Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics

Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. Pre-frailty is still not well understood but it has been associated with global imbalance in several physiological systems, including inflammation, and in nutrition. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is essential to move toward more personalized care. The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics, in order to identify specific biomarkers, and study their stability over time. The approach was based on the NU-AGE project (clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65–79 y.o., men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models. Metabolomics enabled to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. The best resulting models included four different metabolites for each gender. They showed very good prediction capacity with AUCs of 0.93 (95% CI = 0.87–1) and 0.94 (95% CI = 0.87–1) for men and women, respectively. Additionally, early and/or predictive markers of pre-frailty were identified for both genders and the gender specific models showed also good performance (three metabolites; AUC = 0.82; 95% CI = 0.72–0.93) for men and very good for women (three metabolites; AUC = 0.92; 95% CI = 0.86–0.99). These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time at a very early stage

Untargeted metabolomic approach by GC-QTOF : From low to high resolution

Les approches métabolomiques non ciblées par GC-MS ont pu évoluer grâce au développement de nouveaux instruments de plus haute résolution, comme le GC-QToF. Cette approche est utilisée au laboratoire dans le cadre de projets scientifiques pour la recherche de biomarqueurs permettant la carac- térisation de phénotypes métaboliques. Une méthode d’analyse non ciblée pour la détermination de profils métaboliques de biofluides par GC- QToF a été adaptée d’une méthode basse résolution (Gao et al (1)) basée sur une double dérivation oximation /silylation. Cette technique, plus sensible et plus résolutive, nécessite des outils de traitement des données spéci- fiques dédiés. Aussi, nous avons dû adapter des outils développés par notre laboratoire pour le traitement de données métabolomiques à ce type de données. Ces outils comprennent l’extraction des données par xcms sous la plateforme Galaxy (W4M, (2)), ainsi que tout le workflow conduisant à l’annotation des ions extraits après filtration et correction des effets batch. Parallèlement, nous déployons une stratégie de déconvolution, à partir d’outils constructeurs afin de com- pléter les résultats obtenus sous Galaxy. A ce jour, les bibliothèques GC-MS (NIST, Golm, Massbank) restent très utilisées pour l’identification des métabolites mais ne contiennent aucun spectre avec des masses précises bien que certains provi- ennent de GC-EI-ToF. Par conséquent, nous constituons une bibliothèque interne en haute résolution avec des standards purs et en matrices biologiques qui alimentera la base de données PeakForest de l’infrastructure française MetaboHUB. La mesure des masses précises ainsi que le développement de nouveaux outils d’automatisation du traite- ment de données devraient permettre de lever certains verrous rencontrés dans la recherche de biomar- queurs concernant l’identification des métabolites

Metabolomics data analysis I & II

This workshop will introduce W4M and how to use it for metabolomics data analysis.Metabolomics data analysis is a complex, multistep process, which is constantly evolving with the development of new analytical technologies, mathematical methods, and bioinformatics tools and databases. The Workflow4Metabolomics (W4M) project aims to develop full LC/MS, GC/MS, FIA/MS and NMR pipelines using Galaxy framework for data analysis including preprocessing, normalization, quality control, statistical analysis and annotation steps.This workshop will introduce W4M and how to use it for metabolomics data analysis

In silico prediction of metabolism as a tool to identify new metabolites of dietary monoterpenes

Dietary terpenes have been little studied, despite the fact that they are well absorbed and display a range of biological properties. Better knowledge about their metabolism will help understanding the health effects of plant foods, herbs and spices and may provide new biomarkers of food intake. As part of the FoodBAll project we are investigating the metabolism of terpenes, identifying metabolites and biotransformations involved in their metabolism.PhytoHub (database that compiles all known metabolites of dietary phytochemicals, including terpenes) and Nexus Meteor (in silico prediction of metabolism), were used to identify biotransformations involved in the metabolism of monoterpenoids. Selected biotransformations were used to predict the metabolism of camphene, camphor, carvacrol, carvone, caryophyllene, 1,4-cineole, 1,8-cineole, citral, citronellal, cuminaldehyde, p-cymene, fenchone, geraniol, limonene, linalool, menthol, myrcene, nootkatone, perillyl alcohol, pinene, pulegone, terpinen-4-ol and thymol. Wistar rats received a chemically defined diet with or without 0.05% of the referred compounds. Before and after 5 days of the exposure to the dietary monoterpenes, urine was collected and untargeted metabolomics analysis performed using high-resolution mass spectrometry (UPLC-QToF). We identified twenty-two enzymatic reactions involved in the metabolism of monoterpenoids leading to the synthesis of monoterpenoid metabolites described in the literature. In average, 10 metabolites per compound were identified in rat urine, including new and known ones. Identification of metabolites was based on monoisotopic mass and formula match, presence of adducts and specific mass losses indicative of glucuronidation and conjugation to amino acids. Validation of identification is being done using orbitrap MS/MS and hydrogen-deuterium exchange experiments.The combination of in silico prediction and in vivo experiment allowed the identification of known and new metabolites of different dietary terpenoids. Predicted metabolites of terpenes will be added in PhytoHub to complement the database of known metabolites

A hybrid and exploratory approach to knowledge discovery in metabolomic data

International audienceIn this paper, we propose a hybrid and exploratory knowledge discovery approach for analyzing metabolomic complex data based on a combination of supervised classifiers, pattern mining and Formal Concept Analysis (FCA). The approach is based on three main operations, preprocessing, classification, and postprocessing. Classifiers are applied to datasets of the form individuals×features and produce sets of ranked features which are further analyzed. Pattern mining and FCA are used to provide a complementary analysis and support for visualization. A practical application of this framework is presented in the context of metabolomic data, where two interrelated problems are considered, discrimination and prediction of class membership. The dataset is characterized by a small set of individuals and a large set of features, in which predictive biomarkers of clinical outcomes should be identified. The problems of combining numerical and symbolic data mining methods, as well as discrimination and prediction, are detailed and discussed. Moreover, it appears that visualization based on FCA can be used both for guiding knowledge discovery and for interpretation by domain analysts

Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets

Flash poster 180 secondes session 2 : FP5 (p 61-61) Poster 78 – Associé FP5Metabolomics generates massive and complex data that need dedicated workflows to extract the meaningful information and to enrich our knowledge of biological systems. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining and statistical methods. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters such as correlation coefficient, retention time and mass information from known isotopes, adducts or fragments, the algorithm principle is to group features coming from the same analyte, and to propose one single representative per group. We chose to compare the present tool to one of the most commonly used free package proposing a grouping method: ‘CAMERA’, using its Galaxy version ‘CAMERA.annotate’ available in Workflow4Metabolomics (W4M; http://workflow4metabolomics.org). To illustrate the ‘Analytic correlation filtration tool’ functionalities, a published dataset available on W4M was used as an example: ‘Sacurine’ (Thevenot et al., 2015).Within the 3,120 ions of the urine dataset, 14% of ions are proposed to be filtered because of analytical redundancies. While CAMERA generated more than 20 groups of more than 10 ions, the proposed tool subdivided them into smaller ones corresponding to individual annotated metabolites, thus demonstrating the efficiency and relevance of the present approach.As a key element in metabolomics data analysis, the tool will be available via the web-based galaxy platform W4M with different output files for network vizualisation and for further data analysis within workflows

A new tool for multi-block PLS discriminant analysis: application to metabolomic data in systems epidemiology

International audienc

Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data

INTRODUCTION: Metabolomics is a powerful phenotyping tool in nutrition and health research, generating complex data that need dedicated treatments to enrich knowledge of biological systems. In particular, to investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses are generally performed separately on metabolomic datasets, complemented by associations with metadata. Another relevant strategy is to simultaneously analyse thematic data blocks by a multi-block partial least squares discriminant analysis (MBPLSDA) allowing determining the importance of variables and blocks in discriminating groups of subjects, taking into account data structure. OBJECTIVE: The present objective was to develop a full open-source standalone tool, allowing all steps of MBPLSDA for the joint analysis of metabolomic and epidemiological data. METHODS: This tool was based on the mbpls function of the ade4 R package, enriched with functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions. RESULTS: To illustrate the potential of this tool, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort. The availability of different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes. CONCLUSION: In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS discriminant analysis method

A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology

Metabolomics is a powerful phenotyping tool in nutrition and health research, generating massive and complex data that need dedicated treatments to enrich our knowledge of biological systems. In particular, to deeper investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses performed separately on metabolomic datasets, are often complemented by associations with metadata (anthropometric, clinical, nutritional and physical activity data…). Another relevant strategy is to perform a multi-block partial least squares discriminant analysis (MBPLSDA) that simultaneously analyses data available from different sources, allowing determining the importance of variables and variable blocks in discriminating groups of subjects, taking into account data structure in thematic blocks.In order to propose a full open-source standalone tool, the present objective was to develop an R package allowing all steps of MBPLSDA analysis for the joint analysis of metabolomic and additional data.The tool was based on the mbpls function of the ade4 R package, enriched with different functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions. To illustrate the potential of the proposed tool and the associated procedure, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort.The availability of the different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes. In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS-discriminant analysis method

Metabolomics as a new key actor in systems biology for better understanding nutrition-health relationship

Poster Session 5. Systems metabolomics and its relation to diseaseMetabolomics as a new key actor in systems biology for better understanding nutrition-health relationship. 1. Conference of the European Association of Systems Medicine (EASyM