuroblast-like cells. To evaluate the Sericoside metabolic activity ex vivo analysis on cells using HR-MAS NMR spectroscopy, was performed. This methodology enabled us to compare cellular activity and metabolic alterations effectively. Preliminary findings indicated notable changes in choline-containing compounds—crucial for membrane biosynthesis—correlated with cell regeneration outcomes measured via scratch tests. These observations suggest a relationship between membrane metabolism and regenerative responses modulated by sericoside, offering valuable insights into its biological effects. In addition, an ex vivo transdermal delivery study was performed using Franz diffusion cells to evaluate the skin penetration of Sericoside. The amount of compound absorbed into the skin layers and receptor fluid was quantified employing HPLC-MS analysis, offering detailed insights into its percutaneous absorption profile. References 1. M. Meunier, et al. (2021) ”Skin Cellular Reprogramming as an Innovative Anti-Aging Strategy for Cosmetic Application: A Clinical Study of Sericoside”, Front. Biosci. (Landmark Ed) 2023, 28(6), 112. https://doi.org/10.31083/j.fbl2806112 50 The 2nd Meeting of the Italian Metabolomics Network NMR-BASED METABOLOMIC APPROACH IN SEPSIS-INDUCED ACUTE KIDNEY INJURY D. Lalli1*, S. Cerruti1, C. Cassino1, F. Gavelli2, L. Castello2, M. Botta1 1Università del Piemonte Orientale, Department of Science and Technological Innovation, viale T. Michel 11, 15121, Alessandria, Italy 2Università del Piemonte Orientale, Department of Translational Medicine, via Solaroli 17, 28100, Novara, Italy Sepsis-induced acute kidney injury (SI-AKI) is a frequent complication in critically ill patients, associated with high mortality [1]. The lack of specific biomarkers hampers the early recognition of SI-AKI, which is essential to provide supportive treatment and prevent further organ damage [2]. NMR-based metabolomics is a powerful approach for identifying specific metabolic fingerprints of kidney diseases [3]. Here, we studied 89 urine samples of patients with sepsis to identify urinary metabolomic profiles for the early diagnosis and prevention of SI-AKI. Preliminary statistical analyses showed promising correlations between clinical variability and metabolic alterations. Specifically, multiple factor analysis (MFA) highlighted a distinctive metabolic signature in a group of patients with severe renal and metabolic impairment. These analyses lay the foundation for future investigations aimed at improving diagnostic and therapeutic strategies for patients with sepsis-induced AKI and septic shock. References 1. Zarbock, A. et al. (2023) ”Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup”, Nature Reviews Nephrology, 19, p. 401–417. doi: 10.1038/s41581-023-00683-3. 2. Uchino, S. et al. (2005) ”Acute renal failure in critically ill patients: a multinational, multicenter study”, JAMA, 294, p. 813-818. doi:10.1001/jama.294.7.813. 3. Pandey, S. et al. (2025) ”Metabolomics for the Identification of Biomarkers in Kidney Diseases”, Nanotheranostics, 9, p. 110-120. doi: 10.7150/ntno.108320. 51 The 2nd Meeting of the Italian Metabolomics Network INTEGRATIVE METABOLOMIC AND FLUXOMIC PROFILING REVEALS METABOLIC SHIFTS IN CANCER AND IMMUNE CELLS FOR REFINING PERSONALIZED THERAPEUTIC STRATEGIES AND IDENTIFYING CIRCULATING BIOMARKERS L. La Sala1*, A. Leone1, T. Moccia1, R. Affatato1, R. Migliorino1, E. Perfetto1, P. Bagnara1, S. Costantini1, M. Ametrano1, C. Vitagliano1, M. S. Roca1, F. Iannelli1, C. Ciardiello2, A. Avallone3, C. De Vitis4, R. Mancini4, S. Sciacchitano5, M. Rocco6, A. Budillon7and E. Di Gennaro1 1Experimental Pharmacology Unit-Laboratory of Naples and Mercogliano (AV)- Istituto Nazionale Tumori-IRCCSFondazione G. Pascale, Naples, Italy; 2Preclinical Models of Tumor Progression UnitIstituto Nazionale Tumori-IRCCSFondazione G. Pascale, Naples, Italy; 3Experimental Clinical Abdominal Oncology, Istituto Nazionale Tumori-IRCCSFondazione G. Pascale, Naples, Italy; 4Department of Clinical and Molecular Medicine, Sant’ Andrea Hospital-Sapienza University of Rome, Italy 5Department of Life Sciences, Health and Health Professions, Link Campus University, Rome, Italy. 6Department of Clinical and Surgical Translational Medicine, Sapienza University, Rome, Italy; 7Scientific Directorate; Istituto Nazionale Tumori-IRCCSFondazione G. Pascale, Naples, Italy Despite the significant progress achieved through chemotherapy and emerging therapeutic approaches, including molecularly targeted drugs and immunotherapy, the prognosis of many cancers, especially in advanced stages, remains unfavorable [1]. These considerations highlight the need for early disease diagnosis and the development of new therapeutic strategies. In this scenario, the identification of circulating biomarkers from biological samples could allow for an early diagnosis and for patient stratification to the therapy. Moreover, the techniques used for the analysis of circulating biomarkers stand out for their lower complexity, invasiveness, and cost [2]. Recently, cellular metabolism has been reported as new relevant hallmark of cancer [3]. Unlike normal cells, which primarily rely on oxidative phosphorylation for energy production, tumor cells exhibit increased glucose consumption and lactate production through aerobic glycolysis. These metabolic features contribute to tumor progression and therapy resistance. Moreover, lactate secretion leads to acidification of the tumor microenvironment, promoting tumor growth, migration, and invasion [4–6]. In this study, we aim to understand the metabolic change underlined to anticancer therapy-resistance, with the ultimate goal of designing new combination therapeutic strategies to treat cancer patients. Then, considering that mitochondrial dysfunction could affect differently several types of cells, we lastly aim to identify metabolic shift in different immune cell populations, in order to propose mitochondrial function as potential biomarker [7–11]. To address our first aim, we took advantage of established cell lines, sensitive or resistant to different anti-cancer therapy, as well as of more complex cancer model as organoids. In detail, the day after seeding the cells or organoids, we performed a dedicated assay to monitor mitochondrial respiration. By using Extracellular Flux Analyzer, we measured oxidative phosphorylation as oxygen consumption rate (OCR) and the glycolysis as extracellular acidification rate (ECAR). Our data showed that progression from an earlier stage of cancer to a more advanced one or from sensitive to resistant cells, induced a basal metabolic shift towards energy-demanding and mitochondria-driven metabolism, as evidenced by differential levels of both OCR and ECAR. Similarly, in 3D cultures, which more accurately recapitulate the tumor microenvironment, both OCR and ECAR values were lower than those observed in 2D cultures. Furthermore, in resistant models, mitochondrial dysfunction was associated to change in endo-metabolites levels, as ATP, lactate and glucose measured by NMR approach. Intriguingly, we observed that, after treatment with appropriate drugs, both sensitive and resistant cells showed lower levels of OCR and ECAR compared to untreated controls. Fluxomics and metabolomic analyses unveil a metabolic shift in drug-resistant tumor cells. Finally, with the aim to identify circulating biomarkers from biological samples and to define metabolic changes in immune cells, we are conducting 52 The 2nd Meeting of the Italian Metabolomics Network a comprehensive metabolic analysis on total PBMCs, T and B cells from healthy donors and patients admitted to intensive care units of Sant’ Andrea Hospital. Specifically, PBMCs isolated were sorted, using a gating strategy by Cell Sorter, to obtain T and B cells. Then, all populations were subjected to mitochondrial respiration analysis to assess their metabolic profiles. Preliminary results showed that total PBMCs, T cells, and B cells from patients exhibited reduced ECAR levels compared to healthy donors, indicating decreased glycolytic activity across all populations. Additionally, OCR levels were reduced, particularly in total PBMCs and T cells, suggesting impaired mitochondrial respiration. Interestingly, PBMCs derived from blood sample collected from metastatic colorectal cancer patients, enrolled within a phase 3 study active at IRCCS Fondazione Pascale showed lower OCR and ECAR values compared to healthy donors, corroborating in vitro findings. Overall, our findings suggest that integrating fluxomic approaches enhances the understanding of cancer metabolism, facilitating the identification of predictive and prognostic metabolic biomarkers that may refine personalized therapeutic strategies. References 1. U. Anand, Genes Dis. 2022, doi: 10.1016/j.gendis.2022.02.007 2. S. M. Batool, Cell Rep Med, 2023, doi: 10.1016/j.xcrm.2023.101198 3. D. Hanahan, Can Disc, 2022, doi: 10.1158/2159-8290.CD-21-1059 4. W. Zhang, Mol Med, 2025 doi: 10.1186/s10020-025-01205-6 5. Y. Sun, Drug Res up, 2025 doi: 10.1016/j.drup.2025.101248 6. J. Meiser, Embo J 2024 doi: 10.1038/s44318-024-00098-1 7. L. M. Beckett, Phys Gen, 2025 doi: 10.1152/physiolgenomics.00141.2024 8. I. Yoo, Mol and Cells, 2024, doi: 10.1016/j.mocell.2024.100095 9. J. A. Espinosa, Curr Prot, 2021, doi: 10.1002/cpz1.75 10. R. Y. Pan, Cell Metab, 2022, doi: 10.1016/j.cmet.2022.02.013 11. M. G. Vander Heiden, Science, 2009, doi: 10.1126/science.1160809 Supported by the Ministry of Health (grant No PNRR-MAD-2022-12376767 and Ricerca Corrente Project 2022-2024) 53 The 2nd Meeting of the Italian Metabolomics Network UNCOVERING METABOLIC RESPONSES TO FLAVESCENCE DORÉE PHYTOPLASMA INFECTION IN GRAPEVINE LEAVES USING AN UNTARGETED IMAGING APPROACH A. Locatelli*, D. Magrin1, L. Galetto2, C. Marzachì2, R. Bagnati1, A. Passoni1 1Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Environmental Health Sciences, Mass Spectrometry for One-Health Unit, Via Mario Negri 2, Milan, Italy 2Consiglio Nazionale delle Ricerche, Istituto per la Protezione Sostenibile delle Piante IPSP-CNR, Strada delle Cacce 73, Turin, Italy Flavescence dorée (FD) is a serious grapevine disease widespread in Europe, especially in Italy in recent decades [2]. The disease is caused by the so-far-undescribed ‘Candidatus Phytoplasma vitis’ and transmitted by the leafhopper insect vector Scaphoideus titanus [1]. FD represents a major threat to viticulture worldwide, leading to significant economic losses due to reduced grape yield and quality. For instance, in 2005, Italy allocated €34 million to refund affected growers. Given its economic impact and ongoing spread, there is an unmet need for early detection methods and effective strategies to mitigate phytoplasma infections [3]. The project aims to investigate the metabolic mechanism underlying phytoplasma infection and to identify early candidate diagnostic biomarkers for FD that can differentiate non-infected from infected grapevines through an untargeted mass spectrometry imaging (MSI) approach. Grafted cuttings Vitis vinifera plants (cultivar Barbera) were exposed to FD-infected insect vectors, sampled 3 months after, at symptom appearance. Unexposed plants were used as controls. Leaves from control, exposed and symptomatic, as well as exposed and asymptomatic grapevines were analyzed, sampling the upper and lower surfaces, with four biological replicates for each experimental group. Samples were coated with the α-Cyano-4-hydroxycinnamic acid (CHCA) matrix using a SunCollect Sprayer (SunChrom) and analyzed with a high-resolution mass spectrometer (Orbitrap Q-Exactive - Thermo Scientific) coupled with an AP-MALDI (MassTech) source. Data were processed with MSiReader Pro (MassTech) and matched to reference databases for metabolite annotation. Preliminary analyses of non-infected leaves using two databases (HMDB and PMHub 1.0) enabled acquisition of the grapevine leaf’s fingerprint and supported the creation of an in-house plant metabolism database. Final leaf data from non-exposed, symptomatic, and asymptomatic exposed plants were analyzed and about 5100 features present in at least three of four replicates were identified. Given the high number of features, it is necessary to prioritize them in order to proceed with annotation [4]. Statistical comparison between the upper and lower leaves for each experimental group revealed that 189, 193, and 5 features were significant for non-exposed, asymptomatic, and symptomatic conditions, respectively. Further statistical analysis will identify significant features across the different experimental groups. The features obtained will subsequently be annotated with MSi Reader Pro software using the previously created in-house database. Future experiments will employ LC-MS/MS analysis to confirm and ensure the accurate identification of annotated metabolites. This strategy could significantly advance and facilitate rapid and early phytoplasma detection. Furthermore, this approach holds potential for broader application to other plant–pathogen interactions and pave the way for improved and sustainable approaches to disease control. References 1. Bertazzon, N. et al. (2019) ”Grapevine comparative early transcriptomic profiling suggests that Flavescence dorée phytoplasma represses plant responses induced by vector feeding in susceptible varieties”, BMC Genomics, 20(1). doi:10.1186/s12864-019-5908-6. 54 The 2nd Meeting of the Italian Metabolomics Network 2. Boulent, J. et al. (2020) ”Automatic Detection of Flavescence Dorée Symptoms Across White Grapevine Varieties Using Deep Learning”, Frontiers in Artificial Intelligence , 3. doi:10.3389/frai.2020.564878. 3. Chuche, J. and D. Thiéry (2014) ”Biology and ecology of the Flavescence dorée vector Scaphoideus titanus: A review”, Agronomy for Sustainable Development . EDP Sciences, pp. 381–403. doi:10.1007/s13593-014-0208-7. 4. Domingo-Almenara, X. et al. (2018) ”Annotation: A Computational Solution for Streamlining Metabolomics Analysis”, Analytical Chemistry. American Chemical Society, pp. 480–489. doi:10.1021/acs.analchem.7b03929. 55 The 2nd Meeting of the Italian Metabolomics Network UNVEILING THE IMPACT OF EMERGING POLLUTANTS ON LUNG EPITHELIAL CELL LINES VIA METABOLOMICS MASS SPECTROMETRY A. Magnani1*, S. Arpaia1, S. Causo1, S. Vietti Michelina1, P. Calza2, F. Dal Bello1 1University of Turin, Department of Molecular Biotechnology and Health Science, Piazza Nizza 44, 10126, Torino, Italy; 2University of Turin, Department of Chemistry, Via P. Giuria 5, 10125, Torino, Italy. E-mail: [email protected] Cell metabolomics represents a powerful approach for exploring cellular responses to environmental or chemical stressors. In this study, we applied an HPLC-HRMS-based metabolomic analysis to investigate metabolic alterations in normal (BEAS-2B) and oncogenic (BEAS-2B KRAS G12C) human lung epithelial cell lines exposed to contaminants of emerging concern (CECs). To assess acute toxicity, cells were incubated with two fluoroquinolone antibiotics, ofloxacin and ciprofloxacin, and their transformation products (TPs) generated by TiO2-mediated heterogeneous photocatalysis. Cell viability assays revealed increased mortality following exposure to TPs (Figure 1). Figure 1: Cell viability results of ciprofloxacin TPs treatment on BEAS B2 and BEA 2B KRAS G12C Metabolomic profiling was performed using an HPLC-HRMS method using ODC and HILIC columns for chromatographic separation and high-resolution mass spectrometric detection operated in data dependent analysis aquisition mode with a resolving power of 60k. A total of 141 and 39 metabolites were annotated [1] in positive and negative ionization modes, respectively. Comparison of metabolite profiles across five exposure conditions (1. ciprofloxacin; 2. photocatalyzed ciprofloxacin; 3. water; 4. TiO2 filtrate; and 5. untreated control; same conditions for ofloxacin) led to the identification of several altered metabolites. Among them, guanidinosuccinic acid (GSA) was significantly overexpressed in TPs-exposed cells. This observation aligns with literature reports [2] describing GSA as a derivative of arginine metabolism capable of inducing oxidative damage, potentially resulting in cellular apoptosis. References 1. Schymanski, EL. et al. (2014) ”Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence”, Environ. Sci. Technol. 48 (4) 2097–2098. 2. Nissenson, AR, et al. (2017). Handbook of Dialysis Therapy (Fifth Edition). Elsevier. 56 The 2nd Meeting of the Italian Metabolomics Network A HIGH-RESOLUTION MASS SPECTROMETRY APPROACH TO PROMOTE SORGHUM BICOLOR BIOMASS VALORISATION D. Magrin1*, A. Locatelli1, A. Morabito1, S. Caronni2, S. Citterio2, I. Milanesi2, R. Bagnati1, L. Brunelli1, A. Passoni1 1Mario Negri Institute for Pharmacological Research IRCCS, Department of Environmental Health Sciences, Milan (Italy). 2University of Milan Bicocca, Department of Earth and Environmental Sciences, Milan (Italy). 3Politecnico di Milano, Milan (Italy) Sorghum (L. Moench) subsp. bicolor (hereafter S. bicolor) is an edible grain species native to Africa widely cultivated worldwide due to its adaptability to difficult environment and diverse secondary metabolism, that make it a valuable resource for food, feed and biomass re-use [1]. Despite its potential, a great part of S. bicolor biomass still remains largely undervalued and is often treated as waste. Recent growing interest in its nutritional value and possible health benefits has further highlighted its high potential but the understanding of how environmental conditions influence its metabolic composition is still not complete. Therefore, this study aims to develop a robust mass-spectrometry method for the analysis of the impact of three among the most relevant environmental factors triggering S. bicolor develop and growth, the species secondary metabolism, through a comprehensive metabolomic analysis. Twelve pots were prepared according to a fully cross-cutting experimental design to assess the effects of three considered environmental factors which were water availability, soil enrichment with phosphorous (Ca(H2PO4)2) and growth-promotion, obtained using an ad-hoc consortium of fungi and bacteria (Micosat F 002). Endogenous metabolites in the leaves of the plants were identified using liquid chromatography (Agilent Technologies, 1200 Series) coupled to high-resolution mass spectrometry (Orbitrap Q-Exactive, Thermo Scientific), operated in both positive and negative heated electrospray ionization (HESI) mode. Analyses were run at four time points to monitor the metabolite production during plant growth phases. Our work starts from a workflow previously applied to study Lepidium sativum (cress) metabolism that was adapted and further optimized for the untargeted identification of Sorghum bicolor metabolites. A linear mixed model was fitted to the longitudinal data to account for repeated measurements and to assess statistical significance across different conditions and time points. Preliminary analysis revealed an extended panel of metabolites belonging from ammino acids, lipids, carbohydrates, phenols and carotenoids classes. These preliminary findings also demonstrated the enhanced efficiency and reliability of this workflow in plant metabolomics. The analysis should highlight key metabolic shifts, providing valuable insights into strategies for enhancing metabolite synthesis. Overall, these findings contribute to the valorisation of underexploited plant resources [2]. Within the framework of the circular economy, valorising plant biomass is essential to minimize waste production, promote environmental sustainability, and obtain high-valuable bioactive compounds. Overall, this study establishes a basis for the metabolic characterization of S. bicolor under different environmental conditions and supports its potential for future valorisation. References 1. Stefoska-Needham, A. (2024) ”Sorghum and health: An overview of potential protective health effects”, Journal of Food Science, 89(S1), pp. A30–A41. Available at: https://doi.org/10.1111/1750-3841.16978. 2. Van Nguyen, T.T. et al. (2022), ”Valorization of agriculture waste biomass as biochar: As first-rate biosorbent for remediation of contaminated soil”, Chemosphere, 307(Pt 3), p. 135834. Available at: https://doi.org/10.1016/j.chemosphere.2022.135834. 57 The 2nd Meeting of the Italian Metabolomics Network MEASURING ETHANOL CONCENTRATION IN ALCOHOLIC BEVERAGES BY 1H-NMR: METHODS EVALUATION AND A CASE STUDY ON RED AND WHITE WINES M. Sozzi1*, V. Aru2, N. Cavallini1, B. Khakimov2, F. Savorani1, S. B. Engelsen2 1Department of Applied Science and Technology, Polytechnic of Turin, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; 2Chemometrics & Analytical Technology, Department of Food Science, University of Copenhagen, Rolighdsvej 26, 1958 Frederiksberg C, Denmark. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique that is now widely used in the field of food and beverages. Many recent applications are related to quality control, authentication, traceability, fraud detection but also process monitoring such as fermentation and aging effects. Even if NMR spectroscopy is commonly used for quantification purposes, in the recent literature there are few studies focused on the possibility of using this technique to quantify ethanol in alcoholic beverages [1]. Indeed, some recent works highlighted problems associated with this application, often related to low accuracy [2], occurring also with higher magnetic field intensities [3]. In this context we explore the causes of these results by planning a detailed experimental design using a series of ethanol solutions with increasing concentrations and considering different 1H NMR acquisition parameters, data processing techniques and methods related to ethanol quantification. To try to avoid the known quantification problems [2,3] related to the use of the proton-proton coupling triplet signal at 1.18 ppm (i.e., one of the two “main” ethanol signals), the carbon-related satellite peak at 0.08 ppm was also tested, building upon the work of Lopez-Rituerto et al. [4]. The main and the satellite signals were modelled separately and compared, and a new correction method to make the satellites quantitative was developed. In addition, from the data analysis and processing point of view, two different quantification approaches were tested (namely, row sum, and multivariate curve resolution). Also, to evaluate the effects of two deuterated solvents (D2O and DMSO) and of the suppression of the water signal, ANOVA Simultaneous Component Analysis (ASCA) was applied. Additionally, both internal and external standard quantification approaches were evaluated during this study. To test and further evaluate the model performances, different external wine samples were analyzed following an optimized operating procedure. References 1. Caleja-Ballesteros H.J.R., et al., Microchemical Journal, 2021, 164, 105999. 2. Matviychuk Y., et al., Analytica Chimica Acta, 2021, 1182, 338944. 3. Isaac-Lam M. F., International Journal of Spectroscopy, 2016, 2526946. 4. Lopez-Rituerto E., et al., Journal of Agricultural and Food Chemistry, 2009, 57, 2112–2118. 64 The 2nd Meeting of the Italian Metabolomics Network FROM RAW DATA TO LONGITUDINAL INSIGHT: AN OPEN-SOURCE WORKFLOW FOR UNTARGETED METABOLOMICS OF ULVAN POLYSACCHARIDE FERMENTATION B. Zonfrillo1*, C. M. S. Ambrosio2, N. Mulinacci1, J. Rubert2,3 1University of Florence, Department of NEUROFARBA, Via Ugo Schiff 6, 50019, Sesto Fiorentino, Italy 2Wageningen University and Research, Division of Human Nutrition and Health, P.O. Box 17, 6700 AA, Wageningen, The Netherlands 3Wageningen University and Research, FQD group, P. O. Box 17, 6700 AA, Wageningen (The Netherlands) Fermentation systems are useful tools to assess the impact of dietary treatments on the gut microbiota in vitro. When combined with untargeted metabolomics, they can reveal temporal and treatment-related metabolic dynamics but require reproducible data processing and robust statistical approaches. Following best practices in nutrimetabolomics [2], this study presents an integrated workflow for data processing, annotation, and longitudinal analysis applied to [3], was subjected to in vitro digestion (INFOGEST 2.0) and colonic fermentation [1], sampling at 0-12-24-48 h. LC-QToF data were acquired in randomized order, with blanks, pooled QC, and external standards ensuring reproducibility. Data processing in MZmine 3 included smoothing, peak detection, alignment, and gap filling. Annotation with SIRIUS 5.8.6 (CSI:FingerID, ZODIAC, CANOPUS, COSMIC) and data filtering retained features with RSD < 20%, and high variance (top 60%). The final dataset (943 ESI� and 485 ESI� features) included 173 features with MSI 2a confidence. Dynamic responses were modeled using repeated-measures ASCA (RM-ASCA) in RStudio. PC1 (66-72% variance) described the global fermentation trajectory, showing decreased peptides and bile acids and a rise in fatty acid derivatives, while PC2-PC3 captured treatment-specific effects, distinguishing ulvan from control samples. Two cyclic proline dipeptides, cyclo-(Pro-Pro) and cyclo-(Pro-Met), showed opposite time trends and were tentatively linked to Lactobacillus and Bacteroides via MicrobeMASST, suggesting selective stimulation of beneficial microbial groups. Additional ulvan-specific sulfur-containing metabolites, not previously described, may represent markers. This study, building on a previous targeted metabolomic analysis, integrates open-source tools with advanced longitudinal modeling, providing a workflow for untargeted metabolomics data interpretation of ulvan fermentation by human microbiota. References 1. Pérez-Burillo, S. et al. (2021) ”An in vitro batch fermentation protocol for studying the contribution of food to gut microbiota composition and functionality”,Nature Protocols, 16(7), pp. 3186–3209. Available at: https://doi.org/10.1038/s41596-021-00537-x. 2. Ulaszewska, M.M. et al. (2019) ”Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies”, Molecular Nutrition & Food Research, 63(1), p. 1800384. Available at: https://doi.org/10.1002/mnfr.201800384. 3. Zonfrillo, B. et al. (2025) ”Multivariate optimization of ulvan extraction applying Response Surface Methodology (RSM): the case of Ulva lactuca L. from Orbetello lagoon”, Carbohydrate Polymers, 354, p. 123340. Available at: https://doi.org/10.1016/j.carbpol.2025.123340. 65