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# Impaired postprandial skeletal muscle metabolism in a minipig model of insulin resistance: insights from arteriovenous and biopsy-based metabolomics analyses

Marie Tremblay-Franco, Nathalie Poupin, Florence Castelli, Jérémie David, Marianne Jarzaguet, Didier Remond, Francois Fenaille, Dominique Dardevet, Sergio Polakof

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## To cite this version:

Marie Tremblay-Franco, Nathalie Poupin, Florence Castelli, Jérémie David, Marianne Jarzaguet, et al.. Impaired postprandial skeletal muscle metabolism in a minipig model of insulin resistance: insights from arteriovenous and biopsy-based metabolomics analyses. 12. Journées Scientifiques du Réseau Francophone de Métabolomique et Fluxomique RFMF, May 2019, Clermont-Ferrand, France. 168 p., 2019, Livre des résumés. hal-02285596

HAL Id: hal-02285596

https://hal.science/hal-02285596

Submitted on 2 Jun 2020

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12<sup>èmes</sup> Journées Scientifiques du
Réseau Francophone de
Métabolomique et Fluxomique

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RÉSEAU FRANÇOPHONÈ DE MÉTABOLOMIQUE ET FLUKOMIQUE

21 – 23 Mai 2019 Clermont-Ferrand

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# Table des matières

* Partenaires des 12èmes Journées Scientifiques

* Les comités Scientifique et Organisation

* Programmes détaillés

* Résumés des Ateliers

* Résumés des Communications Invités

* Résumés des Communications Orales

* Résumés des Communications Posters

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RÉSEAU FRANCOPHONE DE MÉTABOLOMIQUE ET FLUXOMIQUE
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## Partenaires Institutionnels des 12èmes Journées RFMF

La Région

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Auvergne-Rhône-Alpes

+

clermont
auvergne
 métropole

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METABO HUB

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I-SITE
CLERMONT
Clermont Auvergne Project

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INRA
SCIENCE & IMPACT

UNIVERSITÉ
Clermont
Auvergne

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BIOASTER
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Partenaires Industriels Platinum des 12èmes Journées RFMF

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SHIMADZU

Excellence in Science

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PROTEIGENE

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PROTEOMIC SOLUTIONS

ThermoFisher

SCIENTIFIC

Waters

THE SCIENCE OF
WHAT'S POSSIBLE.®

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Agilent

Trusted Answers
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Partenaires Industriels Bronze des 12èmes Journées RFMF

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Eurisotop

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LES COMITES

ORGANISATION et SCIENTIFIQUE

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# Les Comités d'Organisation

## Comité local d'organisation

* Blandine Comte (INRA) : Unité de Nutrition Humaine, Clermont-Ferrand

* Estelle Pujos-Guillot (INRA) : Plateforme PFEM-MetaboHUB, Unité de Nutrition Humaine, Clermont-Ferrand

* Cyril Jousse (Université Clermont Auvergne) : Plateforme PFEM-MetaboHUB, Institut de Chimie de Clermont-Ferrand

* Binta Diémé (Université Clermont Auvergne) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

* Charlotte Joly (INRA) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

* Marie Lagrée (Université Clermont Auvergne) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

* Bernard Lyan (INRA) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

* Carole Migné (INRA) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

* Stéphanie Monnerie (doct. INRA) : Unité de Nutrition Humaine, Clermont-Ferrand

* Mélanie Pétéra (INRA) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

* Corinne Pouyet (INRA) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

## Comité francophone d'organisation

* Anne-Emmanuelle Hay-de Bettignies (MCU, Université de Lyon 1, ISPB) : UMR Ecologie Microbienne 5557,

* Frédérique Courant (MCU, Université Montpellier) : UMR Hydrosciences

* Floriant Bellvert (IE, CNRS) : Plateforme MetaToul-MetaboHUB

* Fabien Jourdan (DR, INRA) : UMR Toxalim, Plateforme MetaToul-MetaboHUB Toulouse

* Patrick Giraudeau (Pr, Université de Nantes) : CEISAM, Nantes, France

* Yann Guitton (IR, Oniris) : Laboratoire d'Etude des Résidus et Contaminants dans les Aliments, France
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# Les Comités Scientifiques

Le Comité Scientifique des 12ième JS RFMF Clermont Ferrand 2019 est constitué des membres du Conseil d'Administration du RFMF, du RFMF junior et des membres des universités et instituts locaux:

## Conseil d'Administration du RFMF

* Floriant Bellvert (IE, CNRS) : LISBP, Plateforme MetaToul-MetaboHUB, Toulouse, France

* Julien Boccard (Maître-assistant, Université de Genève) : Ecole de Pharmacie Genève-Lausanne Suisse

* Alain Bouchereau (PU, Université de Rennes) : IGEP, Rennes, France

* Frédérique Courant (MCU, Université Montpellier) : UMR Hydrosciences, Montpellier, France

* Pascal de Tullio (Maître de Recherche, Université de Liège) : CIRM, Liège, Belgique

* Patrick Giraudeau (Pr, Université de Nantes) : CEISAM, Nantes, France

* Yann Guitton (IR, Oniris) : Laboratoire d'Etude des Résidus et Contaminants dans les Aliments, France

* Anne-Emmanuelle Hay-de Bettignies (MCU, Université de Lyon 1) : UMR Ecologie Microbienne 5557, Lyon, France

* Fabien Jourdan (DR, INRA) : UMR Toxalim, Plateforme MetaToul-MetaboHUB Toulouse, France

* Jean-Charles Martin (DR, INRA) : UMR NORT, plateformes BiomeT et CRIBIOM, Marseille, France

* Etienne Thevenot (Chercheur CEA, Saclay) : CEA LIST, MetaboHUB, Saclay, France

## Comité scientifique local

* Blandine Comte (DR, INRA) : Unité de Nutrition Humaine, Clermont-Ferrand

* Anne-Marie Delort (DR, CNRS) : Plateforme PFEM-MetaboHUB, Institut de Chimie de Clermont-Ferrand, Clermont-Ferrand

* Erwan Engel (DR, INRA) : Unité Qualité des Produits Animaux, Clermont-Ferrand

* Franck Giacomoni (IE, INRA) : Plateforme PFEM-MetaboHUB, Clermont-Ferrand

* Cyril Jousse (IR, Université Clermont Auvergne) : Plateforme PFEM-MetaboHUB, Institut de Chimie de Clermont-Ferrand, Clermont-Ferrand

* Estelle Pujos-Guillot (IR, INRA) : Plateforme PFEM-MetaboHUB, Unité de Nutrition Humaine, Clermont-Ferrand

* Damien Richard (PH, CHU Gabriel Montpied) : Service Pharmacologie Médicale, CHU, Clermont-Ferrand

* Jean-Marie Bonny (DR, INRA) : plateforme AgroResonance, Unité Qualité des Produits Animaux, Clermont-Ferrand
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PROGRAMMES

ATELIERS ET 12 JS

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# Programme des ateliers des 12èmes Journées

Lundi 20 mai 2019

Université Clermont Auvergne, IAE School of Management

11h-13h : Metabo MOOC (usemetabo) au service des enseignants (Rolin Dominique & Alain Bouchereau).

11h-13h : Juniors (RFMF Junior).

14h-16h : Intégration de données en métabolomique (Marion Brandolini & Julien Boccard).

14h-18h : Les réseaux moléculaires, découverte et prise en main (Amina Bouslimani, Pieter Dorrestein, Nicolas Elie, Yann Guitton, Louis Felix Nothias & David Touboul).

16h30-19h30 : MIMOSA, a mass spectrometry application for processing and analysis of metabolomics data – Application to lipidomics (Djomangan Adama Ouattara, Joséphine Abi-Ghanem).
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# Programme des 12èmes Journées Scientifiques

## MARDI 21 MAI

**8h00-9h00** Accueil des participants

**9h00-9h30** Bienvenue

**9h00-9h20** Allocution de Bienvenue

**9h20-9h30** Introduction aux 12èmes journées Scientifiques du RFMF
**Fabien Jourdan**, Président du RFMF et **Blandine Comte, Cyril Jousse** et **Estelle Pujos-Guillot**, Coordinateurs du Comité Local d'Organisation

**9h30-12h05** Session Santé/Pharmacologie
Modérateurs : **Estelle Pujos-Guillot** - **Sylvain Dechaumet**

**9h30-10h15** Metabolomics: recent developments in the analytical process - **Coral Barbas** Université San Pablo, Madrid, Espagne

**10h15-10h35** Stable isotope labeling highlights enhanced fatty acid and lipid metabolism in human acute myeloid leukemia - **Justine Bertrand Michel**. METATOUL, Inserm, Toulouse, France

**10h35-11h05** Pause, visite de l'exposition

Modérateurs : **Blandine Comte** - **Justine Leenders**

**11h05-11h35** Methodological developments in Ultra-High-Resolution NMR for a deeper understanding of metabolic processes in relapsed/refractory diffuse large B-cell lymphoma - **Nicolas Giraud**. Laboratoire de Chimie et Biochimie Toxicologiques et Pharmacologiques, Université de Paris, Sorbonne Paris Cité, Université Paris Descartes - Paris V, France

**11h35-11h50** The microbiome and drug metabolism - **Marine Letertre**. Imperial College London, Royaume-Uni

**11h50-12h10** Suivi longitudinal du métabolisme cérébral par imagerie spectroscopique de Résonance Magnétique Nucléaire chez des modèles murins progressifs de la maladie de Parkinson **Carine Chassain**. CHU Gabriel Montpied, Clermont-Ferrand, France

**12h10-12h25** Session constructeur: Waters - MetaboQuan et LipidQuan - Méthodes clés en main pour l'analyse ciblée des lipides et des métabolites - **Christophe Siroit**. Waters SAS, Saint-Quentin en Yvelines, France

**12h25-13h15** Déjeuner

**13h15-14h25** Session posters : Présence des auteurs devant leurs posters
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**13h40-14h10 Session parallèle constructeur:** Thermo Fisher Scientific - Improved metabolome coverage and increased confidence in unknown identification through novel automated acquisition strategy combining sequential injections and MSn - **Marie Pierre Pavageau**. Thermo Fisher Scientific, San Jose, CA, USA

**14h25-16h45 Session Microbiologie/Biotechnologie**
*Modérateurs : Fabien Jourdan - Binta Dieme*

**14h25-15h10** Using mass spectrometry to connect the worlds chemistry of life - a big data problem for natural products **Peter Dorrestein** Université de Californie, USA

**15h10-15h30** Expanding the BCAA pathway of E. coli reveals the molecular basis of its robustness and homeostasis **Mickael Dinclaux**. Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Université de Toulouse, CNRS, INRA, INSA, Toulouse, France

**15h30-15h45** Y flux: A high throughput fluxomic workflow for exploration of metabolic phenotypes - **Cécilia Berges**. Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Université de Toulouse, CNRS, INRA, INSA, Toulouse, France

**15h45-16h00 Session constructeur:** Accelerate your lab capabilities combining a metabolomic workbench and the new 6546 LC-QTOF Agilent - **Jérémy Jeudy**. Agilent Technologies France, Les Ulis, France

**16h00-16h10 Flash posters session 1:**
*Modérateurs : Floriant Bellvert - Blandine Comte*

- **Noémie Butin**: A combined metabolomics and lipidomics approach enables the stratification of acute-on-chronic liver failure patients according to their severity. CEA, INRA, Université Paris Saclay, Laboratoire d'Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-Sur-Yvette, France

- **David Mallet**: Metabolic characterization of different phases of Parkinson's disease. Grenoble Institut des Neurosciences - CEA, CHU Grenoble, Inserm U1216, Université Grenoble Alpes, France

- **Sergio Polakof**: Impaired postprandial skeletal muscle metabolism in a minipig model of insulin resistance: insights from arteriovenous and biopsy-based metabolomics analyses. Université Clermont Auvergne, INRA, UMR1019 Unité de Nutrition Humaine, Clermont-Ferrand, France

**16h10-16h40** Pause, visite de l'exposition

**16h40-17h40 Session Microbiologie/Biotechnologie**
*Modérateurs : Floriant Bellvert - Grégory Genta-Jouve*
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**16h40-17h10** Improved riboflavin production with Ashbya gossypii from vegetable oil based on 13C metabolic network analysis with combined labeling analysis by NMR and mass spectrometry - **Lindsay Peyriga**. Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Université

**17h10-17h25 Session constructeur** : Shimadzu - Applications en omics du nouveau QTOF Ultra-Fast MS LCMS-9030 - **Etienne Maout**. Shimadzu France, Marne-la-Vallée, France.

**17h25-17h40** Feature-based molecular networking of untargeted mass spectrometry data: Bridging MS-DIAL, MZmine2, OpenMS, and XC-MS, with the GNPS web-platform - **Louis Félix Nothias**. University of California, San Diego, USA

**17h40-17h50 Flash posters session 2** : Modérateurs : Floriant Bellvert - Cyril Jousse

- **Nicolas Elie** : MetGem Software for the Generation of Molecular Networks Based on the t-SNE Algorithm. Institut de Chimie des Substances Naturelles, CNRS, UPR2301, France

- **Stéphanie Monnerie** : Analytic correlation filtration: A new tool to reduce analytical complexity of meta bolomic datasets. Université Clermont Auvergne, INRA, UMR1019 UNH, Mapping, Clermont-Ferrand, France

- **Matthieu Schoumacher** : New insight in Metabolomics based study of Age Related Macular Degeneration (AMD): Lipoprotein profile and subclass analysis. Centre Indisciplinaire de Recherche sur le Médicament (CIRM), Belgique

**17h50-19h50** Assemblée générale RFMF

**19h50-21h30** Apéritif et session posters

## MERCREDI 22 MAI

**9h00-10h25 Session développements analytiques**
Modérateurs : Patrick Giraudeau - Justine Leenders

**9h00-9h45** Pros and cons of fingerprinting and profiling in NMR based metabolomics - **Claudio Luchinat**, Université de Florence, Italie

**09h45-10h05** Supercritical fluid chromatography coupled to high resolution mass spectrometry for dereplication and quantification of natural products - **David Touboul**. Institut de Chimie des Substances Naturelles (ICSN), CNRS UPR2301, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette, France
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**10h05-10h25** Apport de la spectrométrie de masse à haute résolution et à mobilité ionique dans les analyses méta bo lomiques à haut débit et à large échelle - **Aurélie Delvaux**. Institut Parisien de Chimie Moléculaire IPCM, Université Pierre et Marie Curie (UPMC) - Paris VI, France

**10h25-10h55** Pause, visite de l'exposition

**10h55-12h15** Session développements analytiques

**10h55-11h10** Session constructeur : Proteomic solution Proteigene - Assessing gut microbiota-host crosstalk: Development of a standardized and quantitative targeted metabolomics assay - **Barbara Wolf**. BIOCRATES Life Sciences AG, Innsbruck, Austria

**11h10-11h30** 13C-labeled yeast extracts: a powerful tool for normalization and quantification in metabolomics and lipidomics? - **Thaïs Hautbergue**. Service de Pharmacologie et d'Immunoanalyse, Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, Gif-sur-Yvette, France

**1h30-11h45** Advanced high-field and benchtop NMR methods for lipid profiling of microalgae
- **Dylan Bouillaud**. Génie des Procédés-Environnement-Agro-alimentaire - Université de Nantes, CNRS : UMR6144, France

**11h45-12h15** Illuminating lifestyles using metabolomics of personal objects - **Amina Bouslimani**. University of California, San Diego, USA

**12h15-13h15** Déjeuner

**13h15-14h15** Session posters : Présence des auteurs devant leurs posters

**14h15-15h00** Session Environnement/Plantes

Modérateurs : Cyril Jousse - Guillaume Meiffren

**14h15-15h00** Metabolomics in the research and development of plant and protection products: current state and future perspectives - **Konstantinos A. Aliferis** - Université d'Athènes, Grèce

**15h00-15h30** Large scale micropollutants screening in a wetland ecosystem using a non-targeted high resolution mass spectrometry approach - **Loïc Maurer**. Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie, Université de Strasbourg, UMR7357, CNRS FR3627, Ecole Nationale du Génie de l'Eau et de l'Environnement de Strasbourg, INSA Strasbourg, France

**15h30-15h50** Anti-leishmaniasis and Metabolomic approach from the leaves extract of Psidium guajava L. from Lao PDR **Chiobouaphong Phakeovilay**. UMR 152 Pharma Dev, Université de Toulouse, IRD, UPS, Toulouse, France ; Université Paul Sabatier - Toulouse III, France

**15h50-16h00** Flash posters session 3 :
Modérateurs : Floriant Bellvert - Estelle Pujos-Guillot

- **Christelle Ghaffar** : Étude de la réponse métabolique de Pseudomonas
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syringae 32b-74 isolée des nuages à une exposition sub-létale au mercure.
Université Clermont-Auvergne, CNRS, Institut de Chimie de Clermont-
Ferrand (ICCF), Clermont-Ferrand, France

- **Ines Rémy-Larsonnier**: Profilage métabolique de molécules d'intérêt du piment (Capsicum) en nutrition animale. Laboratoire d'Ecologie Microbienne/CESN, Villeurbanne, France

**Roland Wedekind**: Syringol metabolites as biomarkers of smoked meat intake. Centre International de Recherche contre le Cancer - International Agency for Research on Cancer, Organisation Mondiale de la Santé/World Health Organization

**16h00-16h30** Pause, visite de l'exposition

**16h30-16h45 Session constructeur**: Bruker - Analyses du plasma par RMN chez les adultes phenylcétonuriques sous prise en charge nutritionnelle montrent des déséquilibres métabolomiques - **Claire Cannet**. Bruker BioSpin, Wissembourg, France

**16h45-17h25 Session Bioinformatique/Base de données**
Modérateurs : Franck Giacomoni - Grégory Genta-Jouve

**16h45-17h05** The Exposome-Explorer database in 2019: recent development on dietary biomarkers and their association with cancer risk - **Vanessa Neveu**. Centre International de Recherche contre le Cancer - International Agency for Research on Cancer, Organisation Mondiale de la Santé/World Health Organization Office, France

**17h05-17h25** Dynamic retention time databases for steroidomics - **Santiago Codesido**. Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Suisse

**17h25-17h45** RFMF d'Honneur - **Anne-Marie Delort** - Cloud microorganisms: what does metabolomics tell us? Université Clermont-Auvergne, CNRS, Institut de Chimie de Clermont-Ferrand (ICCF), Clermont-Ferrand, France

**17h45-19h45** Assemblée générale RFMF Junior

**20h00** Dîner de gala

## JEUDI 23 MAI

**9h30-10h40 Session Statistiques**
Modérateurs : Julien Boccard - Mélanie Pétéra

**9h30-9h50** Fusion de données de transcriptomique et de métabolomique pour une meilleure compréhension des effets d'un contaminant alimentaire - **Marie Tremblay-Franco**. UMR1331 Toxalim, Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
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**9h50-10h10** A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology - **Marion Brandolini-Bunlon**. Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France

**10h10-10h25** Metabolomics and proteomics data integration and feature selection for high-throughput phenotyping
- **Alyssa Imbert**. CEA, LIST, Laboratoire Analyse de Données et Intelligence des Systèmes, MetaboHUB, Gif-Sur-Yvette, France

**10h25-10h40** **Session constructeur:** Sciex - Industrializing Flux Capabilities - Yoann Fillâtre. AB Sciex, Les Ulis, France

**10h40-11h10** Pause, visite de l'exposition

**11h10-12h35** **Session Alimentation/Qualité des aliments**
Modérateurs : Erwan Engel - Binta Dieme

**11h10-11h40** Discovery and validation of food intake biomarkers using untargeted metabolomics in human intervention and cross-sectional studies - **Claudine Manach**. Université Clermont Auvergne, INRA, Unité de Nutrition Humaine, UMR1019, Clermont-Ferrand, France

**11h40-12h00** Pratiques culturales, qualité des graines de colza et santé du consommateur: une étude preuve de principe « multi-omique » chez la souris au métabolisme dérégulé - **Jean-Charles Martin**. Centre recherche en CardioVasculaire et Nutrition, INSERM 1263, INRA 1260, Université Aix Marseille, Marseille, France

**12h00-12h15** Découverte de marqueurs précoces de l'altération microbiologique des ovoproduits par des approches de métabolomique croisée (RMN-spectrométrie de masse) - **Rémy Coat**. Laboratoire de génie des Procédés-Environnement-Agro-alimentaire, Université Bretagne Loire, IMT Atlantique Bretagne-Pays de la Loire, Oniris, CNRS UMR6144, Université de Nantes, France

**12h15-12h35** Analyse non ciblée de contaminants dans les aliments : validation d'une approche de type métabolomique sur deux technologies UHPLC-HRMS et deux scenarii de contamination « en aveugle » **Mathieu Cladière**. UMR Ingénierie Procédés Aliments (GENIAL), INRA UMR1145, AgroParisTech, Université Paris-Saclay, Massy, France

**12h35-12h55** Prix de thèse - **Florent Olivon** - Nouvelle stratégie de priorisation pour l'étude des produits naturels par l'ap proche des réseaux moléculaires multi-informatifs. Institut de Chimie des Substances Naturelles, CNRS-ICSN, UPR 2301, Université Paris-Saclay, Gif-sur-Yvette, France

**13h00** Clôture
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## RESUMES DES ATELIERS

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RÉSEAU FRANCOPHONE DE MÉTABOLOMIQUE ET FLUXOMIQUE
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# Atelier 1 - metabo MOOC (usemetabo) au service des enseignants

animateur(s) : Rolin Dominique (dominique.rolin@inra.fr) & Alain Bouchereau
alain.bouchereau@univ-rennes1.fr)

Plus les autres contributeurs du MOOC que vous retrouverez sur le site de FUN à cette adresse :
https://www.fun-mooc.fr/courses/course-v1:cnrs+136001+session01/about

Public envisagé et prérequis :

- Public : Enseignants-chercheurs, vacataires d'enseignements, conférenciers auprès d'étudiants, en quête de ressources pour leurs enseignements/interventions dans le domaine de la métabolomique dans leurs établissements.

- Prérequis : enseigner, intervenir auprès d'étudiants, dans un cursus universitaire, niveaux LM

Contexte et objectifs de l'atelier :

Les communautés bio-informatique et métabolomique, fédérées autour d'infrastructures nationales (Institut Français de Bioinformatique (IFB) et MetaboHUB), ont notamment pour mission de contribuer à la formation aux nouveaux usages technologiques et aux champs de leurs applications.
Les MOOC (Massive Open Online Courses) ont récemment fait une apparition dans le paysage de l'enseignement supérieur mais aussi de la recherche, suscitant de forts intérêts. C'est dans ce contexte et grâce aux soutiens de l'IFB, de MetaboHUB, du Réseau Francophone de Métabolomique et Fluxomique (RFMF) et de l'Université de Rennes 1 qu'il a été décidé de créer un MOOC dédié à la métabolomique : Usemetabo.

Usemetabo, dont la première sortie a eu lieu en octobre dernier a eu pour principal objectif de présenter en français pour des étudiants de niveau licence les concepts et méthodologies appliqués au profilage métabolique et à la métabolomique, en révéler les principaux usages et en illustrer les principaux champs d'applications. Il a connu un franc succès.

Les ressources vidéo sont d'ores et déjà disponibles sur un site WEB.

Cet atelier est conçu sous la forme d'une séance d'échanges entre ceux qui ont participé à l'élaboration de ce MOOC (20 contributeurs) et ceux qui ont ou auraient la volonté d'enseigner la métabolomique dans leur établissement. Ce MOOC est appelé à vivre, à évoluer et à fournir d'autres formes de ressources pédagogiques facilement accessibles aux enseignants. Il nous semble essentiel de pouvoir partager ces projets avec la communauté enseignante, dans le cadre du RFMF, et recueillir les besoins/les envies en matière d'outils pédagogiques et de contenus valorisables sous différents formats pour faciliter l'enseignement de la métabolomique et de la fluxomique à l'échelle nationale.

Références (publications, sites, outils, etc.) :

Voir le site de FUN pour le MOOC de la métabolomique :

https://www.fun-mooc.fr/courses/course-v1:cnrs+136001+session01/about

Voir la vidéo introduisant usemetabo : https://www.dailymotion.com/video/x6mt32w
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# Atelier 2 - MIMOSA: A mass spectrometry application for processing and analysis of metabolomics data – Application to lipidomics

Animateur(s) : Djomangan Adama Ouattara (Djomangan.OUATTARA@bioaster.org), Joséphine Abi-Ghanem (Josephine.ABIGHANEM@bioaster.org)

## Public envisagé et prérequis :

- Public : Bioinformaticians and biochemists in mass spectrometry

- Prérequis : Mass spectrometry in metabolomics / Basic knowledge of MS data processing (peak picking, alignment and annotation)

## Objectif de l'atelier :

The objective of this workshop is to introduce the main fundamentals of the bioinformatics tools developed at BIOASTER for the processing and analysis of mass spectrometry data in metabolomics, with a focus on lipidomics. MIMOSA (Mass spectrometry processIng of MetabOlomicS DatA) is an application module for BIOCODE, which is a transversal computational framework for metabolomics. MIMOSA allows building simple to complex pipelines to process and analyze metabolomics data with a high reproducibility and traceability. MIMOSA is ready-to-use, fast and extendable. It is specially designed to handle large metabolomics data (up to hundreds of raw files) in a reasonable time laps on a classical laptop. It allows interfacing available software like ProteoWizard, OpenMS and built-in functions for data normalization, statistical analysis and the identification of metabolites using Sirius, Metfrag and LipidMatch. As well, other tools can easily be interfaced into the MIMOSA to build customized workflows. MIMOSA is available as open Matlab scripts and a standalone application to non-developer users. Its extension to more open programming languages is planned.

## Compétences acquises à la sortie de l'atelier :

- Processing of raw MS metabolomics data (with custom metadata), feature annotation, feature identification, basic MS data analyses (unsupervised, supervised analyses)

- Use of MIMOSA using Matlab scripts

- Use of MIMOSA using the standalone application (for non-developer users)

- Use of OpenMS, ProteoWizard, LipidMatch tools for untargeted metabolomics and lipidomics

- Use of the BIOCODE framework under Matlab environments
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Références (publications, sites, outils, etc.) :

Abi-Ghanem et al, MIMOSA: A computational application for mass spectrometry preprocessing of metabolomics data, to submit to BMC Bioinformatics February 2019

Ouattara et al, BIOCODE: A transversal framework for computational workflow standardization – Fundamental concepts of the PRISM architecture, to submit to BMC Bioinformatics February 2019

Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods. 2016;13:741–8

Koelmel JP, Kroeger NM, Patterson RE, Cochran JA, Beecher CWW, Garrett TJ, et al. LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinformatics. 2017;18:331.

Ruttkies C, Schymanski EL, Wolf S, Hollender J, Neumann S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminform. 2016;8:3

Pré-requis pour les participants : afin de profiter pleinement de cet atelier, merci de bien prendre en compte les configurations matérielles requises (si besoin, merci de contacter les animateurs pour confirmation).

- Personnal computers

- Free Matlab (2017, 2018 or 2019) pre-installed (Warning: the installation time is long)

* At least the Bioinformatics Toolbox is required

* It is recommended to install all the toolboxes by default

- ProteoWizard 3.0.x pre-installed (will be provided otherwise)

- OpenMS 2.3 preinstalled (will be provided otherwise)
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# Atelier 3 - Intégration de données en métabolomique

Animateur(s) : Marion Brandolini - [marion.brandolini-bunlon@inra.fr](mailto:marion.brandolini-bunlon@inra.fr) et Julien Boccard - [julien.boccard@unige.ch](mailto:julien.boccard@unige.ch)

Public envisagé et les prérequis :

- Public : Chercheurs, ingénieurs, étudiants travaillant en métabolomique et en quête d'informations sur les concepts, méthodes et outils d'intégration de données.

- Prérequis : Notions de base en traitement de données métabolomiques

Objectif de l'atelier :

L'objectif de cet atelier est, dans un premier temps, d'introduire les concepts clefs (structure des données, niveaux de fusion) et les stratégies expérimentales d'intégration (bioinformatique ou chimiométrique) des données métabolomiques, et d'en illustrer quelques-unes à travers différents exemples d'applications.

Il est, dans un deuxième temps, de créer des interactions entre participants, avec des échanges sur les différentes stratégies au regard de la nature des données et du type de question de recherche posée.

Compétences acquises à la sortie de l'atelier :

- Compréhension globale des différentes stratégies d'intégration des données métabolomiques

- Capacité de s'orienter vers une approche adéquate pour intégrer ses données expérimentales
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# Atelier 4 - Les réseaux moléculaires, découverte et prise en main

animateur(s) : Amina Bouslimani (abouslimani@ucsd.edu), Pieter Dorrestein (pdorrestein@ucsd.edu), Nicolas Elie (nicolas.elie@cnrs.fr), Yann Guitton (yann.guitton@oniris-nantes.fr), Louis Felix Nothias (nothias@ucsd.edu), David Touboul (david.touboul@cnrs.fr)

Public envisagé et les prérequis :

- Public : Utilisateurs de spectrométrie de masse (débutants ou expérimentés) appliquée à la métabolomique.

- Prérequis : Spectrométrie de masse et métabolomique, Notions de base en spectrométrie de masse en tandem (MS/MS), acquisition automatique de spectres MS/MS, annotation de spectres MS/MS.

Contexte et objectifs de l'atelier :

L'atelier 'Réseaux Moléculaires' (Molecular Network) a pour but d'introduire une approche bioinformatique permettant d'organiser, visualiser et interpréter des données de spectrométrie de masse en tandem (MS/MS) obtenues en mode non ciblé, utilisé communément en métabolomique. La méthode des réseaux moléculaires est basée sur l'évaluation du degré de similarité spectrale et permet de comparer, aligner et grouper des spectres MS/MS collectés à partir de différents échantillons afin de mettre en évidence des familles de molécules ayant des structures chimiques similaires. A travers l'analyse de 'Réseaux Moléculaires', il est également possible d'annoter les spectres MS/MS en les comparant aux spectres de référence des banques publiques. Durant cet atelier, nous présenterons les concepts de bases, et les étapes clés requises pour créer un réseau moléculaire avec la plateforme GNPS (http://gnps.ucsd.edu, Global Natural Products Social Molecular networking) ainsi que le logiciel MetGem (https://metgem.github.io/, pour la génération de réseaux moléculaires avec l'outil t-SNE). Notamment, vous apprendrez à créer et visualiser un réseau moléculaire à partir de données MS/MS. Cet atelier sera également l'occasion de vous familiariser avec les outils permettant la conversion de vos données aux différents formats de fichiers (mzML, mgf...). Vous utiliserez par la suite l'outil 'Library Search' qui permet d'annoter des spectres MS/MS grace aux banques publiques de spectres de références. Au cours de cet atelier, des fichiers de démonstrations seront mis à disposition, toutefois il est possible d'utiliser vos propres fichiers MS/MS. L'atelier se terminera par une présentation des autres outils disponibles sur la plateforme GNPS, tels que le Molecular Blast (MASST) permettant de rechercher un spectre MS/MS dans toutes les données publiques déposées sur GNPS/MassIVE, et le feature based molecular networking qui permet l'intégration d'OpenMS, MZmine2, MS-DIAL, XC-MS et MetaboScape, avec GNPS, ou d'autres logiciels d'analyses de données MS/MS.

Compétences acquises à la sortie de l'atelier :

Les participants apprendront à :
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i) Gérer les formats de données

ii) Créer et visualiser un réseau moléculaire sur la plateforme GNPS.

iii) Naviguer sur la plateforme GNPS afin d'accéder aux résultats d'analyses et de les interpréter.

iv) Chercher un spectre MS/MS dans les données publiques déposées sur GNPS/MassIVE.

v) Accéder à d'autres outils et ressources disponibles sur GNPS.

vi) Créer, visualiser et interpréter des réseaux avec le logiciel MetGem.

Références (publications, sites, outils, etc.) :

Wang, M. et al. *Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking*, Nature Biotechnology 34, 828-837 (2016) - https://www.nature.com/articles/nbt.3597

Olivon et al. *MetGem Software for the Generation of Molecular Networks Based on the t-SNE Algorithm*, Analytical Chemistry 90(23), 13900-13908 (2018) - https://pubs.acs.org/doi/10.1021/acs.analchem.8b03099

GNPS website- https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash2.jsp

GNPS documentation - https://ccms-ucsd.github.io/GNPSDocumentation/

GNPS YouTube channel - https://www.youtube.com/channel/UCufTdDIUPjfoN604lgv_29g/ videos

MetGem - https://metgem.github.io/

Pré-requis pour les participants : afin de profiter pleinement de cet atelier, merci de bien prendre en compte les configurations matérielles requises (si besoin, merci de contacter les animateurs pour confirmation).

Les participants doivent disposer d'ordinateurs personnels (Windows, MacOS, linux) et de connaissances de base sur la spectrométrie de masse en mode tandem. Le logiciel MetGem est téléchargeable à l'adresse suivante: https://metgem.github.io/ et installable sous Windows.
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# Atelier 5 - Juniors

animateur(s) : RFMF Junior (rfmf.junior@gmail.com)

Public envisagé et les prérequis :

Public : Cet atelier vise plus particulièrement la communauté junior mais reste ouvert à tous !

Prérequis : Aucuns

Objectif de l'atelier : Créer des échanges par groupes autour des outils analytiques, statistiques et méthodologiques utilisés en métabolomique et fluxomique.

Compétences acquises à la sortie de l'atelier : Une vision plus éclairée des différents outils disponibles et utilisés dans le domaine de la métabolomique, fluxomique ainsi que des nouveautés en cours de développement qui viendront compléter et améliorer nos ressources.

Références (publications, sites, outils, etc.) : http://www.rfmf.fr/rfmf-junior.html
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COMMUNICATIONS INVITEES

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# Présentation des Invités

**Pr Coral Barbas** Universidad San Pablo Madrid, CEMBIO

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Prof Coral BARBAS is currently Full Professor of Analytical Chemistry at Pharmacy Faculty, Universidad CEU San Pablo, Madrid (Spain) and Director for the "Centre for Metabolomics and Bioanalysis" (CEMBIO) at this Faculty. She is also Director for CEU International School of Doctorate (CEINDO); Visiting Professor at Imperial College London, Department of Surgery and Cancer and at Bialystok Medical University. As previous appointments she was Vice-Chancellor for Research at Universidad CEU San Pablo and Marie Curie Fellow at Kings College London. She is the author of more than 200 papers, with current research interests in all the steps in metabolomics workflow: experimental design, sample pretreatment, analytical methods for targeted and untargeted metabolomics, method validation, data treatment, compound identification and interpretation. Her researcher is focused on multiplatform analysis with GC-MS, LC-MS and CE-MS of all kind of biological samples searching for disease biomarkers, prognostic biomarkers, mechanisms of action of a drug, diet, etc. Her awards include the medal of Bialystok Medical University and she was named to the 2016 Power List, the 50 Most Influential Women in Analytical Chemistry in the World, *The Analytical Scientist* and recently she has received the award of the Belgian Society of Pharmaceutical Sciences (BSPS 2018).

**Pieter Dorrestein** University of California - San Diego, Collaborative Mass Spectrometry Innovation Center

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Dorrestein is Professor at the University of California - San Diego. He is the Director of the Collaborative Mass Spectrometry Innovation Center and a Co-Director, Institute for Metabolomics Medicine in the Skaggs School of Pharmacy & Pharmaceutical Sciences, and Departments of Pharmacology and Pediatrics. Since his arrival to UCSD in 2006, Prof. Dorrestein has been pioneering the development of mass spectrometry methods to study the chemical ecological crosstalk between populations of microorganisms, including host interactions for agricultural, diagnostic and therapeutic applications. He participated in panels for the white house science and technology office of president on the launch of a national microbiome initiative and has been on panels for the National Academy of Sciences on the Chemistry of the Microbiome. He has co-authored over 220 publications and his work has been featured by the wall street journal, CNN, NYTimes, Fox, BBC and hundreds of other news outlets. He has been recognized with several awards, among them are awards from the Beckman foundation, V-foundation in cancer research, EUREKA award for unconventional and enabling research, Hearst Foundation, Pharmaceutical Research and Manufacturing Association research award and the Abel award in pharmacology. For a more detailed biography see http://www.nature.com/news/the-man-who-can-map-the-chemicals-all-over-your-body-1.20035
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**Claudio Luchinat** University of Florence, CERM

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Claudio Luchinat is full Professor of Chemistry at the University of Florence, Director of CERM (Center of Magnetic Resonance) and of CIRMMP (Interuniversity Consortium on Magnetic Resonance of MetalloProteins). His research interests include development of NMR-based structural methodologies, electron and nuclear relaxation, NMR of paramagnetic species, relaxometry, bioinorganic chemistry and metabolomics. His h-index is 79 (Scholar), and his papers have been quoted more than 25.000 times.H has held seminars in many prestigious universities and research institutions worldwide, and plenary lectures in International Workshops, Symposia and Conferences. He has been awarded the 1989 "Raffaello Nasini" gold medal, the 1994 Federchimica Award "For an Intelligent Future", the 1996 SBIC European Medal for Biological Inorganic Chemistry, the 2001 GDRM gold medal, the 2017 Sapio Senior Award and the 2018 Richard R. Ernst Prize in Magnetic Resonance. http://www.cerm.unifi.it/people/claudio-luchinat

**Konstantinos A. Aliferis** Agricultural University of Athens, PMG

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Konstantinos A. Aliferis is Professor of Pesticide Science at the Agricultural University of Athens (Greece), head of the Pesticide Metabolomics Group (Dept of Plant Science). Since late 2017, Dr Aliferis is also Adjunct Professor at McGill University in Plant Science Dept. His skills are covering plant functional genomics (metabolomics), advanced crop protection, pesticide research and development, research on bioactive plant and fungal metabolites-natural products (bioactivity, MoA, and toxicological risk assessment), cannabis R&D, fungal resistance to fungicides and fungal metabolomics (GC/MS, FT-ICR/MS, Orbitrap MS/MS, & NMR analyzers), GM plant risk assessment, metabolic networking, shotgun proteomics, RNA sequencing data analyses, multi-level "omics" data integration, biomarker discovery, quantitative structure-activity relationship (QSAR) modeling, and food chemistry (chemoinformatics, pesticide residues). Dr Aliferis is also an expert in the state-of-the-art mega-variate analyses (PCA, O2PLS, PLS-DA, OPLS-DA, SIMCA, HCA, Heatmaps), "omics" data fusion, bioinformatics analysis, metabolic networking, and biological interpretation of large-scale "omics" data. For further information : Pesticide Metabolomics Group (PMG): http://www.aua.gr/pesticide-metabolomicsgroup/
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## Oral Invité 1– OI 1

# METABOLOMICS: RECENT DEVELOPMENTS IN THE ANALYTICAL PROCESS

### Coral Barbas

¹Center for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, San Pablo CEU University, Madrid, Spain

Metabolomics is the omics with a closer relationship with Analytical Chemistry due to the analytical difficulties to detect such broad range of compounds in terms of physic-chemical properties and concentrations. Even more, I would say that Metabolomics is producing an evolution in analytical terms and concepts. Metabolomics workflow mimics the classical steps in the “Analytical Process” and our group has been developing tools to increase analytical quality and confidence at every step of the workflow.

Some researchers tend to think that working without a priori hypothesis means working in a blind way where everything is valid. Indeed a proper design and development of a metabolomics assay requires a tight control of all factors to avoid artifactual findings. Technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample; however meaningful results can be only obtained if all the steps are carefully considered.

The final success of untargeted metabolomics depends on applying the principles of the Analytical Process to every step in the workflow: i) Clear definition of the objective of the analysis. ii) Sampling and sample storage to guarantee a representative and homogeneous sample. iii) Sample pretreatment to obtain a sample as complete and non-biased as possible while being compatible with the instrumental techniques. iv) Analytical methods with a proper quality control. v) Use of appropriate statistics and data treatment. vi) Identification of statistically significant metabolites. vii) Interpretation of results

Our group has been working in improving the methodology in many of these areas while at the same time applying our developments in real world studies because the path is made by walking.

Examples of different studies to avoid black boxes in untargeted metabolomics will be presented.

1.-Sampling and sample collection: Brain tissue obtained from autopsy is practically the only source of normal brain in humans. The post-mortem time (PT)-induced changes that may occur at both the metabolomics and anatomical levels in the brains will affect the conclusions obtained. and work in better known environments such as post-mortem brain sample evolution (1). In another study a
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popular method of cell isolation (fluorescence-activated cell sorting (FACS)) was considered. The impact of FACS on the cell metabolome was deeply investigated (2).

2.-Identification. CEU Mass Mediator (CMM) is an on-line open source tool developed at CEMBIO (3). It aims aiding researchers when performing metabolite annotation. Its database integrates several metabolomic databases including Human Metabolome Database (HMDB), KEGG and LipidMaps and 672,042 simulated compounds from MINE. In addition, CMM scores the annotations which matched the query parameters using 122 rules based on expert knowledge.

Since this last major revision, CMM has continued to grow, expanding the knowledge base of its expert system and including new services to support researchers in the metabolite annotation and identification process (4). The information from external databases has been refreshed, and an in-house library with oxidized lipids not present in other sources has been added. This has increased the number of experimental metabolites up 332,665 and the number of predicted metabolites to 681,198. Furthermore, new taxonomy and ontology metadata have been included. CMM has expanded its functionalities with a service for the annotation of oxidized glycerophosphocholines, a service for spectral comparison from MS2 data, and a spectral quality-assessment service.

## References

1.- C. Gonzalez-Riano et al. Metabolomics and neuroanatomical evaluation of post-mortem changes in the hippocampus. Brain Structure and Function 2017. 222, 6, 2831-2853.

2.- Binek A et al. Flow Cytometry Has a Significant Impact on the Cellular Metabolome. J Proteom Res 2018 (on line publication).

3.-Gil de la Fuente et al. Knowledge-based metabolite annotation tool: CEU Mass Mediator. J. Pharm. Biomed. Anal. 2018 154, 138-149.

4.-Gil-De-La Fuente, et al. CEU Mass Mediator 3.0: a metabolite annotation tool. J. Proteome Res 2019, 18 (2), 797-802.
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## Oral Invité 2– OI 2

# Using Mass Spectrometry to Connect The Worlds Chemistry of Life
– a Big Data Problem For Natural Products

Peter Dorrestein

Imagine if we could figure out the chemical composition of any natural sample and understand the relationships of the molecules to all other samples in seconds – akin to a Google search but instead of using a text we use mass spectrometry information. This would transform the life sciences. The foundation for such infrastructure is being created through community wide knowledge capture and analysis tool development. In this lecture I will describe the steps we have taken with GNPS, MASST, ReDU and other tools to achieve that goal.
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## Oral Invité 3– OI 3

# Pros and cons of fingerprinting and profiling in NMR-based
metabolomics

Claudio Luchinat

Magnetic Resonance Center (CERM) and Department of Chemistry – University of Florence - Italy

The two main approaches to metabolomics are fingerprinting and profiling. Their characteristics have been recently reviewed by us[1]. Fingerprinting by NMR is the global evaluation of the whole spectrum, viewed as a snap-shot of all (assigned or unassigned) detectable metabolites present in the sample. This can be done by transforming NMR spectra in a data matrix through bucketing, or using full resolution spectra. In the latter case, specific algorithms for peak alignment are needed. Fingerprinting is essentially used to provide fast sample classification by multivariate statistical techniques. However, due the need of finely comparing different sets of data, fingerprinting is not robust to bias introduced by non-perfectly standardized pre-analytical and analytical procedures. Profiling, in contrast, deals with the determination of the concentrations of all quantifiable metabolites in the sample. Profiling provides more meaningful data from a biochemical perspective, as it also enables the identification of metabolites and metabolic pathways associated with a specific pathology and is more robust to (non-critical) sample variations. However, the analysis required to assign and deconvolute the NMR signals for quantification is non-trivial and not yet completely automated. Also, the molecules quantifiable via profiling are presently significantly less numerous than those contributing to the fingerprint: the latter, therefore, is still the best tool for sample classification and statistical model building. However, if progress in automated peak assignment and quantitation continues, the gap in information content between profiling and fingerprinting will be progressively reduced. Promising approaches to this end will be discussed[2].

[1] A. Vignoli, V. Ghini, G. Meoni, C. Licari, P. G. Takis, L. Tenori, P. Turano, C. Luchinat, Angew. Chem. Int. Ed Engl. 2019, 58, 968–994.

[2] P. G. Takis, H. Schäfer, M. Spraul, C. Luchinat, Nat. Commun. 2017, 8, 1662.
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## Oral Invité 4– OI 4

# Metabolomics in the research and development of plant protection products: current state and future perspectives

Konstantinos A. Aliferis

Pesticide Science Laboratory, Agriculture University of Athens, Iera Odos 75, 18855 Athens, Greece

Department of Plant Science, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, Quebec, Canada, H9X 3V9

konstantinos.aliferis@aua.gr, konstantinos.aliferis@mcgill.ca

Plant protection products (PPPs) represent the backbone of the agri-food sector worldwide in the effort to secure food quality and supply. Nonetheless, the sector is facing major challenges such as, among others, the development of resistance to PPPs pest and pathogens, the presence of residues of PPPs in the food and the environment, toxicity issues to non-target organisms, and the increasing concerns of the public and international organizations on food safety. In order to address such challenges, the implementation of advanced bioanalytical tools for the discovery of new sources of bioactivity, which will ideally exhibit new or alternative mode(s)-of-action (MoA), improved efficacy and toxicological profiles, is necessary. Based on its unique capacities, metabolomics could serve as an ideal tool towards this direction, and additionally could accelerate the research and development (R&D) of PPPs, which is a time consuming and costly process. Within this context, my research program is focusing on the in-depth study of the mechanism by which plant pathogens develop resistance to PPPs, the discovery and assessment of new sources of bioactivity as PPPs, in vitro and in planta (e.g., endophytes, natural products, bioelicitors, nanomaterials), and the development of advanced metabolomics for the discovery of the MoA of bioactive compounds. Here, the current state and future perspectives of the application of metabolomics in the R&D of PPPs will be presented.
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COMMUNICATIONS ORALES

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## Oral 1 – O1

# Stable Isotope Labeling Highlights Enhanced Fatty Acid and Lipid Metabolism in Human Acute Myeloid Leukemia

Lucille Stuani<fnref n="1" />, Fabien Riols<fnref n="2" />, Pierre Millard<fnref n="3" />, Aurélie Batut<fnref n="2" />, Fanny Viars<fnref n="2" />, Marie Sabatier<fnref n="2" />, Estelle Saland<fnref n="2" />, Jean-Charles Portais<fnref n="3" />,<fnref n="4" />, Jean-Emmanuel Sarry<fnref n="1" />, *Justine Bertrand-Michel*<fnref n="2" />

**Background:** In Acute Myeloid Leukemia (AML), a complete response to chemotherapy is usually obtained after conventional chemotherapy but overall patient survival is poor due to highly frequent relapses. As opposed to chronic myeloid leukemia, B lymphoma or multiple myeloma, AML is one of the rare malignant hemopathies the therapy of which has not significantly improved during the past 30 years despite intense research efforts. One promising approach is to determine metabolic dependencies in AML cells. Moreover, two key metabolic enzymes, isocitrate dehydrogenases (IDH1/2), are mutated in more than 15% of AML patient, reinforcing the interest in studying metabolic reprogramming, in particular in this subgroup of patients. **Methods:** Using a multi-omics approach combining proteomics, lipidomics, and isotopic profiling of [U-13C] glucose and [U-13C] glutamine cultures with more classical biochemical analyses, we studied the impact of the IDH1 R132H mutation in AML cells on lipid biosynthesis. **Results:** Global proteomic and lipidomic approaches showed a dysregulation of lipid metabolism, especially an increase of phosphatidylinositol, sphingolipids (especially few species of ceramide, sphingosine, and sphinganine), free cholesterol and monounsaturated fatty acids in IDH1 mutant cells. Isotopic profiling of fatty acids revealed that higher lipid anabolism in IDH1 mutant cells corroborated with an increase in lipogenesis fluxes. **Conclusions:** This integrative approach was efficient to gain insight into metabolism and dynamics of lipid species in leukemic cells. Therefore, we have determined that lipid anabolism is strongly reprogrammed in IDH1 mutant AML cells with a crucial dysregulation of fatty acid metabolism and fluxes, both being mediated by 2-HG (2-Hydroxyglutarate) production<fnref n="1" />. 1Stuani *et al*, Int. J. of Molecular Sciences, 2018

**Mots-Clés:** lipidomique, marquage isotopique, cancer

<footnote n="1">¹ Team ResistAML, Drug resistance oncometabolism in acute myeloid leukemia, CRCT – Inserm :
UMR1037, CNRS : UMR5294, Université Paul Sabatier-Toulouse III - UPS – 2 avenue Hubert Curien TOULOUSE</footnote>

<footnote n="2">² Plateforme MetaToul Lipidomique, I2MC – Inserm : UMR1048 – Toulouse</footnote>

<footnote n="3">³ LISBP UMR5504, 135 Avenue de rangueil 31077 Toulouse cedex 04, France</footnote>

<footnote n="4">⁴ Plateforme MetaToul Réseaux métabolique, LISBP Toulouse</footnote>
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## Oral Keynote 1 – OK1

# Methodological Developments in Ultra-High-Resolution NMR
## For a Deeper Understanding of Metabolic Processes in Relapsed/Refractory Diffuse Large B-Cell Lymphoma

Gildas Bertho<fnref n="1" />, Leonardo Lordello<fnref n="2" />, Stéphanie Nuan-Aliman<fnref n="2" />, Cédric Caradeuc<fnref n="1" />, Catherine Thieblemont<fnref n="3" />,<fnref n="2" />, Véronique Baud<fnref n="2" />, Nicolas Giraud<fnref n="1" />

Lymphoma refers to the group of blood cancers that develop from lymphocytes, which are key infection-fighting cells of the immune system. Among the many different forms of lymphomas, Diffuse Large B-Cell Lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma that accounts for around 40% of cases. Although DLBCL treatment outcome has significantly improved, to date up to 40% of patients will either be refractory or relapse and die. Over the last years, metabolism has become a major topic in the field of cancer research, unveiling new approaches to decipher the mechanism of the disease, as well as tools for developing new treatments.[1] In this context, we are carrying out a collaboration to overcome drug resistance in relapsed/refractory diffuse large B-cell lymphoma (DLBCL). Prof. C. Thieblemont (Hospital Saint-Louis, Paris) has recently successfully set up a phase 1 clinical trial using a combination of three metabolic inhibitors that was shown to induce a global remission in 75% of relapsed/refractory DLBCL patients included in the trial.[2] These results were validated by Dr. V. Baud (INSERM, Univ. Paris Descartes) who showed that combining anti-metabolic drugs induces massive DLBCL cell death.

Here we will show how 1H NMR can be used to get a deeper insight into the metabolism of DLBCL and decipher the mechanism of action of metabolic inhibitors. We will describe how standard protocols have been applied on the cellular endo- and exo-metabolomes studied on DLBCL cell lines and shed light on how these antimetabolic drugs impact on DLBCL metabolomes. We will also present the methodological developments that are in progress in our group to acquire broadband homonuclear decoupled spectra on these complex biofluids. Despite their intrinsic lower sensitivity, the most recent methods in the field of "pure shift" NMR [3] yield significant resolution enhancements when they are implemented in standard pulse sequences designed for NMR-based metabolomics, which paves the way to a higher level of information and to new protocols for statistical analyses.

Mots-Clés: Lymphoma, Metabolism, NMR, Pure Shift

<footnote n="1">¹ Laboratoire de Chimie et Biochimie Toxicologiques et Pharmacologiques, Université de Paris, Sorbonne Paris Cité – Université Paris Descartes - Paris 5 : UMR8601 – France</footnote>

<footnote n="2">² NF-kappaB, Différenciation et Cancer, Université de Paris, Sorbonne Paris Cité – Université Paris Descartes - Paris 5 : EA7324 – France</footnote>

<footnote n="3">³ Assistance Publique-Hopitaux de Paris – Hopital-Saint-Louis, Hematooncology-Universite Paris Diderot, Sorbonne Paris-Cite, Paris, France – France</footnote>
<figure><img src="image_64.png" /></figure>

## Oral Junior 1 – OJ1

# The microbiome and drug metabolism

**Marine Letertre**<sup>1</sup>, **Nyasha Munjoma**<sup>2</sup>, **Adele Costabile**<sup>3</sup>, **Lesley Hoyles**<sup>4</sup>, **Aadra Bhatt**<sup>5</sup>, **Matthew Redinbo**<sup>5</sup>, **Jeremy Nicholson**<sup>6</sup>, **Jonathan Swann**<sup>7</sup>, **Ian Wilson**<sup>1</sup>

Understanding drug-microbiome-host interactions should now be considered as a key point to determine the pharmacological profile of a drug as well as its toxicological profile. On one side, the gut microbiota can, directly and indirectly, affect the pharmacological profile of orally administered drugs (or metabolites excreted into the gut via e.g. the bile), and on the other, the drug itself can perturb the microbiome structure and its functionality with potential ramifications for host health. Pharmaco-metabonomics, a useful tool to patient stratification, was applied to a multifaceted approach in order to cover the wide range of gut microbial-drug interactions, and to develop an appropriate in vitro tool. The simultaneous use of targeted and untargeted UPLC-MS metabolic profiling allowed (1) to monitor the detoxification of methotrexate by the bacterial enzyme FGCP and (2), to observed dose-related effects of the drug on the urine and faecal metabolic profile and microbial community in rats. As it was reported that the microbial metabolite *p*-cresol, coming from the microbial degradation of tyrosine, can negatively impact paracetamol metabolism and sulfate-conjugation pathway in general, a targeted UPLC-MS method to quantify *p*-cresol and catabolites has been developed and it will soon be applied in a fit-for-purpose method on mice samples which received two weeks of high-tyrosine diet to manipulate their sulfate metabolism prior to receiving a sub-lethal dose of acetaminophen. Understand the interactions occurring between a given drug and the gut microbiota can also help to therapeutically target the gut microbiota and limits adverse-drug reactions of an existing drug. Indeed, in order to enhance irinotecan efficacy and limits its toxicity, a bacterial *β*-glucuronidase inhibitor is being developed. Its administration in mice model seems promising without significantly influence the metabolome of the animals. Finally, preliminary results of an in vitro model based on miniaturized batch cultures and semi-targeted UPLC-MS showed drug and donor-specific effects on compound metabolism. We are currently working on scaling-down the model to move toward a high-throughput method to easily automate and to implant it in pharmaceutical and clinical laboratories.

Mots-Clés: Pharmaco, metabonomics, microbiome, UPLC, MS, methotrexate, acetaminophen, *p*-, cresol, *B*, glucuronidase

<footnote n="1">¹ Imperial College London – Royaume-Uni</footnote>

<footnote n="2">² Waters Corporation – Royaume-Uni</footnote>

<footnote n="3">³ University of Roehampton – Royaume-Uni</footnote>

<footnote n="4">⁴ Nottingham Trent University – Royaume-Uni</footnote>

<footnote n="5">⁵ University of North Carolina – États-Unis</footnote>

<footnote n="6">⁶ Murdoch University – Australie</footnote>
<figure><img src="image_65.png" /></figure>

## Oral 2 – O2

# Suivi longitudinal du métabolisme cérébral par imagerie spectroscopique de Résonance Magnétique Nucléaire chez des modèles murins progressifs de la maladie de Parkinson

**Carine Chassain**<sup>1</sup>, **Christophe Melon**<sup>2</sup>, **Guilhem Pages**<sup>3</sup>, **Yann Le Fur**<sup>4</sup>, **Paolo Gubellini**<sup>5</sup>, **Pascal Salin**<sup>5</sup>, **Lydia Kerkerian-Le Goff**<sup>2</sup>, **Franck Durif**<sup>6</sup>

Les modèles animaux de la maladie de Parkinson (MP), reproduisant la dégénération progressive des neurones dopaminergiques de la substance noire pars compacta (SN), ouvrent de nouvelles opportunités pour en étudier l'évolution. L'imagerie spectroscopique de Résonance Magnétique Nucléaire (MRSI) est utilisée pour suivre au cours du temps l'évolution des profils métaboliques dans les composants clés des ganglions de la base (striatum dorsal, cortex moteur, noyaux sous-thalamique (NST) et SN).

Les acquisitions MRSI sont réalisées à 11,7T. Des images anatomiques permettent de visualiser les régions d'intérêt et de positionner la MRSI. Cette technique permet de collecter des spectres provenant de volumes d'intérêt de tout un plan de coupe avec une résolution de 1,6x1,6x2 mm3. Les données MRSI sont analysées sous CSIAPO puis la quantification est effectuée avec le logiciel LCModel. Des rats reçoivent une injection stéréotaxique unilatérale d'un substrat inhibiteur des transporteurs au glutamate, le PDC (n=7) ou du véhicule (n=5) dans la SNc gauche. Pour le modèle α-synucléine (n=7), des vecteurs viraux adéno-associés recombinants sont injectés aux mêmes sites. L'imagerie spectroscopique a été réalisée 30, 60 et 120 jours après la chirurgie. A chaque point, l'activité motrice des rats est mesurée et à la fin de l'étude les rats sont sacrifiés pour quantifier la dénervation dopaminergique nigrale.

Les taux de glutamate sont plus élevés au niveau du striatum chez les deux modèles. La quantification du glutamate au niveau du NST montre une augmentation dans le NST ipsilatéral à la lésion, mais aussi du côté contralatéral. Les deux modèles ont des atteintes motrices et la dénervation DA est vérifiée.

La MRSI est une technique utile pour la caractérisation longitudinale des profils de métabolites sur des modèles animaux de MP. Elle souligne l'implication de la réactivité glutamatergique du NST contralatéral à l'atteinte primaire des neurones dopaminergiques de la SN dans la progression de la MP. Comprendre les circuits et les mécanismes interhémisphériques impliqués pourraient permettre de proposer des nouvelles stratégies thérapeutiques pour ralentir cette progression.

**Mots-Clés:** Imagerie spectroscopique, RMN, maladie de Parkinson, modèles murins

<footnote n="1"><sup>1</sup> CHU Gabriel Montpied (CHU) – CHU Clermont-Ferrand – 58 rue Montalembert 63000 Clermont-Ferrand, France</footnote>

<footnote n="2"><sup>2</sup> IBDM, UMR 7288 CNRS / Aix-Marseille Université, Marseille CNRS – France</footnote>

<footnote n="3"><sup>3</sup> AgroResonance-UR370 QuaPA, Saint-Genes Champanelle – INRA Clermont-Ferrand-Theix – France</footnote>

<footnote n="4"><sup>4</sup> CRMBM, UMR 7339 CNRS / Aix-Marseille Université Marseille – CNRS – France</footnote>

<footnote n="5"><sup>5</sup> IBDM, UMR 7288 CNRS / Aix-Marseille Université, Marseille – CNRS – France</footnote>

<footnote n="6"><sup>6</sup> Université Clermont Auvergne (UCA), EA7280 NPSY-Sydo, Clermont-Ferrand – Université Clermont Auvergne (UCA) – France</footnote>
<figure><img src="image_66.png" /></figure>

## Oral Sponsor 1 – OS1
Platinum

Waters
THE SCIENCE OF
WHAT'S POSSIBLE.®

MetaboQuan et lipdQuan – Methodes clés en main pour l'analyse ciblée des lipides et des métabolites

Christophe Siroit, Waters
<figure><img src="image_67.png" /></figure>

**Oral Sponsor 2 – OS2**
Platinum

**ThermoFisher**
SCIENTIFIC

# Re-inventing the Untargeted Metabolomics Workflow with the Thermo Scientific™ Orbitrap ID-X™ Tribrid™ MS

Marie Pierre Pavageau, ThermoFisher Scientific

Compound identification is a current bottleneck in the broad implementation of metabolomics. The development of an automated acquisition strategy implemented on a Tribrid™ mass spectrometer, enabling multistage fragmentation (MS¹) associated with an integrated software and library has increased the number of detected metabolites in samples and enables confident biological interpretation of the results.
<figure><img src="image_68.png" /></figure>

# Expanding the BCAA pathway of E. coli reveals the molecular basis of its robustness and homeostasis

Mickael Dinclaux<fnref n="1" />, Pierre Millard <fnref n="1" />, Fabien Létisse<fnref n="1" />

Escherichia coli K-12 MG1655 strain (F- lambda- ilvG- rfb-50 rph-1) is a model organism for scientific research and a workhorse for biotechnology, which carries frameshift mutations in its genome. Among them, a reported frameshift mutation concerns *ilvG* gene (ilvG-) which encodes the acetohydroxy-acid synthase II (AHAS; EC 2.2.1.6), leading an inactive gene product and a valine sensitivity characterized by a growth defect in minimal medium supplemented with valine. Here, we wondered if the lack of AHAS II enzyme induced other disorders at the metabolic level despite of the presence of the two isoenzymes, AHAS I and AHAS III. By using an exometabolome comparative analysis using 1D-H NMR between the wild-type strain and a strain repaired for this specific mutation, we identified the accumulation of three metabolites, namely propionate, DHIV (2,3-dihydroxy-isovalerate) and DHMP (2,3-dihydroxy-3-methylpentanoate). The two latter metabolites are intermediates of the branched chain amino acids (BCAA) pathway, both being the substrate of dihydroxy acid dehydratase, for the formation of valine and leucine, and for the formation of isoleucine respectively. Surprisingly, the reaction catalyzed by the dihydroxy-acid dehydratase takes place downstream of that catalyzed by the AHAS, making the accumulation of DHIV and DHMP not obvious to explain. Propionate is reported to be produced from ketobutyrate in anaerobiosis by pyruvate formate lyase. An investigation of propionate accumulation observed in this study in aerobic condition show the existence of a new metabolic process in connection with the absence of AHAS II. Combining metabolomics approaches based on both MS and NMR, molecular biology tools and in silico models, we pinpointed local metabolic reorganizations explaining the accumulation of these metabolites in culture supernatants. This works demonstrates the molecular basis of the robustness and homeostasis of this metabolic node in E. coli which contributes to the adaptative ability of metabolism to genetic modifications.

Mots-Clés: E. coli, BCAA, homeostasis, robustness

<footnote n="1">¹ Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP) – LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France – France</footnote>
<figure><img src="image_69.png" /></figure>

# Y flux: A high throughput fluxomic workflow for exploration of metabolic phenotypes

Cécilia Berges<fnref n="1" />,<fnref n="2" />, Edern Cahoreau <fnref n="1" />,<fnref n="2" />, Lindsay Peyriga <fnref n="1" />,<fnref n="2" />, Stephanie Heux <fnref n="1" />, Pierre Millard <fnref n="1" />, Floriant Bellvert <fnref n="1" />,<fnref n="2" />, Jean-Charles Portais <fnref n="1" />,<fnref n="2" />,<fnref n="3" />

Despite the progress that has been made in sequencing technology and bioinformatic methods, a non-negligible percentage of genes are still not characterized. Even for an organism very well known as *E. coli*, we can find more than 40% of genes with unknown or putative function. Highthroughput (HT) omics approaches enable the large-scale analysis of biological molecules and try to get a comprehensive understanding of living organism and their regulation. Among those, fluxomics aims at measuring the actual rates of biochemical reactions in metabolic networks. However, a 13C fluxomic experiment requires several complex experimental and computational steps as weel as large input of manual steps. These makes classical fluxomics a low throughput approach, which limits the number of organisms, strains and conditions which can be investigated at once. To increase fluxomics throughput and robustness while reducing experimental costs and human efforts, we designed a complete workflow in order to automatize, parallelize, integrate and optimize each step of fluxomics experiments. This includes (1) experimental design, (2) automated labelling experiment, (3) isotopic and metabolomics analyses, (4) Data processing (5) Flux calculation and Statistical analysis. We then decided to challenge our workflow and evaluate the impact of unknown function genes mutation on central metabolic fluxes of *E. coli*. For this purpose, we selected 182 mutant strains of the Keio collection for which the function was not characterized. Flux maps were obtained for every strain on labelled glucose condition; they were statistically analyzed and compared to reference flux maps of known strains. Among these 182 strains investigated, two strains (ydcS and ybjP) showed significant modifications of central metabolism with metabolic fluxes changes compared to the wild type strain without any significant variation of physiology. Thanks to this development, we are able to quantify flux responses to environmental or genetic perturbations in particular for screening of genes functions in complement to other omics approaches. It opens the way to very large-scale fluxomics screening experiment and it can be applied in many fields : biotechnology, biomarker, drug development, medical science, toxicology or pharmacology.

Mots-Clés: Flux analysis, High throughput, Fluxomic workflow, Biotechnology, y genes

<footnote n="1">¹ LISBP – LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France – France</footnote>

<footnote n="2">² MetaToul - MetaboHub - ANR-11-INBS-0010 – France</footnote>

<footnote n="3">³ Stromalab – Université de Toulouse, CNRS ERL5311, EFS, INP-ENVT, Inserm U1031, UPS – France</footnote>
<figure><img src="image_70.png" /></figure>

## Oral Sponsor 3 – OS3
Platinum

<figure><img src="image_71.png" /></figure>

Agilent

Trusted Answers

Accelerate our lab capabilities combining a metabolomic
workbench and the new 6546 LC-QTOF Agilent

Jeremy Jeudy, Agilent
<figure><img src="image_72.png" /></figure>

## Oral Flash 1 – FP1

# A combined metabolomics and lipidomics approach enables the stratification of acute-on-chronic liver failure patients according to their severity

**Noemie Butin**<sup>1</sup>, **François Fenaille**<sup>1</sup>, **Benoit Colsch**<sup>1</sup>, **Christophe Junot**<sup>1</sup>, **Joan Clària**<sup>2,3</sup>, **Richard Moreau**<sup>4,5</sup>, **Vicente Arroyo**<sup>5</sup>

Acute-on-Chronic Liver Failure (ACLF) is a recently recognized syndrome characterized by acute decompensation (AD) of cirrhosis, an organ/system failure(s) and extremely poor survival. ACLF can be triggered by a precipitating event (e.g. bacterial infection) and is invariably associated with exacerbated systemic inflammation. According to the European Foundation for the study of Chronic Liver Failure (EF-CLIF), patients with ACLF can be classified into three groups, essentially according to the number of impaired organs. In the present project, we investigated whether metabolomics and lipidomics can identify potential new diagnostic biomarkers of ACLF. In our study, a cohort of more of 800 serum samples from decompensated cirrhotic patients with and without ACLF were analyzed and compared to healthy subjects by LC-HRMS. Data mining procedures using multivariate and univariate analyses were then performed to highlight discriminant metabolites with a preliminary work on data processing to normalize data and to overcome analytical biases occurring in the analysis of a large biological cohort. Our data confirmed the metabolic and lipidomic cirrhosis signatures obtained in previous studies, especially regarding to the levels of glycerophosphatidylcholines, amino acids and energy metabolites. Furthermore, our approach enabled to discriminate between decompensated cirrhotic patients with ACLF and those without ACLF, and a specific metabolite signature associated with the ACLF grade was obtained. Moreover, we found that Kynurenine pathway is activated in patients with acute decompensation, culminate in patients with ACLF and may be involved in the pathogenesis of ACLF, clinical course and mortality.

<footnote n="1">¹ CEA Saclay, Laboratoire d'Etude du Métabolisme des Médicaments, MetaboHUB-Paris, DRF/IBITEC-S/SPI, 91191 Gif-sur-Yvette Cedex, France. – CEA, CNRS, Université Paris-Saclay, CEA Saclay 91191 Gif sur Yvette France – France</footnote>

<footnote n="2">² European Foundation for the Study of Chronic Liver Failure (EF-CLIF), Barcelona, Spain – Travessera de Gràcia, 11, Planta 7ª, 08021 Barcelona, Spain, Espagne</footnote>

<footnote n="3">³ Department of Biochemistry and Molecular Genetics, Hospital Clinic, Institut d'Investigacions Biomèdiques August-Pi-Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain – Carrer del Rosselló, 149, 08036 Barcelona, Spain, Espagne</footnote>

<footnote n="4">⁴ INSERM and Université Paris Diderot-Paris 7, Centre de Recherche sur l'Inflammation (CRI), Paris and Clichy, France – Institut National de la Santé et de la Recherche Médicale - INSERM – France</footnote>

<footnote n="5">⁵ European Foundation for the Study of Chronic Liver Failure (EF-CLIF), Barcelona, Spain – Espagne</footnote>
<figure><img src="image_73.png" /></figure>

## Oral Flash 2 – FP2

# Metabolic characterization of different phases of Parkinson's disease

David Mallet<fnref n="1" />, Thibault Duffourd<fnref n="1" />, Carole Carcenac<fnref n="1" />, Sébastien Carnicella<fnref n="1" />, Emmanuel Barbier<fnref n="1" />, Sabrina Boulet<fnref n="1" />, Florence Fauvelle<fnref n="1" />

**Context:** Parkinson's disease (PD) is the second most common neurodegenerative disorder, affecting almost 1% of the population beyond the age of 60. Currently, its diagnosis relies on the expression of the well-known motor symptoms (akinesia, rigidity, and tremor) which appear in the late stage of the disease. Detecting the disease earlier represents a key step to develop curative treatments which are so far only symptomatic. Long considered as a purely motor disease, PD is nevertheless also characterized by neuropsychiatric disorders (apathy, depression, anxiety...) that can develop during the early stages of the disease as well as later on.

**Objectives:** Our aim is to find specific molecular markers of early phases of PD, when only the neuropsychiatric

symptoms are expressed, using 1HNMR-based metabolomics on serum samples and on brain tissues of a rodent model allowing investigation of different phases of PD.

**Methods:** The animal model is based on a specific, partial, bilateral 6-OHDA-induced lesion in dopaminergic neurons. For each rat, motor functions and apathetic-like behaviors were assessed using a stepping test and operant sucrose self-administration, respectively.

Both serum and intact tissue samples were analyzed using a CPMG pulse sequence by respectively liquid NMR at 950 MHz (IBS Grenoble) and HRMAS-NMR at 500 MHz (IRMaGE, CEA Grenoble). Spectra were submitted to multivariate statistics (SIMCA-v14) in order to investigate if metabolic profile is correlated to behavioral and histological data.

**Results/conclusion:** In our animal cohort we observed a gradation in the symptoms, well in line with PD progression, from only neuropsychiatric, to the expression of neuropsychiatric associated with motor symptoms. For more precise evaluation, a score based on behavioral performances and on the striatal dopaminergic denervation was built. Serum and tissue spectra showed a good correlation of this score with the gradation of symptoms at different phases of PD.

In both samples, the energetic pathways seems to be modified with the progression of the disease, while some amino acids, like alanine and serine, are also dysregulated prior to the appearance of motors symptoms. We will further use the same methodology in serum with *De Novo* patients.

**Mots-Clés:** Parkinson, Biomarkers, liquid 1HNMR, Tissu, HRMAS NMR (High resolution magic angle spinning NMR)

<footnote n="1">¹Grenoble Institut des Neurosciences – CEA, CHU Grenoble, Inserm : U1216, Université Grenoble Alpes – France</footnote>
<figure><img src="image_74.png" /></figure>

## Oral Flash 3 – FP3

# Impaired postprandial skeletal muscle metabolism in a minipig model of insulin resistance: insights from arteriovenous and biopsy-based metabolomics analyses

Marie Tremblay-Franco <fnref n="1" />,<fnref n="2" />, Nathalie Poupin <fnref n="1" />, Florence Castelli, Jérémie David, Marianne Jarzaguet, Didier Rémond, François Fenaille <fnref n="3" />, Dominique Dardevet <fnref n="4" />, Sergio Polakof <fnref n="4" />

The postprandial phase (PP) represents one of the most challenging phenomena in wholebody metabolism, as the metabolism must adapt to major changes in blood composition following meal intake. This capacity of adaptation is strongly dependent on the metabolic flexibility of the individual, which is compromised in insulin resistant (IR) conditions, particularly at the skeletal muscle level (1). We applied an untargeted LC/MS-based metabolomics approach to skeletal muscle biopsies obtained before (0) and 1.5, 3 and 6 h after a regular meal in a minipig (dexamethasone treated) model of IR (2). Biopsies analyses were complemented by the assessment of the incoming (arterial) and outgoing (venous) blood metabolomes. As the consequence of the anabolic effect of the meal, in healthy animals we observed increased PP muscle levels of metabolites related to glucose (lactate, glucose-1,6biP, lactoylglutathione), energy (citrate, malate), amino acids (AA; methionine, glutamate, aspartate) and nucleotide (GTP, inosine, guanosine) metabolisms. In contrast, these metabolites showed a blunted PP profile in IR minipigs, which illustrates the loss of response to the meal induced by such condition. These include the inability to uptake and use glucose through glycolysis, stimulate protein synthesis due to the lack of AA entry into the muscle and net loss of other nitrogen resources (nucleotides). For some of these metabolites (lactate, AA) the observed changes in the biopsies were in line with the arteriovenous differences across the muscle, which confirms that they were actively uptaken, while for other (nucleotides) the altered profiles in the muscle were the consequence of intramuscular metabolic changes. As a whole, our high-throughput arteriovenous metabolomics approach across the muscle allowed us to further explore the PP metabolic changes observed in the muscle biopsies from IR minipigs. This strategy could be therefore useful to determine in the systemic circulation metabolites (potential biomarkers) able to sign the IR condition without having access to the skeletal muscle biopsies.

(1) Galgani et al. Am J Physiol. 2008;295(5):E1009-17.

(2) Revel et al. PLoS ONE. 2017;12(10):e0186204.

<footnote n="1">¹ UMR 1331 Toxalim - INRA-ENVT-INP-EIP-UPS - INRA [Toulouse] - France</footnote>

<footnote n="2">² Metatoul Axiom platform - Metabohub national Infrastructure - Toulouse - France</footnote>

<footnote n="3">³ CEA-INRA UMR 0496/DRF/Institut Joliot/SPI/LEMM, Université Paris Saclay, MetaboHUB - CEA - Centre d'Etudes Nucléaires de Saclay, 91191 Gif-sur-Yvette, France, France</footnote>

<footnote n="4">⁴ INRA UMR1019 Unité de Nutrition Humaine - Université Clermont Auvergne, INRA, UNH - France</footnote>
<figure><img src="image_75.png" /></figure>

## Oral Keynote 2 – OK2

# Improved riboflavin production with Ashbya gossypii from vegetable oil based on 13C metabolic network analysis with combined labeling analysis by NMR and mass spectrometry

**Lindsay Peyriga**<sup>1</sup>, **Susanne Katharina Schwechheimer**<sup>3</sup>, **Judith Becker**<sup>3</sup>, **Jean-Charles Portais**<sup>2,4,5</sup>, **Christoph Wittmann**<sup>3</sup>

The fungus *Ashbya gossypii* is an important industrial producer of riboflavin, i.e. vitamin B2. In order to meet the constantly increasing demands for improved production processes, it appears essential to better understand the underlying metabolic pathways of the vitamin. Here, we used a highly sophisticated set-up of parallel 13C tracer studies with labeling analysis by NMR and mass spectrometry to resolve carbon fluxes in the overproducing strain A. gossypii B2 during growth and subsequent riboflavin production from vegetable oil as carbon source, yeast extract, and supplemented glycine. The studies provided a detailed picture of the underlying metabolism. Glycine was exclusively used as carbon-two donor of the vitamin's pyrimidine ring, which is part of its isoalloxazine ring structure, but did not contribute to the carbon-one metabolism due to the proven absence of a functional glycine cleavage system. The pools of serine and glycine were closely connected due to a highly reversible serine hydroxymethyltransferase. Transmembrane formate flux simulations revealed that the onecarbon metabolism displayed a severe bottleneck during initial riboflavin production, which was overcome in later phases of the cultivation by intrinsic formate accumulation. The transiently limiting carbon-one pool was successfully replenished by time-resolved feeding of small amounts of formate and serine, respectively. This increased the intracellular availability of glycine, serine, and formate and resulted in a final riboflavin titer increase of 45%.

Mots-Clés: Ashbya gossypii, Riboflavin, 13C tracer, Metabolic flux, Carbon, one metabolism, NMR, MS

<footnote n="1"><sup>1</sup> Metatoul - Metabohub - Institut national de la recherche agronomique (INRA) : UMR792, CNRS : UMR5504, INSA - Institut National des Sciences Appliquées - France</footnote>

<footnote n="3"><sup>3</sup> Institute of Systems Biotechnology, Saarland University - Allemagne</footnote>

<footnote n="4"><sup>4</sup> STROMALAB - Institut National de la Santé et de la Recherche Médicale - INSERM - France</footnote>

<footnote n="5"><sup>5</sup> Laboratoire d'ingénierie des Systèmes Biologiques et des Procédés - Institut National de la Recherche Agronomique : UMR0792, Institut National des Sciences Appliquées - Toulouse, Centre National de la Recherche Scientifique : UMR5504 - France</footnote>
<figure><img src="image_76.png" /></figure>

## Oral Sponsor 4 – OS4
Platinum

<figure><img src="image_77.png" /></figure>

SHIMADZU

Excellence in Science

### Applications en Omics du nouveau QTOF Ultra-Fast MS 9030

Etienne Maout, Schimadzu
<figure><img src="image_78.png" /></figure>

## Oral Junior 3 – OJ3

# Feature-Based Molecular Networking of Untargeted Mass Spectrometry Data: Bridging MS-DIAL, MZmine2, OpenMS, and XC-MS, with the GNPS web-platform

Louis-Félix Nothias<fnref n="1" />, Daniel Petras<fnref n="1" />, Mingxun Wang<fnref n="1" />, Robin Schmid<fnref n="1" />,<fnref n="2" />, Abinesh Sarvepalli<fnref n="1" />, Zheng Zhang<fnref n="1" />, Melissa Esposito<fnref n="1" />, Ricardo Da Silva<fnref n="1" />, Alexander Aksenov<fnref n="1" />, Pieter Dorrestein<fnref n="1" />

Molecular networking has become an essential bioinformatic tool to annotate non-targeted tandem mass spectrometry data (MS/MS). Available on the GNPS web-platform (http://gnps.ucsd.edu), molecular networking accelerates the annotation of molecular/spectral families by propagating spectral library matches across the networks. Based on the MS-Cluster algorithm, molecular networking enables the large scale metabolomics meta-analysis, up to thousands of files. However, as MS-Cluster uses the MS/MS spectral counts as a proxy for the ion distribution between samples, other methods are needed to estimate the relative ion abundance, especially when analysing a single study.

In the present work, we introduce feature-based molecular networking, a collection of computational tools integrated together into a seamless LC-MS data processing pipeline combined with molecular networking on the GNPS web-platform (https://ccms-ucsd.github.io/GNPSDocumentation/featureba Utilizing software with advanced graphical user interface (MS-DIAL, MZmine2, MetaboScape) or advanced computational libraries (OpenMS, and XC-MS), the workflows serves both experimentalists, bioinformaticians, and software developers.

We benchmarked feature-based molecular networking on dilutions of a reference serum samples (NIST 1950 SRM), along with various mixtures of fecal and plant extracts, on both Orbitrap and QTOF mass spectrometers. The benchmarking of feature-based molecular networking showed that: 1) it outperformed MS-Cluster based molecular networking for the estimation of the ion relative distribution between samples, 2) it reduced the number of spectral artefacts, and 3) it prevented the merging of isomeric MS/MS spectra. These results were observed for both Orbitrap and QTOF instruments, and different sample types, indicating that feature-based molecular networking has significant advantages over MS-Cluster based molecular networking. The feature-based molecular networking is proposed to serve as an essential complementary tool to the "classical" MS-Cluster-based molecular networking which is a robust and nearly parameterless method, and is the only method capable of performing large scale meta-analysis of untargeted LC-MS/MS datasets.

Mots-Clés: molecular networking, GNPS, mass spectrometry, feature, based molecular networking

<footnote n="1">¹ University of California [San Diego] – États-Unis</footnote>

<footnote n="2">² University of Münster – Allemagne</footnote>
<figure><img src="image_79.png" /></figure>

## Oral Flash 4 – FP4

# MetGem Software for the Generation of Molecular Networks Based on the t-SNE Algorithm

Nicolas Elie<fnref n="1" />, Florent Olivon<fnref n="1" />, Gwendal Grelier<fnref n="1" />, Fanny Roussi<fnref n="1" />, Marc Litaudon<fnref n="1" />, David Touboul<fnref n="1" />

Metabolomic studies based on MS produce large amount of data requiring dedicated chemo/bioinformatics tools to explore them. Whereas many algorithms are available for the data treatment at MS1level, methodologies for MS2 data are scarce. One of the most popular is the generation of molecular networks (MNs) through the calculation of cosine score on a collection of MS2 spectra available on the online GNPS platform. We introduce here dedicated software, called MetGem (https://metgem.github.io), allowing the generation of GNPS-like MNs together with a t-distributed stochastic neighbor embedding (t-SNE) based visualization of the cosine score matrix. Starting from the .mgf file, all spectra detected are compared to each other using the GNPSbased cosine score calculation system.

One way to represent these results is to conceive a square matrix gathering together highdimensional objects, i.e. MS2 spectra, whose dimensions contain the similarities (cosine score values), taken pairwise, between all the spectra of the dataset. As it is difficult to apprehend these high-dimensional objects and visualize them in a meaningful manner, several manifold learning algorithms have been developed for dimensionality reduction purposes and pattern recognition. The idea developed herein was to feed the t-SNE algorithm with the pairwise similarity matrix. Considering that only small distances are reliable in high-dimensional spaces, t-SNE aggregates local data points closer in the lower-dimensional space. By focusing preferentially on the local structure of the data, t-SNE tends to extract better clustered local groups of point. Thus, it allows distinguishing easily patterns lying in different manifolds by simple visual analysis and in an unsupervised way.

Performances and advantages of the t-SNE output have been evaluated on two datasets, i.e. LC-MS2 analyses of fractions from a supercritical CO2 extraction of S. lineata leaves and a bark extract of C. peltatum. In both cases, t-SNE allows us to annotate more nodes compared to the GNPS-based cosine score calculation system and thus a deeper exploration of the experimental datasets was demonstrated.

Finally, in terms of network calculation and visualization, our interface running on laptop computer is up to 20 times faster for small and medium datasets (< 1000 MS2 spectra) and competes equally with GNPS for larger files (up to 35000 MS2 spectra).

**Mots-Clés:** cosine, similarity, MS2, t, SNE, molecular networking, python, desktop, chemo, informatics, bioinformatics, mass spectrometry, algorithm, metgem, mgf, GNPS, manifold, machine learning

<footnote n="1">¹Institut de Chimie des Substances Naturelles – Centre National de la Recherche Scientifique : UPR2301 – France</footnote>
<figure><img src="image_80.png" /></figure>

## Oral Flash 5 – FP5

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

**Stéphanie Monnerie**<fnref n="1" />, **Mélanie Pétéra** <fnref n="2" />, **Bernard Lyan** <fnref n="2" />, **Pierrette Gaudreau** <fnref n="3" />, **Blandine Comte** <fnref n="1" />, **Estelle Pujos-Guillot** <fnref n="2" />,<fnref n="1" />

Metabolomics generates massive and complex data that need dedicated workflows to extract the meaningful information and to enrich our knowledge of biological systems. For biological interpretation, experts are mainly focusing on metabolites rather than on the redundant different analytical species. Moreover, 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 that may exist within samples. The algorithm principle is to group features coming from the same analyte and to propose one single representative per group. The user can define grouping criteria with various options including correlation coefficient, retention time, mass information from a reference list of isotopes, adducts and fragments. Thresholds for all these criteria can be fixed and the representative feature can be determined following four methods according to the user needs and the analytical technology.

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' also available in Workflow4Metabolomics (W4M; http:// workflow4metabolomics.org). To illustrate the 'Analytic correlation filtration tool' functionalities and the results obtainable on typical experimental data, a published dataset available on W4M was used as an example. This dataset named 'Sacurine' was obtained from human urine samples analysed by LC-HRMS in negative ionization mode (Thevenot et al., 2015).

Within the 3,120 ions of the urine dataset, the tool allowed creating 2,651 groups, meaning that 15% 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 vizualisation for further data analysis within workflows.

Mots-Clés: analytical redundancies, filtration, workflow

<footnote n="1">¹ Université Clermont Auvergne, INRA, UNH, Mapping – (INRA) : UMR1019 – France</footnote>

<footnote n="2">² Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont - INRA
Clermont-Ferrand-Theix : UMR1019 – France</footnote>

<footnote n="3">³ Centre de Recherche du Centre hospitalier de l'Université de Montréal ; Département de médecine, Université de Montréal – Canada</footnote>
<figure><img src="image_81.png" /></figure>

## Oral Flash 6 – FP6

# New insight in Metabolomics based study of Age Related Macular Degeneration (AMD): Lipoprotein profile and subclass analysis.

Matthieu Schoumacher<fnref n="1" />, Vincent Lambert, Justine Leenders <fnref n="1" />, Bernadette Govaerts <fnref n="2" />, Sylvain Hansen, Jean-Marie Rakic, Agnès Noël, Pascal De Tullio <fnref n="1" />

Age-related macular degeneration (AMD) is the leading cause of blindness among the elderly population in developed countries. 90% of all vision loss due to AMD result from the exudative form of this pathology, which is characterized by a choroidal neovascularization (CNV). Currently, diagnosis of AMD relies on ophthalmologic exams and treatments of the exudative form are based on the use of anti-angiogenic drug targeting vascular endothelial growth factors. Despite these advances, several clinical challenges have to be overcome. Among those, the identification of biomarkers that could allow to refine patient stratification, to follow disease progression and evaluate responses to treatment are mandatory. For this purpose, we decide to apply NMR-based metabolomics approach on both AMD patients and on a laser-induced murine choroidal neovascularization experimental model. In the clinical study, the metabolomics approach does not allow a complete differentiation between control and AMD patients. However, focusing only on AMD group, a clear-cut separation between active and non-active phases could be highlighted.

In the mouse model, discrimination between laser-induced and control mice occurs only when CNV is install ed. In both human and animal studies, lactate and lipoprotein profile were identified as the main biomarkers. Mechanistically, we demonstrated that lactate plays a critical role in the onset of the inflammatory and angiogenic phases and could be correlated with the CNV development. Then, controlling lactate level appears as a new therapeutic approach of AMD.

On the other hand, lipoprotein profile is of particular interest for patient follow-up. Indeed, evaluation of lipoprotein profile change trough a simple methods allowed us to establish clear modification of profiles according to the active or non-active status of the patient and to the induced or non-induced status of the mice.

This work focuses on the lipoprotein profile and on the development of a methodology that could be used to characterize and compare the different profiles in the human and the mice studies.

Mots-Clés: NMR, metabolomics, AMD, Lipoprotein

<footnote n="1">¹ Centre Indisciplinaire de Recherche sur le Médicament (CIRM) – Institut de Pharmacie Quartier HOPITAL - Av. Hippocrate, 15 (Bât. B36) Sart-Tilman B-4000 Liège, Belgique</footnote>

<footnote n="2">² Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA) – Université Catholique de Louvain, Voie du Roman Pays 20, 1348 Louvain-La-Neuve, Belgique</footnote>
<figure><img src="image_82.png" /></figure>

## Oral 4 – 04

# Supercritical fluid chromatography coupled to high resolution mass spectrometry for dereplication and quantification of natural products

Laurent Laboureur<fnref n="1" />, Thi Phuong Thuy Hoang, Morgane Barthélemy<fnref n="1" />, Pierre Champy³, Véronique Eparvier<fnref n="1" />, David Touboul<fnref n="1" />

The development of the most valuable analytical strategies need to include optimization of analytical parameters such as accuracy, sensitivity, reproducibility, speed, while operator safety and environmental impacts are never considered at the same level. An absurd situation has emerged, due to the side effects of analytical methods, especially those developed to investigate hydrophobic compounds: a large amount of chemical waste is generated. Taking into account growing public concerns on environmental questions, the development of environmentally friendly analytical methods is mandatory. Supercritical Fluids (SF), such as SF-CO2, can be considered as a green solvent for chromatography showing solvation properties closed to the ones of hexane. The advantages of SFC coupled to high resolution tandem mass spectrometry (SFC-HRMS/MS) will be exemplified with the dereplication of complex mixtures of secondary metabolites, *i.e.* acetogenins (environmental toxins) and N-Acyl homoserine lactones (AHLs) implied in bacterial quorum sensing.

For the dereplication of Annonaceous acetogenins from *Annona muricata* L., SFC-HRMS/MS method was developed and showed shorter retention times, higher number of separated isomers and sensitivity compared to LC-HRMS/MS. In order to improve the quality of structural information by modifying the fragmentation pathways, a post-column cationization by lithium salts has been implemented for the first time. Due to the CO2 depressurization before the entrance of the mass spectrometer, the lithium salt was directly added in the make-up solvent leading an efficient exchange of sodium or proton with lithium for the cationization under ESI. For the quantification of AHLs playing crucial roles in microorganism communication, SFCHRMS offers the unique advantage to make possible the sample injection in pure organic solvents such as ethyl acetate or chloroform. In fact, acetonitrile is often used to prepare samples containing AHLs due to their fast hydrolysis in alcohol or water. Nevertheless, long chain AHLs (C16 and higher) are not soluble in acetonitrile making the identification of long chain AHLs not possible by classical LC-MS/MS approach. SFC-HRMS is therefore improving the quantitative analysis of such long chain AHLs within retention times lower than 10 min.

Mots clefs : supercritical fluid chromatography, acetogenin, N, Acyl homoserine lactone, mass spectrometry

<footnote n="1">¹ Institut de Chimie des Substances Naturelles (CNRS-ICSN) – CNRS : UPR2301 – ICSN, CNRS UPR2301, Université Paris-Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198 Gif-sur-Yvette, France,</footnote>

<footnote n="2">² CNRS UMR 8076 BioCIS - BIOMolécules : Conception, Isolement et Synthèse (BioCIS) – CNRS : UMR8076 – CNRS UMR 8076 BioCIS - BIOMolécules : Conception, Isolement et Synthèse Bureau D5 309 - Bâtiment D5 - 5, rue J.B. Clément 92296 Châtenay-Malabry, France, France</footnote>
<figure><img src="image_83.png" /></figure>

## Oral 5 – O5

# Apport de la spectrométrie de masse à haute résolution et à mobilité ionique dans les analyses métabolomiques à haut débit et à large échelle

Aurélie Delvaux<fnref n="1" />, Sandra Alves<fnref n="1" />, Estelle Rathahao - Paris<fnref n="2" />

A l'heure actuelle, la RMN et la MS sont les deux techniques analytiques majoritairement employées pour conduire des études métabolomiques. La MS, grâce à sa sensibilité et à sa sélectivité permet de détecter un grand nombre de métabolites dans des matrices biologiques complexes. En outre, le couplage avec des méthodes séparatives permet d'augmenter sa couverture métabolomique. Néanmoins cette approche augmente drastiquement le temps d'analyse et reste limitée pour la distinction de beaucoup d'isomères.

La mobilité ionique (IM) est une technique permettant de séparer des composés ionisés en phase gazeuse sous l'action d'un champ électrique. La mobilité ionique couplée à la spectrométrie de masse présente une grande capacité de séparation des isomères comparés aux autres techniques en couplage avec la MS. De plus, les analyses en mobilité relevant du domaine des millisecondes, le couplage de l'IM – à la MS présente également l'avantage de réaliser des analyses rapides (de l'ordre de la minute contre des dizaines de minute pour la LC) ouvrant l'application à l'analyse d'un grand nombre d'échantillons.

Ainsi, notre projet vise à développer une méthode d'analyse métabolomique à haut débit et à large échelle utilisant la technique de mobilité ionique de type TIMS (Trapped Ion Mobility Spectrometer) couplée à un analyseur à temps de vol. Nous avons tout d'abord évalué l'influence des paramètres instrumentaux sur la détection des métabolites de faibles masses moléculaires. D'autres conditions analytiques telles que la concentration des métabolites ont également été étudiées. Des composés isomères standards ont été analysés dans le but d'évaluer la capacité du TIMS pour la séparation de différents types d'isomères (énantiomères, isomères de fonction, position etc). Enfin, nous avons évalué le potentiel de l'instrument TIMS pour l'analyse directe d'un mélange complexe, tel que l'urine, ainsi que sa capacité à séparer des métabolites isomères dans ce mélange. Nous avons pu détecter et séparer les métabolites isomères d'un xénobiotique et fragmenter spécifiquement un couple.

Mots-Clés: mobilité ionique, spectrométrie de masse, isomères, haut débit

<footnote n="1">¹ Institut Parisien de Chimie Moléculaire IPCM – Université Pierre et Marie Curie [UPMC] - Paris VI - France</footnote>

<footnote n="2">² UMR Service de Pharmacologie et Immunoanalyse – CEA INRA Saclay – France</footnote>
<figure><img src="image_84.png" /></figure>

## Oral Sponsor 5 – OS5
Platinum

<figure><img src="image_85.png" /></figure>

PROTEIGENE

# Assessing Gut Microbiota-Host Crosstalk: Development of a Standardized and Quantitative Targeted Metabolomics Assay

PHAM, Tuan Hai¹, SOMMER, UIf¹, HEISCHMANN, Svenja¹, KIRCHBERG, Doreen¹, IWANOWA, Xenia¹, WOLF, Barbara¹, BURATTI, Martin¹, ROEHRING, Cornelia¹, KOAL, Therese¹

¹BIOCRATES Life Sciences AG, Innsbruck, Austria, barbara.wolf@biocrates.com

## Introduction

Targeted metabolomics provides a snapshot of the metabolic phenotype and is increasingly acknowledged across scientific fields. Here, we developed a standardized, quantitative targeted assay for multiplexed analysis of central host and gut bacteria-derived metabolites by mass spectrometry.

## Material & Methods

10 µL of plasma and fecal samples were pipetted on a 96 well-plate, preloaded with internal standards. After derivatization and extraction, LC-MS and FIA-MS analyses were performed (Agilent 1290 Infinity UHPLC – SCIEX QTRAP® 5500). MetIDQ™ software was used for the entire automated workflow, from sample registration to quality-controlled results.

## Results

In total, over 500 metabolites from 14 small molecule and 11 lipid classes were analyzed by the newly developed standardized, quantitative targeted assay. The LC-MS analysis provided quantitative results of small molecules covering bile acids, indole derivatives, amino acids and related compounds, amongst other classes. Acylcarnitines, lipids, and monosaccharides were analyzed by FIA-MS. Using the 96-well plate format, 80 samples, together with blank, quality controls, and calibrators, can be analyzed within 35 hours.

## Conclusion

The newly developed standardized, quantitative targeted assay enables multiplexed analysis of over 500 host and gut microbiota-derived metabolites. Future applications will demonstrate the power of this solution for metabolic phenotyping specifically assessing gut microbiota-host crosstalk.
<figure><img src="image_86.png" /></figure>

## Oral 6 – 06

# 13C Labeled Yeast extracts: a powerful tool for normalization and quantification in metabolomics and lipidomics?

Thaïs Hautbergue<fnref n="1" />, Cécilia Bergès<fnref n="2" />, Emeline Chu-Van<fnref n="1" />, A. Tournadre<fnref n="3" />, Annelaure Damont<fnref n="1" />, Florence Castelli<fnref n="1" />, Justine Bertrand-Michel<fnref n="3" />, François Fenaille<fnref n="1" />, Floriant Bellvert<fnref n="2" />, Benoit Colsch<fnref n="1" />

Batch correction and compound quantification are two major challenges in metabolomic and lipidomic studies by mass spectrometry. Absolute quantification of molecules requires labeled standards often difficult to synthesized or not available. In addition, due to the emergence of a large cohort analysis, batch normalization must be applied in order to correct differences in signal intensities between analyses of samples.

To overcome these limitations, we evaluate an original approach using extracts of fully 13C labeled *Saccharomyces cerevisiae* as a pool of internal standards for the normalization and quantification of hundreds of metabolites and lipids in human biofluids.

Unlabeled and 13C-labeled *Saccharomyces cerevisiae* were produced via fermentation in medium enriched with unlabeled or labeled glucose respectively. Yeast metabolites were extracted using a Methanolic solution, while lipids were extracted using a modified Folch method. Extracts were analyzed by LC-HRMS.

Comparison of unlabeled and labeled extracts allowed validation of 150 robust metabolites whose equivalent ions were found in both extracts with a mass difference consistent with their number of carbon atoms. These metabolites corresponded to the following major metabolic pathways: amino acid metabolism, citrate cycle, purine and pyrimidine metabolism, pentose phosphate pathway, glycolysis and glutathione metabolism. Untargeted lipidomic analysis of labeled and unlabeled extracts allowed detection of several lipid classes such as glycerophospholipids, glycerolipids, sphingolipids and fatty acids. However, some sterols and glycolipids were different between yeast and mammal due to incorporation of ergosterol instead of cholesterol for example. To go further in the evaluation of this approach, yeast extract will be spiked in NIST plasma for which concentration of several metabolites are known. Finally, labeled yeast extracts will allow normalizing analytical batches considering the specific chemical behavior of each class of molecules and will allow identifying and quantifying hundreds of compounds. This also will help in structural characterization of unknown molecules commonly detected in samples and yeast extracts by indicating the number of carbon atoms of each ion in full scan or tandem mass spectrometry analyses.

<footnote n="1">¹ Service de Pharmacologie et d'Immunoanalyse, Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France</footnote>

<footnote n="2">² MetaToul- MetaboHUB, Toulouse – INSA - Institut National des Sciences Appliquées – France</footnote>

<footnote n="3">³ MetaToulLipidomics Facility, INSERM UMR1048, MetaboHUB, Toulouse INSERM – France</footnote>
<figure><img src="image_87.png" /></figure>

# Advanced high-field and benchtop NMR methods for lipid profiling of microalgae

**Dylan Bouillaud**<sup>1,2</sup>, **Arturo Vladimir Heredia Marquez**<sup>1</sup>, **Thomas Castaing Cordier**<sup>2</sup>, **Delphine Drouin**<sup>1</sup>, **Olivier Gonçalves**<sup>1</sup>, **Jonathan Farjon**<sup>2</sup>, **Patrick Giraudeau**<sup>2,3</sup>

Microalgae are increasingly studied in both academy and industry owing to their broad applicative potential. Indeed, they are able to create biomass with significant productivities compared to traditional crops. Besides, there is a great biodiversity in microalgae and some species can produce valuable compounds in an ecological way, since microalgae consume CO2, a highly emitted gas largely involved in the greenhouse effect.

Lipids are important molecules in the list of valuable compounds produced by microalgae. They can be used in food industry or for biofuel production. Some species of microalgae have the ability to produce reserve lipid –mainly triglycerides– under nitrogen starvation conditions: microalgae are then disrupted and a metabolic shift is provoked. In order to better understand this phenomena and find the best way to produce lipids, biological and process research works are carried out by a substantial research community.

NMR has a great potential to study the lipidic metabolism of microalgae in a non-destructive and reproducible fashion. In this context, we aim at bringing some of the most recent NMR developments into the bioprocess community, in order to maximize the observable information. On the one hand, advanced high-field NMR methods offering the best performance in sensitivity and resolution are investigated for the lipid characterization of microalgae extracts. Different quantitative pulse sequences (1H NMR, 13C NMR and our recently developed quantitative 2D NMR methods [1]) are evaluated in this study in terms of analytical performance and available information on lipids.

On the other hand, we investigate the potential of recently developed benchtop NMR spectroscopy [2] as an online lipid sensor. We demonstrate the potential of this transportable setting for the real-time and in vivo monitoring of microalgae cultivations. Thanks to the implementation of advanced solvent suppression NMR pulse sequences [3], a lipidic profile can be obtained and quantified in order to monitor the lipid concentration in real time, in a flow-compatible and non-invasive way.

Mots-Clés: NMR, benchtop NMR, microalgae, lipid, profiling, monitoring

<footnote n="1">¹ Génie des Procédés-Environnement-Agro-alimentaire – Université de Nantes, CNRS : UMR6144,</footnote>

<footnote n="2">² Chimie Et Interdisciplinarité : Synthèse, Analyse, Modélisation – Université de Nantes, Centre National de la Recherche Scientifique : UMR6230 – France</footnote>

<footnote n="3">³ Institut Universitaire de France – Ministère de l'Éducation nationale, de l'Enseignement supérieur et de la Recherche, Ministère de l'Éducation nationale, de l'Enseignement supérieur et de la Recherche, Ministère de l'Éducation nationale, de l'Enseignement supérieur et de la Recherche – France</footnote>
<figure><img src="image_88.png" /></figure>

## Oral Keynote 3 – OK3

# Illuminating lifestyles using metabolomics of personal objects

**Amina Bouslimani**¹, **Louis-Félix Nothias**¹, **Kathleen Dorrestein**¹, **Chris Callewaert**¹, **Ricardo R. Da Silva**¹, **Alan Jarmusch**¹, **Rob Knight**¹, **Pieter C. Dorrestein**¹

¹ University of California [San Diego] – États-Unis

Due to the exchange of molecules between humans and the external environment, an individual's personal belongings can yield a large amount of meaningful chemical information. Over 1200 samples collected from hands and personal items (phone, keys, computer, wallet) of 120 individuals were analyzed using an untargeted high resolution UPLC-QTOF MS/MS based workflow. Molecular networking based on spectral similarities and the GNPS knowledgebase platform were applied for rapid characterization of chemistries recovered from objects and for translating them into lifestyle readouts. Data connecting hands of individuals to their personal objects provides insights into unambiguous lifestyle characteristics such as health, diet, preference for cosmetics, frequented locations and even environmental exposures. Many skin-associated molecules were detected on several everyday items. Particularly, food derived molecules indicative of a person's diet were found alongside signatures associated to an individual's health such as antidepressants, antihistamines and medications used to treat high blood pressure and skin disorders. Hygienic status such as beauty products, sunscreens in addition to insect repellents, pesticides and fungicides were also revealed. The chemical information from objects also allowed narrowing down pools of individuals that share the same lifestyle characteristics. Specifically, 30 individuals used sunscreen, 9 went on camping or hiking trips, while 22 used medications. Lifestyle profiles were constructed using chemical traces recovered from personal items. For example, two prescribed medications were found on objects of individual 58: a muscle relaxant Cyclobenzaprine and an antidepressant Lorazepam. Additionally, Viagra was also detected which suggests that these objects belong to a male individual; and the illicit drug cocaine was also found on objects of this same individual 58. The ability of lifestyle profiling to accurately connect items to individual lifestyles makes this method amenable to understanding the habits of suspects within a criminal investigation based on trace evidence. This work and the methods used could also be applied in clinics by inspiring non-invasive approaches to measure drug metabolism, patient compliance and environmental exposures.

**Mots-Clés:** lifestyle, mass spectrometry, metabolomics, skin, objects.
<figure><img src="image_89.png" /></figure>

## Oral 7 – 07

# Large scale micropollutants screening in a wetland ecosystem using a non targeted high resolution mass spectrometry approach

Loïc Maurer <fnref n="1" />,<fnref n="2" />, Claire Villette <fnref n="1" />,<fnref n="2" />, Adrien Wanko <fnref n="1" />, Dimitri Heintz <fnref n="2" />

Many studies have focused on micropollutants and their potential environmental impacts. The release of micropollutants to the environment has several origins; wastewater (WW) is one of the major sources of the release of diversified micropollutants. Indeed, wastewater treatment plants (WWTP) were initially not designed to remove micropollutants. Most of the studies investigated targeted micropollutants in WWTP (ex, Hijosa-Valsero et al, 2010). The development of metabolomics databases and high-resolution mass spectrometry (HRMS) helps performing nontargeted analyses and to obtain a global view of this pollution.

Constructed wetlands (CW) are common WWTP in rural areas in France. Micropollutants distribution in CW was obtained using a nontargeted analysis in three compartments of the ecosystem (water, plants, sludge). To this end, an extraction method based on a double extraction and freeze-drying was developed. Then the investigation was performed in LC -HRMS using the TargetScreener method (Bruker). The identification was performed using Metaboscape 4.0 software (Bruker). The Schymanski classification (Schymanski et al, 2015) was used to annotate the obtained molecular features in each compartment. Molecular formula (4th level) were generated using SmartFormula and putative identifications (3rd level) were achieved by comparing experimental data with public databases (plants, toxics ...).

This approach highlighted emerging substances that are not routinely monitored yet. A trend in the distribution of micropollutants could be shown: around 50% of the tentative identifications were anthropogenic molecules (drugs, ...). These classes of molecules were found in each compartment. These results shed new light on the diffusion of toxic molecules in the environment. This approach coupled with the study of environmental risk, could help the management of sludge generated by CW.

Mots-Clés: micropollutants, wetland ecosystem, non targeted analyses, high resolution mass spectrometry (HRMS)

<footnote n="1">¹ Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie – université de Strasbourg : UMR7357, Centre National de la Recherche Scientifique : FR3627, Ecole Nationale du Génie de l'Eau et de l'Environnement de Strasbourg, INSA Strasbourg – France</footnote>

<footnote n="2">² Institut de biologie moléculaire des plantes – université de Strasbourg, Centre National de la Recherche Scientifique : UPR2357 – France</footnote>
<figure><img src="image_90.png" /></figure>

## Oral 8 – 08

# Anti-leishmaniasis and Metabolomic approach from the leaves extract of Psidium guajava L. from Lao PDR.

Chiobouaphong Phakeovilay<sup>1,2</sup>, Sandra Bourgeade-Delmas<sup>*1</sup>, Pierre Perio<sup>1</sup>, Karine Reybier<sup>1</sup>, Françoise Nepveu<sup>1</sup>, Guillaume Marti<sup>1</sup>

1 UMR 152 Pharma Dev, Université de Toulouse, IRD, UPS, Toulouse, France – Université Paul Sabatier - Toulouse III – France

2 Faculty of Pharmacy, University of Health Sciences, Ministry of Health, Lao PDR – Laos

Introduction Leishmaniasis disease is the top five of communicable diseases via parasites and vectors. The available treatments involve many side effects, including toxicity, non-specific targeting and resistance development. Thus, new antileishmanial chemical entities are of utmost interest to fight against this disease.

**Objectives** To identify potential antileishmanial components from Psidium guajava leaves by applying a metabolomic approach and correlation analysis.

**Method** Crude extracts Psidium guajava leaves were submitted to liquid-liquid separation between polar and apolar compounds and analyzed by liquid chromatography coupled to high resolution mass spectrometry and together with screening of antileishmanial activity. The putative active compounds were highlighted by multivariate correlation analysis between the biological response and LC-MS profiles of several P. guajava samples.

**Results** Our results showed that apolar phase from the P. guajava extracts were the most actives (IC50 = 2.886±0.02446 µg/mL). Multivariate data analysis of polar extracts highlighted a family of triterpenes compounds like Jacoumaric acid (IC50 = 1.318±0.05497 µg/mL) and its derivatives.

**Conclusion** Our approach allowed the identification of antileishmanial potential compounds from the complex crude extracts in few steps and can be easily implemented to several other natural product discovery workflow.
<figure><img src="image_91.png" /></figure>

## Oral Flash 7 – FP7

# Étude de la réponse métabolique de Pseudomonas syringae 32b-74 isolée des nuages à une exposition sub - létale au mercure

Christelle Ghaffar, Martine Sancelme<fnref n="1" />, Binta Diémé<fnref n="2" />, Marie Lagrée<fnref n="3" />, Cyril Jousse<fnref n="4" />,<fnref n="5" />, Pierre Amato<fnref n="3" />

Ces dernières décennies, les fortes activités industrielles ont conduit à une augmentation de la pollution aux métaux lourds de l'atmosphère, et notamment au mercure (1,7 ng d'Hg / m3 d'air) 1. Celui-ci est présent sous différentes formes dans l'environnement : élémentaire (Hg0), ionique (Hg2+) et méthylé (Me – Hg). Ce dernier agit sur les groupes thiols des protéines, siège de sa toxicité. Certaines bactéries présentent un mécanisme de résistance codé par l'opéron *mer* et sous le contrôle du régulateur transcriptionnel *MerR* 2.

Des études montrent que des microorganismes subsistent dans l'atmosphère et les nuages, avec des conséquences possibles sur les processus physico-chimiques atmosphériques 3. Ces derniers sont donc exposés à la pollution atmosphérique du mercure, et notamment à la forme ionique Hg2+.

Nous étudions ici l'impact du mercure à dose sub - létale sur le métabolisme d'une souche bactérienne précédemment isolée d'eau de nuage et dépourvue de l'opéron *mer* : *Pseudomonas syringae* 32b74 5. L'objectif est d'identifier des marqueurs métaboliques spécifiques de l'exposition au mercure, dans l'optique d'une utilisation des microorganismes atmosphériques comme traceurs environnementaux.

La souche *P. syringae* 32b74 est ainsi incubée en absence et en présence de mercure biodisponible Hg2+ (5 µg/mL). Un profilage métabolique est réalisé après 2h et 24h d'exposition. Le métabolisme des cellules est stoppé par immersion dans l'azote liquide, et les métabolites intracellulaires sont extraits par un mélange de solvant avant d'être analysés par LC-MS. Par la suite, une analyse en composante principale (ACP) est réalisée afin de discriminer les potentiels signaux biomarqueurs.

L'impact du mercure sur l'écosystème atmosphérique est une question environnementale importante. L'utilisation de la métabolomique nous permettra de l'observer plus finement sur cette souche isolée de l'atmosphère.

Mots-Clés: Mercure, Atmosphere, Pseudomonas syringae 32b74, Métabolomique

<footnote n="1">¹ Institut de Chimie de Clermont Ferrand (ICCF) – Université Clermont Auvergne, CNRS, SIGMA Clermont – France</footnote>

<footnote n="2">² Université François-Rabelais, INSERM U930 "Imagerie et Cerveau", CHRU de Tours, 10 Bv Tonnellé, 37044 Tours, France –</footnote>

<footnote n="3">³ Institut de Chimie de Clermont-Ferrand - Clermont Auvergne (ICCF) – SIGMA Clermont, Université Clermont Auvergne : UMR6296, Centre National de la Recherche Scientifique : UMR6296 – 24 Avenue des Landais / 63177 Aubière Cedex, France</footnote>

<footnote n="4">⁴ Institut de Chimie de Clermont-Ferrand (ICCF) – SIGMA Clermont, Université Clermont Auvergne : UMR6296, Centre National de la Recherche Scientifique : UMR6296 – 24 Avenue des Landais / 63177 Aubière Cedex, France</footnote>

<footnote n="5">⁵ Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont-Ferrand, Clermont-Ferrand, France (PFEM) – Université Clermont Auvergne, INRA, Université Clermont Auvergne – France</footnote>
<figure><img src="image_92.png" /></figure>

## Oral Flash 8 – FP8

# Comparaison des profils métaboliques et des propriétés antimicrobiennes de produits formulés à base de piment (Capsicum)

Inès Remy-Larsonnier<sup>a</sup>, Marjolaine Rey<sup>a</sup>, Carine Commun<sup>b</sup>, Anne Doléans-Jordheim<sup>b</sup>, Gilles Comte<sup>a</sup>, Marie-Geneviève Dijoux-Franca<sup>a</sup>, Camille Rozier<sup>c</sup>

Réduire l'usage des antibiotiques en santé humaine et animale est devenue une absolue nécessité afin de limiter l'émergence de souches bactériennes antibio-résistantes. Les entreprises développant des additifs destinés aux animaux d'élevage tel qu'ID4FEED utilisent les propriétés biologiques des plantes comme base principale de leurs produits. Parmi les sources d'actifs ciblées par ID4FEED, le piment (Capsicum chinense) s'est révélé être un candidat majeur[1]. Les capsaïcinoïdes, responsables de la sensation de chaleur provoquée par le piment, sont la famille de métabolites très caractérisés jusqu'à présent chez le piment car ils possèdent une activité biologique d'importance en santé animale (antimicrobienne, antiinflammatoire, analgésique, radical-scavenging)[2]. Il a néanmoins été mis en évidence que d'autres classes de métabolites apportaient leurs activités dans le totum que représente la poudre de piment tels que des antioxydants et des antimicrobiens (vitamines A et E, caroténoïdes, polyphénols, phénols, terpènes).

L'objectif de ce travail a été de réaliser un suivi quantitatif et qualitatif des capsaïcinoides et des composés minoritaires de la poudre de piment et de comparer les profils métaboliques de la matière première et des produits commerciaux issus de cette matière première.

Une stratégie d'analyse globale par GC-QQQ et UHPLC-DAD-Q-TOF nous a permis de mettre en évidence des différences (quantitatives et qualitatives), notamment de certains flavonoïdes et volatiles contenus dans le piment. Le lien potentiel entre ces différences de profils métaboliques et les activités biologiques (antimicrobiennes et radical-scavenging notamment) de la matière première et des produits commerciaux est de même évalué en parallèle et nous permettra de renforcer notre compréhension des propriétés biologiques des préparations industrielles.

[1] Do Rêgo, E. R., Finger, F. L., & do Rêgo, M. M. (2012). Consumption of pepper in Brazil and its implications on nutrition and health of humans and animals.

[2] De Aguiar, A. C., Coutinho, J. P., Barbero, G. F., Godoy, H. T., & Martínez, J. (2016). Comparative study of capsaicinoid composition in capsicum peppers grown in Brazil. *International Journal of Food Properties*, 19(6), 1292-1302.

Mots clés : Capsicum ; additifs ; capsaïcinoides ; UHPLC-DAD-Q-TOF ; GC-QQQ

<footnote><sup>a</sup> Université Lyon 1, UMR CNRS 5557/INRA 1418, Ecologie Microbienne, CESN, Villeurbanne, France</footnote>

<footnote><sup>b</sup> Université Lyon 1, UMR CNRS 5557/INRA 1418, Ecologie Microbienne, Lyon, France</footnote>

<footnote><sup>c</sup> ID4FEED SAS square du Rhône, 12 avenue du Rhône, BP 10051, Annecy, France</footnote>
<figure><img src="image_93.png" /></figure>

## Oral Flash 9 – FP9

# Syringol metabolites as biomarkers of smoked meat intake

Roland Wedekind<fnref n="1" />, Pekka Keski-Rahkonen <fnref n="1" />, Vivien Viallon <fnref n="1" />, Pietro Ferrari <fnref n="1" />, Erwan Engel <fnref n="2" />, Marc Gunter <fnref n="1" />, Inge Huybrechts <fnref n="1" />, Augustin Scalbert<fnref n="1" />

**Background:** Intake of processed meat has been associated with an increased risk of colorectal cancer but the estimation of intake of this heterogeneous food group in epidemiological studies is still challenging because of the lack of sufficient details in dietary questionnaires. **Objective:** The objective of this work is to identify in a dietary intervention study novel biomarkers for processed meat intake.

**Design:** A metabolomic approach based on high-resolution mass spectrometry was applied to 21 processed meat products previously digested *in vitro* and a randomized cross-over dietary intervention in which 12 volunteers consumed successively 3 processed meat products (bacon, salami, hot dog) and two other foods used as controls during 3 days. Identified biomarkers were tested for replication in 474 subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC) cross-sectional study for which a detailed 24h dietary recall and food frequency questionnaires were available.

**Results:** Syringol and four derivatives of syringol were found to be characteristic of digests of smoked meat products. The same compounds present as sulfate esters in urine showed increased levels at 2 and 12 hours following consumption of smoked meat products (hot dog, bacon) in the intervention study. The same syringol sulfates also showed increased urinary excretion in participants of the EPIC cross-sectional study reporting recent or habitual consumption of smoked meat products. These markers showed good ability to predict smoked meat intake with receiver operator characteristic areas under the curve ranging from 0.78 to 0.86 and 0.74 to 0.79 for acute and habitual intake respectively.

**Conclusions:** The biomarkers of smoked meat intake identified in the present study may improve assessment of smoked meat intake in epidemiological studies.

**Mots-Clés:** Smoked meat, biomarker, metabolomics

<footnote n="1">¹ Centre International de Recherche contre le Cancer - International Agency for Research on Cancer – Organisation Mondiale de la Santé / World Health Organization Office – France</footnote>

<footnote n="2">² UR 370 Qualité des Produits Animaux, Equipe Micro-contaminants Arôme et Sciences Séparatives (MASS) (QuaPA, MASS) – Institut National de la Recherche Agronomique - INRA (FRANCE) – INRA Centre de recherches Auvergne-Rhône-Alpes Theix 63122 Saint-Genès-Champanelle, France</footnote>
<figure><img src="image_94.png" /></figure>

## Oral Sponsor 6 – OS6
Platinum

<figure><img src="image_95.png" /></figure>

Analyses du plasma par RMN chez les adultes phenylcetonuriquessous prise en charge nutritionnelle montrent des désiquilibres métabolomiques

Claire Cannet, Bruker
<figure><img src="image_96.png" /></figure>

## Oral 9 – 09

# The Exposome-Explorer database in 2019: recent development on dietary biomarkers and their association with cancer risk

**Vanessa Neveu**<fnref n="1" />, **Geneviève Nicolas**<fnref n="1" />, **Augustin Scalbert**<fnref n="1" />

Exposome-Explorer (http://exposome-explorer.iarc.fr/), the first database on biomarkers of exposure to dietary and environmental factors, aims at providing comprehensive data on all known biomarkers of exposures to the diet, pollutants and contaminants measured in population studies.

This data is systematically and manually collected from the scientific literature, evaluated and stored in the database. All data is accessible on-line through a user-friendly web interface. This data includes nature of biomarkers (currently 491 biomarkers), biospecimens and population where measured, analytical methods, concentrations, reproducibility over time.

Since its first official release in February 2017, Exposome-Explorer has been expanded with a list of 180 new putative biomarkers of dietary exposure identified from recent metabolomics studies that can be subjected to further tests for validation purpose. In addition, new information on the application of dietary biomarkers in epidemiological studies on cancer risk has been collected. More than 300 scientific papers describing associations of dietary biomarkers with cancer risk have been compiled. This information is now accessible online.

This recent new development of Explorer-Explorer will be presented. Illustrations of the use of the database by researchers to compare the performance and field of application of various biomarkers of exposure and to identify the specific biomarkers or panels of biomarkers most useful for biomonitoring or disease aetiology studies will be presented.

**Mots-Clés:** Biomarkers, Databases, Cancer, Environmental exposure, Biocuration

<footnote n="1">¹Centre International de Recherche contre le Cancer - International Agency for Research on Cancer – Organisation Mondiale de la Santé / World Health Organization Office – France</footnote>
<figure><img src="image_97.png" /></figure>

## Oral 10 – O10

# Dynamic retention time databases for steroidomics

Santiago Codesido<fnref n="1" />, Giuseppe Marco Randazzo <fnref n="1" />, Fabio Lehmann <fnref n="2" />, Victor Gonzalez Ruiz <fnref n="1" />, Arnaud Garcia <fnref n="1" />,
Ioannis Xenarios <fnref n="2" />, Robin Liechti <fnref n="2" />, Alan Bridge <fnref n="2" />, Julien Boccard <fnref n="1" />, Serge Rudaz <fnref n="1" />

On top of the usual analytical challenges found in metabolomics, steroidomics faces the additional difficulty of working with a large family of compounds with very similar structures. Liquid chromatography (LC) is often the method of choice for their separation, but such structural similarity can be compromising for this separation due to the consequently similar physico-chemical properties. The problem is usually overcome by careful tuning of the mobile phase gradient, to focus the separation on compounds of interest. However, in a standard workflow this is highly problematic for annotation. It requires characterizing a library of known compounds for every fine-tuned configuration. We present a software solution, DynaStl, capable of annotating LCMS (liquid chromatography-mass spectrometry) features by dynamically generating the retention times from a database containing intrinsic properties for a library of metabolites. In this way, the chemical characterization of the library only needs to be performed once, and the generated retention times are adapted to the parameters of each gradient on the fly. We study the influence of experimental vs. in-silico compound properties on the quality of the prediction and the annotation, and we introduce a calibration mechanism to increase accuracy and compensate for deviations in the input parameters. We run tests on both standards and real samples, and observe that the algorithm produces reliable predictions, suitable for steroidomics compound annotation.

Mots-Clés: steroidomics, liquid chromatography, prediction, dynamic, database, annotation

<footnote n="1">¹ Analytical Sciences, School of Pharmaceutical Sciences, University of Geneva, University of Lausanne – Suisse</footnote>

<footnote n="2">² Swiss Institute of Bioinformatics – Suisse</footnote>
<figure><img src="image_98.png" /></figure>

## Oral Membre d'honneur

# Cloud microorganisms: what does metabolomics tell us?

Anne Marie Delort¹

¹Institut de Chimie de Clermont-Ferrand - Clermont Auvergne - SIGMA Clermont, Université Clermont Auvergne : UMR6296, Centre National de la Recherche Scientifique : UMR6296 - 24 Avenue des Landais / 63177 Aubière Cedex, France

A few years ago our team discovered the presence of metabolically active microorganisms in clouds (1). These microorganisms are aerosolized from the earth surface, travel in the air, condense water as any aerosol to form a droplet and become thus part of the cloud. They can finally go back to the earth *via* the precipitations. During this atmospheric cycle they face different stresses (cold temperature, UV light, oxidants, pollutants...), they bio-transform the organic matter in clouds, and finally they also interact with the terrestrial or aquatic ecosystems when they return to the soil.

We have used metabolomics combining LC-MS and NMR to study cloud microorganisms all along this atmospheric cycle.

When exposed to cold stress(2) or to H2O2(3), cloud bacteria completely reorganize their metabolism, as a result this change could impact cloud chemistry.

Work is in progress to study the modulation of the cloud bacteria metabolome facing atmospheric mercury. We also study photosynthetic organisms, and more particularly the interactions between atmospheric algae present in clouds (and rain) with cyanobacteria present in lakes.

Up to now metabolomics' experiments have been performed with isolated strains isolated from clouds sampled at the puy de Dôme station. In future work we plan to perform meta-metabolomics based on the whole cloud microbiota.

(1) A.-M. Delort and P. Amato (eds), Microbiology of aerosols. Willey, 2017. https://onlinelibrary.wiley.com/doi/

(2) C. Jousse, C. Dalle, I. Canet, M. Lagrée, M. Traïkia, B. Lyan, C. Mendes, M. Sancelme, P. Amato, A.-M. Delort*. Metabolomic study of the response to cold shock in a strain of *Pseudomonas syringae* isolated from cloud water. *Metabolomics*, 2018, 14, 11.

(3) N. Wirgot, M. Lagrée, M. Traïkia, L. Besaury, P. Amato, I. Canet, M. Sancelme, C. Jousse, B. Diémé, B. Lyan and A.-M. Delort*. Metabolic modulations and resilience of *Pseudomonas graminis* facing H2O2 in cloud water. *Scientific reports*, 2019 in revision.

Mots-Clés: Cloud, microorganisms, stresses, ecosystem, NMR, LC, MS
<figure><img src="image_99.png" /></figure>

## Oral 11 – O11

# Fusion de données de transcriptomique et de métabolomique pour une meilleure compréhension des effets d'un contaminant alimentaire

**Marie Tremblay-Franco** <fnref n="1" />,<fnref n="2" />, Yannick Lippi <fnref n="1" />, Cécile Canlet <fnref n="1" />,<fnref n="2" />, Roselyne Gautier <fnref n="1" />,<fnref n="2" />, Claire Naylies <fnref n="1" />, Manon Neves <fnref n="1" />, Philippe Pinton <fnref n="1" />, Imourana Alassane-Kpembi <fnref n="1" />, Laurent Debrauwer <fnref n="1" />,<fnref n="2" />, Isabelle Oswald <fnref n="1" />

L'analyse d'un seul bloc de données " omiques " peut s'avérer insuffisante pour fournir une image globale des modulations engendrées par un stimulus biologique. Les métabolites sont les produits finaux de réactions enzymatiques et pourraient donc refléter des modifications d'expression de gènes.

Dans ce contexte, ce travail a pour objet de comparer des méthodes d'intégration de données de transcriptomique et de métabolomique, pour évaluer les corrélations entre ces deux niveaux fonctionnels et identifier d'éventuels gènes candidats à la modification du métabolome suite à une exposition à un contaminant alimentaire.

Nous avons tout d'abord évalué deux familles de méthodes pour sélectionner des variables corrélées intra et inter-blocs : l'analyse des corrélations canoniques (CCA) " sparse ", méthode à variables latentes, et les cartes auto organisées de Kohonen (SOM, 1982), méthode de projection utilisée pour classer les objets dans les unités d'une carte bi-dimensionnelle. La pénalisation " sparse " (Wilms et al. 2016) permet de résoudre les problèmes de sur-ajustement et sélectionner les transcrits et métabolites les plus importants. La méthode O2PLS (Bouhaddani et al. 2016) a ensuite été utilisée pour étudier la relation entre les variables sélectionnées à la 1ère étape et l'exposition au contaminant alimentaire.

Les simulations effectuées ont montré que la méthode SOM, à l'opposé de la méthode sCCA, était très sensible mais très peu spécifique, et permettait une meilleure discrimination des observations en fonction de l'exposition.

Les méthodes sCCA/SOM+O2PLS ont ensuite été appliquées à des données générées à partir de fragments d'intestin exposés à un contaminant alimentaire, qui peut se retrouver dans les denrées alimentaires destinées à la consommation humaine et animale. L'identification de biomarqueurs d'exposition est donc importante pour le suivi de la santé humaine et animale. Le modèle O2PLS ajusté en utilisant les variables sélectionnées par la méthode SOM a permis une meilleure séparation en fonction de l'exposition. Les liens entre les métabolites et les gènes discriminants sont en cours de détermination (réseaux de corrélations et métaboliques).

Mots-Clés: Intégration de données, Transcriptomique, Metabolomique, Contaminant alimentaire

<footnote n="1">1 UMR1331 Toxalim, Université de Toulouse, INRA, ENVT, INP-Purpan, UPS INRA - France</footnote>

<footnote n="2">2 Axiom platform, MetaToul-MetaboHUB, national infrastructure for metabolomics and fluxomics INRA</footnote>
<figure><img src="image_100.png" /></figure>

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

Marion Brandolini-Bunlon<fnref n="1" />, Mélanie Petera<fnref n="1" />, Pierrette Gaudereau <fnref n="2" />,<fnref n="3" />, Blandine Comte <fnref n="4" />, Stéphanie Bougeard⁵,
Estelle Pujos-Guillot <fnref n="1" />

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.

Mots-Clés: multi block PLS discriminant analysis, metabolomics, epidemiology

<footnote n="1">¹ Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB
Clermont – Institut national de la recherche agronomique (INRA) : UMR1019 – F-63000 Clermont-Ferrand, France</footnote>

<footnote n="2">² Département de médecine, Université de Montréal – Canada</footnote>

<footnote n="3">³ Centre de recherche du centre hospitalier de l'université de Montréal – Canada ⁴ Université Clermont Auvergne, INRA, UNH – Institut national de la recherche agronomique (INRA) : UMR1019 – F-63000 Clermont-Ferrand, France</footnote>

<footnote n="4">⁴ Département d'épidémiologie – Anses – France</footnote>
<figure><img src="image_101.png" /></figure>

## Oral Junior 5 – OJ5

# Metabolomics and proteomics data integration and feature selection for high-throughput phenotyping

**Alyssa Imbert**<sup>1</sup>, **Magali Rompais**, **Julia Novion Ducassou**, **Emmanuelle Mouton-Barbosa**, **Mohammed Selloum**,
**Florence Castelli**, **Charlotte Joly**, **Emeline Chu-Van**, **Aurélie Hirschler**, **Marie Courçon**,
**Yohann Coute**, **Christophe Lechaplais**, **Natacha Lenuzza**, **Arthur Tenenhaus**, **Laurent Vasseur**, **Marion Brandolini-Bunlon**,
**Ludovic Cottret**, **Yves Vandenbrouck** , **Olivier Sand**, **Bruley Christophe**, **Franck Giacomoni**, **Fabien Jourdan**, **Odile Schiltz**, **Pierre Le Ber**, **Claudine Medigue**, **Jérôme Garin**, **Yann Herault**, **Dominique Rolin**,
**Christophe Junot**, **Marcel Salanoubat**, **Virginie Brun**, **Alain Perret**, **Myriam Ferro**, **Tania Sorg**, **Estelle Pujos-Guillot**,
**Anne Gonzalez-De-Peredo**, **François Fenaille**, **Christine Carapito**, **David Vallenet**, **Etienne Thevenot**<sup>1</sup>

How complementary are metabolomics and proteomics approaches? What benefit does their combination provide in terms of prediction performance and data interpretation? Which methods and pipelines would enable robust and high-throughput data integration?

To address these pivotal and yet open questions, the ProMetIS consortium, gathering 10 laboratories from the 5 national infrastructures for mouse phenogenomics (PHENOMIN), genomics (France Génomique), proteomics (ProFI), metabolomics and fluxomics (MetaboHUB), and bioinformatics (IFB), has designed a case study focusing on the high-throughput phenotyping of mouse models. Liver and plasma from homozygous mutant mice for the "Linker For Activation Of T Cells" gene (LAT), together with their wild-type littermates (WT), were analyzed by clinical phenotyping, proteomics, and metabolomics complementary LC-MS technologies, resulting in a total of 11 datasets.

To combine such data, we focused on the regularized generalized canonical correlation analysis method (rGCCA). This multi-block approach enables a broad exploration of the relationships between the individual datasets, in an unsupervised (e.g. canonical correlation) or supervised context (e.g. partial least squares), as well as feature selection.

Here, we optimized the design of the relationships between the blocks and the tuning of the penalization parameter to achieve robust and efficient proteomics and metabolomics integrated phenotyping. By comparing the results to the separate analysis of each dataset, we demonstrate the impact of data integration in terms of prediction performance and biological interpretation.

Mots-Clés: Data integration, feature selection, metabolomics, proteomics, high, throughput phenotyping, rGCCA

<footnote n="1"><sup>1</sup> CEA, LIST, Laboratoire Analyse de Données et Intelligence des Systèmes, MetaboHUB – CEA – France</footnote>
<figure><img src="image_102.png" /></figure>

**Oral Sponsor 7**
Platinum

<figure><img src="image_103.png" /></figure>

Industrializing Flux capabilities

Yoann Fillâtre AB Sciex
<figure><img src="image_104.png" /></figure>

## Oral Keynote 4 – OK4

# Discovery and validation of food intake biomarkers using untargeted metabolomics in human intervention and cross-sectional studies

Natalia Vazquez-Manjarrez ¹, Christoph Weinert ², Marynka Ulaszewska ³, Carina I Mack ², Pierre Micheau ¹,
Mélanie Pétéra ⁴, Stéphanie Durand ⁴, Estelle Pujos-Guillot ⁴, Bjorn Egert ², Fulvio Mattivi ³, Achim Bub ², Sabine
E. Kulling ², Lars Ove Dragsted ⁵, Claudine Manach ¹

¹ Unité de Nutrition Humaine – INRA): UMR1019, Université Clemont Auvergne – France

² Max Rubner-Institut – Allemagne

³ Fondazione Edmund Mach - Edmund Mach Foundation [Italie] – Italie

⁴ PFEM – INR): UMR1019 – France

⁵ University of Copenhagen – Danemark

⁶

**Background:** Banana is one of the most widely consumed fruits in the world. However, scarce information is available regarding its health effects. Biomarkers of banana intake would allow a more accurate assessment of its consumption in nutrition studies.

**Objective:** Using an untargeted metabolomics approach, we aimed to identify the bananaderived metabolites present in urine after consumption, and among them new candidate biomarkers of banana intake.

**Methods:** A randomized controlled study with a crossover design was performed on 12 healthy subjects, with two dietary interventions: 1) 250ml control drink (Fresubin) and 2) 240g banana+150ml control drink. Urine samples collected over 24h were analyzed with UPLC-QTOFMS and GC×GC-MS. The discovered biomarkers were confirmed in a cross-sectional study (KarMeN) where 72 subjects were selected reflecting high, low, and non-consumers of banana. The confirmed biomarkers were examined singly or in combinations, for established criteria of validation for biomarkers of food intake.

**Results:** We identified 33 potentially bioactive banana metabolites, some of them being newly described. A panel of five metabolites, methoxyeugenol-glucuronide, dopamine-sulfate, salsolinolsulfate, xanthurenic-acid and 6-hydroxy-1-methyl-1,2,3,4-tetrahydro-b-carboline-sulfate, were confirmed as candidate intake biomarkers. We demonstrated that the combination of methoxyeugenolglucuronide and dopamine-sulfate performs best in predicting banana intake in high (AUCtest=0.92) and low (AUCtest=0.87) consumers. The new biomarkers met key criteria establishing their current applicability in nutrition and health research for assessing occurrence of banana intake.

**Conclusions:** Our metabolomics study revealed new putative bioactive metabolites of banana and a combined biomarker of intake. Those findings will help to better decipher the health effects of banana in future focused studies.

**Funding:** JPI HDHL FoodBAII project, #ANR-14-HDHL-0002-02

**Mots-Clés:** Biomarkers of food intake, banana, untargeted metabolomics, dopamine, salsolinol
<figure><img src="image_105.png" /></figure>

## Oral 13 – 013

# Pratiques culturales, qualité des graines de colza et santé du consommateur : une étude preuve de principe " multi-omique " chez la souris au métabolisme dérégulé

Djawed Bennouna <fnref n="1" />, Franck Tourniaire <fnref n="2" />, Jean-François Landrier <fnref n="1" />,<fnref n="3" />, Thierry Durand <fnref n="4" />, Jean-Marie Galano <fnref n="4" />, Frédéric Fine <fnref n="6" />, Sherazade Benatia <fnref n="1" />, Clément Rosique <fnref n="1" />, Karl Fraser 4, Claire Vigor ⁸, Jean-Charles Martin <fnref n="1" />

Les graines de colza (Brassica napus) sont riches en composés phytochimiques bénéfiques pour la santé humaine ou animale, tels que polyphénols et glucosinolates. Le contenu de ces composés peut fluctuer en fonction des conditions de culture. Nous avons estimé si de telle différence pouvait moduler la valeur nutritionnelle des graines. Parmi les 8 cultivars x 8 régions, 2 cultivars provenant d'Ile et Vilaine (cultivar Bonanza) ou de la Somme (ES-Mambo) présentaient des contenus phytochimiques très contrastés (84 métabolites secondaires mesurés par LCHRMS et MS2). Les extraits ont été administrés pendant 6 semaines à des souris ob. Un extrait commercial de Brassica oleracea (Brocoli) a été ajouté comme référence dans l'essai nutritionnel. La réponse biologique ou " responsome " a été déterminée dans le métabolome intestinal et plasmatique, le transcriptome et le métabolome hépatiques. 768 métabolites, 436 lipides et 24974 transcrits par souris ont été identifiés, avec 64 variables cliniques et 25 types d'isoprostanes (analyses LCMS). Les données ont été regroupées en 72 ensembles fonctionnels ou biochimiques pour le métabolome, 14 pour les transcrits, et examinés par analyse multibloc.

Seul l'extrait de " mambo " réduisait le stress oxydatif, indiquée par la diminution d'isoprostanoïdes plasmatiques, associé aux fonctions de stress oxydatif " omiques ". Ces actions sur le stress oxydant paraissent favorables dans le cadre de la protection vasculaire. En dehors de ces régulations, le " responsome " montrait une proximité constante entre l'extrait de colza " mambo " et l'extrait de brocoli, liée à des composés phytochimiques bioactifs communs, et ciblaient des régulations métaboliques bénéfiques comme le métabolisme du tryptophane. L'analyse des régulations conjointes multi-échelles montrait que 70% des fonctions biologiques avaient une régulation coordonnée et soulignait l'impact régulateur primordial des extraits sur le microbiote et sa propagation à l'hôte.

**Conclusion:** les modifications du contenu phytochimique induites par différentes conditions de culture de B. napus peuvent modifier leur valeur santé.

**Mots-Clés:** colza, environnement, nutrition, santé, multiomique

<footnote n="1">1 Centre recherche en CardioVasculaire et Nutrition – INRA UMRs1263</footnote>

<footnote n="2">2 CriBioM – Aix-Marseille Université - AMU – 27 Bd Jean Moulin Faculté de médecine, La Timone, France</footnote>

<footnote n="3">3 UMR INRA 1260</footnote>

<footnote n="4">4 Institut des Biomolécules Max Mousseron [Pôle Chimie Balard] – Centre National de la Recherche Scientifique : UMR5247, Université de Montpellier, Ecole Nationale Supérieure de Chimie de Montpellier – France</footnote>

<footnote n="5">5 terre innovia – TERRES INOVIA – France</footnote>

<footnote n="6">6 AgResearch – Nouvelle-Zélande</footnote>
<figure><img src="image_106.png" /></figure>

# Découverte de marqueurs précoces de l'altération microbiologique des ovoproduits par des approches de métabolomique croisée (RMN-spectrométrie de masse)

Rémy Coat<fnref n="1" />, Jack Legrand <fnref n="1" />, Abdellah Arhaliass <fnref n="1" />, Olivier Goncalves <fnref n="1" />

L'ovoproduit est une matrice biologique complexe qui une fois cassé et homogénéisé perd une bonne part de ses propriétés de conservation, devenant ainsi pour certaines bactéries un excellent milieu de croissance. Ce développement bactérien non maitrisé provoque des altérations biochimiques modifiantes fortement les qualités organoleptiques de l'ovoproduit. L'objectif du projet ALTOVOP est d'étudier les mécanismes biochimiques impliqués par des approches de métabolomique combinées (ciblée et non ciblée) afin de décrire et de comprendre ces mécanismes jusqu'alors peu ou pas caractérisés et d'identifier in fine des marqueurs précoces de ces altérations microbiologiques.

L'approche analytique ciblée concerne des métabolites identifiés comme impliqués dans la dégradation des ovoproduits. Dans le cadre de l'approche analytique non ciblée, des techniques de profilage des macromolécules (FTIR, NIR, Raman) et des métabolites (NMR, GC-MS) ont été employées afin d'établir des profils d'ovoproduits contrastés (frais et dégradés) et d'identifier des marqueurs potentiels d'altération.

Deux études cinétiques de la dégradation réalisées sur des Ovoproduits stériles préparés en laboratoire avec différentes espèces bactériennes ont permis d'établir et de suivre plus en détail l'évolution de la concentration des différents métabolites identifiés au cours de la dégradation et d'identifier différentes voies métaboliques impliquées. Différents liens entre ces voies d'altération et l'évolution des propriétés fonctionnelles des produits ont également pu être proposés. L'altération des propriétés émulsifiantes et gélifiantes, par exemple, serait liée au catabolisme des lipides et aux activités protéolytiques.

Les molécules ainsi identifiées (Glucose et produits de son catabolisme, glycérol et acides organiques, acides aminés...) pourraient être utilisées pour détecter de manière précoce l'altération microbiologique des ovoproduits, permettant un meilleur suivi qualité des produits, affinant leurs DLC et ce faisant évitant de régulières pertes de produit liées à leur altération en milieu industriel.

<footnote n="1">¹ Laboratoire de génie des procédés - environnement - agroalimentaire – Université Bretagne Loire, IMT Atlantique Bretagne-Pays de la Loire, Oniris, Centre National de la Recherche Scientifique : UMR6144, Université de Nantes – France</footnote>
<figure><img src="image_107.png" /></figure>

## Oral 14 – O14

# Analyse non ciblée de contaminants dans les aliments : validation d'une approche de type métabolomique sur deux technologies
UHPLC-HRMS et deux scénarios de contamination " en aveugle "

Grégoire Delaporte ¹, Mathieu Cladière ¹, Valérie Camel ¹

¹ UMR Ingénierie Procédés Aliments (GENIAL) – Institut National de la Recherche Agronomique :
UMR1145, AgroParisTech, Université Paris-Saclay, Université Paris-Saclay – 1 avenue des Olympiades, F-91744 Massy, France

Le récent intérêt pour les approches métabolomiques dans l'analyse non-ciblée de contaminants dans les aliments a permis le développement de premières approches aux performances contrastées : niveaux de concentrations élevés (mg/kg) et gamme de composés analysés relativement importante [1]. De nouveaux travaux à des concentrations plus faibles (~10 µg/kg) ont été publiés, mais sans aucune étude de transférabilité sur différents instruments ou scénarios de contamination [2].

Nous avons mis au point une méthode d'analyse non-ciblée de contaminants dans le thé basée sur une extraction générique par solvant, une analyse par UHPLC-ToF-MS, l'extraction des signaux par XCMS (opéré sur W4M) et un prétraitement des données avant une analyse multivariée par analyse en composantes indépendantes (ICA) sur Matlab. A l'issue de ce protocole, les ions suspects sont annotés grâce à une combinaison de recherches dans des bases de données et d'analyse des profils isotopiques [3]. La validation de cette méthode nécessite de la confronter à différents scénarios de contamination, et de vérifier son applicabilité à d'autres technologies HRMS. Ainsi, deux scénarios de contamination ont été étudiés " en aveugle " sur deux instruments (ToF et Orbitrap) : 1) thé vert / 3 groupes / 1 mélange de dopage / teneurs < 30 µg/kg (MTBLS771) ; 2) thé noir / 3 groupes / 2 mélanges de dopage / teneurs < 10 µg/kg (MTBLS772) [4].

L'application de notre méthode aux données Orbitrap a nécessité une adaptation de la stratégie d'imputation des valeurs manquantes en raison d'un bruit trop faible pour le module xcms.fillPeaks. En termes de performances, l'étude " en aveugle " du scénario 1) a montré des réponses sensiblement équivalentes pour les deux instruments avec environ 60% de détection, ainsi qu'un faux positif trouvé sur le ToF. Concernant le scénario 2), plus complexe, les résultats montrent une complémentarité des deux appareils avec 2 (ou 3) molécules détectées uniquement sur le ToF (ou l'Orbitrap), soit un taux global de détection de 100%. Ces résultats sont novateurs car pour la première fois une même méthode d'analyse non-ciblée de contaminants peut être mise en œuvre sur deux plateformes HRMS de technologies différentes, avec la capacité à détecter plusieurs scénarios de contamination.

Mots-Clés: sécurité sanitaire, thé, contaminants, non ciblé, HRMS, ToF, orbitrap
<figure><img src="image_108.png" /></figure>

# Prix de Thèse RFMF 2019

Nouvelle stratégie de priorisation pour l'étude des produits naturels par l'approche des réseaux moléculaires multi-informatifs,

Florent Olivon
<figure><img src="image_109.png" /></figure>

Communications Posters

Session Santé

<figure><img src="image_110.png" /></figure>
<figure><img src="image_111.png" /></figure>

Poster 1 – P1

Pathogenic E. coli induces a metabolic reprogramming in colonic
epithelial cells through the production of colibactin

Romain Villéger <fnref n="1" />, Binta Diémé <fnref n="2" />,<fnref n="3" />, Marie Lagrée <fnref n="2" />,<fnref n="3" />, Julien Delmas <fnref n="1" />, Gwenaëlle Roche <fnref n="1" />, Nicolas Barnich <fnref n="1" />, *Cyril Jousse* <fnref n="2" />,<fnref n="3" />, Mathilde Bonnet <fnref n="1" />

Introduction: Recent studies suggest that colonization of the colonic mucosa by pathogenic *E. coli* could be involved in the development of colorectal cancer (CRC), especially through the production of genotoxins such as colibactin. However, the mechanisms underlying the colibactin-independent carcinogenesis are still to explore. We propose to use an untargeted metabolomic approach to investigate the mechanisms sustaining the carcinogenic potential of colibactin-positive *E. coli*.

Methods: Human colon carcinoma T84 cells were infected with a colibactin-positive *E. coli* strain 11G5, or its colibactin-negative mutant 11G5-ΔclbQ. Expression of genes coding for enzymes responsible for colibactin synthesis was evaluated by RT-qPCR after 3h, 24h and 48h post-infection. Metabolites were extracted from infected cells by a methanol/water/acetonitrile mixture (2:1:2) 24h post-infection. Extracts were investigated using 1H-NMR / LC-MS and data were processed with XCMS package (Galaxy W4M). PCA and PLS-DA were applied to analyze profiling data.

Results: Colibactin-positive *E. coli* were found intracellularly 3h post-infection, then persisted and multiplied in cells during the following 24h and 48h. A higher expression of *clbC*, *clbN*, *clbP* and *clbQ* genes was observed after 24h when compared to 3h and 48h. At the 24h post-infection time point, 1H-NMR metabolomic analysis of cell lysate retained 881 buckets. For LC-MS analysis, 776 and 413 features were respectively kept in positive and negative ionization mode for multivariate analysis. PCA analysis revealed strong discrimination between non-infected cells and cells infected with 11G5 *E. coli*, but also between cells infected with 11G5-ΔclbQ *E. coli* versus 11G5 *E. coli*. Discriminant metabolites between the 11G5 and the 11G5-ΔclbQ groups suggest the specific effect of colibactin on the disturbance of colonic cell metabolome. Alterations in the amino acids and nucleosides seems to characterize the colibactin-dependent metabolic reprogramming in T84 cells.

Conclusion: These results reveal a specific metabolic landscape in colonic epithelial cells following colibactin-positive *E. coli* infection, and highlight a colibactin-dependent metabolic reprogramming that could reveal attractive targets to inhibit CRC development.

Mots-Clés: colorectal cancer, E. coli, colibactin, metabolomics

<footnote n="1">¹ Microbes, Intestin, Inflammation et Susceptibilité de l'Hôte - Clermont Auvergne - INSERM UMR1071, Université Clermont Auvergne, Centre de Recherche en Nutrition Humaine d'Auvergne, INRA [Auvergne/Rhône-Alpes] - France</footnote>

<footnote n="2">² Institut de Chimie de Clermont-Ferrand - Clermont Auvergne - SIGMA Clermont, Université Clermont Auvergne : UMR6296, Centre National de la Recherche Scientifique : UMR6296 - France</footnote>

<footnote n="3">³ Plateforme d'Exploration du Métabolisme MetaboHUB Clermont, Clermont-Ferrand, France – INRA (FRANCE), Université Clemont Auvergne – France</footnote>
<figure><img src="image_112.png" /></figure>

Poster 2 – P2

# Endometriosis: diagnosis, phenotyping and physiopathology from NMR-based metabolomics

Gildas Bertho<fnref n="1" />, Fatiha Kateb <fnref n="1" />, Pietro Santulli <fnref n="2" />,³, Charles Chapron <fnref n="2" />

Endometriosis is a gynecological disease, characterized by the growth and proliferation of endometrium-like tissues outside the uterus. The symptoms include painful periods, painful ovulation, pain during or after sexual intercourse, heavy bleeding, chronic pelvic pain, fatigue, as well as infertility, and can impact on general physical, mental, and social well-being. Endometriosis affects around 10% of women during their reproductive years, which corresponds to approximately 176 million women in the world. Endometriosis can start as early as a girl's first period, and menopause may not resolve the symptoms. A general lack of awareness by both women and health care providers, due to a "normalization" of symptoms, results in a significant delay from the apparition of symptoms to the diagnosis and treatment. There is no known cure and today laparoscopy is the most common procedure used to diagnose and remove endometriotic tissue.

We used 1H-NMR-based metabolomics to decipher the mechanism inherent to the disease. This work in collaboration with physician at hospital Cochin in Paris consists in a prospective cohort study, including 75 patients: 50 with histologically proved endometriosis and 25 controls with no macroscopic endometriotic lesions during laparoscopic surgery. Endometriosis was precisely classified into 2 groups of 25 patients: endometrioma (OMA) and deep infiltrating endometriosis (DIE).

The serum metabolomic profile of endometriosis patients was characterized by decreased concentrations of amino acids compared with healthy women. Conversely, the concentrations of free fatty acids and ketone bodies biomarkers were significantly increased in the endometriosis group. Therefore, in endometriosis patients the lipolysis is activated followed by an increased ketogenesis where the inflammatory role of 3-hydroxybutyrate could have of major importance. Interestingly, OMA and DIE phenotypes also displayed specific metabolic profiles.

1H-NMR-based metabolomics demonstrated its potential to identify metabolic changes associated to endometriosis and endometriosis phenotypes in serum samples. This information is useful to get a better understanding of the pathogenesis of endometriosis, thus providing support to the rapid and noninvasive diagnosis of this pathology.

Mots-Clés: Endometriosis, Biomarkers, NMR, Serum, Phenotyping, Metabolic Pathways

<footnote n="1">¹ Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques – Université Paris Descartes - Paris 5 : UMR8601, Centre National de la Recherche Scientifique : UMR8601 – France</footnote>

<footnote n="2">² Service de Gynécologie et Obstétrique II [Cochin] – CHU Cochin [AP-HP] – France ³ Service de Médecine de la Reproduction – Hôpital Cochin – France</footnote>
<figure><img src="image_113.png" /></figure>

Poster 3 – P3

Colorectal Cancer : Biomarkers and Effect Size

Nicolas Di Giovanni<fnref n="1" />, Marie-Alice Meuwis <fnref n="2" />, Edouard Louis <fnref n="2" />, Jean-François Focant <fnref n="1" />

Colorectal cancer affects more than one million new persons each year worldwide, and kills more than 700.000.
Nevertheless, its diagnosis is still largely based on invasive tissue sampling and gaps remain in the understanding of
its pathogenesis, with complex combinations between lifestyle, genetics, epigenetics, chronic inflammation (IBD) and
microbiota. Untargeted metabolomics is one of the approaches that can be used to solve these issues.
In the present study, we analyzed serum samples from patients affected by colorectal cancer (CRC, n = 18) and by
colorectal cancer in remission (R-CRC, n = 17), and samples from healthy patients matched for biases (HC, n = 19 and
R-HC, n = 17). The aim was to find candidate biomarkers able to diagnose the active state of the disease as well as to
compare the concentration levels of the molecules of interest with the remission state to better understand the
biolocal processes beneath the observed clinical and metabolic symptoms.

To do so, an optimized and validated (NIST SRM 1950) comprehensive GC×GC-(HR)TOFMS method we developed
was used, that also included an in-house QC system and data processing based on multiple statistical techniques.

Because the experimental design prevented a direct comparison between the active and remission samples, which
were not directly matched for biases, we used a measure called effect size that has the advantage over significance
testing to focus on effect (here signal/concentration variation) magnitude.

This presentation will therefore discuss effect size interest and application in metabolomics. Along with the results
obtained in terms of the highlighted candidate biomarkers? Candidates that were identified using full mass spectrum,
linear retention indices and accurate mass provided by state-of-the-art high-resolution (HR) time-of-flight mass
spectrometry, and which discrimination potential was assessed using supervised and unsupervised models,
discriminant analysis and ROC curves.

Mots-Clés: Colorectal cancer, gas chromatography, effect size

<footnote n="1">1 Organic and Biological Analytical Chemistry Group, MS Lab, Université de Liège – Belgique</footnote>

<footnote n="2">² Translationnal Gastroenterology Unit, GIGA-R, CHU (University Hospital), Liège – Belgique</footnote>
<figure><img src="image_114.png" /></figure>

Poster 4 – P4

Increased content of Lysophospholipids, diacylglicerides and sphingomyelins in isolated triglycerides-rich lipoproteins and HDL from patients with coronary artery disease

Ljubica Svilar¹, Magali Barchuk, Patricia Ancel, Veronica Miksztowicz, Graciela Lopez, Miguel Rubio, Laura Schreier, Anne Dutour, Benedicte Gaborit, Jean-Charles Martin, Gabriela Berg

¹ CriBioM – Aix-Marseille University, INSERM, INRA, C2VN – France

Introduction: android obesity and the atherogenic dyslipidemia, with increased TG-rich lipoproteins remnants (TRL) and decreased HDL levels, are associated with higher risk of coronary artery disease (CAD). Plasma lipidomics studies have revealed that particular molecular lipid species are involved in the development of atherosclerosis and CAD. However, the lipidomic analysis of isolated lipoproteins (TRL and HDL) has been scarcely studied so far. This qualitative analysis is of interest as it would allow differentiating specific lipid species contribution to the development of CAD.

Materials and Methods: lipidomic analysis was performed in 15 patients undergoing coronary artery bypass graft (CAD) and 12 patients with valve replacement (No CAD). Serum samples were obtained before surgery and TRL and HDL were isolated by ultracentrifugation. For lipidomics, lipids were extracted from each fraction with MTBE. Lipidomics was carried out on a LC C18 column hyphenated to a Q-Exactive plus mass spectrometer, using both positive and negative ionization mode, and acquired in full MS and data dependent MSMS mode. Lipid annotation was performed using a combination of the open source XCMS and ThermoFischer LipidSearch software.

Results: a total of 242 lipid species were identified in both lipoproteins fractions. In CAD patients, higher content of total LPC (p< 0.002) and LPE (p< 0.02) in TRL were found. In this lipoprotein higher levels of LPC(20:4), LPC(38:5) and LPC(16:0) as well as LPE(18:1) and LPE(16:0) were observed in CAD, while lower content of MG(18:2p) was detected compared to no CAD patients (p< 0.04). No differences in the total content of each lipid in HDL were observed between groups, although CAD patients presented higher levels of DG(18:1/20:4) and SM(22:0/20:2) (p< 0.04).

Conclusion: The distribution of lipid species, especially bioactive lipids, in TRL and HDL could differentially contribute to atherosclerosis progression and CAD development. Assessment of molecular lipids from lipoproteins may allow a better understanding of the role of these species in the development of atherosclerosis and the risk of CAD.

Mots-Clés: lipidomics, lipoproteins, HDL, coronary artery disease
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## Poster 5 – P5

# New isoprostanoid biomarkers for prostate cancer

Claire Vigor<fnref n="1" />, Amandine Rocher<fnref n="1" />, Guillaume Reversat<fnref n="1" />, Amandyne Linares-Maurizi<fnref n="1" />, Camille Oger<fnref n="2" />, Jean Marie Galano<fnref n="1" />, Thierry Durand<fnref n="1" />, Pierre-Jean Lamy<fnref n="3" />, Clément Peigné<fnref n="3" />, Bruno Segui<fnref n="3" />, Vincent Abd El Fattah<fnref n="3" />, Grégoire Poinas<fnref n="3" />, Xavier Rébillard<fnref n="3" />

**Context** : At present, early detection of prostate cancer relies primarily on digital rectal examination and PSA assays after identifying life expectancy and personal risk factors.[1] The requirement of new biomarkers is real for an improvement of the diagnosis, for a better classification of potentially aggressive cancer allowing the orientation of the patient care. In recent years, numerous epidemiological, experimental or clinical studies have shown a close link between oxidative stress (OS) and prostate cancer.[2] Isoprostanes (IsoPs) derived from the radical oxidation of polyunsaturated fatty acids[3], constitute excellent biomarkers of lipid peroxidation in vivo and thus of OS.[4] The goal of our study is to discover new diagnostic biomarkers through the establishment of an LC-MS/MS profile of isoprostanoids and to assess their relevance and performance in the context of early detection or of identification of population subgroups. **Experimental study** : This study consists of a qualitative and quantitative profile of about 50 IsoP derivatives in urine of patients cohort made of: 30 "healthy" patients (no suspicion of prostate cancer), 30 risk patients (suspicion of cancer, negative biopsy), 30 patients with localized prostate cancer and 30 patients with locally advanced or metastatic prostate cancer.

**Preliminary results** : Preliminary data allow observation of trends. Of the 50 isoprostanoids in the method, 5 appear to be higher in patients with highly advanced disease (locally advanced or metastatic) compared to healthy patients. In addition, patients in the risk group according to conventional markers (TR + PSA), but having a negative biopsy, comparable to healthy patients, also have isoprostanoid levels comparable to healthy patients. The analysis of isoprostanoids could help to classify individuals at risk in the category of healthy individuals and avoid the implementation of a biopsy. This preliminary study should be pursued in particular by the comparison between MRI results and pathology for patients with negative biopsies.

**Mots-Clés** : Biomarker, diagnostic, prostate cancer, isoprostanoids

<footnote n="1">¹ Institut des Biomolécules Max Mousseron [Pôle Chimie Balard] – CNRS : UMR5247 – France</footnote>

<footnote n="2">² Institut des Biomolécules Max Mousseron [Pôle Chimie Balard] – Centre National de la Recherche Scientifique : UMR5247, Université de Montpellier, Ecole Nationale Supérieure de Chimie de Montpellier – France</footnote>

<footnote n="3">³ Clinique Beau Soleil [Montpellier] – Université Montpellier I – France</footnote>
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## Poster 6 – P6

# Combattre le glioblastome en combinant le témozolomide aux nanoparticules : analyse de l'exométabolome par Résonance Magnétique Nucléaire

Manon Courcelle¹, Ferdaous Sahli¹, Célia Arib¹, Maroua Ben Haddada¹, Nadia Bouchemal¹, Mohamed Triba¹, Philippe Savarin¹, Jolanda Spadavecchia¹, Tony Palama¹

¹Laboratoire Chimie, Structures, Propriétés de Biomatériaux et d'Agents Thérapeutiques (CSPBAT) – Université Paris 13, Sorbonne Paris Cité, CNRS : UMR7244 – France

Les glioblastomes sont des tumeurs cérébrales malignes très fréquentes qui surviennent à tout âge mais dans 70% des cas entre 45 et 70 ans. Ce type de cancer présente un taux de mortalité élevé ainsi qu'un taux important de récidive. Le traitement actuel consiste en une chirurgie d'ablation la plus large possible suivi d'une radio-chimiothérapie. La molécule utilisée en chimiothérapie est le témozolomide (TMZ), appartenant à la famille des agents alkylants. Cependant, les traitements oncologiques conventionnels sont insuffisants et ont une efficacité limitée ainsi qu'une toxicité non négligeable. L'utilisation de nanoparticules permettrait de pallier à ces inconvénients. En effet, elles permettent de cibler le transport du médicament jusqu'à la tumeur et réduisent la dégradation de ce dernier.

La lignée cellulaire U87MG a été étudiée et leur exométabolome a été analysé par résonance magnétique nucléaire (RMN). Les cellules ont été mises en culture dans du milieu DMEM supplémenté en sérum à 10%. Dans un premier temps, la concentration inhibitrice médiane (IC50) a été mesurée afin de déterminer l'efficacité du TMZ seul. Ensuite, des nanoparticules d'or ont été synthétisées contenant à l'intérieur, le témozolomide et en surface un polymère, le polyéthylène glycol (PEG) qui est un agent stabilisant. Ces nanoparticules sont mises en contact avec les cellules U87MG afin de suivre la viabilité cellulaire au cours du temps. A chaque expérience, les milieux de culture des cellules ont été récupérés pour mesurer les métabolites consommés et produits par les cellules, ie l'exométabolome.
Ces résultats contribueront à une meilleure compréhension du métabolisme des cellules U87MG lors de l'utilisation de nanoparticules pour les combattre.

**Mots-Clés:** nanoparticules, exométabolome, rmn, glioblastome, cancer, témozolomide
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## Poster 7 – P7

# Mechanisms of gastric cancer suppression by taxanes and statins
using a pluri-omics approach

George Alzeeb <fnref n="1" />, Laurent Corcos <fnref n="1" />, Catherine Le Jossic-Corcos <fnref n="1" />

Gastric cancer (GC) is globally the fourth most common cancer and a leading cause of death amongst cancer patients, who are mostly diagnosed at advanced stages of the disease where treatment options are limited and survival rate is poor. Therapeutic options might include combinations of available drugs. Previous studies by our lab have shown that the association of statins (Lovastatin) and taxanes (Docetaxel) was a powerful trigger of HGT-1 human gastric cancer cells apoptosis. Statins are major anti-hypercholesterolemia drugs, that also trigger apoptosis of many types of cancer cells. Docetaxel is a potent microtubule-stabilising agent. To identify biomarkers of response to these treatments, a project was conducted in collaboration with mass spectrometry (GC-MS and LC-MS) and NMR platforms of the CORSAIRE metabolomics and lipidomics network. The levels of more than 100 metabolites were significantly altered by the treatments (especially lovastatin), including amino acids, organic acids, sugars, and several families of lipids. While most metabolites were increased, fumarate was decreased at all time points and L-Glutamine was the metabolite altered the most, pointing to an important metabolic adaptation of cancer cells. Transposed onto the KEGG pathway (Kyoto Encyclopedia of Genes and Genomes) of the "Central Carbon Metabolism in Cancer", our metabolomics, lipidomics and transcriptomics data led to the proposition of a model of the effect of the combined treatment. We aim next to extend this analytical approach by studying the molecular mechanisms (genes) and biochemicals (enzymes and transporters) responsible for the changes in the metabolome and lipidome. Furthermore, our team showed that the combination of docetaxel and lovastatin allowed reduction of tumor volume in a xenograft model in immunocompromised mice. These models, with cells in culture and tumors in mice offer us the opportunity to analyze the metabolic changes resulting from treatments both ex vivo and in vivo.

**Mots-Clés:** Gastric cancer, statin, taxane, metabolomics, lipidomics, transcriptomics, L-Glutamine.

<footnote n="1">¹ Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) – EFS, Université de Brest, Institut National de la Santé et de la Recherche Médicale : U1078 – France</footnote>
<figure><img src="image_118.png" /></figure>

## Poster 8 – P8

# Identification de biomarqueurs dans le Trouble de Déficit d'Attention avec ou sans Hyperactivité (TDAH)

Camille Dupuy <fnref n="1" />, Laurent Galineau <fnref n="1" />, Pierre Thomas-Castelnau <fnref n="1" />, Patrick Emond ³

Le Trouble Déficit d'Attention avec ou sans Hyperactivité (TDAH) est un trouble du neurodéveloppement hétérogène touchant 3 à 5% des enfants scolarisés et caractérisé par un déficit d'attention, une hyperactivité et une impulsivité. Actuellement le diagnostic se fait principalement à l'aide de tests cognitifs avec un risque non négligeable d'erreurs diagnostiques.

A l'heure actuelle aucune étude ne propose un/des biomarqueurs fiable(s) du TDAH. L'identification de biomarqueurs reste donc un défi important pour le diagnostic précoce et le suivi thérapeutique adapté des patients. Associé à la recherche de biomarqueurs circulants, l'étude du métabolisme central est déterminante pour mieux comprendre la physiopathologie du TDAH. A ce titre, l'utilisation de modèles animaux est pertinente pour étudier le métabolome cérébral. Le modèle de rat le mieux caractérisé et le plus utilisé est la souche SHR/NCrl comparée à la souche témoin WKY/NHsd.

L'objectif de cette étude est d'identifier des biomarqueurs métaboliques, au niveau central et périphérique, chez des rats SHR/NCrl. Pour cela, dix régions cérébrales ont été prélevées, ainsi que des prélèvements périphériques (sang, urines et fèces), puis analysés en LC-HRMS. Les données obtenues ont été traitées par des analyses multivariées, univariées et les voies métaboliques discriminantes ont été recherchées.

Cette étude permet de montrer une discrimination entre les deux souches basée sur leur métabolisme. L'analyse de voies permet en effet de différencier deux réseaux fonctionnels : le réseau ventral limbique et le réseau dorsal cognitif. De plus, l'altération statistiquement significative de voies métaboliques communes est retrouvée dans les régions cérébrales et les compartiments périphériques.

A l'avenir, des études de métabolomiques sur des échantillons périphériques cliniques (urines et sang) vont être réalisées afin de mieux caractériser ce trouble sévère et fréquent.

Mots-Clés: TDAH, biomarqueurs, LC, MS, cerveau

<footnote n="1">¹ Imagerie et cerveau – Université de Tours : U930, INSERM : U930 – France</footnote>

<footnote n="2">² Université François-Rabelais, INSERM U930 "Imagerie et Cerveau", CHRU de Tours, 10 Bv Tonnellé, 37044 Tours, France
– Inserm – 10 Bv Tonnellé, 37044 Tours, France</footnote>
<figure><img src="image_119.png" /></figure>

Poster 9 – P9

METABOLOMIC NMR STUDIES AT PRESYMPTOMATIC AND SYMPTOMATIC STAGES OF HUNTINGTON'S DISEASE, ON DROSOPHILA MODEL

Marylène Bertrand <fnref n="1" />, Martine Decoville, Hervé Meudal, Serge Birman, Céline Landon <fnref n="1" />

Huntington's disease (HD) is an inherited neurodegenerative disorder, for which diagnosis development and discovery of new therapeutic targets are urgently required. For this study, a model of Huntington's disease has been generated in *Drosophila melanogaster*, in order to identify metabolic biomarkers, at presymptomatic (10 days-old flies) and at symptomatic stages of the disease (16 days-old flies). The pan-neuronal expression of a pathogenic polyglutaminated huntingtin protein (htt-93Q) in transgenic flies induces neuropathology, with most of the characteristics of the human disease. The discriminant metabolites between the disease carrier flies and their controls were identified by 1H-NMR and OPLS-DA multivariate analyses. This study identifies, for the first time, metabolites at a pre-symptomatic stage and at a symptomatic stage of Huntington's disease modeled in *Drosophila melanogaster*. Ten metabolites (NAD+, AMP, fumarate, asparagine, β-alanine, dimethylamine, glutamine, succinate, glutamate, and ethanol) were identified as biomarkers at a presymptomatic stage of the disease and six metabolites (succinate, phosphocholine, acetate, pyruvate and 2-oxoglutarate) as biomarkers at an advanced stage of the disease. These metabolites reveal dysfunctions in energy production, leading to mitochondrial dysfunction and to a disruption of the glycolysis, TCA and glutamate-glutamine cycles. Our data allow a better understanding of the metabolism impairment of HD at presymptomatic and symptomatic stages of the disease, and demonstrate that the metabolites perturbations evolve during the development of the disease.

Mots-Clés: Huntington's disease, neurodegenerative disease, Drosophila, metabolomics, NMR, presymptomatic

<footnote n="1">¹ Centre de biophysique moléculaire – Université d'Orléans : UPR4301, Institut National de la Santé et de la Recherche Médicale : UPR4301, Centre National de la Recherche Scientifique : UPR4301 – France</footnote>
<figure><img src="image_120.png" /></figure>

## Poster 10 – P10

# High throughput metabolomics using DI-HRMS on a FT-ICR equipped with a dynamically harmonized cell: a methodology for improving the assessment of metadata quality in epidemiological studies

**Baninia Habchi**<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, **Sandra Alves**<fnref n="2" />, **Michèle Guillaume**<fnref n="4" />, **Sylvie Streel**<fnref n="4" />, **Anne-Françoise Donneau**<fnref n="4" />, **Brice Appenzeller**<fnref n="5" />, **Douglas N. Rutledge**<fnref n="1" />, **Alain Paris**<fnref n="6" />, **Estelle Rathahao-Paris**<fnref n="2" />,<fnref n="7" />

Some chronic diseases in human could be explained by exposure to toxicants (Sears et al., J Environ Public Health. 2012;2012). So, an analytical monitoring of population exposome based on high throughput methods applied to urine, serum or hair matrices is required. The recent development in metabolomics by mass spectrometry provides strong insights in such context. In the present study, a high-throughput approach based on direct introduction high resolution mass spectrometry (DI-HRMS) using a Fourier transform ion cyclotron resonance (FT-ICR) on a 7 T-instrument equipped with a dynamically harmonized cell is proposed to get large cohort phenotyping. Urine samples of approximately 500 subjects enrolled in the "Nutrition, Environment and Cardiovascular Health – NESCaV" study were obtained from the Service de Chimie médicale, University Hospital Centre of Liège, and analyzed using flow injection analysis (FIA). A total of 626 injections was performed in positive mode in less than 74 hours.

The so generated data was pre-processed using in-house Matlab scripts. A Principal Component Analysis (PCA) was applied without any known metadata. Analytical data quality was evaluated in a previous work based on more than 50 injections of a quality control sample (Habchi et al., Anal Bioanal Chem, 2018).

PCA showed a separation of some individuals according to drugs administered. As an example, distinction of 16 subjects is related to specific ions at *m/z* 130.109 annotated putatively as [M+H]+ ions of dimethylbiguanide (2 ppm error). This drug is prescribed for non-insulindependent diabetes, a cardiovascular risk factor (CRF) that was studied in the NESCaV study. A second PC separated three individuals from all others based on two variables detected at *m/z* 273.127 and 274.13. These ions correspond probably to [M+H]+ ions of sotalol (1 ppm error) and the corresponding 13C isotope peak, respectively. The sotalol is a drug used for life-threatening arrhythmias, another studied CRF in this cross-sectional study. In both cases, our structural hypotheses resulting from HMDB queries were hopefully confirmed by checking metadata. These results demonstrate the efficiency of the DI-HRMS approach as a very promising tool for high throughput epidemiological studies, specially, to correlate analytical results with metadata.

Mots-Clés: Metabolomics, Epidemiology, Metadata, DIMS, HRMS, PCA

<footnote n="1">¹ UMR Ingénierie Procédés Aliments – AgroParisTech, Inra, Université Paris-Saclay, 91300 Massy, France – France</footnote>

<footnote n="2">² Sorbonne Université – Faculté des Sciences et de l'Ingénierie, IPCM, 75005 Paris, France – France</footnote>

<footnote n="3">³ Aix Marseille Université (AMU) – NORT, UMR INSERM 1062, INRA 1260, F13005 Marseille, France – France</footnote>

<footnote n="4">⁴ Department of Public Health, University of Liège, 4000 Liège, Belgium – Belgique</footnote>

<footnote n="5">⁵ Human Biomonitoring Research Unit, Luxembourg Institute of Health (LIH), Rue Henri Koch 29, Luxembourg</footnote>

<footnote n="6">⁶ Sorbonne Universités – Muséum national d’Histoire naturelle, CNRS, UMR7245 MCAM, 75005 Paris, France – France</footnote>

<footnote n="7">⁷ UMR Service de Pharmacologie et d’Immunoanalyse – CEA-INRA, Université Paris-Saclay, 91191 Gif-sur-Yvette – France</footnote>
<figure><img src="image_121.png" /></figure>

## Poster 11 – P11

# Study of the toxic effect of diphenyl phosphate on the liver metabolism of mice

Jesús Marín-Sáez <fnref n="1" />,<fnref n="2" />, Aurélie Fildier <fnref n="2" />, Audrey Buleté <fnref n="2" />, Léa Payen <fnref n="3" />, Samia Ruby <fnref n="3" />, Myriam Abdallah <fnref n="3" />, Jessica Garcia <fnref n="3" />, Arnaud Vigneron <fnref n="3" />, Emmanuelle Vulliet <fnref n="2" />

Diphenyl phosphate (DPP) is one of the main metabolites of triphenyl phosphate (TPP), organophosphorus additives widely used as flame retardant in substitution of polybrominated diphenylethers and polybrominated biphenyls. In term of exposure, the migration of organophosphate flame retardants constitutes a serious concern for the health of the population. As few studies, mainly carried during in vitro experiments, deal with the impact of DPP on human health, a multi-omics in vivo study on rodent model was performed.

Mice of 3 weeks were fed with water at three concentrations of DPP (0.1, 1, 10 mg/mL) during seven weeks, dose chosen through their relevance in term of human exposure. After a solid-liquid extraction, the extract was split in two before to be analyzed by HPLC-HRMS on a C18 and HILIC columns. The extraction was performed by two operators and from eight independent samples for each concentration to obtain a suitable statistical analysis. The analysis were carried out with an UHPLC system (U3000 Thermo R) coupled to a QToF mass spectrometer (MaxisPlus, Bruker R). The data were processed using MetaboScape 4.0 and were statistically treated using univariate and supervised statistical tools.

The principal discriminant compounds were fatty acyls (oleic acid, linoleic acid) and their carnitinederivatives involved in their mitochondrial oxidation. Confirming these results, RNAseq (NextSeq™ 500 and HiSeq R X Systems) performed on separate treated livers unmask the existence of a disturbed genetic network associated with lipids and fatty acid catabolism. Finally, multiple immunohistochemistry analysis demonstrated aberrant expression of main regulators of lipids metabolism and lipid droplets contents in liver exposed to DPP.

The results indicated clearly a disruption of the control pathways of lipid catabolism and bioenergetics, explaining weight alteration of exposed animals. To the best of our knowledge, this is the first time that the effect of DPP is studied in vivo experiments. This work confirms the hypothesis of other studies where other OPEs have disturbed lipid metabolism and transportation. The successful combination of analytical and biological methodologies shows the huge potential of these techniques to evaluate the effect of these and other kind of compounds in the body functioning.

Mots-Clés: diphenyl phosphate, metabolism, HPLC, HRMS, RNAseq

<footnote n="1">¹ Department of Chemistry and Physics, Analytical Chemistry Area, University of Almería, Research Centre for Agricultural and Food Biotechnology (BITAL), Agrifood Campus of International Excellence ceiA3, Carretera de Sacramento s/n, E-04120 Almería, Spain – Espagne</footnote>

<footnote n="2">² Institut des Sciences Analytiques, UMR 5280 CNRS - Université de Lyon 1, 5 rue de la Doua, 69100 Villeurbanne, France – CNRS : UMR5280 – France</footnote>

<footnote n="3">³ Universite de Lyon, Universite Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Leon Berard, Cancer Research Center of Lyon, Equipe Labellisée Ligue contre le Cancer, Lyon, France – Centre Hospitalier Lyon Sud [CHU - HCL] – France</footnote>
<figure><img src="image_122.png" /></figure>

## Poster 12 – P12

# Glioma biomarker discovery in a Drosophila melanogaster model using NMR spectroscopy

Marion Maravat<fnref n="1" />, Martine Decoville<fnref n="2" />,<fnref n="3" />, Marylène Bertrand<fnref n="3" />, Celine Landon<fnref n="3" />, Séverine Morisset-Lopez<fnref n="3" />, Franck Fayon<fnref n="1" />, Vincent Sarou-Kanian<fnref n="1" />

Human malignant glioma is the most common type of primary brain tumor. Composed of glial cells and their precursors, it is aggressive, highly invasive and resistant to standard therapies which leads to a poor prognosis. Despite the mutated pathways responsible for tumorigenesis being identified, targeted therapies have proven mostly ineffective so far. Novel therapeutic approaches are needed to improve patients' life expectancy and comfort. Studying glioma cells metabolism could lead to the identification of new therapeutic targets.

*Drosophila melanogaster* is a compelling genetic model to study human neurological diseases because of its high conservation in signaling pathways and cellular content of the brain. In this regard, tumor-like proliferation was successfully induced in *Drosophila* by constitutively co-activating the Epidermal Growth Factor Receptor (EGFR) and the Phosphatidyl-Inositol-3 Kinase (PI3K) signaling pathways in glial cells.

Different Nuclear Magnetic Resonance (NMR) spectroscopy techniques were used to obtain metabolic profiles of dissected third instar larval brains. Fresh organs were directly analyzed by High Resolution – Magic Angle Spinning (HR-MAS) and brain extracts were analyzed by solution-state 1H-NMR. Biomarkers were then identified through statistical analyses of the NMR data.

The results of this study not only validate further the *Drosophila melanogaster* model for human glioma but also open the possibility of a better understanding of the biochemical mechanisms of these tumors. The identified metabolites provide the possibility of metabolic therapy by targeting specific pathways but also a mean to follow tumor evolution during pharmacologic treatments.

Mots-Clés: glioma, nmr, biomarker, Drosophila melanogaster

<footnote n="1">¹ Conditions Extrêmes et Matériaux : Haute Température et Irradiation – Université d'Orléans, Centre National de la Recherche Scientifique : UPR3079 – France</footnote>

<footnote n="2">² Université d'Orléans – Ministère de l'Enseignement Supérieur et de la Recherche Scientifique – 6 Avenue du Parc Floral, 45067 Orléans Cédex 2, France</footnote>

<footnote n="3">³ Centre de biophysique moléculaire – Université d'Orléans : UPR4301, Centre National de la Recherche Scientifique : UPR4301 – France</footnote>
<figure><img src="image_123.png" /></figure>

## Poster 13 – P13

# Etude longitudinale d'une exposition aiguë à uranium par approche métabolomique

Baninia Habchi<fnref n="1" />, Stéphane Grison<fnref n="2" />, Dimitri Kereselidze <fnref n="2" />, Celine Gloaguen <fnref n="2" />, Christelle Elie <fnref n="2" />, Victor Magneron <fnref n="2" />, Jean-Charles Martin <fnref n="1" />, Mâamar Souidi <fnref n="2" />

Ce travail cherche à mettre en évidence des signatures métabolomiques associées à une contamination aiguë à l'uranium chez le rat dans le cadre d'une étude basée sur les effets des expositions environnementales ou accidentelles aux faibles doses de radionucléides.

Des prélèvements de plasma et urines ont été réalisés périodiquement pendant 9 mois tout au long du protocole expérimental à partir d'animaux issus de quatre groupes de doses d'expositions croissantes (2 non toxiques et 2 toxiques) et d'un groupe témoin. 900 échantillons d'urine et 600 échantillons de plasma ont été analysés par chromatographie liquide couplée à un spectromètre de masse (Q-Exactive Plus) en mode switch positif-négatif sur deux colonnes C18 et HILIC. Un échantillon de contrôle qualité a permis de contrôler la qualité des analyses et corriger les dérives analytiques entre les différents batchs d'analyse. Plusieurs étapes de prétraitements et différents outils d'analyses multivariées ont été appliqués et utilisés.

Dans l'urine, une ACP (Analyse en composante principale) a permis de montrer l'existence d'un effet temporel. L'analyse discriminante PLSDA (Partial Least Square - Discriminant Analysis) a révélé l'existence d'un effet dose entre les témoins et les doses toxiques mais aussi entre les témoins et les doses non toxiques. Des sous modèles PLSDA, ont aussi été réalisés entre les témoins et les doses non toxiques pour rechercher des marqueurs discriminants selon les périodes temporelles. A partir de ces marqueurs, des scores composites ont pu être calculés et validés par courbe ROC (Receiver Operating Characteristic). Ces scores pourraient être utilisés pour élaborer des tests de diagnostic d'exposition. Des étapes de validations des biomarqueurs discriminants sont encore nécessaires, tout comme l'exploitation des données plasmatiques qui pourraient être complémentaires aux résultats obtenus dans l'urine.

Mots-Clés: Métabolomique, Biomarqueurs, uranium, exposition aiguë, HRMS, LC/MS, PLS, DA.

<footnote n="1">¹ Aix Marseille Université – Aix Marseille Université (AMU), NORT, UMR INSERM 1062, INRA 1260, F13005 Marseille, France – France</footnote>

<footnote n="2">² Institut de Radioprotection et de Sûreté Nucléaire – Institut de Radioprotection et de Sûreté Nucléaire, PSE-SANTE, SESANE, LRTOX, Fontenay-aux-Roses, F-92260 France – France</footnote>
<figure><img src="image_124.png" /></figure>

## Poster 14 – P14

# Leukemic cells develop metabolic reprogramming to adapt their proliferation to nutrient deprivation.

Caroline Lo Presti <fnref n="1" />,<fnref n="2" />, Florence Fauvelle <fnref n="3" />,<fnref n="4" />, Pascal Mossuz <fnref n="1" />,<fnref n="2" />

**Background:** Acute Myeloid Leukemia are a set of malignant proliferations leading to an accumulation of blasts in the bone marrow and blood. The prognosis is pejorative due to their molecular complexity and pathways implicated in leukemogenesis. Studying the metabolism of leukemic cells would help to better characterize AML. For that aim, we have used HRMAS-NMR based metabolomics on different leukemic cell lines. This method has the advantage of allowing the analysis of intact cells, with few preparation, which could facilitate the transfer of our results to clinic.

**Objectives:** The objective was to compare the metabolic profiles of leukemic cells in basal conditions of culture and in deprivation conditions, in order to investigate their behavior under metabolic stress.

**Methods:** Five human leukemic cell lines, KG1, K562, HEL, HL60 and OCIAML3 were studied. For stress experiments, the cells were cultured without serum and samples for HRMAS NMR were prepared at three time points: 2h, 24h and 48h. The samples were analyzed on a 500MHz Bruker Avance III spectrometer (IRMaGE, CEA Grenoble). MAS spin rate was set at 4000 Hz and sample temperature at 277K. A CPMG pulse sequence was used to minimize lipid contribution. All the spectra were corrected for phase and baseline distortions and the spectral region between 8.5 and 0.5 ppm was divided into buckets with equal width of 0.001 ppm using the NMRprocflow software. Buckets were transferred to the SIMCA R V14.1 software for multivariate statistics (PCA and OPLS-DA), in order to find metabolites that are over or under expressed in the different cell lines and depending on the experimental conditions.

**Results:** We showed that each cell line has a specific metabolic profile at basal state, characterized by overexpression of a specific metabolite: glutamine for HL60, phosphocholine for KG1, myoinositol for HEL, creatine/phosphocreatine for K562 and aspartate for OCIAML3.

When cultured in serum-free medium, they developed a rapid metabolic adaptation which allows them to continue to proliferate despite the lack of nutrients.

**Conclusion:** Our results showed that human leukemic cell lines display different metabolic fingerprint in their basal state and develop a common metabolic reprogramming that allowed them to survive under stress conditions.

**Mots-Clés:** acute myeloid leukemia, metabolomics, HRMAS NMR

<footnote n="1">¹ Department of biological hematology, CHU Grenoble Alpes - Centre Hospitalier Universitaire [Grenoble], Centre Hospitalier Universitaire [Grenoble] - France</footnote>

<footnote n="2">² Institute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) – Institut National de la Santé et de la Recherche Médicale : U1209, Centre National de la Recherche Scientifique : UMR5309, Université Grenoble Alpes – France</footnote>

<footnote n="3">³ Grenoble Institut des Neurosciences (GIN) – Université Grenoble Alpes, U1216 Inserm – Chemin Fortuné Ferrini Batiment Edmond J Safra F-38700 La Tronche, France</footnote>

<footnote n="4">⁴ IRMaGe (IRMaGe) – UGA université Grenoble Alpes, Inserm US17, CNRS UMS 3552, Gesellschaft für Schwerionenforschung – Chemin Fortuné Ferrini Batiment Edmond J Safra F-38700 La Tronche, France</footnote>