Various spectrometric methods can be used to conduct metabolomics studies. Nuclear magnetic resonance (NMR) or mass spectrometry (MS) coupled with separation methods, such as liquid or gas chromatography (LC and GC, respectively), are the most commonly used techniques. Once the raw data have been obtained, the real challenge lies in the bioinformatics required to conduct: (i) data processing (including preprocessing, normalization, and quality control); (ii) statistical analysis for comparative studies (such as univariate and multivariate analyses, including PCA or PLS-DA/OPLS-DA); (iii) annotation of the metabolites of interest; and (iv) interpretation of the relationships between key metabolites and the relevant phenotypes or scientific questions to be addressed. Here, we will introduce and detail a stepwise protocol for use of the Workflow4Metabolomics platform (W4M), which provides user-friendly access to workflows for processing of LC-MS, GC-MS, and NMR data. Those modular and extensible workflows are composed of existing standalone components (e.g., XCMS and CAMERA packages) as well as a suite of complementary W4M-implemented modules. This tool suite is accessible worldwide through a web interface and is hosted on UseGalaxy France. The extensible Virtual Research Environment (VRE) provided offers pre-configured workflows for metabolomics communities (platforms, end users, etc.), as well as possibilities for sharing among users. By providing a consistent ecosystem of tools and workflows through Galaxy, W4M makes it possible to process MS and NMR data from hundreds of samples using an ordinary personal computer, after step-by-step workflow optimization. (c) 2025 Wiley Periodicals LLC.Basic Protocol 1: W4M account creation, working history preparation, and data uploadSupport Protocol 1: How to prepare an NMR zip fileSupport Protocol 2: How to convert MS data from proprietary format to open formatSupport Protocol 3: How to get help with W4M (IFB forum) and how to report a problem on the GitHub repositoryBasic Protocol 2: LC-MS data processingAlternate Protocol 1: GC-MS data processingAlternate Protocol 2: NMR data processingBasic Protocol 3: Statistical analysisBasic Protocol 4: Annotation of metabolites from LC-MS dataAlternate Protocol 3: Annotation of metabolites from NMR data

Introduction Les toxicités à long terme liées au traitement, telles que les toxicités neurologiques et métaboliques, constituent un problème majeur en oncologie, en particulier dans le cas du cancer du sein où les patientes bénéficient d'une très bonne survie à long terme. La compréhension et la prédiction des toxicités à long terme est donc essentielle pour prévenir ces toxicités et pouvoir adapter les traitements. Nous avons étudié le lien entre le profil métabolomique mesuré au diagnostic et les toxicités à long terme liées au traitement dans la cohorte prospective Unicancer CANTO (CANcer TOxicities). Méthodes Des profils métabolomiques non ciblés à haute résolution ont été acquis au diagnostic pour 992 patientes ER+/HER2- de la cohorte CANTO, en utilisant la chromatographie liquide couplée à la spectrométrie de masse. Quatre profils métabolomiques sériques par patiente ont été produits : (i) métabolites partagés et annotés (m=233), (ii) métabolites annotés mais pas toujours communs (m=471), (iii) métabolites partagés mais pas toujours annotés (m=766) et (iv) tous les métabolites (m=1935). Afin de contrôler les facteurs de confusion tels que la saisonnalité de l'échantillon sanguin, une stratégie de modélisation basée sur les résidus a été développée. Nous avons comparé des modèles statistiques adaptés à l'analyse de données de grande dimension (LASSO, LASSO adaptatif, apprentissage automatique et apprentissage profond) afin de sélectionner les meilleurs modèles pour la prédiction. L'échantillon a été divisé en ensembles de découverte et de validation pour contrôler le surajustement et une correction pour les tests multiples a été appliquée pour contrôler le taux de fausses découvertes. Résultats Des données cliniques et métabolomiques au diagnostic étaient disponibles pour 857 patientes répartis entre les ensembles de découverte (n=572) et de validation (n=285). Parmi tous les modèles comparés, le LASSO adaptatif était la méthode statistique la plus intéressante avec un biais d'optimisme limité. La méthode du LASSO adaptatif permettait également de sélectionner un sous-ensemble de métabolites particulièrement intéressants pour la recherche translationnelle future. L'ajout de métabolites à faible fréquence ainsi que de métabolites non annotés augmentait de manière significative le pouvoir prédictif des modèles. Après correction pour les tests multiples, les données métabolomiques été statistiquement associées à divers profils de toxicité neurologique et métabolique dans l'ensemble de validation. Par rapport aux données cliniques seules, l'ajout des données métabolomiques permettait d'atteindre une capacité prédictive modeste mais statistiquement significative, en particulier pour les toxicités neurologiques. Conclusion Le profil métabolomique mesuré au diagnostic a une capacité modérée à prédire les toxicités liées au traitement, en plus des variables cliniques, pour les patientes atteintes d'un cancer du sein. La métabolomique haute résolution non ciblée permet d'obtenir de meilleures performances en prenant en compte l'exposition environnementale, les métabolites liés au microbiote et les métabolites de faible fréquence.

Purpose: Long-term treatment-related toxicities, such as neurologic and metabolic toxicities, are major issues in breast cancer. We investigated the interest of metabolomic profiling to predict toxicities.Experimental Design: Untargeted high-resolution metabolomic profiles of 992 patients with estrogen receptor (ER)+/HER2- breast cancer from the prospective CANTO cohort were acquired (n = 1935 metabolites). A residual-based modeling strategy with discovery and validation cohorts was used to benchmark machine learning algorithms, taking into account confounding variables.Results: Adaptive Least Absolute Shrinkage and Selection (adaptive LASSO) has a good predictive performance, has limited optimism bias, and allows the selection of metabolites of interest for future translational research. The addition of low-frequency metabolites and nonannotated metabolites increases the predictive power. Metabolomics adds extra performance to clinical variables to predict various neurologic and metabolic toxicity profiles.Conclusions: Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.

Long term treatment related toxicity is a major issue for breast cancer patients in the adjuvant setting. Predicting toxicities may allow us to adapt the treatment strategy. We assessed whether the metabolomic profile of patients may predict long-term toxicities. High-resolution untargeted metabolomics was performed at baseline for 857 ER-positive, HER2- breast cancer patients from the CANTO prospective cohort. Four metabolomic profiles per patient were produced: (i) shared and annotated metabolites (n=224), (ii) annotated but not always common metabolites (n=456), (iii) annotated but not always shared metabolites (n=766) and (iv) all metabolites (n=1693, FullMet). Samples were split into a discovery and validation set. We benchmarked algorithms adapted for high dimensional analysis (LASSO, Adaptive LASSO, machine learning, and deep learning) in order to select best models for prediction. 30.0% of patients were >65 years old, 24.4% <50 years old, 20.4% had BMI>30, 12.7% had previous history of neurological disorders, 6.1% had diabetes. 69.6% presented with pT1, 25.7% with pT2 and 3.4% with pT3; 11.1% had lymph node involvement. Among all benchmarked, adaptive LASSO was the most interesting statistical method with limited optimism bias. It also allows the selection of a subset of metabolites of particular interest. The addition of rare metabolites as well as non-annotated metabolites significantly increase the predictive power of models. Metabolic toxicity prediction mainly relied on endogenous metabolites while neurological toxicities were partly predicted using exogenous/environmental metabolites. In the validation set, compared to clinical data alone (AUC 0.50-0.54), addition of metabolomics data shows moderate (AUC = 0.55-0.60) but significant (p<0.05 adjusted for multiple comparison) predictive ability for neurological and metabolic toxicities. Breast cancer patient metabolomic profile at baseline improves toxicity prediction after adjuvant chemotherapy, similar to what is reported for genomic fingerprints. Untargeted metabolomics allows the achievement of higher performance by taking into account environmental exposure, metabolites linked to microbiota as well as rare and uncommon metabolites.

La métabolomique est l'analyse de l'ensemble des petites molécules présentes et accessibles dans un système biologique. L'intérêt de cette approche multidisciplinaire est qu'elle permet d'obtenir une vision globale du métabolisme dans l’étude des relations nutrition-santé humaine.

Metabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Oxylipins are a superfamily of lipid mediators regulating many cardiometabolic functions. Plasma oxylipin signature could provide a new clinical tool to enhance the phenotyping of MetS pathophysiology. A high-throughput validated mass spectrometry method, allowing for the quantitative profiling of over 130 oxylipins, was applied to identify and validate the oxylipin signature of MetS in two independent nested case/control studies involving 476 participants. We identified an oxylipin signature of MetS (coined OxyScore), including 23 oxylipins and having high performances in classification and replicability (cross-validated AUCROC of 89%, 95% CI: 85–93% and 78%, 95% CI: 72–85% in the Discovery and Replication studies, respectively). Correlation analysis and comparison with a classification model incorporating the MetS criteria showed that the oxylipin signature brings consistent and complementary information to the clinical criteria. Being linked with the regulation of various biological processes, the candidate oxylipins provide an integrative phenotyping of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways. This may help identify patients at higher risk of cardiometabolic diseases. The oxylipin signature of patients with metabolic syndrome enhances MetS phenotyping and may ultimately help to better stratify the risk of cardiometabolic diseases.

This study explored plasma biomarkers and metabolic pathways underlying feed efficiency measured as residual feed intake ( RFI ) in Charolais heifers. A total of 48 RFI extreme individuals (High-RFI, n = 24; Low-RFI, n = 24) were selected from a population of 142 heifers for classical plasma metabolite and hormone quantification and plasma metabolomic profiling through untargeted LC-MS. Most efficient heifers (Low-RFI) had greater (P = 0.03) plasma concentrations of IGF-1 and tended to have (P = 0.06) a lower back fat depth compared to least efficient heifers. However, no changes were noted (P ≥ 0.10) for plasma concentrations of glucose, insulin, non-esterified fatty acids, β-hydroxybutyrate and urea. The plasma metabolomic dataset comprised 3,457 ions with none significantly differing between RFI classes after false discovery rate correction (FDR > 0.10). Among the 101 ions having a raw P < 0.05 for the RFI effect, 13 were putatively annotated by using internal databases and 6 compounds were further confirmed with standards. Metabolic pathway analysis from these 6 confirmed compounds revealed that the branched chain amino acid metabolism was significantly (FDR < 0.05) impacted by the RFI classes. Our results confirmed for the first time in beef heifers previous findings obtained in male beef cattle and pointing to changes in branched-chain amino acids metabolism along with that of body composition as biological mechanisms related to RFI. Further studies are warranted to ascertain whether there is a cause-and-effect relationship between these mechanisms and RFI.