# Machine learning-based classification to improve Gas Chromatography-Mass spectrometry data processing.

Yoann Gloaguen, Nico Borgsmüller, Tobias Opialla, Eric Blanc, Emilie Sicard, Anne Lise Royer, Bruno Le Bizec, Stéphanie Durand, Carole Migné, Mélanie Pétéra, et al.

► To cite this version:

Yoann Gloaguen, Nico Borgsmüller, Tobias Opialla, Eric Blanc, Emilie Sicard, et al. Machine learning-based classification to improve Gas Chromatography-Mass spectrometry data processing. European RFMF Metabomeeting 2020, Jan 2020, Toulouse, France. 263 p., 2020, Oral and poster abstracts European RFMF Metabomeeting 2020. hal-02505901

HAL Id: hal-02505901

https://hal.science/hal-02505901

Submitted on 2 Jun 2020

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Distributed under a Creative Commons Attribution 4.0 International License
European RFMF
Metabomeeting
2020

JANUARY 22 - 24, 2020

TOULOUSE, FRANCE

WITH THE PARTICIPATION OF EUROPEAN REGIONAL NETWORKS

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Metabolic
Profiling
Forum

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ORAL AND POSTER ABSTRACTS

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We are very pleased to welcome you to the 2020 European RFMF-Metabomeeting conference in Toulouse. This conference gathers together two metabolomics and fluxomics networks, the RFMF (French-speaking Metabolomics and Fluxomics Network) and the MPF (Metabolic Profiling Forum) with more than 10 years of existence. MPF and RFMF share the same objectives which are to catalyze metabolomics and fluxomics research, organize conferences and promote early career scientists. Both networks also put forward networking and social aspects as strong leverage to achieve cutting edge science. For all these reasons we decided to organize this joint event.

With the growing interest in metabolomics, several regional and national networks have been created across Europe (and affiliated islands) within the last years. That is the reason why we decided to also include them in the organization of this event. They were particularly instrumental in creating a high-level scientific program and providing travel grants (a staggering 21 travel grants!).

We expect this conference to demonstrate the recent breakthroughs in metabolomics and fluxomics in a wide range of application fields. We made the choice to keep the scope of the conference as broad as possible (in terms of techniques and applications) since we think that interactions between communities is key in creating new paradigms, collaborations and scientific hypothesis.

One of our aims in this conference is to promote early career scientist participation. That is the reason why we did our best to keep registration prices as low as possible. As mentioned, networks made a strong effort to offer travel grants. Early careers were also strongly involved in the scientific organization of the conference. In fact, an early career committee was set up, led by Alison Woodward, and selected the early career presentations that you will see during the conference. They also proposed and organized workshops and social events. Finally, they will co-chair sessions with the (less early career) scientists.

Good science is generally the outcome of hard work within a friendly and supportive environment. All organisers agreed on the fact that this friendly atmosphere is of upmost importance for the organization of the conference. That is the reason why we did our best to have exciting social and networking events before and during the conference. We also aim at following RFMF mantra of "Good food Good science" with a very local and original gala dinner.

Finally, we would like to acknowledge all the academic and industrial sponsors for supporting this event and previous ones. We also thank all the people involved in the scientific and practical organization of this conference. MetaboHub-Metatoul metabolomics and fluxomics facility contribution was outstanding and our warmest thoughts go to Justine Bertrand Michel and Floriant Bellvert for their hard work and unbreakable optimism.

We wish you a fruitful scientific, human and enjoyable conference!

Jules Griffin (MPF)
Fabien Jourdan (RFMF)

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Page 4 Sponsors

Page 10 Committees

Page 14 Workshops

Page 20 Plenary and Keynote speakers

Page 30 Program

Page 35 Oral abstracts

Page 80 Poster abstracts
Partners and Sponsors
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EUROPEAN REGIONAL AND NATIONAL
NETWORKS AND SOCIETIES

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SCOTTISH
METABOLOMICS
NETWORK

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NORDIC
METABOLOMICS
SOCIETY

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Spanish Metabolomics
Society

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Metabolic
Profiling
Forum

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Committees
SCIENTIFIC COMMITTEE

<table><tr><td>Floriant Bellvert</td><td>RFMF</td><td>CNRS</td></tr><tr><td>Justine Bertrand Michel</td><td>RFMF</td><td>INSERM</td></tr><tr><td>Lorraine Brennan</td><td>Ireland</td><td>University College Dublin</td></tr><tr><td>Karl Burgess</td><td>Scottish Metabolomics Network</td><td>The University of Edinburgh</td></tr><tr><td>Marta Cascante</td><td>Spanish Metabolomics Society</td><td>University Barecelona</td></tr><tr><td>Frédérique Courant</td><td>RFMF</td><td>Université de Montpellier</td></tr><tr><td>Pietro Franceschi</td><td>Italian Metabolomics Network</td><td>Fondazione Edmund Mach</td></tr><tr><td>Patrick Giraudeau</td><td>RFMF</td><td>Université de Nantes</td></tr><tr><td>Roy Goodacre</td><td>MPF</td><td>University of Liverpool</td></tr><tr><td>Jules Griffin</td><td>MPF</td><td>Imperial College</td></tr><tr><td>Robert Hall</td><td>Netherlands Metabolomics Centre</td><td>Wageningen University</td></tr><tr><td>Anne-Emmanuelle Hay</td><td>RFMF</td><td>Université de Lyon 1</td></tr><tr><td>Fabien Jourdan</td><td>RFMF</td><td>INRA</td></tr><tr><td>Christophe Junot</td><td>RFMF</td><td>CEA</td></tr><tr><td>Dong-Hyun Kim</td><td>MPF</td><td>University of Nottingham</td></tr><tr><td>Steffen Neumann</td><td>German Society for Metabolomics Research</td><td>Leibniz Institute for Plant Biochemistry</td></tr><tr><td>Matej Oresic</td><td>Nordic Metabolomics Society</td><td>Orebro University</td></tr><tr><td>Jean-Charles Portais</td><td>RFMF</td><td>Université Paul Sabatier</td></tr><tr><td>Serge Rudaz</td><td>Swiss Metabolomics Society</td><td>Université de Genève</td></tr><tr><td>Andrew Southam</td><td>MPF</td><td>University of Birmingham</td></tr><tr><td>Alison Woodward</td><td>MPF</td><td>University of Nottingham</td></tr></table>

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EARLY CAREER SCIENTIFIC COMMITTEE

<table><tr><td>Jordi Capellades</td><td>Spanish Metabolomics Society</td></tr><tr><td>Sylvain Dechaumet</td><td>RFMF junior</td></tr><tr><td>Yoric Gagnebin</td><td>Swiss Metabolomics Society</td></tr><tr><td>Kate Gallagher</td><td>MPF</td></tr><tr><td>Natasa Giallourou</td><td>MPF</td></tr><tr><td>Corentine Goossens</td><td>RFMF junior</td></tr><tr><td>Katharina Herzog</td><td>Nordic Metabolomics Society</td></tr><tr><td>Justine Leenders</td><td>RFMF junior</td></tr><tr><td>Youzhong Liu</td><td>YoungNMC</td></tr><tr><td>Clément Regnault</td><td>Scottish Metabolomics Network</td></tr><tr><td>Sara Tortorella</td><td>Italian Metabolomics Network</td></tr><tr><td>Alison Woodward</td><td>MPF</td></tr></table>

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LOCAL ORGANISING COMMITTEE

<table><tr><td>Floriant Bellvert</td><td>MetaboHub-Metatoul</td></tr><tr><td>Cécilia Berges</td><td>MetaboHub-Metatoul</td></tr><tr><td>Justine Bertrand Michel</td><td>MetaboHub-Metatoul</td></tr><tr><td>Maxime Chazalviel</td><td>MedDay Pharmaceuticals</td></tr><tr><td>Frédérique Courant</td><td>Université de Montpellier</td></tr><tr><td>Sandrine Ellero-Simatos</td><td>INRA-Toxalim</td></tr><tr><td>Yoann Gloaguen</td><td>Berlin Institute of Health</td></tr><tr><td>Anne-Emmanuelle Hay</td><td>Université de Lyon</td></tr><tr><td>Fabien Jourdan</td><td>MetaboHub-Metatoul</td></tr><tr><td>Hanna Kulyk Barbier</td><td>MetaboHub-Metatoul</td></tr><tr><td>Loïc Mervant</td><td>MetaboHub-Metatoul</td></tr><tr><td>Jean-Charles Portais</td><td>MetaboHub-Metatoul</td></tr><tr><td>Pablo Rodriguez Mier</td><td>INRA-Toxalim</td></tr></table>

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Workshops
**Workshop 1: W1**

*Meet the editor*
(January 21st 8:30pm-10pm)

**Purpose of the workshop:**

Attendees will meet editors and reviewers to discuss the following points:

* Scientific inspiration versus literary perspiration: What makes a good (Metabolomics) paper?

* What you should never do in papers.

* Refereeing process: what happens once I have submitted my paper to the abyss...

* Scientific ethics: objectivity versus subjectivity

**Skills acquired at the end of the workshop:**

* A general understanding of the publication process.

* How to be a good reviewer and how to respond to reviewers (good and bad!)

* Some further ideas to make your paper better.

**Intended audience and prerequisites:**

* **Public:** priority to early career but can go beyond

* **Prerequisites:** did publish or plan to

**Organisers:**

Katharina Herzog, Kate Gallagher, Natasa Giallourou, Roy Goodacre and Jules Griffin.
Workshop 2: W2

*Interactive tutorial, How to process LC-MS data with workflow4metabolomics.org*
*(January 21st, 9am-12am)*

**Purpose of the workshop:**

Metabolomics data analysis is a complex, multistep process, which is constantly evolving with the development of new analytical technologies, mathematical methods, and bioinformatics tools and databases.

The Workflow4Metabolomics(W4M) project aims at developing full LC/MS,GC/MS,FIA/MS and NMR pipelines using Galaxy framework for data analysis including preprocessing, normalization, quality control, statistical analysis and annotation steps.

https://workflow4metabolomics.org

**Intended audience and prerequisites:**

* Public: Researchers or students involved in metabolomics by LC-MS

* Prerequisites: basic knowledge's of the usual data processing steps for LC-MS metabolomics

**Skills acquired at the end of the workshop:**

* What is Galaxy?

* Comprehend the diversity of LC-MS metabolomic data analysis.

* Get familiar with the main steps constituting a metabolomic workflow for untargeted LC-MS analysis.

* Evaluate the potential of a workflow approach when dealing with LC-MS metabolomic data.

**Organisers:**

Franck Giacomoni, Yann Guitton and Marie Tremblay Franco

**References:**

https://workflow4metabolomics.org

Guitton et al (2017) Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online
Workshop 3: W3

metaRbolomics hackathon plus

(January 21st 2020, 1pm-3:45pm.)

**Purpose of the workshop:**

The idea of this hackathon is to have a quiet space where active developers and advanced users can work on integrating their packages in R, adding new data to wikidata or wikipathways, mapping identifiers with BridgeDb, tailor-making MetFrag databases to answer identification questions. The hackathon will start with Jo Rainer presenting the new R packages at https://RforMassSpectrometry.org and then we will just hack away! With the new packages it will be relatively easy to implement custom "backends" to load e.g. MGF files into R, or provide access to custom, lab specific spectral libraries - lots of community contributions are possible!

**Intended audience and prerequisites:**

- **Public**: Computational metabolomics pros – active developers or advanced users

- **Prerequisites**: Problems to solve, questions to ask, data to integrate in open tools, ideas to discuss with active developers, desire to hack and not just talk – organisers will be happiest hidden behind laptop screens solving problems. BYO laptop(s).

**Skills acquired at the end of the workshop:**

Ability to integrate your R code into metaRbolomics collection, ability to add new data to wikidata/wikipathways, custom design your own high throughput non-target screening with MetFrag, map your identifiers with BridgeDb etc etc

**Organisers:**

Johannes Rainer, Emma Schymanski, Egon Willighagen, Denise Slenter

**References:**

The metaRbolomics Toolbox in Bioconductor and beyond:

https://www.mdpi.com/2218-1989/9/10/200

BridgeDb tutorial: https://tess.elixir-europe.org/materials/bridgedbr-tutorial
Workshop 4: W4

How to chair sessions
(January 21st 2020, 2:45pm-4:00pm)

**Purpose of the workshop:**

The aim of the workshop is to provide guidelines and exchange on the role of session chairs. Robert Hall and Jennifer Kirwan will share their experience and answer questions.

**Intended audience and prerequisites:**

- **Public:** early career scientists

- **Prerequisites:** no specific requirements

**Skills acquired at the end of the workshop:**

Guidelines on how to efficiently chair a session and be part of a fruitful conference.

**Organisers:**

Natasa Giallourou, Robert Hall and Jennifer Kirwan
Workshop 5: W5

*Advanced approaches for the analysis of metabolomics data: From experimental design to knowledge discovery*
*(January 21st 2020, 4pm-6pm.)*

**Purpose of the workshop:**

The objective of this workshop is to introduce key concepts and chemometric methods (experimental strategies, data structures, multivariate models) for the analysis of multifactorial, multiblock and longitudinal metabolomic data. Several examples will be used to illustrate these different aspects.

**Intended audience and prerequisites:**

- **Public**: Researchers, engineers or students working in metabolomics and looking for information on data analysis methods and tools dedicated to complex data structures.

- **Prerequisites**: Basics of metabolomic data analysis.

**Skills acquired at the end of the workshop:**

- Overall understanding of the different experimental data structures.

- Selection of appropriate chemometric methods to extract knowledge from metabolomic data.

**Organisers:**

Serge Rudaz & Julien Boccard

**References:**

1. Harnessing the complexity of metabolomic data with chemometrics. J. Boccard, S. Rudaz. Journal of Chemometrics (2014), 28(1), 1-9.

2. Exploring Omics data from designed experiments using Analysis of Variance Multiblock Orthogonal Partial Least Squares. J. Boccard, S. Rudaz. Analytica Chimica Acta (2016), 920, 18-28.

3. Integration of metabolomic data from multiple analytical platforms: toward extensive coverage of the metabolome in Data Analysis for Omic Sciences: Methods and Applications, J. Boccard, S. Rudaz. Elsevier (2018).

4. A scoring approach for multi-platform acquisition in metabolomics. J. Pezzatti, V. González-Ruiz, S. Codesido, Y. Gagnebin, A. Joshi, D. Guillarme, J. Schappler, D. Picard, J. Boccard, S. Rudaz. Journal of Chromatography A (2019), 1592, 47-54.
Plenary and Keynote Speakers
Plenary Speaker P1:

**Pr. Warwick (Rick) Dunn**

Professor of Analytical and Clinical Metabolomics, School of Biosciences and Phenome Centre Birmingham, University of Birmingham, UK

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Professor Warwick (Rick) Dunn is Professor of Analytical and Clinical Metabolomics, Director of Mass Spectrometry in the £8M Phenome Centre Birmingham and is Director of the Birmingham Metabolomics Training Centre at the University of Birmingham in the UK. Rick trained as an analytical chemist with a PhD focussed on placing mass spectrometers on industrial scale chemical process plants. Rick has been applying metabolomics for 20 years and now applies LC and MS platforms to the study of metabolites in mammalian biological systems though targeted and untargeted metabolomics approaches to study human health and disease across the life course. One of his research passions is increasing the efficiency and confidence of metabolite annotation in untargeted studies. His research includes the characterization of the complexity of electrospray ionization data to enhance metabolite annotation through to the optimal acquisition of MS/MS data to increase the number of metabolites annotated. His second research passion is studying how metabolites impact across time on biological processes in human health, disease and ageing in diverse areas including endocrinology, cancer, exercise/nutrition, ageing, trauma and immunological diseases.
**Plenary Speaker P2:**
**Dr. Etienne Lefai**

Université d'Auvergne, INRAE, Unité de Nutrition Humaine, Clermont-Ferrand, France.

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Dr. Etienne Lefai, is a research director at the INRAE in Theix, whose work on the Scandinavian brown bear should make it possible to develop new strategies to combat muscle atrophy. Etiene Lefai studies the underlying mechanisms of brown bear hibernation which have not been understood yet. Together with a large international network of collaborator he develops new approaches combining molecular and cellular studies of bear muscle as well as human muscle cells exposed to bear serum. His recent demonstration of trans-species effects of bear serum controlling protein degradation in cultured human muscle cell holds promising potential. His exploration of winter bear serum therefore holds potential for developing new tools to fight human muscle atrophy and related metabolic disorders.
Plenary Speaker P3:
**Dr. Ingela Lanekoff**

Department of Chemistry - BMC, Analytical Chemistry, University of Uppsala, Sweden.

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Dr. Ingela Lanekoff is a Docent and Associate Professor in Analytical Chemistry at Uppsala University, Sweden, where she heads the Division of Analytical Chemistry and her research group in surface sampling and imaging mass spectrometry. Ingela earned her PhD from Gothenburg University, Sweden, in 2011 after working with Prof. Andrew Ewing and joined the group of Prof. Julia Laskin (currently at Purdue University) at PNNL, USA, as a post doc the same year. In 2014, she started her own group at Uppsala University based on external funding from the Swedish Research Council and the Swedish Foundation for Strategic Research. Her research is focused on method and technique developments to generate 2d maps of metabolite and lipid distributions in tissue sections and detection of metabolites in single cells. Her research also includes collaborations to understand metabolic alterations in disease and dysfunction, such as diabetes and premature birth, by developing both targeted and untargeted analytical methods that enhance the signals of low abundant species and provide novel data.
Plenary Speaker P4:

Pr. Marcel Utz

Head of Magnetic Resonance, School of Chemistry, University of Southampton, UK

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Marcel obtained a MSc in materials science from ETH Zürich in 1994. He then joined the research groups of Prof. Ueli Suter in Polymer Science, and of Prof. Richard R. Ernst in Physical Chemistry at ETH as a doctoral candidate. In 1998, he obtained his doctorate, with a thesis on solid-state NMR investigations of glassy polymers under plastic deformation. In 1999, he joined the group of Prof. Pablo DeBenedetti at Princeton University as a postdoc, with a stipend from the Swiss National Science Foundation. A year later, he joined the Institute of Materials Science and the Department of Physics at the University of Connecticut as an Assistant Professor. He was promoted to Associate Professor with Tenure in 2006. In the same year, he moved to the University of Virginia, where he joined the department of Mechanical and Aerospace Engineering. In 2012, Marcel moved to the UK, joining the School of Chemistry at the University of Southampton. He was promoted to a personal chair in 2014. He currently heads the section of Magnetic Resonance within the School of Chemistry. Marcel lives in Winchester, UK, with his wife Song and their son Nicolas.
Plenary Speaker P5:
**Dr. Maria Fedorova**

AG Bioanalytik, Institut für Bioanalytische Chemie, Fakultät für Chemie und Mineralogie, Universität Leipzig.

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Maria Fedorova studied Biochemistry at Saint-Petersburg State University, Russia and obtained her PhD at Faculty of Chemistry and Mineralogy, Leipzig University, Germany. Now she is a group leader at the Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, at the University of Leipzig. Her research is focused on development and optimization of chromatography and mass spectrometry methods for analysis of lipids and their modified forms. Dr Fedorova group works on implementation of high throughput LC-MS methods in discovery lipidomics targeting in-depth identification and quantification of human lipidome in variety of tissues. By combining lipidomics data with investigation of related proteins and protein post-translational modifications via systems medicine approach, Dr Fedorova aims for a deeper understanding of pathophysiology of obesity, insulin resistance, type II diabetes and cardiovascular disorders.
Keynote Speaker K1:

Pr. Zoran Nikoloski

Max-Planck-Institut für Molekulare Pflanzenphysiologie, Wissenschaftspark Potsdam-Golm, Germany.

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Pr. Zoran Nikoloski is a Chair of Bioinformatics at the University of Potsdam and leads a Cooperative Research Group at the Max Planck Institute of Molecular Plant Physiology in Potsdam, Germany. Zoran trained as a computer scientist with a PhD focused on network-based research from the University of Central Florida, Orlando, USA. The focus of his groups is on systems biology and mathematical modeling of plant metabolism and its relations to protein-protein interaction and gene regulatory networks. His research includes the development of large-scale metabolic networks of model and crop plants and their usage in conjunction with large-scale metabolomics, proteomics, and transcriptomics data to predict complex, agronomically relevant traits. Recent work includes integration of metabolomics data and large-scale metabolic models with genomics data to bridge the gap between genotype and phenotype.
**Keynote Speaker K2**

**Pr. Marta Cascante**

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Marta Cascante is Full Professor at the Department of Biochemistry and Molecular Biology at University of Barcelona and leader of the Integrative Systems Biology, Metabolomics and Cancer team. She has authored over 250 publications. She is partner of European translational research projects (H2020) in the field of systems medicine and metabolomics and member of the editorial advisory boards of Metabolomics and BMC systems Biology. She has been distinguished in 2015 with Icrea Academia Prize and with the Narcis Monturiol Medal of the Catalan Government for her scientific merits.

Research interests are focused on cancer and metabolic diseases with the goal of elucidating the networks and pathways that are fundamental in their development and progression. More specifically, the Integrative Systems Biology, Metabolomics and Cancer team's mission is to devise new therapeutic strategies to forestall therapeutic resistance in cancer through integrative approaches that afford a rational identification of molecular targets, with emphasis on metabolic regulators, for effective synergistic combinations with chemotherapeutics with demonstrated efficacy and/or natural products. To tackle our mission, our team assembles high-level expertise in comprehensive metabolic analysis, bio-computational approaches, generation of cellular and pre-clinical animal models, functional genomics and drug discovery. We have generated proof-of-principle evidences that resulted in publications and a patent application of the effectiveness of this approach. We intend to generalize our approaches to inhibitors currently in use as first-in-line or emerging therapies, by unveiling and targeting the corresponding adaptive metabolic responses in order to overcome therapeutic resistance in cancer. Furthermore, in the framework of H2020 project "PheNoMenal", coordinated by EBI, her team has been contributing to develop and deploy an e-infrastructure that makes it feasible for healthcare researchers to process analyze and mine molecular phenotyping data, to facilitate large-scale data analysis in the coming age of Precision Medicine.
Keynote Speaker K3.

**Dr. Emma Schymanski**

Environmental Cheminformatics and FNR ATTRACT Fellow. LCSB, University of Luxembourg.

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Associate Professor Emma Schymanski is head of the Environmental Cheminformatics group at the Luxembourg Centre for Systems Biomedicine, University of Luxembourg. In 2018 she received a Luxembourg National Research Fund (FNR) ATTRACT Fellowship to establish her group in Luxembourg. She has over 50 peer-reviewed publications, book chapters and a book. Her research combines cheminformatics and computational mass spectrometry approaches to elucidate the unknowns in complex samples, primarily with non-target screening and relate these to environmental causes of disease. An advocate for open science, she is involved in and organizes several European and worldwide activities to improve the exchange of data, information and ideas between scientists to push progress in this field, detailed here:

https://wwwen.uni.lu/lcsb/research/environmental_cheminformatics
Keynote Speaker K4

**Pr. Tuulia Hyötyläinen**

Professor of Chemistry at Örebro University, Sweden.

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Professor Tuulia Hyötyläinen is Professor of Chemistry at Örebro University, Sweden. She has a PhD in analytical chemistry from University of Helsinki, Finland. She has over 20 years of experience in analytical chemistry, mass spectrometry, environmental analysis and metabolomics. One of her main focus areas is the development of robust workflows from sampling, sample preparation, analysis to data preprocessing and data mining, and quality control as well as development of methodologies for the identification of unknown metabolites. Currently, her main emphasis is to elucidate the impact of environmental factors in human health, and to identify effect-based markers of environmental exposure and the metabolic pathways behind the adverse outcomes due to the exposure. Particularly, she focuses on the impact of exposure during fetal development and early life, with focus on autoimmune diseases, particularly T1D, and most recently, also coeliac disease and irritable bowel syndrome.
Program
Tuesday January 21st 2020

Workshop day

University of Toulouse

<table><tr><td>9:00 am - 12:00 pm</td><td colspan="2">W1: Interactive tutorial - How to process LC-MS data with workflow4metabolomics.org</td></tr><tr><td>12:00 pm - 1:00 pm</td><td colspan="2">LUNCH BREAK - ON YOUR OWN</td></tr><tr><td colspan="3">Parallel Workshop A (room TBA)</td></tr><tr><td>1:00 pm - 3:45 pm</td><td>W2: metaRbolomics hackathon plus</td><td>2:45 pm - 4:00 pm W5: How to chair sessions</td></tr><tr><td>4:00 pm - 6:00 pm</td><td colspan="2">W4: Advanced approaches for the analysis of metabolomics data</td></tr><tr><td>6:00 pm - 6:30 pm</td><td colspan="2">BREAK</td></tr><tr><td>6:30 pm - 8:00 pm</td><td colspan="2">Social event : Toulouse walking tour</td></tr><tr><td>8:30 pm - 10 pm</td><td colspan="2">W6: Meet the editor</td></tr></table>
Wednesday 22nd 2020

Auditorium Marthe Condat

<table><tr><td>8:00 am - 9:00 am</td><td>Registration open</td></tr><tr><td>9:00 am - 9:30 am</td><td>Opening Ceremony</td></tr></table>

Methodological & Technological developments

Chairs: Justine Leenders & Marta Cascante

<table><tr><td>9:30 am - 10:15 am</td><td><b>Plenary 1:</b> From analytical chemistry to clinical metabolomics: reflections on the experimental workflow using adrenal cancer and hypervitaminosis A case studies<br/>Rick Dunn, UK</td></tr><tr><td>10:15 am - 10:35 am</td><td><b>O1:</b> Compact NMR spectroscopy and advanced pulse sequences: the perfect match for the online and real-time monitoring of bioprocesses<br/>Jonathan Farjon, France</td></tr><tr><td>10:35 am - 11:00 am</td><td>Coffee break</td></tr></table>

Microbiology & Biotechnologies

Chairs: Alison Woodward & Karl Burgess

<table><tr><td>11:00 am - 11:20 am</td><td><b>O2:</b> ScalaFlux: a scalable approach to quantify fluxes in metabolic subnetworks<br/>Pierre Millard, France</td></tr><tr><td>11:20 am - 11:40 am</td><td><b>O3:</b> Rational optimization of a novel synthetic pathway for methanol utilization in <i>E. coli</i><br/>Camille Peiro, France</td></tr><tr><td>11:40 am - 11:55 am</td><td><b>F1:</b> Characterising the bidirectional biomolecular interactions between the intestinal microbiota and host drug metabolism<br/>Marine Letertre, UK<br/><b>F2:</b> Rapid kinetics of lipid second messengers controls secretion in Toxoplasma parasites<br/>Nicholas Katris, France<br/><b>F3:</b> Searching for differential metabolites between Mycobacterium tuberculosis and the MTBVAC vaccine using non-targeted approach metabolomics<br/>Caridad Diaz Navarro, Spain</td></tr><tr><td>11:55 am - 12:10 pm</td><td><b>S1:</b> <i>Sciex</i>: A New Tool in Metabolomics: SWATH® Acquisition Analysis for Global Profiling and Quantitation<br/>Joerg Schlottlerbec, Sciex</td></tr><tr><td>12:10 pm - 1:00 pm</td><td rowspan="3">Lunch & Posters<br/>Career information<br/>S2: Bruker lunch seminar. TBA</td></tr><tr><td>1:00 pm - 1:45 pm</td></tr><tr><td>1:50 pm - 2:20 pm</td></tr></table>

Agriculture & Food

Chairs: Loic Mervant & Steffen Neumann

<table><tr><td>Chair:</td><td><b>Plenary 2:</b> Physical inactivity and preservation of muscle mass: Lessons from hibernation<br/>Etienne Lefai, France</td></tr><tr><td>3:15 pm - 3:35 pm</td><td><b>O4:</b> Mass Spectrometry Imaging (MALDI-MSI) and LC-MS/MS reveal spatial distribution of metabolic responses to Leptosphaeria maculans infection in stems of Brassica napus<br/>Anani Amegan Missinou, France</td></tr><tr><td>3:35 pm - 4:05 pm</td><td><b>Keynote 1:</b> Insights from integration of metabolomics data in large-scale plant metabolic models<br/>Zoran Nikoloski, Germany</td></tr><tr><td>4:05 pm - 4:20 pm</td><td><b>F4:</b> Elicitor-specific reprogramming of potato's metabolome<br/>Rafaela Martin, France<br/><b>F5:</b> Biosource-guided network annotation and visualization for untargeted metabolomics<br/>Santiago Codesido, Switzerland<br/><b>F6:</b> Recommending substructures for unknown tandem mass spectra<br/>Youzhong Liu, Belgium</td></tr><tr><td>4:20 pm - 5:00 pm</td><td>Coffee break</td></tr></table>

Statistical & Computational developments

Chairs: Sara Tortorella & Lorraine Brennan

<table><tr><td>5:00 pm - 5:20 pm</td><td><b>O5:</b> Mix it up! How to combine multiple LC-MS modes and design of experiments to understand multifactorial biological phenomena<br/>Victor Gonzalez Ruiz, Switzerland</td></tr><tr><td>5:20 pm - 5:35 pm</td><td><b>EC1:</b> ASICS: identification and quantification of metabolites in complex 1H NMR spectra<br/>Gaëlle Lefort, France</td></tr><tr><td>5:35 pm - 5:55 pm</td><td><b>O6:</b> Modelling intrahepatic metabolic rewiring during the onset of obesity using arterio-venous blood metabolomics profiles<br/>Nathalie Poupin, France</td></tr><tr><td>5:55 pm - 6:10 pm</td><td><b>S3:</b> Shimadzu. Profiling experiments with DIA on the Ultra-Fast Q-TOF LCMS-9030<br/>Thierry Legoupil</td></tr><tr><td>6:10 pm - 8:00 pm</td><td>Welcome drinks & Posters</td></tr></table>
Thursday 23rd 2020

Auditorium Marthe Condat

Methodological & Technological developments

Chairs: Jordi Capellades & Pietro Franceschi

<table><tr><td>9:00 am - 9:45 am</td><td><strong>Plenary 3:</strong> Spatial metabolomics in untargeted and quantitative studies<br>Ingela Lanekoff, Sweden</td></tr><tr><td>9:45 am - 10:05 am</td><td><strong>O7:</strong> Coupling metabolomic information coming from both LC-MS and Imaging-MS to characterize experimental induction of coral larvae metamorphosis<br>Alain Paris, France</td></tr><tr><td>10:05 am - 10:25 am</td><td><strong>O8:</strong> NMR metabolomics: a new tool in forensic sciences<br>Emanuela Locci, Italy</td></tr><tr><td>10:25 am - 10:40 am</td><td><strong>EC2:</strong> Machine learning-based classification to improve Gas Chromatography-Mass Spectrometry data processing<br>Yoann Gloaguen, Germany</td></tr><tr><td>10:40 am - 11:05 am</td><td><strong>Coffee break</strong></td></tr></table>

Human health

Chairs: Corentine Goossens & Matej Oresic

<table><tr><td>11:05 am - 11:35 am</td><td><strong>Keynote 2:</strong> Unveiling the metabolic phenotypes and vulnerabilities underlying metastasis and drug resistance<br>Marta Cascante, Spain</td></tr><tr><td>11:35 am - 11:50 am</td><td><strong>EC3:</strong> High throughput untargeted surface metabolite profiling of formalin-fixed paraffin embedded (FFPE) brain tumour tissue microarrays (TMA) using LESA-MS/MS and OrbiSIMS<br>Joris Meurs, UK</td></tr><tr><td>11:50 am - 12:05 pm</td><td><strong>EC4:</strong> Computational modeling of p53 metabolic functions<br>Carlo De Blasio, France</td></tr><tr><td rowspan="3">12:05 pm - 12:20 pm</td><td><strong>F7:</strong> Towards precision diagnostics: Untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients<br>Kulkarni Purva, NL</td></tr><tr><td><strong>F8:</strong> Metabolic maturation in the first two years of life in resource-constrained settings and its association with postnatal growth<br>Natasa Giallourou, UK</td></tr><tr><td><strong>F9:</strong> Rapid LA-REIMS and comprehensive UHPLC-HRMS for metabolomics of biofluids in metabolic disorders<br>Lieven Van Meulebroek, Belgium</td></tr><tr><td>12:20 pm - 1:30 pm</td><td><strong>Lunch & Posters</strong></td></tr><tr><td>1:30 pm - 2:00 pm</td><td><strong>Lunch & Posters</strong><br><strong>S4:</strong> Thermo lunch seminar<br>Escape the Identity Crisis with Intelligence<br>Driven Mass Spectrometry<br>Amanda Souza, Marketing Manager, Life Sciences Mass Spectrometry & Anas Kamleh, Sales Support Expert - Thermo Fisher Scientific</td></tr></table>

Environmental science & Plants

Chairs: Sylvain Dechaumet & Anne-Emmanuelle Hay

<table><tr><td>2:00 pm - 2:30 pm</td><td><strong>Keynote 3:</strong> Environmental Cheminformatics: Case Study of Thirdhand Smoke in House Dust<br>Emma Schymanski, Luxembourg</td></tr><tr><td>2:30 pm - 2:50 pm</td><td><strong>O9:</strong> Relating metabolomics and yield in a network of experiments<br>Sylvain Prigent, France</td></tr><tr><td>2:50 pm - 3:10 pm</td><td><strong>O10:</strong> Understanding the regulation of proline content in source leaves of winter oilseed rape by using transcriptomic and 15N-isotope tracing approaches<br>Younes Dellèro, France</td></tr><tr><td>3:10 pm - 3:25 pm</td><td><strong>S5, Waters:</strong> Multi-dimensional Metabolomics using Ion Mobility Workflows - Future Advances<br>Sarah Lennon, Waters</td></tr><tr><td>3:25 pm - 4:00 pm</td><td><strong>Coffee break</strong></td></tr></table>

Methodological & Technological developments

Chairs: Youzhong Liu & Patrick Giraudeau

<table><tr><td>4:00 pm - 4:45 pm</td><td><strong>Plenary 4:</strong> Micro-NMR for Metabolomic Observation of Microfluidic Culture Systems<br>Marcel Utz, UK</td></tr><tr><td>4:45 pm - 5:05 pm</td><td><strong>O11:</strong> Coupling UPLC-MS with Microdialysis for Metabotyping Burn Injury<br>Elizabeth Want, UK</td></tr><tr><td>5:05 pm - 5:20 pm</td><td><strong>EC5:</strong> Proton detected 31P NMR methods for metabolomics<br>Neil Cox, France</td></tr><tr><td>5:20 pm - 5:40 pm</td><td><strong>O12:</strong> The power of LC-MS based multiomics to explore human adipogenesis<br>Evelyn Rampler, Austria</td></tr><tr><td>5:40 pm - 7:15 pm</td><td><strong>Posters Session</strong></td></tr><tr><td>8:00 pm - 12:00 am</td><td><strong>Gala Dinner</strong></td></tr></table>
Friday 24th 2020

Auditorium Marthe Condat

Environmental science

Chairs: Natasa Giallourou & Serge Rudaz

<table><tr><td>9:00 am - 9:30 am</td><td>Keynote 4: Prenatal chemical exposure modulates neonatal serum phospholipids, increasing later risk of type 1 diabetes<br/>Tuulia Hyötyläinen, Sweden</td></tr><tr><td>9:30 am - 9:50 am</td><td>O13: TheWormJam 2019 vintage - New developments in the international research community for C. elegans systems biology and metabolic modelling<br/>Horst Joachim Schirra, Australia</td></tr><tr><td>9:50 am - 10:05 am</td><td>EC6: Child multi-omics signatures of the early life exposome<br/>Léa Maitre, Spain</td></tr><tr><td>10:05 am - 10:35 am</td><td>Coffee break</td></tr></table>

Human health

Chairs: Kate Gallagher & Justine Bertrand Michel

<table><tr><td>10:35 am - 11:20 am</td><td>Plenary 5: Lipidomics signatures of metabolic diseases<br/>Maria Fedorova, Germany</td></tr><tr><td>11:20 am - 11:40 am</td><td>O14: Optimisation of Biofluid and Tissue Metabolite and Lipid Extraction for Clinical Metabolic Phenotyping<br/>Andrew Southam, UK</td></tr><tr><td>11:40 am - 12:00 pm</td><td>O15: Integrated metabolomics and transcriptomics analysis of intra-tumour heterogeneity in paediatric brain tumours<br/>Alison Woodward, UK</td></tr><tr><td>12:00 pm - 12:20 pm</td><td>O16: Identifying novel activators of regulatory T-cell metabolism<br/>Celia Berkers, The Netherlands</td></tr><tr><td>12:20 pm - 12:35 pm</td><td>Closing Ceremony & Awards</td></tr></table>

O: Oral Presentation - EC: Early Career oral presentation - F: Flash presentation (180 seconds) - S: Sponsor presentation
Oral abstracts
Plenary 1: P1

*From analytical chemistry to clinical metabolomics: reflections on the experimental workflow using adrenal cancer and hypervitaminosis A case studies*

William NASH<fnref n="1" />, Lukas NAJDEKR<fnref n="1" />,<fnref n="2" />, Elliott PALMER<fnref n="1" />, Andrew SOUTHAM<fnref n="1" />,<fnref n="3" />, Andris JANKEVICS<fnref n="1" />,<fnref n="4" />, Thomas LAWSON<fnref n="1" />, Gavin LLOYD<fnref n="1" />,⁵, Anthony OXLEY<fnref n="1" />, Kieran FINNEY<fnref n="1" />, Vasilis CHORTIS<fnref n="1" />, Alessandro PRETE<fnref n="1" />, Mark VIANT<fnref n="1" />, Ralf WEBER<fnref n="1" />,<fnref n="2" />, Helen COOPER<fnref n="1" />, Georg LIETZ<fnref n="1" />, Wiebke ARLT<fnref n="1" /> and Warwick DUNN<fnref n="1" />,<fnref n="4" />

**Introduction**

The application of untargeted and semi-targeted metabolomics tools for clinical research is increasing at an exponential rate worldwide. The metabolomics workflow [1,2] from experimental design and sample collection/preparation through data acquisition to data processing and statistical analysis is well defined though developments and validation of many of these processes are required in areas including sample collection for large human studies, standardized analytical assays [3], quality assurance and quality control [4,5] as well as metabolite identification [6]. In this presentation, I will discuss recent advances in Birmingham in analytical chemistry approaches and metabolite annotation strategies and demonstrate their application using case studies.

**Technological and methodological innovation**

New advances in the metabolomics workflow applying ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) and bioinformatics are providing enhanced coverage of the metabolome, are increasing sample throughput, and are increasing annotation/identification rates. Dried blood spots are an inexpensive and simple strategy for individuals to collect blood samples; their stability, volume normalisation and analysis will be described [7]. The recent development of standardized UHPLC-MS assays to enhance metabolite annotation and enable medium-throughput data collection for hundreds of biofluid, cell and tissue samples will be discussed. Finally, new developments in the collection and use of chromatographic retention time, full scan MS1 and MS/MS data will be described with demonstrations on the quantity and quality of metabolite annotations.

**Results and impact**

The application of standardized untargeted UHPLC-MS metabolomics assays in clinical metabolomics will then be described. Case studies will include (1) understanding hypervitaminosis A in animal models and humans along with biomarker identification and (2) defining metabolic changes in adrenal tumours (adrenocortical carcinoma and adrenocortical adenomas). I will finish with some reflections on the next steps to strengthen metabolomics in clinical studies.

**References**

[1] Brown et al. 2005. Metabolomics. 1, 39-51

[2] Dunn et al. 2011. Chemical Society Reviews. 40, 387-426

[3] Dunn et al. 2011. Nature Protocols. 6, 1060-1083

[4] Broadhurst et al. 2018. Metabolomics. 14, 72

[5] Beger et al. 2019. Metabolomics. 15, 4

[6] Nash et al. 2018. Trends in Analytical Chemistry. Article 115324

[7] Palmer et al. 2019. Analytical Chemistry. In press, doi: 10.1021/acs.analchem.9b02577

<footnote n="1">¹ School of Biosciences, University of Birmingham, Birmingham, UK</footnote>

<footnote n="2">² Phenome Centre Birmingham, University of Birmingham, Birmingham, UK</footnote>

<footnote n="3">³ Human Nutrition Research Centre, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, UK</footnote>

<footnote n="4">⁴ Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK</footnote>
Oral 1: 01

Compact NMR spectroscopy and advanced pulse sequences: the perfect match for the online and real-time monitoring of bioprocesses

Jonathan FARJON<fnref n="1" />, Dylan BOUILLAUD<fnref n="1" />,<fnref n="2" />, Benoît CHARRIER<fnref n="1" />, Delphine DROUIN<fnref n="2" />, Vladimir HEREDIA<fnref n="2" />, Olivier GONCALVES<fnref n="2" />, Patrick GIRAUDEAU<fnref n="1" />

Introduction

Microalgae family host a tremendous source of chemodiversity. Moreover, thanks to their metabolic plasticity, they are able to redirect their carbon fluxes toward the biosynthesis of biomolecules of interest, i.e. lipids when grown in nitrogen limited cultures [1]. In order to monitor and therefore optimize their culture conditions, it is possible to exploit NMR spectroscopy as a suitable non-destructive approach in order to provide real time structural, kinetic and quantitative information.

Technological and methodological innovation

Emerging benchtop NMR spectrometers are low-cost, transportable and robust tools, but they suffer from a low sensitivity and a weak spectral resolution. In order to circumvent these limitations, we recently implemented advanced high-resolution NMR techniques on a 1T benchtop NMR instrument equipped with a gradient coil such as solvent suppression schemes [2]. Thanks to these developments, benchtop NMR can be applied to monitor complex bioprocesses in real time [3].

## Results and impact

Our compact NMR approach was applied to the monitoring of microalgae cultures in their cultivation medium [4]. The benchtop spectrometer was coupled to a photobioreactor to monitor the total lipid production kinetics over 3 weeks under real culture conditions. Results are in perfect agreement with those obtained relying on a reference off-line GC-FID [5]. This online, non-invasive and automated approach helps making NMR accessible to the real-time monitoring and control of metabolic processes.

References

[1] Y. Christi et al. 2008. Trends Biotechnol. 26 and 126-131

[2] B. Gouilleux et al. 2017. Magn. Reson. Chem. 55 and 91-98

[3] D. Bouillaud et al. 2019. Magn. Reson. Chem. In press and 1-11

[4] D. Bouillaud et al. 2019. Algal Res. https://doi.org/10.1016/j.algal.2019.101624

[5] D. Bouillaud et al. 2019. In preparation

<footnote n="1">¹ Université de Nantes, CEISAM, UMR CNRS 6230, BP 92208. 2 rue de la Houssinière, 44322 Nantes Cedex 3</footnote>

<footnote n="2">² Université de Nantes, GEPEA, UMR CNRS 6144, 37 boulevard de l'Université, 44600 Saint-Nazaire Cedex</footnote>
## Oral 2: O2

### ScalaFlux: a scalable approach to quantify fluxes in metabolic subnetworks

Pierre Millard<fnref n="1" />; Uwe Schmitt<fnref n="2" />; Patrick Kiefer<fnref n="3" />; Julia A. Vorholt<fnref n="3" />; Stéphanie Heux<fnref n="1" />; Jean-Charles Portais<fnref n="4" />,<fnref n="5" />

## Introduction

¹³C-metabolic flux analysis (¹³C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology [1]. Current ¹³C-MFA approaches model label propagation starting from the extracellular ¹³C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point.

## Technological and methodological innovation

We have developed a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis [2], can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current ¹³C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability.

## Results and impact

We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of *Saccharomyces cerevisiae* mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches [3]. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

## References

[1] Heux et al. 2017. Current opinion in biotechnology. DOI: 10.1016/j.copbio.2016.10.010

[2] Millard et al. 2019. BioRxiv preprint. DOI: 10.1101/735308

[3] Castano-Cerezo et al. 2019. Metabolomics. DOI: 10.1007/s11306-019-1580-8

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

<footnote n="2">² Scientific IT Services, ETH Zurich, 8092, Zurich, Switzerland</footnote>

<footnote n="3">³ Institute of Microbiology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland</footnote>

<footnote n="4">⁴ MetaToul-MetaboHUB, National infrastructure of metabolomics and fluxomics, Toulouse, France</footnote>

<footnote n="5">⁵ STROMALab, Université de Toulouse, INSERM U1031, EFS, INP-ENVT, UPS, Toulouse, 11 France</footnote>
Oral3: O3

Rational optimization of a novel synthetic pathway for methanol utilization in E. coli

Camille PEIRO¹, Alessandro DE SIMONE¹, Cláudia VICENTE¹, Brice ENJALBERT¹, Floriant BELLVERT¹, Stéphanie HEUX¹

¹ TBI, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France

Introduction

Methanol is an attractive feedstock due to its low price and the possibility to be produced from renewable sources. Integrating methylotrophy in *Escherichia coli* has attracted increasing attention for the production of value-added compounds. Here, we are working on a novel synthetic pathway for methanol assimilation in *E. coli* composed of a methanol dehydrogenase (Mdh) and a dihydroxyacetone synthase (Das), which ultimately produce dihydroxyacetone (DHA) and glyceraldehyde-3-phosphate (GA3P) [1].

Technological and methodological innovation

We used a high-throughput double transformation robotic platform to construct a combinatorial library of Mdh and Das orthologs resulting in 266 different combinations. To identify the most efficient one, we tested the *in vivo* activity of each combination. A high-throughput screening was developed using ¹³C-methanol and we followed by MS the mean enrichment of a metabolite (i.e. PEP) that serves as a proxy for methanol assimilation.

Results and impact

One combination of Mdh and Das was of great interest as more than 20% of mean enrichment was reached at the PEP level [1]. We identified genetic targets [2] and demonstrated that their modification could further improve methanol assimilation. Over all, these results bring novel knowledge on synthetic methylotrophy while opening the way for developing new cell factories for methanol-based production of chemicals.

References

[1] Heux et al. 2018. PCT/EP2018/066762

[2] Peiro et al. 2019. Appl Environ Microbiol. Volume 85 No.15
**Flash Poster 1: F1**

*Characterising the bidirectional biomolecular interactions between the intestinal microbiota and host drug metabolism*

Marine Letertre¹, Annie Thaikkatil¹, Aadra Bhatt², Matthew Redinbo², Ian Wilson¹ & Jonathan Swann¹

¹Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Exhibition Road, South Kensington, London SW7 2AZ, UK. ²Department of Microbiology and Immunology, University of North Carolina, Chapel Hill NC27599.

**Introduction**

The gut microbiota, with trillions of microbial cells and 150 time more genes that the human genome itself, synthesises enzymes and metabolites that can influence the metabolism and toxicity of drugs in the host [1]. Conversely, drugs themselves have also been identified to exert an impact on the community structure of the gut microbiota and thus modify its overall functionality [2]. Such bidirectional interactions can have implications for host health and the outcomes of treatment strategies.

**Technological and methodological innovation**

The simultaneous use of targeted and untargeted LCMS and 1H NMR spectroscopy based-metabolic profiling was applied to explore (1) the potential for the gut microbiome to indirectly modulate the drug metabolism of the host[3] by feeding mice with a high-tyrosine diet and increase the microbial production of p-cresol in the presence and absence of antibiotics, and (2) the benefits of inhibiting a specific bacterial enzyme implicated in the toxicity of irinotecan to improve the outcomes of this drug[4].

**Results and impact**

This work demonstrates (1) the influence of microbial p-cresol on the sulfation capacity of the host liver and its effect on the phase II drug metabolism of paracetamol and (2) that selective inhibition of microbial enzymes could be an attractive target to complement the development of future drugs. The gut microbiota can explain a notable proportion of toxicity and variability in drug responses and evaluating an individual's microbiome prior to drug treatment could help to improve drug outcomes.

**References**

[1] Zimmermann M. et al., 2019, Nature 570, 462-467.

[2] Maier L. et al., 2018, Nature 555, 623-628.

[3] Clayton T.A. et al., 2009, Proc. Natl. Acad. Sci. U.S.A. 106, 14728-14733.

[4] Wallace B.D. et al., 2010, Science 330, 831-835.
**Flash Poster 2: F2**

*Rapid kinetics of lipid second messengers controls secretion in Toxoplasma parasites.*

Nicholas KATRIS<fnref n="1" />; Yoshiki YAMARYO-BOTTE<fnref n="1" />; Christophe ARNOLD<fnref n="1" />; Ross Waller, Cyrille BOTTE<fnref n="1" />

**Introduction**

Signalling of secretion in Toxoplasma parasites is regulated by lipid second messengers, phosphatidylinositol phosphates (PIPs) and diacylglycerol (DAG). Additionally, phosphatidic acid (PA)1 is implicated in vesicle fusion for release of invasion factors. However, the full network is poorly defined. We investigate the role of multiple signalling proteins including Calcium Dependent Protein Kinases (CDPKs) and an apical P-Type ATPase/Guanylyl cyclase2 (TgGC) to map the lipid signalling network.

**Technological and methodological innovation**

We use lipidomics approaches, notably Gas Chromatography Mass Spectrometry (GC-MS) to perform an exhaustive lipidomic analysis of the TgGC mutant. Furthermore, we used GC-MS lipid analysis to monitor lipid kinetics over time in wild type and mutant cells and observed changes in lipid second messenger turnover within a matter of seconds. Our findings are novel in the field of Toxoplasma parasite biology, where our study is the first to monitor lipid second messenger kinetics.

**Results and impact**

We see PA increases in WT cells less than 10 seconds after stimulus, a response which is ablated in TgGC depleted cells. We also see rapid kinetics in a TgRNG2 mutant, where a large DAG spike appears within 5 secs of stimulus, then drops to resting levels within 10 secs. We analyse proteins TgGC, TgRNG23, TgCDPK14 and TgPKG to map out the network to show that lipid signalling is crucial to control parasite secretion, which is regulated by rapid turnover of lipid second messengers.

**References**

[1] Bullen et al. 2016. Cell Host Microbe.

[2] Yang et al. 2019. Journal of Biological Chemistry

[3] Katris et al. 2014. Plos Pathogens.

[4] Lourido et al. 2010. Nature.

<footnote n="1">¹ ApicoLipid Team, IAB, Universite Grenoble Alpes, La Tronche UMR5309, INSERM 1209</footnote>

<footnote n="2">² Dept. of Biochemistry, University of Cambridge</footnote>
**Flash Poster 3: F3**

*Searching for differential metabolites between Mycobacterium tuberculosis and the MTBVAC vaccine using nontargeted approach metabolomics*

Caridad Díaz<fnref n="1" />, José Pérez del Palacio<fnref n="1" />, Patricia Mena<fnref n="1" />, Francisca Vicente<fnref n="1" />, Irene Pérez<fnref n="2" />,³, Carlos Martín<fnref n="2" />,³,<fnref n="4" />, Olga Genilloud<fnref n="1" />, Antonio Sánchez Pozo<fnref n="5" /> and Jesús Gonzalo-Asensio<fnref n="2" />,³,<fnref n="6" />

**Introduction**

MTBVAC is a live attenuated M. tuberculosis vaccine based on two genetic deletions in the *phoP* and *fadD26* virulence genes. The MTBVAC vaccine is currently in Phase 2 clinical trials with newborns and adults in South Africa<fnref n="1" />. Our objective is to explore *in vitro* differences in intracellular and extracellular metabolites between the vaccine candidate MTBVAC, the *phoP* mutants and their parental strains of *M. tuberculosis*.

**Technological and methodological innovation**

Four different modes of liquid chromatography coupled to high resolution mass spectrometry analysis were developed for a non-targeted metabolomics approach. In this study, we used a combination of reverse-phase liquid chromatography and hydrophilic interaction liquid chromatography in positive and negative ionization ion mode. For data analysis, we used a multi- and univariate statistical analysis and for metabolite identification we used several public and private database.

**Results and impact**

We have found significant differences in extracellular and intracellular metabolites between M. tuberculosis and theirs phoP mutants, including the MTBVAC vaccine candidate. These results expand our knowledge of the PhoP regulatory network particularly in the context of the lipid metabolism2. Differential metabolites between M. tuberculosis and MTBVAC might constitute potential biomarkers of tuberculosis infection or vaccination respectively and pave the way for future studies in vivo.

**References**

1. Tameris, M., Mearns, H., Penn-Nicholson, A., Gregg, Y., Bilek, N., Mabwe, S., Geldenhuys, H., Shenje, J., Luabeya, A.K.K., Murillo, I. and Doce, J., 2019. Live-attenuated *Mycobacterium tuberculosis* vaccine MTBVAC versus BCG in adults and neonates: a randomised controlled, double-blind dose-escalation trial. The Lancet Respiratory Medicine, 7(9), pp.757-770.

2. Díaz Navarro, C., Pérez del Palacio, J., Valero-Guillén, P.L., Mena García, P., Pérez, I., Vicente, F., Martin, C., Genilloud, O., Sánchez Pozo, A. and Gonzalo Asensio, J., 2019. Comparative Metabolomics between *Mycobacterium tuberculosis* and the MTBVAC Vaccine Candidate. ACS Infectious Diseases.

<footnote n="1">¹ Fundación MEDINA. Parque Tecnológico de Ciencias de la Salud. Av del Conocimiento, 34,18016, Granada, Spain.</footnote>

<footnote n="2">² Grupo de Genética de Micobacterias, Departamento de Microbiología y Medicina Preventiva. Facultad de Medicina, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain. ³ CIBER Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.</footnote>

<footnote n="4">⁴ Servicio de Microbiología, Hospital Universitario Miguel Servet, Zaragoza, Spain</footnote>

<footnote n="5">⁵ Department of Biochemistry and Molecular Biology 2, University of Granada, Spain.</footnote>

<footnote n="6">⁶ Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Zaragoza, Spain.</footnote>
**Sponsor 1: S1 Sciex: A New Tool in Metabolomics: SWATH® Acquisition Analysis for Global Profiling and Quantitation**

Joerg Schlotterbec, Sciex

<figure><img src="image_47.png" /></figure>
Sponsor 2: S2 Bruker

High Speed Untargeted 4D-OMICS LC-MS/MS Workflows with Parallel
Accumulation Serial Fragmentation (PASEF)

Aiko Barsch,Head of Application Development Metabolomics / Lipidomics

*Endometriosis phenotypes are associated with specific metabolomic profiles on patients' blood and follicular fluids*

Gildas Bertho, University Paris Descartes

<figure><img src="image_48.png" /></figure>
Plenary 2: P2

Physical inactivity and preservation of muscle mass: Lessons from hibernation

Etienne Lefai¹

·Proteostasis Team, UNH 1019, INRA, Université Clermont Auvergne France

Muscle atrophy is one of the main deleterious consequences of ageing, diseases (e.g. cancers and AIDS), and physical inactivity. It is especially detrimental to locomotion, heat production, and metabolism thus leading to frailty, increased dependency and metabolic disorders. Apart from being a major clinical problem for older people, muscle loss is also observed during physical inactivity, which has become a major leading cause of death worldwide. Although basic knowledge regarding the underlying mechanisms of muscle atrophy is continuously growing, essentially from rodent models and clinical trials in humans, there are still no efficient therapeutic strategies for its prevention and treatment.

Hibernating bears exhibit a strong and unique ability to preserve muscle mass in conditions of muscle disuse and food deprivation, conditions during which muscle atrophy is observed in human. Bears remain inactive in winter during up to seven months without arousal episodes (without eating, drinking, urinating or defecating), with only very limited loss in muscle protein content and strength, whereas muscle and fibre cross-sectional area are preserved.

Underlying mechanisms have not been understood yet, but our approaches combine molecular and cellular studies of bear muscle as well as human muscle cells exposed to bear serum. Our recent demonstration of trans-species effects of bear serum controlling protein degradation in cultured human muscle cell holds promising potential. Hence, exploration of winter bear serum therefore holds potential for developing new tools to fight human muscle atrophy and related metabolic disorders.
## Oral 4: 04

### *Mass Spectrometry Imaging (MALDI-MSI) and LC-MS/MS reveal spatial distribution of metabolic responses to Leptosphaeria maculans infection in stems of Brassica napus*

Anani A. MISSINOU<fnref n="1" />, Anne LEVREL<fnref n="1" />, Mélanie LAGARRIGUE-REBOUTIER<fnref n="2" />, Régis LAVIGNE<fnref n="2" />, Yann GUITTON<fnref n="3" />, Alain BOUCHEREAU<fnref n="1" />, Régine DELOURME<fnref n="1" />, Maria MANZANARES-DAULEUX<fnref n="1" />, Antoine GRAVOT<fnref n="1" />

## Introduction

Partial quantitative resistance to blackleg, caused by Leptosphaeria maculans, is expressed during the steminfection stage, thus limiting the extent of necrosis in partially resistant genotypes [Pilet-Nayel et al., 2017]. To get a better understanding of the biochemical mechanisms involved in this partial resistance at the stem stage, we developed an approach to spatially apprehend the metabolomic changes occurring in stem areas close to the fungus-infected tissues. Mass Spectrometry Imaging (MSI) [Qin et al., 2018] is a technology that enables the visualization of the spatial distribution of hundreds to thousands of metabolites in the same tissue section simultaneously.

## Technological and methodological innovation

In the present work, we used high-resolution Matrix-Assisted Laser Desorption/Ionization Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry Imaging (MALDI-FT-ICR MSI) to analyze the spatial distribution of metabolites in different zones of stem cross-sections after infection with L. maculans and prospect their potential role in plant response. Two genotypes, 'Bristol' (susceptible) and 'Darmor' (resistant), were analyzed at 14 days following petiole infection with L. maculans (isolate JN2).

## Results and impact

Analysis of specific metabolic responses to infection highlighted that most of the accumulated discriminative features after infection were genotype-specific. In contrast, 50% of the depleted discriminative features were shared by both genotypes. Different metabolic responses were also observed in the proximal and distal tissues from the necrotic cavity in both genotypes. This difference was clearer especially in the vascular tissues of the resistant genotype, revealing that this tissue might play important metabolic functions in the reduction of necrosis extension. In addition, a metabolomic approach conducted using Liquid Chromatography separation coupled with High-Resolution Mass Spectrometry, revealed a large metabolomic shift in infected tissues of B. napus and disease-induced metabolic features (phytoalexins previously refenced [Pedras & Abdoli, 2017] and phytoalexins-like).

This work pointed out the suitability of combining mass spectrometry platforms to analyze and map metabolic responses to biotic stress in tissues of B. napus. These results, which will be consolidated by structural identification of features using direct MALDI-MSI-fragmentation and molecular networks approach, suggest some outlooks for the future understanding of the metabolic response of B. napus to pathogen infection.

## References

- Pilet-Nayel, M.-L., Moury, B., Caffier, V., Montarry, J., Kerlan, M.-C., Fournet, S., ... Delourme, R. (2017). Quantitative Resistance to Plant Pathogens in Pyramiding Strategies for Durable Crop Protection. Frontiers in Plant Science, 8, 1838.

- Pedras, M. S. C., & Abdoli, A. (2017). Pathogen inactivation of cruciferous phytoalexins: detoxification reactions, enzymes and inhibitors. RSC Advances, 7(38), 23633-23646.

- Qin, L., Zhang, Y., Liu, Y., He, H., Han, M., Li, Y., ... Wang, X. (2018). Recent advances in matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) for in situ analysis of endogenous molecules in plants. Phytochemical Analysis, 29(4), 351-364.

<footnote n="1">¹IGEPP, INRA, AGROCAMPUS OUEST, Université Rennes, Le Rheu, France</footnote>

<footnote n="2">²Protim platform, IRSET - INSERM U1085, Rennes, France</footnote>

<footnote n="3">³LUNAM Université, Oniris, Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Nantes, France</footnote>
**Keynote 1: K1**

*Insights from integration of metabolomics data in large-scale plant metabolic models*

Zoran Nikoloski¹

¹Max-Planck-Institut für Molekulare Pflanzenphysiologie, Wissenschaftspark Potsdam-Golm, Germany.

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component's concentration resides in a given range remain elusive. I provide a systematic overview of the existing constraint-based approaches which can be used to integrate and predict metabolite concentrations in large-scale metabolic networks. The emphasis is placed on network structure conditions which can be used to arrive at simplified approaches for prediction of metabolite concentrations even in absence of data on kinetic parameters, provided there is access to metabolomics data from a reference condition. In addition, I will present a constraint-based approach that requires integration of genotype-specific metabolomics data to enable genomic prediction of plant growth. This approach is used to predict rosette growth of *Arabidopsis thaliana* accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. The results demonstrate that, in comparison to classical genomic selection, our approach improves the prediction accuracy of growth within and across nitrogen environments by 32.6% and 51.1%, respectively.
**Flash Poster 4: F4**

*Elicitor-specific reprogramming of potato's metabolome*

Rafaela Lopes MARTIN¹,²; Pauline CLIN¹; Adrián SCHWARZENBERG²; Pauline LE BOULCH¹; Jean-Claude YVIN²; Eric NGUEMA-ONA²; Florence VAL¹

¹AGROCAMPUS-OUEST, INRA-UMR IGEPP 1349, Institut de Génétique, Environnement et Protection des Plantes, 65,
Rue de Saint Brieuc, 35042, Rennes, France

²Centre Mondial de l'Innovation-Laboratoire de Nutrition Végétale, Groupe Roullier, 18 Avenue Franklin Roosevelt, 35400 Saint-Malo, France

**Introduction**

Potato crops are the target of major pathogens, including Phytophthora infestans, causal agent of late blight1. Control methods for this oomycete are based on the fungicides. Current regulations require a reduction of these molecules and one strategy could be to stimulate plant defenses via the PAMP-Triggered Immunity by exogenous elicitors. Unfortunately, this strategy do not always successful in the field. Our hypothesis was that the induced defence responses depend on the elicitors' origin.

**Technological and methodological innovation**

This hypothesis was tested on 2 potato genotypes with different levels of resistance to P. infestans treated respectively with 3 elicitors: A Concentrated Culture Filtrate-CCF, PAMPs from P. infestans2, amino acid-β-aminobutyric acid-BABA3 and an extract from Ulva spp4. The leaflet are analysed 48h after treatment by a non-target metabolomic approach using UPLCqTOF-MSe. Then a focus was carried out on secondary metabolism pathways through targeted metabolomic and transcriptomic approaches.

**Results and impact**

The results showed that each elicitor induce a specific reprogramming of metabolic profile independently of the genotype5. BABA and CCF preferentially regulated isoquinole alkaloids and anthocyanins and Ulva extract phenylpropanoids and sterol alkaloids. The transcript analysis confirmed these induction only for the Ulva extract. The results suggest that the antimicrobial compounds induction depends on the elicitor/genotype interaction which should be taken into account for pathogen management.

**References**

[1] Fry 2016. Annu. Rev. Phytopathol. 54 and 529-547

[2] Saubeau et al. 2014. Molecules. 19 and 15374-15390

[3] Bengtsson et al. 2014. Plant Pathology. 63 and 193-202

[4] Jaulneau et al. 2010. Journal of Biomedicine and Biotechnology. 2010 and 1-11

[5] Martin et al. 2019. Metabolomics (submitted).
**Flash Poster 5: F5**

*Biosource-guided network annotation and visualization for untargeted metabolomics*

Santiago CODESIDO¹, Víctor GONZÁLEZ-RUIZ¹,², Eulàlia OLESTI¹, Julien BOCCARD¹, Serge RUDAZ¹,²

¹ Analytical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO),
1206 Geneva, Switzerland

² Swiss Centre for Applied Human Toxicology (SCAHT),
4055 Basel, Switzerland

**Introduction**

Untargeted metabolomics has demonstrated its usefulness as a tool for biomarker exploration and sample classification.
The problem remains of annotating analytical features with chemical identities, as one cannot obtain MS/MS spectra for
metabolites with low concentrations, or measure LC-MS standards for all possible substances. This is noticeable in human
metabolomics, where on the other hand the network of metabolites and pathways is well described [1,2], providing additional
information.

**Technological and methodological innovation**

The use of biosources to enhance annotation recursively was used in [3] for MS/MS data. We present a Python module for network-guided annotation at several levels of reliability (such as MS/MS, LC-MS, MS), keeping track of the potentially multiple features assigned to each compound, and visualizing the relevant subnetwork. This allows to sort and select annotated features dynamically, by criteria such as their ability to separate sample groups, or their influence on a multivariate analysis.

**Results and impact**

We have found the resulting annotation to be highly useful to explore pathway alterations beyond the small set of
metabolites identifiable via pure standards, by suggesting relevant features in their metabolic neighbourhood. Steroids are
particularly benefitted, given the annotation multiplicity generated by their similar masses and retention times. Data analysis
is also enhanced through the dual visualization of dysregulations both in metabolite levels and ratios between related
compounds.

**References**

[1] Kiyoko Aoki et al. 2005. Current Protocols in Bioinformatics. 11: 1.12.1-1.12.54

[2] Ines Thiele et al. 2013. Nature Biotechnology. 31: 419-425

[3] Xiaotao Shen et al. 2019. Nature Communications. 10: 1516
**Flash Poster 6: F6**

*Recommending substructures for unknown tandem mass spectra*

Youzhong LIU<fnref n="1" />, Aida MRZIC<fnref n="1" />, Pieter MEYSMAN<fnref n="1" />, Thomas DE VIJLDER<fnref n="2" />, Edwin P. ROMIJN<fnref n="2" />, Valkenborg DIRK<fnref n="3" />, Bittremieux WOUT<fnref n="1" />,<fnref n="4" />, Laukens KRIS<fnref n="1" />

**Introduction**

Structural elucidation of unknown natural products, metabolites and drug derivatives from LC-MS/MS spectra remains a challenging task. However, these small molecules may share common substructures, which may result in common features in their spectra (i.e. frequently-occurring product ions and neutral losses). Through molecular networking (GNPS) and mass motif analysis (MS2LDA), an increasing number of spectral patterns have been discovered in high-quality spectral libraries. However, the lack of automated structure annotation system prevents these patterns from being useful for partial identification of unknown metabolites and further biological interpretation.

**Technological and methodological innovation**

We present a method that mines patterns containing both spectral features and substructures from spectral libraries. These novel patterns are represented as " diagnostic rules" that predict substructure presence/absence based on single or several observed spectral features. From GNPS spectral library, we generated 8378 rules allowing the prediction of 712 substructures. The combined use of these rules allows substructure recommendation in an automated fashion.

**Results and impact**

We validated GNPS rules on both expert-annotated spectral patterns (MS2LDA motifs) and independent test spectra from CASMI challenge, both showing a good agreement with the ground-truth. We further applied these rules for the characterization of impurity and degradation products of marketed drugs. From hundreds of signals (mostly unknowns) detected in a pharmaceutical product, our approach efficiently isolated signals where underlying compounds were structurally related to the parent drug (by sharing substructures). These unwanted unknown components probably arise from the manufacturing process or form during storage. The potential chemical transformations (e.g. oxidation, methylation.) were inferred using the precursor mass differences between parent compound and impurities. Combining substructure conservation and mass shifts, we proposed data-driven pathways of drug degradation and validated some of them based on literatures.

Our approach can be applied to biological systems with a goal to achieve system biochemical interpretation of MS/MS data of a complex mixture without identifying individual metabolites. The substructure recommendation system was developed into an openly-available web-tool at messar.biodatamining.be.

<footnote n="1">¹ Department of Mathematics and Computer Science, Advanced Database Research and Modelling (ADReM), University of Antwerp, Antwerp, Belgium</footnote>

<footnote n="2">² Pharmaceutical Development & Manufacturing Sciences (PDMS), Janssen Research & Development, Beerse, Belgium</footnote>

<footnote n="3">³ Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium</footnote>

<footnote n="4">⁴ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, CA, USA</footnote>
Oral 5: 05

Mix it up! How to combine multiple LC-MS modes and design of experiments to
understand multifactorial biological phenomena

Víctor GONZÁLEZ-RUIZ¹,²; Eulàlia OLESTI¹,²; Marianna SILVANO³; Santiago CODESIDO¹; Domitille SCHVARTZ²,⁴;
Jean-Charles SANCHEZ²,⁴; Julien BOCCARD¹,²; Ariel RUIZ i ALTABA³; Serge RUDAZ¹,²;

¹ Analytical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Switzerland

² SCAHT, Swiss Centre for Applied Human Toxicology, Switzerland

³ Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Switzerland

⁴Translational Biomarker Group, Department of Internal Medicine Specialties, University of Geneva, Switzerland

Introduction

Fusion of data from multiple analytical techniques remains challenging, as it does the interpretation of results issued from
of multi-factorial experiments in metabolomics [1]. Herein, we use multi-modal LC-MS and design of experiments to study
metabolic heterogenicity in cancer cells in culture. Different cells exhibiting common or phenotypic traits were compared and
the multi-modal data was merged and interpreted using the AMOPLS (ANOVA Multiblock OPLS) method [2].

Technological and methodological innovation

Metabolomic experiments were carried out to investigate differences between both cells and phenotypes simultaneously.
A combination of complementary LC-MS modes (RP and two HILIC modes, with ESI+ and ESI-) [3] was implemented to
broaden the explored chemical space, spanning from highly apolar lipids to polar and ionized metabolites. Thanks to
AMOPLS, the contribution of each LC-MS mode, as well as individual metabolites could be evaluated with respect to the
two studied experimental effects.

Results and impact

The main source of variability was the cell type (25.5%), while the phenotype accounted for 12.4%. All the three LC-MS
techniques contributed equally to distinguish each experimental effect, with amide-HILIC allowing a better separation
according to the phenotype. Our results show that cells exhibiting shared phenotypic traits present common metabolic
patterns, independently of their genetic differences.

References

[1] González-Ruiz, V. et al. (2017) *Journal of Chromatography A*, **1527**, 53-60

[2] Boccard, J. et al. (2016). *Analytica Chimica Acta*, **920**, 18–28

[3] Pezzatti, J. et al. (2019). *Journal of Chromatography A*, **1592**, 47–54
EC oral 1: EC1

ASICS: identification and quantification of metabolites in complex 1H NMR spectra

Gaëlle LEFORT<fnref n="1" />,<fnref n="2" />; Laurence LIAUBET<fnref n="2" />; Cécile CANLET<fnref n="3" />; Nathalie VIALANEIX <fnref n="1" />; Rémi SERVIEN⁴

**Introduction**

Several high-throughput technologies allow to obtain metabolomic profiles in biological fluids: Mass Spectrometry (MS) or Nuclear Magnetic Resonance (NMR) for instance. Among them, NMR has the advantage of being less expensive and is viewed as a promising tool to detect interesting biomarkers easily. However, the interpretation of the obtained spectra is difficult since the identification and the quantification of the metabolites present in a complex mixture is not automatic.

**Technological and methodological innovation**

To ease and expand the use of NMR, we developed a new R package available on Bioconductor, ASICS (Automatic Statistical Identification in Complex Spectra; [1] and [2]), that proposes a complete pipeline for metabolomic spectra analysis. ASICS contains a statistical method to identify and quantify metabolites in a complex mixture by using a statistical model based on a library of pure metabolite reference spectra.

**Results and impact**

For some datasets, biochemical dosages of several metabolites were also available. Overall, ASICS exhibited a good sensitivity and specificity to retrieve present metabolites and a quantification that was strongly correlated to most metabolite dosages. In conclusion, ASICS allows a faster and simpler direct biological interpretation than the classical bucket approach and better results than other quantification methods such as Batman [3], Bayesil [4] or Chenomx [5].

**References**

[1] Tardivel P. et al. 2017. Metabolomics. 13(10):109

[2] Lefort G. et al. 2019. Bioinformatics. 35(21):4356-4363.

[3] Hao J. et al. 2012. Bioinformatics. 28(15):2088-2090

[4] Ravanbakhsh S. et al. 2015. PLOS ONE.
10(5):e0124219

[5] Weljie A. et al. 2006. Anal. Chem. 78: 4430-4442.

<footnote n="1">¹ MIAT, Université de Toulouse, INRA, Castanet-Tolosan, France</footnote>

<footnote n="2">² GenPhySE, Université de Toulouse, INRA, ENVT, Castanet-Tolosan, France</footnote>

<footnote n="3">³ Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France ⁴ InTheRes, Université de Toulouse, INRA, ENVT, France</footnote>
## Oral 6: 06

### Modelling intrahepatic metabolic rewiring during the onset of obesity using arteriovenous blood metabolomics profiles

Nathalie POUPIN<fnref n="1" />, Marie TREMBLAY-FRANCO<fnref n="1" />,<fnref n="2" />, Cécile CANLET<fnref n="1" />,<fnref n="2" />, Aurélien AMIEL<fnref n="1" />,<fnref n="2" />, Didier REMOND<fnref n="3" />, Laurent DEBRAUWER<fnref n="1" />,<fnref n="2" />, Dominique DARDEVET<fnref n="3" />, Ines THIELE<fnref n="4" />,<fnref n="5" />,<fnref n="6" />, Maike AURICH<fnref n="4" />, Fabien JOURDAN<fnref n="1" />, Isabelle SAVARY-AUZELOUX<fnref n="3" />, Sergio POLAKOF<fnref n="3" />

## Introduction

The metabolite composition of the blood inflowing and outflowing a tissue reflects its metabolic function, with consumed (resp. released) metabolites being in higher (resp. lower) concentration in arterial inflow than venous outflow. The objective of this study was to perform a global metabolic profiling of plasma from incoming and outgoing hepatic vessels using NMR on high fat/high sugar (HFHS)-fed minipigs to explore how the hepatic metabolism is modulated during the onset of obesity [1].

## Technological and methodological innovation

The originality of our approach was to translate NMR arterio-venous metabolic profiles into utilization and release fluxes that we integrated in a tuned hepatic genome-scale metabolic network model to simulate fluxes through intra-tissular metabolic reactions. By setting constraints on model exchange reactions to enforce uptake and release of metabolites fitting the experimental data and using in silico flux calculation methods we could predict associated changes in intrahepatic metabolic fluxes.

## Results and impact

The interest and novelty of the presented approach is to take advantage of accessible circulating metabolites across a tissue to computationally predict rewiring in its metabolism and changes in consumed and released metabolites that could constitute biomarkers of intra-tissular metabolic modulations. Using this metabolic network modelling strategy, we predict that HFHS is associated with changes in tryptophan catabolism [2], which were further supported by biochemical and molecular analyses.

## References

[1] Polakof S et al. 2018. Eur J Nutr. 57(1):119-35

[2] Poupin et al. 2019. Sci Reports. 9(1):12557

<footnote n="1">¹UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, 31300 Toulouse, France</footnote>

<footnote n="2">²Axiom platform, MetaToul-MetaboHUB, national infrastructure for metabolomics and fluxomics, 31027 Toulouse, France</footnote>

<footnote n="3">³Université Clermont Auvergne, INRA, Unité de Nutrition Humaine, UMR1019, 63000 Clermont-Ferrand, France</footnote>

<footnote n="4">⁴Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg</footnote>

<footnote n="5">⁵School of Medicine, National University of Ireland, Galway, University Road, Galway</footnote>

<footnote n="6">⁶Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, University Road, Galway</footnote>
Sponsor 3: S3

Shimadzu. Profling experiments with DIA on the Ultra-Fast Q-TOF LCMS-9030

Thierry Legoupil

<figure><img src="image_49.png" /></figure>

SHIMADZU

Excellence in Science
**Plenary 3: P3**

*Spatial metabolomics in untargeted and quantitative studies*

Ingela LANEKOFF¹

¹ Dept. of Chemistry-BMC, Uppsala University, Sweden

**Introduction**

Spatial metabolomics offers an additional analytical dimension by revealing the localization of individual metabolites in thin tissue sections. In addition, spatial metabolomics enables detection of low abundant analytes that are highly localized to distinct cellular regions, which causes them to be diluted below the limit of detection in bulk analysis. Several studies using spatial metabolomics to find metabolic signatures associated with dysfunctional biological conditions will be presented.

**Technological and methodological innovation**

Nanospray desorption electrospray ionization enables both untargeted and quantitative spatial metabolomics by highly localized liquid extraction followed by electrospray ionization and mass spectrometry analysis. For quantification and chemical modifications, internal standards or reagents are added into the extraction solvent and present during the entire analysis. Surface sampling capillary electrophoresis mass spectrometry offers untargeted analysis directly from the tissue surface.

**Results and impact**

Rodent models for premature birth, stroke, and diabetes were studied using spatial metabolomics. Both untargeted and quantitative analysis (of prostaglandins, neurotransmitters, and lipids) revealed their localization and abundance in healthy tissue and shifts in dysfunctional systems. When compared to bulk analysis using liquid chromatography ion mobility mass spectrometry, spatial metabolomics provided more detailed chemical information with higher significance to disease state.

**References**

[1] Duncan, K. et al. 2019. Anal. Chem. 91 and 7819

[2] Bergman, H. et al. 2019. Anal. Bioanal. Chem. 411 and 2809

[3] Duncan, K., et al. 2018. Anal.Chem. 90 and 7246

[4] Lanekoff, I., et al. 2016. Sci. Rep. 6 and 33023

[5] Bergman, H., et al. 2016. Analyst. 141 and 3686
Oral 7: 07

Coupling metabolomic information coming from both LC-MS and Imaging-MS to
characterize experimental induction of coral larvae metamorphosis

Tianze Wang<sup>1</sup>, Isabelle Domart-Coulon<sup>1</sup>, Théo Mora<sup>1</sup>, Vincent Guérineau<sup>1</sup>, Nicolas Elie<sup>1</sup>, Yong Chen<sup>1</sup>, David Touboul<sup>1</sup>, <u>Alain Paris</u>

**Introduction**

Benthic settlement of swimming planula larvae of corals (Cnidaria, Scleractinia) involves both physical interaction with the reef substrate (mechanosensation) and reception of chemical cues emitted by substrate biofilms (chemosensation). These interactions initiate secretion of adhesion molecules and metamorphosis of larva into polyp1. Experimentally, this sequence of events can be induced by neuropeptides2,3 and phenotyped by metabolomics using LC-MS and complementarily by MS-imaging.

Technological and methodological innovation

Planula larvae emitted by the coral species Pocillopora acuta Lamarck (1816) were collected from individual colonies in February 2019 at the Océanopolis aquarium (Brest, France) and kept in filtered natural sea water under diurnal illumination. Metamorphosis of larvae was induced by a synthetic GLW-amide neuropeptide. Metabolomic analyses were done by LC-MS fingerprinting of polar extracts, MS-imaging with MALDI-ToF-MS4,5 coupled to multivariate analyses and canonical analyses between datasets.

Results and impact

A reorganization of the larval metabolic pathways was demonstrated by metabolomic analysis of polar extracts, revealing a great residual variance explained by a "colony" effect, in addition to the "metamorphosis induction" effect. MS-imaging revealed in situ metabolomic information usable at the tissue scale. Canonical links between these two sets of data were tentatively explored, in order to deconvolute the global metabolomic information at tissue level thanks to bootstrap procedures.

References

[1] Massé et al. 2018. Sci. Rep. 8:2293

[2] Moeller et al. 2019. Sci. Rep. 9:2291

[3] Takahashi et al. 2015. Int. J. Mol. Sci. 16:2610

[4] Woo et al. 2008. Nat. Protoc. 3:1341-1349

[5] Touboul et al. 2015. Methods Mol. Biol. 1203:41-48.

<footnote n="1">¹MCAM, UMR7245, MNHN, Paris, France</footnote>

<footnote n="2">²ICSN, CNRS UPR2301, Gif-sur-Yvette, France</footnote>

<footnote n="3">³UMR 8640 PASTEUR, Ecole Normale Supérieure, Paris, France</footnote>
Oral 8: 08

NMR metabolomics: a new tool in forensic sciences

Emanuela LOCCI<fnref n="1" />; Alberto CHIGHINE<fnref n="1" />; Matteo NIOI<fnref n="1" />; Fabio DE-GIORGIO<fnref n="2" />; Roberto DEMONTIS<fnref n="1" />; Ernesto D'ALOJA<fnref n="1" />

Introduction

Forensic science represents the application of several aspects of the science (such as medicine, chemistry, physics, and biology) in the sphere of criminal investigation and, in a more restrict context, is mainly focused in giving evidences to be used in a legal scenario (either in the criminal or in the civil justice system) by the forensic implementation and validation of scientific methods developed for purposes far from the legal medicine ones.

NMR metabolomics is increasingly used in forensics, due to the possibility of investigating endogenous metabolic profiles and exogenous molecules that may help to describe metabolic patterns and their modifications associated to specific conditions with forensic implications.

Technological and methodological innovation

Compared to the classical analytical methods used in forensic sciences, NMR metabolomics allows to study the global profile of the low molecular weight metabolites present in the sample. It has the advantage of relying not on a single chemical feature but on a fingerprint composed by a multitude of chemical features. It is rapid, it does not need extensive sample preparation and it is non-destructive, meaning that samples are completely recovered after the analysis and available for further investigations.

Results and impact

The attention has been devoted to the identification of peculiar metabolic signatures and specific post-mortem biomarkers in different conditions of legal medicine interest, such as the identification of biological traces [1], the investigation of the metabolomic profile modifications after death for the estimation of the time since death [2, 3], the metabolomic effects of forensic relevant pathological insults such as violent mechanical asphyxia for differential diagnosis of the cause of death [4], the identification of metabolomic pathways related to acute or chronic alcohol intake. The results of these studies highlight how forensic sciences may benefit from a NMR metabolomic approach by gaining additional/complementary information that may help to unravel several forensic conundrums.

References

[1] Scano et al. 2013. Magn. Reson. Chem. 51, 454-462

[2] Rosa et al. 2015. BioMed Res. Int. 2015, 627201

[3] Locci et al. 2019. Metabolomics. 15, 76

[4] Varvarousis et al. 2017. Sci. Rep. 7, 16575

<footnote n="1">¹ Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy</footnote>

<footnote n="2">²Institute of Public Health, Section of Legal Medicine, Catholic University of Rome, Rome, Italy</footnote>
EC oral 2: EC2

*Machine learning-based classification to improve Gas Chromatography-Mass spectrometry data processing.*

Yoann GLOAGUEN<sup>1,2,3</sup>, Nico BORGSMULLER<sup>1</sup>, Tobias OPIALLA<sup>2,3</sup>, Eric BLANC<sup>1,4</sup>, Emilie SICARD<sup>6</sup>, Anne-Lise ROYER<sup>5</sup>, Bruno LE BIZEC<sup>5</sup>, Stéphanie DURAND<sup>6</sup>, Carole MIGNÉ<sup>6</sup>, Mélanie PÉTÉRA<sup>6</sup>, Estelle PUJOS-GUILLOT<sup>6</sup>, Franck GIACOMONI<sup>6</sup>, Yann GUITTON<sup>5</sup>, Dieter BEULE<sup>1,3,4</sup>, Jennifer KIRWAN<sup>2,3</sup>

**Introduction**

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult.

**Technological and methodological innovation**

We present part of the work published in [1] and implemented in our workflow for improved peak picking (WiPP), focusing on the use of machine learning-based classification to optimize and improve different steps of the common GC-MS metabolomics data processing workflow. Our approach evaluates the quality of detected peaks using a machine learning based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis.

**Results and impact**

We benchmarked our workflow to standard compound mixes and a complex biological dataset, demonstrating that peak detection is improved. Furthermore, the approach can provide an impartial performance comparison of different peak picking algorithms. We also discuss the applicability of the approach to liquid chromatography-mass spectrometry data.

**References**

[1] Gloaguen, Y.; Borgsmüller, N. et al. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data. Metabolites 2019, 9, 171.

<footnote n="1"><sup>1</sup>Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany</footnote>

<footnote n="2"><sup>2</sup>Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany</footnote>

<footnote n="3"><sup>3</sup>Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany</footnote>

<footnote n="4"><sup>4</sup>Charité – Universitätsmedizin Berlin, 10178 Berlin, Germany</footnote>

<footnote n="5"><sup>5</sup>LABERCA, Oniris, INRA, Université Bretagne-Loire, 44307, Nantes, France</footnote>

<footnote n="6"><sup>6</sup>Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont-Ferrand, France</footnote>
Keynote 2: K2

*Unveiling the metabolic phenotypes and vulnerabilities underlying metastasis and
drug resistance*

**Marta CASCANTE**<fnref n="1" />,<fnref n="2" />*, Míriam TARRADO-CASTELLARNAU<fnref n="1" />,<fnref n="2" />, Cristina BALCELLS<fnref n="1" />,<fnref n="3" />, Josep TARRAGO<fnref n="1" />,<fnref n="2" />
IGOR MARIN DE MAS<fnref n="1" />,<fnref n="4" />+, ESTHER AGUILAR<fnref n="1" />+, PEDRO DE ATAURI<fnref n="1" />,<fnref n="2" />, ERIKA ZODDA<fnref n="1" />, MÒNICA PONS<fnref n="5" />,
JOSEP J.

CENTELLES<fnref n="1" />,<fnref n="2" />, Balázs PAPP<fnref n="5" />, Francesc Mas<fnref n="3" />, Miquel MUÑOZ-ORDOÑO<fnref n="1" />,<fnref n="2" />, Vitaly SELIVANOV<fnref n="1" />,<fnref n="2" />, Jordi PERARNAU<fnref n="1" />+, Fionnuala MORRISH<fnref n="6" />, David HOCKENBERY<fnref n="6" />, Mariia YUNEVA<fnref n="7" />, Silvia MARIN<fnref n="1" />,<fnref n="2" />, Timothy THOMSON<fnref n="2" />,<fnref n="5" />

Introduction

Despite advances in therapy, progression to metastasis and acquisition of resistance to chemotherapy remain the greatest challenges in cancer. More specifically, new modalities of treatment are urgently needed for metastatic cancer and to cope with acquired drug resistance. Recent studies have highlighted metabolic reprogramming as a key player in the acquisition of metastatic potential and therapeutic resistance, which has led to propose metabolic adaptations as potential therapeutic targets.

Technological and methodological innovation

Here, we have applied a systems biology approach, including experimental data integration into genome-scale metabolic models, to unveil metabolic differences and potential vulnerabilities associated with metabolic heterogeneity of tumor cells subpopulations in metastatic prostate cancer and to cisplatin resistance. For this purpose we use a dual model clonal cell model, consisting of a CSC-subpopulation with an epithelial phenotype and a non-CSC-subpopulation with hallmarks of stable EMT.

Results and impact

We show that EMT and metastasis programmes can display distinct metabolic traits. Briefly, the major differences were observed in differential use of glucose and glutamine to fuel TCA cycle, mitochondrial respiration, one-carbon metabolism, beta-oxidation and eicosanoids metabolism. By applying similar approaches, we have identified metabolic adaptive responses associated with platinum resistance as actionable targets in combined therapeutic strategies. Finally, we have analyzed the metabolic reprogramming associated with the inhibition of the cyclin-dependent kinase CDK4/6 in colorectal cancer cells, having unveiled new mechanisms of cancer cell adaptation whose targeting averts the acquisition of pharmacological resistance to cell cycle inhibitors.

References

[1] Marín de Mas I, et al. 2018 PLoS Comput Biol. 2018 Jan 2;14(1):e1005914.

[2] Tarrado-Castellarnau M, et al. Mol Syst Biol. 2017 Oct 4;13(10):940.

[3] Aguilar E, 2016 Stem Cells. May;34(5):1163-76. doi: 10.1002/stem.2286. Epub 2016 Feb 2. PubMed PMID: 27146024;
PubMed Central

<footnote n="1">¹Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona (UB) and
Institute of Biomedicine of University of Barcelona (IBUB), Barcelona.</footnote>

<footnote n="2">²Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid.</footnote>

<footnote n="3">³Department of Material Sciences and Physical Chemistry and Research Institute of Theoretical and Computational Chemistry (IQTCUB), Barcelona.</footnote>

<footnote n="4">⁴Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Szeged.</footnote>

<footnote n="5">⁵Department of Cell Biology, Barcelona Institute for Molecular Biology (IBMB-CSIC), Barcelona.</footnote>

<footnote n="6">⁶Fred Hutchinson Cancer Research Center, Seattle.</footnote>

<footnote n="7">⁷The Francis Crick Institute, London. ⁷Former affiliations were the work has been done. *Presenting author.</footnote>
EC Oral 3: EC3

High throughput untargeted surface metabolite profiling of formalin-fixed paraffin embedded (FFPE) brain tumour tissue microarrays (TMA) using LESA-MS/MS and OrbiSIMS

Joris MEURS<fnref n="1" />; David SCURR<fnref n="1" />; Lisa STORER²; Richard GRUNDY², Morgan ALEXANDER<fnref n="1" />; Ruman RAHMAN²; Dong-Hyun KIM<fnref n="1" />

**Introduction**

Understanding biochemical tumour profiles within architecturally-intact tissue specimens is important for the development of new treatments. [1] Surgically biopsied FFPE brain tumour tissue has historically only been amenable for TMA-based immunohistochemical analyses. The emergence of advanced analytical methods permits sensitive genome-wide analysis of high and low-abundant oncometabolites, maximising biochemical read-outs from limiting amounts of tissue such as from paediatric brain tumours.

**Technological and methodological innovation**

The potential of untargeted metabolite profiling using LESA-MS and OrbiSIMS has been shown before. [2,3] For the first time, LESA-MS/MS and OrbiSIMS have been explored for untargeted surface metabolite profiling and imaging within distinct intra-tumour regions using paediatric ependymoma TMAs as an example. OrbiSIMS and LESA-MS/MS allow acquisition of complementary data with confident identification and localization of metabolites with high mass and spatial resolution.

**Results and impact**

LESA-MS/MS and OrbiSIMS allowed to find complementary significant metabolites and grouping of patients based on their MS profiles, irrespective of known genetic sub-type. The ability of detecting intra-tumour metabolic difference will provide crucial information towards development of personalized treatment. Conversely, aberrant metabolic signatures consistently detected across patients may identify ubiquitous therapeutic vulnerabilities which circumvent inter-patient genetic heterogeneity.

**References**

[1] Kilday et al. 2009. Molecular Cancer Research. 7 765-786

[2] Meurs et al. 2019. Analytical Chemistry. 90 6001-6005

[3] Passarelli et al. 2017. Nature Methods. 14 1175-1183

<footnote n="1">¹ Advanced Materials & Healthcare Technologies Division, School of Pharmacy, University of Nottingham, UK
² Children's Brain Tumour Research Centre (CBTRC), School of Medicine, Queen's Medical Centre, University of Nottingham</footnote>
EC Oral 4: EC4

Computational models of p53-associated metabolic networks

Carlo DE BLASIO<fnref n="1" />*; Pablo RODRIGUEZ-MIER<fnref n="2" />*; Michela DI MICHELE<fnref n="1" />*; Lindsay PEYRIGA<fnref n="3" />; Maud HEUILLET<fnref n="3" />; Floriant BELLVERT<fnref n="3" />; Laetitia K LINARES<fnref n="1" />; Matthieu LACROIX<fnref n="1" />; Fabien JOURDAN<fnref n="2" />, Jean-Charles PORTAIS<fnref n="3" />; Nathalie POUPIN<fnref n="2" />; Laurent LE CAM<fnref n="1" />

**Introduction**

The TP53 gene is the most commonly inactivated tumor suppressor gene in human cancers [1]. P53 is a transcription factor activated in response to multiple types of stress that regulates the expression of genes controlling cell proliferation, senescence, DNA repair and cell death. Several laboratories have also highlighted a major role of p53 in metabolism and showed that p53-associated metabolic functions contribute to its tumor suppressive activities.

**Technological and methodological innovation**

Our ambitious project aims at developing the first computational model of the metabolic networks regulated by the p53 pathway that we will use to underpin its tumor suppressive functions. To build these networks, we extensively profiled primary mouse embryonic fibroblasts (Mefs) harboring single or combined genetic alterations of genes encoding key components of the p53 pathway (p53, MDM2, E4F1), using different “omics” approaches (RNAseq, Proteomics, and Exometabolomics). These large datasets are integrated into Genome Scale Metabolic Networks (GSMN) that we will use to predict new biomarkers and new therapeutic strategies.

**Results and impact**

We performed NMR-based exometabolomic studies to determine consumption and production rates of key metabolites, as well as RNA-seq studies to determine the expression profiles of metabolic genes in all our cellular models. These analyses are pinpointing unexpected connections between these key components of the p53 pathway and pyruvate, branched-chain-aminoacids (BCAAs), or methionine metabolism. These data highlight the underestimated complexity of the metabolic networks regulated by the p53 pathway.

**References**

[1] Labuschagne, C.F. et al. 2018. BBA - Reviews on Cancer. 1870, 32–42

<footnote n="1">¹ Institut de Recherche en Cancérologie de Montpellier, Institut du Cancer de Montpellier, Montpellier, France</footnote>

<footnote n="2">² INRA – Institut National de la Recherche Agronomique, Toxalim, Toulouse, France</footnote>

<footnote n="3">³ Institut National des Sciences Appliquées, Institut National Polytechnique, Université Paul Sabatier, Toulouse, France</footnote>
**Flash Poster 7: F7**

*Towards precision diagnostics: Untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients*

Purva KULKARNI<fnref n="1" />, Albert GERRITSEN<fnref n="1" />,<fnref n="2" />, Udo F.H. ENGELKE<fnref n="1" />, Brechtje HOEGEN<fnref n="2" />, Siebolt de BOER<fnref n="1" />, Ed van der HEEFT<fnref n="1" />, Marleen C.D.G. HUIGEN<fnref n="1" />, Leo A.J. KLUIJTMANS<fnref n="1" />, Karlien L.M. COENE<fnref n="1" />

**Introduction**

Inborn Errors of Metabolism are inherited conditions caused by genetic defects in enzymes or their cofactors, resulting in a specific metabolite fingerprint showing accumulation of substrate or lack of end-product in patient body fluids. Untargeted metabolomics offers a comprehensive readout of metabolic status on an individual patient basis. This makes it a promising tool for diagnostic screening and treatment monitoring of IEM patients, especially when clinical presentations are non-specific.

**Technological and methodological innovation**

We have previously established Next-Generation Metabolic Screening2 as a metabolomics-based diagnostic tool for individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we have developed an automated computational pipeline to streamline analysis of complex data and make it reproducible. The pipeline features a GUI that converts raw data, detects and aligns features across samples and annotates them to identify significant deviations in patients as compared to controls.

**Results and impact**

Using our automated computational pipeline, we have advanced the application of metabolomics in clinical diagnostic setting to a next level. Our pipeline ensures reproducible and time-efficient metabolomics data management, processing and analysis. To validate this pipeline, we tested samples of IEM patients, including several diagnoses that were not yet measured with NGMS, for example L-2-hydroxyglutaric aciduria. Our results further expand the clinical applicability and IEM portfolio of NGMS.

**References**

1. A. Tebani et al., International Journal of Molecular Sciences. 17 (2016)

2. K. L. M. Coene et al., Journal of Inherited Metabolic Disease. 41, 337-353 (2018)

<footnote n="1">¹Department of Laboratory Medicine, Translational Metabolic Laboratory (TML), Radboud University Medical Center, Geert Groote Plein Zuid 10, 6525, GA, Nijmegen, The Netherlands.</footnote>

<footnote n="2">²Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.</footnote>
**Flash Poster 8: F8**

*Metabolic maturation in the first two years of life in resource-constrained settings and its association with postnatal growth*

N. Giallourou<fnref n="1" />, F. Fardus-Reid<fnref n="1" />, G. Panic<fnref n="1" />, K. Veselkov<fnref n="1" />, B.J.J. McCormick<fnref n="2" />, M.P. Olortegui<fnref n="3" />, T. Ahmed<fnref n="4" />, E. Mduma<fnref n="5" />, P.P. Yori<fnref n="3" />,<fnref n="6" />, M. Mahfuz<fnref n="4" />, E. Svensen<fnref n="5" />,<fnref n="7" />, M.M.M. Ahmed<fnref n="4" />, J. M. Colston<fnref n="8" />, M. N. Kosek<fnref n="3" />,<fnref n="6" />, J. R. Swann<fnref n="1" />

**Introduction**

Malnutrition continues to impact the growth and development of millions of children worldwide, and chronic undernutrition has proven to be largely refractory to interventions. Improved understanding of metabolic development in infancy and how it differs in growth-constrained children may provide insights to inform more timely, targeted and effective interventions.

**Technological and methodological innovation**

Untargeted ¹H NMR spectroscopy was used to characterize the metabolic profile of urine samples (n=2423) collected longitudinally from children from Peru, Bangladesh and Tanzania in the first two years of life. A panel of age-associated metabolites consistent across the three sites were identified and the biochemical age of the children was computed and standardized as a phenome-for-age Z score comparing a child’s phenome age to that of an internal healthy reference population, providing a single continuous measure of metabolic maturity.

**Results and impact**

Predictive models demonstrated that growth-constrained children lag in their metabolic maturity relative to their healthier peers and that metabolic maturity can predict growth 6 months into the future providing a sensitive measure to better predict growth outcomes. The concept of a phenome age can be used to optimize interventions to the temporal metabolic demands of the developing infant and provides a metabolic framework from which nutritional programs may be more precisely constructed and evaluated.

<footnote n="1">¹Department of Metabolism, Digestion and Reproduction, Imperial College London, UK</footnote>

<footnote n="2">²Fogarty International Centre, Bethesda, MD, USA</footnote>

<footnote n="3">³Asociación Benéfica PRISMA, Unidad de Investigación Biomedica, Iquitos, Peru</footnote>

<footnote n="4">⁴International Centre for Diarrhoeal Disease Research, Bangladesh</footnote>

<footnote n="5">⁵Haydom Global Health Institute, Haydom, Tanzania</footnote>

<footnote n="6">⁶Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA</footnote>

<footnote n="7">⁷Haukeland University Hospital, Norway</footnote>

<footnote n="8">⁸International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA</footnote>
**Flash Poster 9: F9**

*Rapid LA-REIMS and comprehensive UHPLC-HRMS for metabolomics of biofluids in
metabolic disorders*

Lieven VAN MEULEBROEK<fnref n="1" />; Ellen DE PAEPE<fnref n="1" />, Kathleen WIJNANT<fnref n="1" />,<fnref n="2" />, Vera PLEKHOVA<fnref n="1" />, Margot DE SPIEGELEER<fnref n="1" />, Bruno LAPAUW<fnref n="3" />, Nathalie MICHELS<fnref n="2" />, Stefaan DE HENAUW<fnref n="2" />, Myriam VAN WINCKEL<fnref n="4" />, Lynn VANHAECKE<fnref n="1" />,<fnref n="5" />

**Introduction**

Metabolomics offers unique possibilities to characterize the human pathophysiological state. However, the involved LC-MS approaches are generally not suited for point-of-care testing in non-laboratory settings. In response, this study established laser-assisted rapid evaporative ionization MS (LA-REIMS) for rapid fingerprinting of biofluids (i.e. plasma, urine, saliva and feces). This concept was used to assess milk allergy in infants, obesity in adolescents, and type 2 diabetes in adults.

**Technological and methodological innovation**

The LA-REIMS platform comprised a Q-ToF mass spectrometer and Opolette™ HE2940 pump that was equipped with a Nd:YAG laser (2940 nm). A free space optics set-up was used for transmission of the laser energy to the biological material, where laser ablation (i.e. matrix-assisted desorption and ionization) took place. This strategy has been applied in surgical intervention (tissue analysis)<fnref n="1" />,<fnref n="2" /> but not yet explored for metabolomics of biofluids. Methods were optimized for the various matrices.

**Results and impact**

LA-REIMS methods were available for each biofluid, with the acquisition time being 0.5 min/sample. Fingerprints enclosed between 1,426 (feces) and 1,818 (plasma) compound ions, and enabled rapid differentiation of samples according to pathophysiological state (OPLS-DA models, *p*-value <0.05 and Q<fnref n="2" /> >0.5). LC-MS<fnref n="3" />,<fnref n="4" />,<fnref n="5" /> achieved similar discrimination and yielded new diagnostic and therapeutic biomarkers. LA-REIMS may support the translation of metabolomics into a clinical environment.

**References**

[1] Schäfer et al. 2009. Angewandte Chemie International Edition. 448. 8240-8242.

[2] Fatou et al. 2016. Scientific Reports. 6. 25919-25932.

[3] Van Meulebroek et al. 2017. Analytical Chemistry. 89. 12502-12510.

[4] Vanden Bussche et al. 2015. Analytical Chemistry. 87. 10927-10934.

[5] De Paepe et al. 2018. Analytica Chimica Acta. 1033. 108-118.

<footnote n="1">¹ Ghent University, Faculty of Veterinary Medicine, Department of Veterinary Public Health and Food Safety, Laboratory of Chemical Analysis, Salisburylaan 133, 9820, Merelbeke, Belgium</footnote>

<footnote n="2">² Ghent University, Faculty of Medicine and Health Sciences, Department of Public Health and Primary Care, Corneel Heymanslaan 10, 9000, Gent, Belgium</footnote>

<footnote n="3">³ Ghent University Hospital, Department of Endocrinology, Corneel Heymanslaan 10, 9000 Ghent, Belgium</footnote>

<footnote n="4">⁴ Ghent University Hospital, Department of Pediatrics and Medical Genetics, Corneel Heymanslaan 10, 9000, Gent, Belgium</footnote>

<footnote n="5">⁵ Queen's University, School of Biological Sciences, Institute for Global Food Security, University Road, BT7 1NN Belfast, Northern Ireland, United Kingdom</footnote>
Sponsor 4: S4

*Escape the Identity Crisis with Intelligence Driven Mass Spectrometry*

Amanda Souza, Marketing Manager, Life Sciences Mass Spectrometry & Anas Kamleh, Sales Support Expert - Thermo Fisher Scientific

ThermoFisher
SCIENTIFIC
**Keynote 3: K3**

*Environmental Cheminformatics: Case Study of Thirdhand Smoke in House Dust*

Emma L. SCHYMANSKI<fnref n="1" />; Sonia TORRES<fnref n="2" />; Noelia RAMIREZ<fnref n="2" />

**Introduction**

The environment and the chemicals to which we are exposed is incredibly complex. Household dust samples contain a summary of household exposure to various chemicals, suitable for exploration using high resolution mass spectrometry (HR-MS). Target and non-target HR-MS analysis for third hand smoke (THS) was performed on 75 dust samples (33 smoker, 42 non-smoker households) in Tarragona, Spain, focusing on tobacco-specific nitrosamines, which have been associated with higher health risk [1].

**Technological and methodological innovation**

Primarily open access/source metabolomics and environmental workflows for HR-MS data analysis were used to interrogate the non-target data. A list of substances in THS was compiled for suspect screening, while an XCMS-based [2] workflow was used to prioritize non-target features of interest that were significantly different between smoker and non-smoker samples. Data was extracted using RMassBank [3], features were annotated using MassBank and MetFrag [4], searching in PubChem and CompTox.

**Results and impact**

This case study will be used to demonstrate the potential (pros and cons) of various environmental cheminformatics approaches to interpret non-target data, and highlight challenges still facing the field, such as limited coverage of mass spectral libraries [5]. A number of other European and world-wide initiatives will be covered to demonstrate the power of HR-MS and “environmental cheminformatics” to reveal information about the exposome and move to “near real-time” analysis.

**References**

[1] Ramírez N. et al. 2014. Environment International. 71, 139-147

[2] Smith, C.A. et al. 2006. Analytical Chemistry. 78, 779-787

[3] Stravs, M.A. et al. 2013. Journal of Mass Spectrometry. 48, 89-99

[4] Ruttkies, C. et al. 2016. Journal of Cheminformatics. 8, 3

[5] Frainay C. et al. 2018. Metabolites. 8, 51

<footnote n="1">¹ Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg</footnote>

<footnote n="2">² University Rovira y Virgili, Tarragona, Spain</footnote>
## Oral 9: O9

### Relating metabolomics and yield in a network of experiments

Sylvain PRIGENT<fnref n="1" />,<fnref n="2" />; Olivier FERNANDEZ<fnref n="1" />,<fnref n="2" />,Stéphane BERNILLON<fnref n="1" />,<fnref n="2" />; Pierre PETRIACQ<fnref n="1" />,<fnref n="2" />; Annick MOING<fnref n="1" />,<fnref n="2" />, Thierry BERTON<fnref n="1" />,<fnref n="2" />, Emilie MILLET<fnref n="3" />,<fnref n="4" />; Llorenç CABRERA-BOSQUET<fnref n="3" />; Claude WELCKER<fnref n="3" />; FrançoisTARDIEU<fnref n="3" /> and Yves GIBON<fnref n="1" />,<fnref n="2" />

## Introduction

Prediction of phenotypic traits based on biomarkers is promising to improve breeding efficiency and our knowledge about the links between genotypes and phenotypes. In this work, we propose a modelling method to predict maize yield from metabolomic data. With a panel of 238 maize hybrid genotypes, we were able to achieve valuable predictions of yield evaluated in 29 field experiments in Europe with metabolomic data gathered from plants grown under controlled conditions in an automated greenhouse.

## Technological and methodological innovation

Generalised multilinear modelling, such as elastic nets, is a powerful method to reveal interesting links between different datasets while disregarding non-informative variables. Using this method, we were able to link metabolomic data to plant performance measured in fields under different meteorological conditions. This enabled yield predictions and the identification of metabolic biomarkers. Environmental indices were also used to improve the quality of the prediction at the field level.

## Results and impact

The models presented here were able to perform good average yield predictions, with correlation between actual and predicted yield ranging around 0.6. Interestingly, the best predictions were obtained using metabolomic data gathered from stressed plants. Regarding predictions at the field level, the introduction of environmental indices in the models further improved the predictive capabilities, especially in drought conditions.

## References

[1] Riedelsheimer et al. 2012. Nature genetics. 44, 217-220

[2] Millet et al. 2019. Nature genetics. 51, 952-956

<footnote n="1">¹Biologie du fruit et pathologie Institut National de la Recherche Agronomique : UMR1332, Université de Bordeaux</footnote>

<footnote n="2">²Plateforme Métabolome Bordeaux, Institut national de la recherche agronomique (INRA) : UMR1332</footnote>

<footnote n="3">³Biometris, Wageningen University & Research Centre</footnote>

<footnote n="4">⁴Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (UMR759 LEPSE)
Institut national de la recherche agronomique (INRA), Montpellier SupAgro
Place Viala, F-34060 Montpellier Cedex 1- France</footnote>
## Oral 10: O10

### Understanding the regulation of proline content in source leaves of winter oilseed rape by using transcriptomic and ¹⁵N-isotope tracing approaches

Younes DELLERO¹, Vanessa CLOUET¹, Nathalie MARNET², Anthoni PELLIZARRO¹, Sylvain DECHAUMET¹, Marie-Françoise NIOGRET¹, Alain BOUCHEREAU¹

¹INRA, UMR 1349 Institut de Génétique, Environnement et Protection des Plantes, INRA, Agrocampus Ouest, Université de Rennes 1, Rennes, France

²Plateau de Profilage Métabolique et Métabolique (P2M2), INRA de Rennes, Le Rheu, France, Centre de Recherche Angers Nantes BIA

### Introduction

Proline metabolism is an essential component of plant adaptation to multiple environmental stress conditions that is also known to participate in specific developmental phases, particularly in reproductive organs [1,2]. Recent evidences suggested a possible role for proline catabolism in *B. napus* for nitrogen remobilization processes during sink to source transition at the vegetative stage [3,4,5].

## Technological and methodological innovation

Here, we analyzed the transcriptional regulation of the *P5CS/ProDH* balance at the vegetative stage during sink to source transition with respect to net proline biosynthesis and degradation fluxes. To do so, we performed ¹⁵N labelling experiment with either ¹⁵NH₄ or ¹⁵N-proline during 4 hours and monitored the redistribution of ¹⁵N towards amino acids by taking advantage of an UPLC-TQD analytical system.

## Results and impact

We showed that the downregulation of three *P5CS1* genes during sink to source progression was accompanied by a reduced commitment of *de novo* assimilated ¹⁵N towards proline biosynthesis and an overall depletion of free proline content. Inversely, the regulation of *ProDH* transcript levels during sink to source transition was not correlated with the evolution of the maximal degradation capacity for proline. Our results suggest a role for proline catabolism in *B. napus* at a late stage of senescence.

### References

[1] Szabados et al. 2010. Trends in Plant Science. 15, 89-97.

[2] Biancucci et al. 2015. Frontiers in Plant Science. 6, 680

[3] Faes et al. 2015. Planta. 241, 403-419

[4] Clement et al. 2018. Journal of Experimental Botany. 69, 891-903

[5] Launay et al. 2019. Journal of Experimental Botany. In Press
**Sponsor 5: S5**

*Multi-dimensional Metabolomics using Ion Mobility Workflows – Future Advances*

Sarah Lennon, Waters

<figure><img src="image_50.png" /></figure>
**Plenary 4: P4**

*Micro-NMR for Metabolomic Observation of Microfluidic Culture Systems*

Marcel Utz¹

¹School of Chemistry, University of Southampton

Quantitative observation of metabolism is the most direct form of studying the activity of biological systems. Our group have developed dedicated NMR probe systems and protocols that allow obtaining high-quality NMR spectra from microfluidic devices with sample volumes around 1 µl. NMR spectroscopy at such small scales must overcome significant challenges in order to provide sufficient resolution and sensitivity for meaningful metabolomic investigations.

Microfluidic lab-on-a-chip systems are increasingly used for the culture of cells, cell assemblies, tissues, and small organisms. They provide detailed environmental control, high efficiency and experimental throughput, and allow large-scale repetition of experiments under stable conditions. While traditional microfluidic assays rely on destructive end-point analysis to probe the transcriptome, proteome, or metabolome of the cultured system, NMR can provide valuable additional information non-invasively. This allows detailed observation of metabolic activity in cultured systems, complementary to the traditional end-point analysis techniques. In this talk, I will provide an overview of the opportunities and challenges associated with microlfluidic NMR, and present recent results on microfluidic perfusion culture of liver tissue slices, as well as culture of cells in adherence and in the form of cell aggregates.
Oral 11: 011

Coupling UPLC-MS with Microdialysis for Metabotyping Burn Injury

Elizabeth WANT<fnref n="1" />; Dominic FRISTON<fnref n="2" />; Helen LAYCOCK<fnref n="2" />; Istvan NAGY<fnref n="2" />

**Introduction**

Burn injury is a devastating trauma affecting millions, resulting in chronic pain, often refractory to treatment. Mechanisms behind burn injury are poorly understood; studies are often destructive, prohibiting longitudinal temporal data.[1-3] Microdialysis permits in vivo collection of solutes primarily from the extracellular interstitium, ideal for studying burns. Metabolomics using mass spectrometry offers a sensitive and unbiased approach for elucidating metabolic changes - to enhance understanding of pathological processes and guide future therapeutic strategies.

**Technological and methodological innovation**

Subcutaneous microdialysis in a novel burn model was coupled with an optimised 12 minute ultraperformance liquid chromatography - mass spectrometry (UPLC-Q-ToF-MS) assay (ESI+/ESI mode). [4] Microdialysis was conducted for 0.5 hrs pre-burn and 3 hours post-burn, collected in 0.5 hr fractions. Data were pre-processed using XCMS software followed by multivariate analysis (PCA and PLS-DA) to elucidate discriminatory metabolite features due to burn. Model robustness was affirmed using CVANOVA. UPLC-MS/MS studies were performed for structural elucidation of key metabolites.

**Results and impact**

We demonstrate the application of metabolomic profiling to microdialysate collected from burns; profiling hundreds of analytes. PCA showed good acquisition stability. Clear metabolic differences were observed after burn injury; with niacinamide and uric acid potentially involved in the pathology. Further understanding metabolic changes induced by burn injury will help guide future therapeutic intervention. As this approach is applicable to other samples and conditions, it may improve our understanding of metabolic changes underlying a variety of pathological processes.

**References**

[1] Torres-Pérez JV, et al. 2018. J Mol Med (Berl). Jan;96(1):75-84.

[2] Torres-Pérez JV, et al. 2017. Sci Rep. Jan 25;7:

[3] White JP, et al. 2011. Eur J Pain. Aug;15(7):683-90.

[4] Friston D, et al. 2019. Anal Chem. May 21;91(10):6541-6548.

<footnote n="1">¹ Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London</footnote>

<footnote n="2">² Section of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Imperial College, London</footnote>
EC oral 5: EC 5

Proton detected ³¹P NMR methods for metabolomics

Neil Cox¹; Pierre Millard¹; Edern Cahoreau¹; Guy Lippens¹

¹Toulouse biotechnology institute, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.

**Introduction**

Phosphorylated metabolites are omnipresent as catabolic intermediates and anabolic building blocks of the cell components (1). NMR is an interesting approach for phosphometabolite characterization. However, despite the 100% natural abundance of the NMR active ³¹P isotope and its large overall sensitivity, one order of magnitude stronger than ¹³C, direct observation of ³¹P signals suffers from line broadening at higher field and exchange broadening from interaction with cations such as Mg²⁺(2).

**Technological and methodological innovation**

We have developed proton detected ³¹P NMR experiments for the identification and quantification of phospho-
metabolites. These NMR sequences can be readily implemented in metabolomics pipelines to offer (i) molecular
fingerprinting of phosphometabolites, (ii) an increase in overall metabolomics data, (iii) simplification of overcrowded spectra
for targeted analysis and (iv) complementarity to MS data for degradation and adducts formation control.

**Results and impact**

³¹P NMR technical developments geared towards metabolomics applications will be discussed. Experimental result will be presented for phosphometabolite fingerprinting of complex media, and the complementarity with mass spectrometry will be demonstrated. The capacity to identify and follow phosphometabolites in active cell extracts allows to follow reaction networks. Additional aspects of ³¹P NMR such as its ability to monitor in real time pH variation during a reaction will also be discussed (3).

**References**

(1) Westheimer, Science, 1987 235:1173-8.

(2) Jaffe & Cohn, Biochemistry, 1978 17:652-7.

(3) Cox et al., Analytical Chemistry, 2019 91:3959-63.
Oral 12: 012

*The power of LC-MS based multiomics to explore human adipogenesis*

Evelyn Rampler<fnref n="1" />; Dominik Egger <fnref n="2" />; Harald Schoeny<fnref n="1" />, Mate Rusz<fnref n="1" />, Cornelia Kasper<fnref n="2" />, Thomas Naegele<fnref n="3" />, Gunda Koellensperger<fnref n="1" />

Introduction

Stem cells are the ideal source to study fat formation as they are pluripotent and their differentiation fate is directly
depending on their environment. In this work, we use human mesenchymal stem cells (MSCs), received from surgery waste,
and differentiate them into fat cells/adipocytes. The combination of several layers of information coming from metabolomics,
lipidomics and proteomics enables in-depth analysis of the biochemical pathways in adipogenesis.

Technological and methodological innovation

We employ a multimolecular approach using an adapted two-phase extraction [1] to monitor the crosstalk between lipid
metabolism and protein-based signaling in a single sample (~10⁵ cells). The innovative analytical workflow includes
standardization with in-house produced ¹³C isotopically labeled metabolites/lipids, dual injection chromatography (HILIC,
RP) hyphenated to high-resolution mass spectrometry for simultaneous untargeted screening and targeted quantification
[2,3,4].

Results and impact

Metabolite and lipid concentrations range over four orders of magnitude and were detected down to the low fmol level.
Comparing MSCs and adipocytes, we observe significant regulation of different metabolites and lipids such as triglycerides,
gangliosides, carnitine and amino acids with 113 fully reprogrammed pathways [5]. The presented workflow is a proof of
principle for the power of LC-MS based multiomics and is fit for purpose for applications such as adipogenic differentiation.

References

[1] Coman et al. 2016, Molecular & Cellular Proteomics, 15, 1453–1466

[2] Schwaiger et al. 2019. Analyst, 144, 220–229

[3] Rampler et al. 2017. Analyst, 142, 1891–1899

[4] Rampler et al. 2018. Analytical Chemistry, 90, 6494–6501

[5] Rampler et al. 2019. Molecules. 24, 3615

<footnote n="1">¹ Institute of Analytical Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria</footnote>

<footnote n="2">² Institute for Cell and Tissue Culture Technologies, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria</footnote>

<footnote n="3">³ Department Biology I, LMU München, Großhaderner Str. 2-4, 82152 Planegg-Martinsried, Germany</footnote>
**Keynote 4: K4**

*Prenatal chemical exposure modulates neonatal serum phospholipids, increasing*
*later risk of type 1 diabetes*

Aidan McGlinchey<fnref n="1" />, Tim Sinioja<fnref n="2" />, Santosh Lamichhane<fnref n="3" />, Johanna Bodin<fnref n="4" />, Heli Siljander<fnref n="5" />, Dawei Geng<fnref n="2" />, Jorma Ilonen<fnref n="6" />, Suvi M. Virtanen<fnref n="7" />, Hubert Dirven<fnref n="10" />, Hanne Friis Berntsen<fnref n="10" />, Karin Zimmer<fnref n="10" />, Unni C. Nygaard<fnref n="11" />, Matej Orešič<fnref n="1" />,<fnref n="3" />, Mikael Knip<fnref n="5" />, *Tuulia Hyötyläinen<fnref n="2" />*

**Introduction**

In the last decade, the increasing incidence of type 1 diabetes (T1D) stabilized in Finland, coinciding with tighter regulation of perfluoroalkyl substances (PFAS) which are environmental chemicals that humans are continuously exposed to as they are widely-used in food packaging materials, paper and textile coatings, and fire-fighting foams (1,2). Exposure to PFAS has been linked with several adverse health outcomes, and particularly changes in lipid metabolism (2,3).

**Technological and methodological innovation**

In a mother-infant cohort study (n=264), we analyzed metabolite profiles of pregnant mothers and their offspring at birth with UHPLC-QTOFMS and GC-QTOFMS (260 identified lipids and metabolites) and quantified 22 PFAS with UHPLC-QqQMS. We then further examined the impact of PFAS exposure on metabolome in non-obese diabetic mouse (NOD) model, using a novel UHPLC-QqQMS method for simultaneous determination of PFAS and bile acids (BA). We also validated the findings in a second infant cohort (n=75).

**Results and impact**

High fetal PFAS exposure associated with reduced phospholipids in the infants and the association was exacerbated with high genetic risk of T1D (3). This lipid profile was similar to that observed in infants progressing to T1D (4,5). We verified the findings in animal models, exposed to PFAS, and also found that altered BA metabolism was associated with the changes in the T1D related lipids. Our findings suggest that fetal PFAS exposure contributes to risk and pathogenesis of T1D in children.

**References**

[1] V. Harjutsalo et al. 2013. JAMA 310, 427-428.

[2] S. Salihovic et al. 2019. Anal.Bioanal. Chem in press

[3] A. McGlinchey et al. BioRxiv 588350, doi: https://doi.org/10.1101/588350M

[4] M. Oresic et al. 2013. Diabetes 62, 3268-3274

[5] R.K. Johansson et al. 2019. Sci. Rep. 9. 14819.

<footnote n="1">¹School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden</footnote>

<footnote n="2">²School of Science and Technology, Örebro University, 702 81 Örebro, Sweden</footnote>

<footnote n="3">³Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520 Turku, Finland</footnote>

<footnote n="4">⁴Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, 0456 Oslo, Norway</footnote>

<footnote n="5">⁵Children's Hospital, University of Helsinki and Helsinki University Hospital, 00290 Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland</footnote>

<footnote n="6">⁶Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, 20014 Turku, Finland</footnote>

<footnote n="7">⁷Department of Clinical Microbiology, Turku University Hospital, 20014 Turku, Finland</footnote>

<footnote n="8">⁸Public Health Promotion Unit, National Institute for Health and Welfare, 00271 Helsinki, Finland</footnote>

<footnote n="9">⁹Center for Child Health Research, Tampere University Hospital, 33520 Tampere, Finland</footnote>

<footnote n="10">¹⁰Norwegian University of Life Sciences, 0102 Oslo, Norway</footnote>

<footnote n="11">¹¹National Institute of Occupational Health, 0363, Oslo, Norway</footnote>
## Oral 13: 013

### The WormJam 2019 vintage –New developments in the international research community for C. elegans systems biology and metabolic modelling

Horst Joachim SCHIRRA¹; Jake P. N. HATTWELL¹; Michael WITING²; Paul R. EBERT³; Nathan LEWIS⁴; Christoph KALETA⁵; Olivia CASANUEVA⁶; On behalf of the WormJam Consortium.

¹ Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland, Australia

² Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, München, Germany

³ School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia

⁴ Department of Pediatrics, & Novo Nordisk Center for Biosustainability at UC San Diego, University of California, San Diego, USA

⁵ Medical Systems Biology, Christian-Albrechts-University Kiel, Kiel, Germany

⁶ Epigenetics, Babraham Institute, Babraham Research Campus, Cambridge, UK

### Introduction

*Caenorhabditis elegans* is an important biological model organism. In recent years, metabolism has been increasingly recognised as being of pivotal importance for ageing, development, or disease, as well as the basis for resistance to toxic insults or adaptation to different environments. In this context, the combination of metabolomics with systems biology techniques enables a more detailed characterisation of this organism and its physiological processes [1-2].

### Technological and methodological innovation

Recent advances in this area are driven by metabolic reconstructions which allow to model metabolism *in silico*. The *WormJam* genome-scale model (GSM) is a consensus metabolic reconstruction of *C. elegans*, created by the *WormJam* consortium [3-4], and represents the most comprehensive knowledgebase for *C. elegans* metabolism. In 2019 we further improved the metabolic reconstruction especially in lipid metabolism, and created an innovative technological framework for continuous community curation.

### Results and impact

We will cover *WormJam*'s aims, 2019 progress, and planned developments. The *WormJam* GSM and NMR-based metabolomics were also applied to characterise *C. elegans* resistance to the toxic gas phosphine, which is used world-wide as agricultural fumigant. The metabolic changes behind phosphine resistance hint at conserved mechanisms for global metabolic regulation [1-2]. A highly curated community-driven consensus *C. elegans* GSM will bring this organism to the forefront of metabolism research.

### References

[1] Schlipalius et al. 2012. Science. 338, 807-810

[2] Ma et al. 2017. bioRxiv. 144386

[3] Hastings et al. 2017. Worm. 6:e1373939, 1-4

[4] Witting et al. 2018. Frontiers Mol. Biosci. 5:96
EC Oral 6: EC6

*Child multi-omics signatures of the early life exposome*

L. Maitre<sup>1,2,3</sup>, M. Bustamante<sup>1,2,3,4</sup>, C. Hernández<sup>1,2,3</sup>, D. Thiel<sup>5</sup>, M. Vives<sup>1,2,3</sup>, C.E. Lau<sup>5</sup>, A. Siskos<sup>5</sup>, X. Basagaña<sup>1,2,3</sup>, L. Chatzi<sup>6,7</sup>, M. Nieuwenhuijsen<sup>1,2,3</sup>, R. Slama<sup>8</sup>, C. Thomsen<sup>9</sup>, J. Wright<sup>10</sup>, M. Coen<sup>5</sup>, J. R Gonzalez<sup>1,2,3</sup>, H. Keun<sup>5</sup>, M. Vrijheid<sup>1,2,3</sup>

**Introduction**

It is widely recognised that early life environmental factors play a critical role in influencing life health outcomes, yet the global molecular impact of the whole exposome in utero and in childhood is poorly characterised. The Lifecycle project offers a unique opportunity to explore the interplay between the early-life exposome and the molecular responses in childhood in multiple European birth cohorts (Maitre et al. 2018).

**Technological and methodological innovation**

Here we present a large population-based study of the exposome in 1,300 children at age 6-11 years incorporating climate, outdoor urban environment, lifestyle, social factors & chemicals. The blood methylome, transcriptome, plasma proteins, serum and urinary metabolites were measured in childhood (Lau et al. 2018) and an exposome-wide association study (ExWAS) performed.

To gain insight into the complex interconnected nature of the exposome and its potential biological signature patterns, we applied network analytic and visualisation methods.

**Results and impact**

We identified 1170 significant associations, which were organized in 3 pregnancy and 11 postnatal network clusters. We found that the child methylome best captured the influence of pregnancy exposures, such as tobacco smoking and supply of essential minerals (Mo, K, Zn). The omics signatures associated with postnatal exposures, revealed the major influence of adiposity and lifestyle factors in mediating the interaction between exposure and phenotype. Clusters of association could also distinguish routes of exposure through diet to chemical pollutants such as heavy metals (As, Hg) and pesticides (organophosphates) known to have significant impact on health later in life.

**References**

Lau C-HE, et al. (2018) Determinants of the urinary and serum metabolome in children from six European populations. BMC Med 16:202. doi: 10.1186/s12916-018-1190-8

Maitre L, et al. (2018) Human Early Life Exposome (HELIX) study: a European population-based exposome cohort. BMJ Open 8:e021311. doi: 10.1136/bmjopen-2017-021311

<footnote n="1"><sup>1</sup>.ISGlobal, Barcelona, Spain</footnote>

<footnote n="2"><sup>2</sup>Universitat Pompeu Fabra (UPF), Barcelona, Spain</footnote>

<footnote n="3"><sup>3</sup>CIBER Epidemiologia y Salud Pública (CIBERESP), Madrid, Spain</footnote>

<footnote n="4"><sup>4</sup>Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain</footnote>

<footnote n="5"><sup>5</sup>Department of Computational and Systems MedicineSurgery & Cancer and Department of Metabolism, Digestion & Reproduction, Imperial College London, UK</footnote>

<footnote n="6"><sup>6</sup>Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA</footnote>

<footnote n="7"><sup>7</sup>Department of Complex Genetics and Epidemiology, CAPHRI School for Public Health and Primary Care, University of Maastricht, Maastricht, The Netherlands</footnote>

<footnote n="8"><sup>8</sup>Team of Environmental Epidemioly applied to Reproduction Respiratory Health, Institut Nationanl de la Santé et de la Recherche Médicale (Inserm), Centre national de la recherche scientifique (CNRS), University Grenoble Alpes, Institute for Advanced Biosciences, Joint Research Center (U1209), La Tronche, Grenoble, France</footnote>

<footnote n="9"><sup>9</sup>Department of Environmental Exposure and Epidemiology, Norwegian Institute of Public Health, Oslo, Norway</footnote>

<footnote n="10"><sup>10</sup>Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK</footnote>
## Oral 14: 014

### Optimisation of Biofluid and Tissue Metabolite and Lipid Extraction for Clinical Metabolic Phenotyping

Andrew D. Southam<fnref n="1" />,<fnref n="2" />; Liam Hagglington<fnref n="1" />; Harriet Pursell<fnref n="1" />; Gianfranco Frigerio<fnref n="3" />; Lukas Najdekr<fnref n="1" />,<fnref n="2" />; Andris Jankevics<fnref n="1" />,<fnref n="2" />; Warwick B. Dunn<fnref n="1" />,<fnref n="2" />,<fnref n="4" />.

### Introduction

Clinical metabolic phenotyping aims to detect and measure 1000s of metabolites and lipids within clinical samples (e.g. biofluids and tissues) to identify changes between metabolic phenotypes (e.g. disease status) and to understand biochemical mechanisms driving the phenotype. Sample extraction is a critical step in clinical metabolic phenotyping: it must be reproducible and give a high extraction yield of metabolites and lipids.

### Technological and methodological innovation

We tested multiple monophasic and biphasic metabolite/lipid extraction methods for biofluids (urine/plasma) and tissue (heart/kidney/liver). We also tested solvent-biofluid incubation time/temperature. Extracts were analysed by UHPLC-MS assays: HILIC (urine, plasma, tissue polar extracts); $C_{18}$ aqueous reversed phase [RP] (urine polar extracts); $C_{18}$ reversed phase (plasma & tissue lipid extracts). Each method was assessed for yield, reproducibility and class of extracted metabolites.

### Results and impact

Based on yield and reproducibility the best methods were: plasma/urine HILIC– monophasic 50:50 methanol (MeOH):acetonitrile (ACN); urine RP – any tested monophasic method except 100% ACN; plasma lipids – monophasic 100% isopropanol (IPA). Altering solvent-biofluid incubation time/temperature had little effect on yield. For tissue, MeOH/CHCl3/H2O was the best all-round method; however, for some specific compounds other methods performed better, e.g. cardiolipins were better extracted by 100% IPA.

<footnote n="1">¹ School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT.</footnote>

<footnote n="2">² Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT.</footnote>

<footnote n="3">³ Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.</footnote>

<footnote n="4">⁴ Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT.</footnote>
Oral 15: 015

Integrated metabolomics and transcriptomics analysis of intra-tumour heterogeneity
in paediatric brain tumours

Alison WOODWARD<fnref n="1" />; Alina PANDELE<fnref n="1" />; Salah ABDELRAZIG<fnref n="2" />; Catharine ORTORI<fnref n="2" />; David BARRETT<fnref n="2" />; Richard GRUNDY<fnref n="1" />; Dong-Hyun KIM<fnref n="2" />; Ruman RAHMAN<fnref n="1" />

Introduction

Despite standard-of-care treatment, up to 50% of childhood ependymomas relapse; the prognosis is then abysmal. One likely explanation for the failure of adjuvant therapy, is the under appreciation of intra-tumour heterogeneity. Integrated metabolomics and transcriptomics offers an opportunity to elucidate aberrant metabolic pathways and reveal new therapeutic avenues in ependymoma tissue neurosurgically resected from different spatial regions of individual tumours.

Technological and methodological innovation

Here we present broad metabolome coverage using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to separately analyse ependymoma metabolites and lipids, and broad transcriptome coverage by RNA sequencing. We homogenised surgically resected ependymoma tissue and extracted polar metabolites and lipids using methanol/water/chloroform (1:1:3), leaving RNA in the solid interphase, ensuring integrated omics analysis will be conducted on the same population of tumour tissue.

Results and impact

First, we compared two subgroups from distinct neuro-anatomical compartments, PF-A (n=10) and ST-RELA (n=5).
Integration of metabolomics with transcriptomics revealed the metabolic network centred on GMP was important in STRELA.
Next, intra-tumour regions within each of 8 PF-A patients were compared, revealing an abundance of metabolites in the
arginine and proline metabolism pathways. The identified intra-tumour heterogeneity exposes new drug targets representing
metabolic vulnerabilities

<footnote n="1">¹ School of Medicine, University of Nottingham, UK</footnote>

<footnote n="2">² School of Pharmacy, University of Nottingham, UK</footnote>
Oral 16: 016

Identifying novel activators of regulatory T-cell metabolism

Anna HOEKSTRA<fnref n="1" />; Sander DE KIVIT<fnref n="2" />,<fnref n="3" />; Mark MENSINK<fnref n="2" />,<fnref n="3" />; Derk AMSEN<fnref n="4" />; Jannie BORST<fnref n="2" />,<fnref n="3" />; Celia BERKERS<fnref n="1" />,<fnref n="5" />

**Introduction**

In a healthy immune response there is a balance between the opposing activities of two T-cell types: conventional T cells (Tconv) and regulatory T cells (Treg). In contrast, immune related diseases are often characterized by a disbalance between these cell types. Upon activation, T cells place unique demands on their metabolism. However, whereas proliferation of Tconv is fully depending on the mTORC1 pathway, Treg proliferation is not, potentially allowing for selective drug targeting.

**Technological and methodological innovation**

We hypothesized that Tconv and Treg differ in their metabolic features. We used a novel procedure to sort and expand human Treg cells without the use of the metabolic inhibitor rapamycin. Subsequently, we combined metabolomics, fluxomics and proteomics analyses to map metabolic differences between resting and activated Tconv and Treg.

**Results and impact**

We here show that unlike Tconv, human Treg cells do not activate a glycolytic program upon CD3 activation and CD28 costimulation. Moreover, we identify a novel co-stimulatory signal that does enable Treg to undergo a metabolic switch and activate glycolysis. Finally, using tracer studies, we identify unique aspects in the Treg glycolytic program. Further exploration of these differences could identify metabolic targets that can be exploited to selectively modulate T cell activity.

<footnote n="1">¹ Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands</footnote>

<footnote n="2">²Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands</footnote>

<footnote n="3">³Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands;</footnote>

<footnote n="4">⁴Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam, The Netherlands</footnote>

<footnote n="5">⁵Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands</footnote>
Poster abstracts
Poster 001: P001
Revealing age-related sex differences in human lipidome: Metabolic phenotyping of Lausanne population

Poster 002: P002
Untargeted metabolomic analysis for the discovery of treatment response biomarkers in breast cancer patients through Hotelling T2 multivariate profiling

Poster 003: P003
Metabolomic investigation of immune cell activation by Liquid Chromatography coupled with High Resolution Mass Spectrometry (LC-HRMS)

Poster 004: P004
GH-OMICs: applying metabolomics to the fight against hormonal doping

Poster 005: P005
NMR metabolomics for understanding the role of alkaline phosphatase in neurotransmission and inflammation processes

Poster 006: P006
A multiplatform approach: HPLC, GC and NMR for urinary metabolomic analysis of muscle-invasive bladder cancer

Poster 007: P007
NMR metabolic profiling can help discriminate between normal primary hepatocytes and diverging hepatic cancer cell lines

Poster 008: P008
Quantitative metabolomics analysis reveals altered lipid profile in women with endometriosis: a pilot case-control study

Poster 009: P009
Steroid profile in human seminal fluid: is it linked to sperm quality?

Poster 010: P010
Combination of two semi-targeted approaches to detect toxicological biomarkers as a tool to understand the relation between red meat consumption and colorectal cancer promotion

Poster 011: P011
Mre11 nuclease activity removes the replication blocking nucleoside analogue Gemcitabine from the nascent strand during DNA replication

Poster 012: P012
Metabolic characterization of Leishmania mexicana-infected macrophages

Poster 013: P013
A comprehensive analysis of metabolome and lipidome in different sleep deprivation models using a multiplatform technique of chromatography-mass spectrometry

Poster 014: P014
Study on Alterations of Metabolome by Blood Transfusion using LC-Q-TOF and GC-MS

Poster 015: P015
Dried Blood Spots for the Determination of Reduced to Oxidized Glutathione Status

Poster 016: P016
Preliminary investigations of alkaloid composition from Thalictrum isopyroides plant of Azerbaijan Flora

Poster 017: P017
Serum targeted and untargeted metabolomics-based analyses in infants with Ureteropelvic Junction Obstruction

Poster 018: P018
Investigating urinary and faecal metabolic differences among native Africans, African Americans and Alaskans using mass spectrometry

Poster 019: P019
Alterations in plasmatic levels of metabolites involved in purine, urea cycle and tryptophan metabolic pathways during right ventricular remodeling in chronic thromboembolic pulmonary hypertension

Poster 020: P020
Molecular networking-based mass spectrometry to decipher tumor cells metabolism

Poster 021: P021
"-Omics- sciences and therapeutic"

Poster 022: P022
Metabolomic study of commercial mistletoe extracts used in alternative anticancer therapy

Poster 023: P023
Optimization of the untargeted metabolomics workflow for studying the impact of dysbiosis on the host

Poster 024: P024
Rapid extraction and High-Resolution Tandem Mass Spectrometry analysis of the acylCoA metabolome

Poster 025: P025
Serum metabolic profiling following traumatic brain injury in rats using 1H Nuclear Magnetic Resonance spectroscopy

Poster 026: P026
Surface analysis of lipids in rat mesenteric fat tissue sections by MALDI-MS imaging to understand the impact of obesity

Poster 027: P027
Combined transcriptomics-metabolomics profiling of the heat shock response in the hyperthermophilic archaeon
Pyrococcus furiosus

Poster 028: P028
Alternative carbon sources for the central metabolism of *Trypanosoma brucei* procyclic form

Poster 029: P029
Characterization of bioherbicidal compounds stemming from micro-algae diversity by differential metabolomic and spectral similarity network assisted dereplication (ALL DREAM)

Poster 030: P030
Threonine metabolism in Leishmania

Poster 031: P031
Fluorinating Putida: Annotating Novel and Undesired Metabolites

Poster 032: P032
Analysis of metabolome time- variations of an endophytic fungal strain of Botryosphaeria mamane through untargeted
LC-MS profiling

Poster 033: P033
Study of the production of (phyto)hormones by mycorrhizal fungi

Poster 034: P034
Metabolites characterization of nine cyanobacteria isolated from thermal mud.

Poster 035: P035
Metabolomics in plant-microbe interactions: "MetaboHUB-MetaToul-plant metabolites" platform facilities

Poster 036: P036
Investigating the effects of future climate scenarios in soft fruit: application of HPLC-PDA-MS metabolite profiling in
blackcurrant cultivated under increased temperature and altered light regimes

Poster 037: P037
The role of food polyphenols in energy metabolism

Poster 038: P038
Study of dietary polyphenols metabolites by a top-down metabolomics approach
Poster 039: P039
NSP19 an original eco-extract from Dioscorea villosa with neuroprotective effects in in vitro Alzheimer's disease models

Poster 040: P040
Digging out the terroir influence on bioactive polyphenols from grape stems: A correlation_network_ driven approach to spatialize metabolomics data.

Poster 041: P041
Development of a UHPLC-MS/MS targeted method in various matrices for the quantification of toxicologically relevant aldehydes produced from lipid peroxidation

Poster 042: P042
LC-MS based untargeted metabolite profiling of wholegrain spelt (Triticum spelta) cultivated in Switzerland

Poster 043: P043
Isotope Labelling and Molecular Networking to Identify New Fungal Secondary Metabolites

Poster 044: P044
Specific derivatization with isotopic labeling introduction and mass spectrometry to monitor lipid peroxidation carbonyl compounds in the intestinal lumen by both targeted and non-targeted approaches.

Poster 045: P045
Characterization of A. thaliana responses to plant defence elicitors using untargeted metabolomics

Poster 046: P046
Metabolomics implemented to the Equine Biologic Passport (EBP) for doping control.

Poster 047: P047
Unravelling the impact of the antidepressant venlafaxine on the metabolome of meagre fish

Poster 048: P048 Comparison of effects produced by the exposure of bisphenol A and estradiol in zebrafish embryos (Danio rerio) using an untargeted metabolomics approach

Poster 049: P049
Let's talk about POÆMS !! Photosynthetic Organisms from Atmospheric Ecosystem in a Multiscale Study

Poster 050: P050
Developing a multi-omics approach for chemical grouping

Poster 051: P051
Microbiome and metabolome analyses of the medaka fish gut during a chronic exposure to toxic cyanobacterial blooms

Poster 052: P052
High-Throughput Targeted Lipidomics Analysis of Dihydroceramide Desaturase-1 (DES1) Knockout Mice

Poster 053: P053
In Vivo Microdialysis of Endogenous and 13C-labeled TCA Metabolites in Rat Brain: Reversible and Persistent Effects of Mitochondrial Inhibition and Transient Cerebral Ischemia

Poster 054: P054
Ion source optimization in liquid chromatography time-of-flight mass spectrometry for metabolomics studies using the design of experiments

Poster 055: P055
A novel multi-targeted quantitative approach for nutrimetabolomics research

Poster 056: P056
FOBI: An ontology to represent food intake data and associate it with metabolomic data

Poster 057: P057
Comparison of different methods for lipidic extraction
Poster 058: P058
Development of an untargeted metabolomics workflow for investigating drug-induced cardiotoxicity using cardiac microtissues.

Poster 059: P059
A metabolomics-like approach for chemical forensic: preliminary study and perspectives

Poster 060: P060
Simultaneous measurement of absolute metabolite concentration and isotope incorporation by mass spectrometry

Poster 061: P061
Evaluation of pure shift NMR method in metabolomics

Poster 062: P062
A high throughput fluxomic workflow for exploration of metabolic phenotypes

Poster 063: P063
Metabolomic fingerprint of aging on red blood cells

Poster 064: P064
Intelligent Acquisition for Comprehensive Metabolome Coverage in Plants, Mammals, and Bacteria

Poster 065: P065
Novel MALDI imaging solution empowered by a dual-source Q-TOF and a dedicated bioinformatics pipeline for identification of peaks from tissue

Poster 066: P066
Establishing a Spectral Library and Accurate Mass Retention Time (AMRT) Database for Pediatric Metabolomics Analysis

Poster 067: P067
Investigation of mevalonate pathway by quantitative metabolomics and isotopologue profiling

Poster 068: P068
High-throughput metabolite profiling of cell media for improved antibody production utilizing a dual separation/mass spectrometry system

Poster 069: P069
The development of selective sorbent for isolation of catecholamines and their metabolites from biological fluids

Poster 070: P070
Data processing workflow to study the exposome by suspect screening

Poster 071: P071
Development of an untargeted approach based on isotope profiling of metabolic networks by high resolution mass spectrometry

Poster 072: P072
Improving lipid annotation coverage using intelligent precursor selection software on an Orbitrap-based mass spectrometer

Poster 073: P073
Comparison of normalization techniques for untargeted metabolomics of urine samples.

Poster 074: P074
Comparison of two sampling and extraction protocols to perform metabolomic studies on human adherent cells

Poster 075: P075
Development of a System Suitability QC Sample for Nano-Electrospray Direct Infusion Mass Spectrometry Metabolomics

Poster 076: P076
Evaluation of U-13C Spirulina (Arthrospira platensis) for stable isotope assisted untargeted metabolomics and liquid chromatography-isotope dilution mass spectrometry (LC-IDMS)

Poster 077: P077
Increased throughput and coverage for the annotation of Saponins using a structurebased MSn approach on a Tribrid Orbitrap mass spectrometer

Poster 078: P078
Application of 13C flux analysis in diagnosis of metabolic disorders

Poster 079: P079
Study on Intracellular Tracking of 13C isotope - labeled Volatile Cancer Markers

Poster 080: P080
Metabolomics profiling urine by Benchtop NMR

Poster 081: P081
Multi-platform and multi-matrix analytical development

Poster 082: P082
A comparison between a targeted and an untargeted approach in the frame of a pilot biomonitoring study

Poster 083: P083
Development and validation of a RPLC-MS/MS method for the quantification of ceramides in human serum of patients with CAD

Poster 084: P084
AlpsNMR: an R package for fully untargeted NMR-based metabolomics workflow

Poster 085: P085
Costs and benefits of switching from vendor-based to open source pipelines for untargeted LC-MS metabolomics

Poster 086: P086
Volatile Biomarker discovery in flatus by GC-MS and GC-IMS to diagnose digestive disorders and diseases.

Poster 087: P087
MetExplore: Omics data analysis in the context of metabolic networks

Poster 088: P088
BARSA: BidimensionAl nmR Spectra Annotation

Poster 089: P089
Assessing the impact of physicochemical parameters on the predictive capabilities of thermodynamics-based stoichiometric approaches under mesophilic and thermophilic conditions

Poster 090: P090
Uniting metabolomics data processing and highly confident annotation across six MS instrumental set ups: MetaboScape 5.0

Poster 091: P091
Integrated workflow with quality control for large cohort and clinical metabolomics research using robust hardware and signal correction

Poster 092: P092
Automatic annotation in untargeted metabolomics: a proof of concept protocol based on recurrent experiments

Poster 093: P093
Metabolomics results interpretation using recommender system embedded in metabolic network visualization

Poster 094: P094
Technological developments and opportunities with Workflow4Metabolomics

Poster 095: P095
Taxonomically informed metabolite annotation enhances confidence in specialized metabolomes annotation

Poster 096: P096
Improving prediction of essential genes using context-specific metabolic network ensembles

Poster 097: P097
Showcasing Longitudinal Metabolomic Datasets: Accessible, Fast and Simple

Poster 098: P098
Developing methods for targeted metablomics by LC-MS

Poster 099: P099
The SMRT dataset for machine learning-based metabolite retention time prediction.

Poster 100: P100
LC-MS/MS data analysis with xcms

Poster 101: P101
Targeting MDM2-dependent serine metabolism as a new therapeutic strategy for liposarcoma

Poster 102: P102
Biofluid Applications in Benchtop Low-frequency 60 MHz NMR: A Metabolomics Investigation

Poster 103: P103
Analysis of Penillium sclerotiorum metabolome by molecular networking

Poster 104: P104
FoodomicsGR National research infrastructure for the comprehensive characterisation of Foods

Poster 105: P105
Bioavailability of dietary long chain-PUFAs in the rat retina: mapping by MALDI imaging mass spectrometry

Poster 106: P106
Colorectal Cancer: Biomarkers and Effect Size

Poster 107: P107
Inflammation signaling by cardiac stromal exosomes

Poster 108: P108
Assessment of Preterm Infant's Nutrition by Untargeted Human Milk Lipidomics

Poster 109: P109
Exploring the use of GC-CI-MS for stable isotope labeling in metabolomics

Poster 110: P110
Development of a targeted urinary metabolic profiling technique for inflammatory bowel disease using Liquid Chromatography Mass Spectrometry with Electrospray Ionization Quantification.

Poster 111: P111 CEU MASS MEDIATOR: THE TOOL TO ANNOTATE COMPOUNDS IN CE-MS FOR METABOLOMICS

Poster 112: P112
Improved Metabolite Identification in a Single Injection with SWATH® Acquisition for Untargeted Metabolomics Workflow

Poster 113: P113
High-throughput single-step sample preparation coupled to targeted LC-MS/MS approach for extended coverage of human plasma metabolome and lipidome

Poster 114: P114
In vivo isotopic tracing experiments reveal lactate as an oxidative substrate for brown/beige adipose tissues

Poster 115: P115
LipostarMSI: Comprehensive and Vendor-Neutral Data Analysis Platform for Mass Spectrometry Imaging

Poster 116: P116
SWATH® Acquisition Allows a Deeper Level of Comprehensive Metabolite Quantitation

Poster 117: P117
Community-based urine sampling methodology and biomarker technology for assessment of dietary exposure

Poster 118: P118
Simultaneous quantification of urine metabolites to allow comprehensive assessment of dietary exposure

Poster 119: P119
Utilizing the semantic web for kinetic modeling of metabolic disease pathways

Poster 120: P120
Untargeted Lipidomic analysis of bioactive lipids in epicardial adipose tissue and the effect of coronary artery disease status

Poster 121: P121
Using metabolomics to investigate the role of the gut microbiota in mediating the effects of diet on appetite in humans

Poster 122: P122
Seeking new biomarkers of atherogenesis in an atherogenic-prone down-sized pig model and their relevance in a cardiometabolic risk human cohort using LC-MS/MS lipidomics

Poster 123: P123
MS-based targeted metabolomics of eicosanoids and other oxylipins: analytical variability and interlaboratory comparison

Poster 124: P124
Migrating from PLS to Artificial Neural Networks – Adapting Interpretation Strategies

Poster 125: P125
Identification of biomarkers in Attention Deficit with Hyperactivity Disorder (ADHD)

Poster 126: P126
Metabolic signatures of urinary schistosomiasis and pharmacometabolomics of praziquantel treatment efficacy in children from rural Côte d'Ivoire

Poster 127: P127
Metabolomics at the top: Characterizing hypoxic responses and chronic mountain sickness in the highest city of the world

Poster 128: P128
Input of deep phenotyping in the metabolic syndrome stratification

Poster 129: P129
The circulating metabolites in the progression to islet autoimmunity and type 1 diabetes

Poster 130: P130
Milk molecular species of triacylglycerols characterized by lipidomic approach in cows and goats fed diets supplemented with various lipid sources

Poster 131: P131
Genome-scale metabolic modeling of human CD4+ T cells reveals ceramides as metabolic signature of Th17 and regulatory T-cell differentiation

Poster 132: P132
How metabolomic data support an innovative read across approach for safety assessment in cosmetics

Poster 133: P133
LC-MS-based semi-targeted lipidomics method: Application to the discovery of lipid biomarkers in diabetes

Poster 134: P134
Optimization of fecal NMR-based metabolomics to study the developing infant gut

Poster 135: P135

Biomarkers involved in glioma development in Drosophila melanogaster model

Poster 136: P136

Differential Mobility Separation Enhances the Quantification of Lysophosphatidic Acid in Plasma

Poster 137: P137 The Ichem'Algae ANR project: Untargeted chemotyping of algae bank by GC-MS and non invasive vibrational spectroscopy

Poster 138: P138 Prolonged Exposure to Milk Casein Results in Depressive Behaviour, Impaired Brian Development and Altered Metabolism in Wistar Rats

Poster 139: P139

Assessment of the Substrate Biodegradability in Anaerobic Co-digestion using a Metabolomic Approach

Poster 140: P40

Comparative metabolomics in Mamiellales

Poster 141: P41

Deciphering B .methanolicus metabolism for a one carbon-based production platform

Poster 142: P142

A Community-led Initiative to Develop and Promote Quality Assurance and Quality Control in Untargeted Metabolomics
Research: the metabolomics Quality Assurance and quality Control Consortium (mQACC)

Poster 143: P43

Analysis of Endophytic Colletotrichum sp. strains by MALDI-TOF mass spectrometry and t-SNE Molecular Networking

Poster 144: P44

Fungal endophyte exo-metabolites alter the morphology and metabolome of the plant pathogen Fusarium graminearum: an LC-MS based metabolomics approach to unravel the biocontrol effect

Poster 145: P145

Evaluation of micro Pillar Array Columns (µPAC™) Combined with High Resolution Mass Spectrometry for Lipidomics

Poster 146: P146

Chemical mappings for the flowers of Abeliophyllum distichum using metabolomics tools

Poster 147: P147

METABOLOMIC INSIGHT IN THE RESPONSES OF STREAM BIOFILMS TO THE HERBICIDE DIURON AND ITS MODULATION BY ENVIRONMENTAL FACTORS

Poster 148: P148

The Power of MS/MSALL Acquisition for High-Throughput Metabolomics Studies

Poster 149: P149

Exploring a volatomic-based strategy to study Lavendula semiochemicals

Poster 150: P150

Plasma metabolomics to identify biomarkers of foie gras quality in mule duck

Poster 151: P151

Effect of organic fertiliser on metabolic profile of hydroponically cultivated tomato

Poster 152: P152

Tackling the antioxidant metabolome of white wine

Poster 153: P153

Metabolomics approach reveals disruption of metabolic pathways in the marine bivalve Mytilus galloprovincialis exposed to WWTP effluent
Poster 154: P154
Molecular networking as a novel approach to unravel chemical diversity of Dinophysis spp. from French coastal waters

Poster 155: P155
Fast quantitative 2D NMR for targeted and untargeted lipidomics and metabolomics.

Poster 156: P156
4D-Lipidomics investigation of in C. elegans daf-2 mutants related to ageing and longevity

Poster 157: P157
Lipid profiling to identify changes in lipid metabolism in Caenorhabditis elegans upon starvation

Poster 158: P158
High speed untargeted 4D-Lipidomics LC-MS/MS workflows with Parallel Accumulation Serial Fragmentation (PASEF)

Poster 159: P159
Electrospray Ionization and samples complexity in Meta-metabolomics: a biomarker or a suppressed ion?

Poster 160: P160
Experimental strategy to discover new bioactive lipopeptides produced by gut microbiota: unknown approach by LC-HRMS.

Poster 161: P161
Novel UHPLC-MS method for detailed analysis of lipid species using a scheduled MS/MS acquisition approach for improved metabolite annotation

Poster 162: P162
Ammonium fluoride as suitable additive for HILIC-based LC-HRMS metabolomics

Poster 163: P163 Metabolomics profiles of Annona species cultivated in Egypt by using bioassay-guided fractionation process as antiproliferative agent.

Poster 164: P164
Targeting Esterified Oxylipins – Optimized Sample Preparation for LC-MS Analysis

Poster 165: P165
Stability of total oxylipins – influence of plasma generation and long-term storage

Poster 166: P166
Understand the metabolic crosstalk in photosymbiosis

Poster 167: P167
Molecular networks as a metabolomic tool to link traditional uses to biological activities and potential valuation of Polynesian plants

Poster 168: P168
A New Tool in Metabolomics: SWATH® Acquisition Analysis for Global Profiling and Quantitation

Poster 169: P169
How to automate boring lipidomic extraction?

Poster 170: P170
Development of lipidomic profiling by SFC-HRMS

Poster 171: P171
Parsimonious 13C Metabolic Flux Analysis

Poster 172: P172
The development of microbore UHPLC-MS assays to enhance sensitivity of untargeted metabolomic analysis of mammalian biofluids.

Poster 173: P173
MeTaQuaC: Quality Control Measures for Targeted Metabolomics Studies
Flash Poster 1: F1

Characterising the bidirectional biomolecular interactions between the intestinal microbiota and host drug metabolism

Flash Poster 2: F2

Rapid kinetics of lipid second messengers controls secretion in Toxoplasma parasites

Flash Poster 3: F3

Searching for differential metabolites between Mycobacterium tuberculosis and the MTBVAC vaccine using non-
targeted approach metabolomics

Flash Poster 4: F4

Elicitor-specific reprogramming of potato's metabolome

Flash Poster 5: F5

Biosource-guided network annotation and visualization for untargeted metabolomics

Flash Poster 6: F6

Recommending substructures for unknown tandem mass spectra

Flash Poster 7: F7

Towards precision diagnostics: Untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients

Flash Poster 8: F8

Metabolic maturation in the first two years of life in resource-constrained settings and its association with postnatal growth

Flash Poster 9: F9

Rapid LA-REIMS and comprehensive UHPLC-HRMS for metabolomics of biofluids in metabolic disorders
**Poster 001: P001 Revealing age-related sex differences in human lipidome:
Metabolic phenotyping of Lausanne population**

Jessica Medina<fnref n="1" />, Tony Teav<fnref n="1" />, Vera van der Velpen<fnref n="1" />, Colin Volet<fnref n="1" />, Pedro Marques-Vidal<fnref n="2" />, Peter Vollenweider<fnref n="2" />, Hector Gallart-Ayala<fnref n="1" /> & Julijana Ivanisevic<fnref n="1" />

**Introduction**

Men and women have different predisposition to acquire metabolic disorders with age. However, beyond sex hormones, the sex-specific lipidome variation with age, in health and in disease, remains unexplored. Lipids have a far-reaching role in the regulation of metabolic processes and given the alterations in lipid metabolism with ageing, the sex-related framework of lipid variation needs to be defined along lifespan in a 'healthy' population, prior to relating altered pathways with a disease.

**Technological and methodological innovation**

We will comprehensively quantify complex lipid species and steroids in human plasma of the apparently healthy sex-matched subjects from 40 to 80 years old. To obtain the complex lipid profile, we have developed a high-throughput single-step extraction with BUME (butanol-methanol 1:1 v/v %) coupled to HILIC-ESI-MS/MS analysis. With our method we can measure approximately 600 lipid species including glycerolipids, cholesterol esters, sphingolipids, glycerophospholipids and free fatty acids.

**Results and impact**

Quantitative data on these structural, fuel and signaling lipids, will allow us to draw a comprehensive picture of plasma lipid profile. The correlation with the steroid profile will allow us to characterize its variation in the context of female and male reproductive cycle and hormonal status. The advanced data analysis will comprise the exploration of sex-differences within specific biochemical pathways in addition to associations with other clinical metadata.

**References**

1. Huynh, K. et al. 2019. Cell Chem. Biol. 26. 71-84

2. Yu, Z. et al. 2012. Aging Cell. 11.960-967 3. Trabado, S. et al. 2017. PLoS One. 12. 1-17.

<footnote n="1">¹ Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Switzerland</footnote>

<footnote n="2">² Internal Medicine, Faculty of Biology and Medicine, University of Lausanne, Switzerland</footnote>
# Poster 002: P002 Untargeted metabolomic analysis for the discovery of treatment response biomarkers in breast cancer patients through Hotelling T2 multivariate profiling

Ariadna Martín-Blázquez1; Leticia Díaz-Beltrán2; Caridad Díaz Navarro1; María Ruiz2; Mónica Fernández2; Capilla de la Torre2; Francisca Vicente1; Olga Genilloud1; José Pérez del Palacio1; Pedro Sánchez Rovira2

## Introduction

Breast cancer is the leading cause of women cancer-related deaths worldwide. Thus, new technologies with enhanced sensitivity and specificity for diagnosis and monitoring are in critical demand. The aim of our study was to identify those metabolites that suffered the greatest variations over time between patients who responded and did not respond to treatment in the four-breast cancer molecular subtypes: Luminal B, Luminal A, Triple Negative and Human Epidermal growth factor Receptor 2 positive.

## Technological and methodological innovation

Plasma samples of 104 breast cancer patients were collected before any type of chemotherapy, prior to the surgery and before the surgery. They were classified into responders or non-responders based on Miller-Payne response. Samples were analyzed following an untargeted approach using liquid chromatography coupled to high-resolution mass spectrometry in positive mode. Multivariate Empirical Bayes Analysis detected differences in temporal profiles in the different biological conditions.

## Results and impact

Hotelling's T2 multivariate profiling allowed us to detect differential metabolites whose expression varied over time between responders and non-responders. It could be used to determine the existence of residual disease after neoadjuvant treatment and thereby contribute to the identification of patients who will absolutely benefit from additional treatment. LC-HRMS plasma metabolomics is a non-invasive, faster and less costly technique than the current screening methods for breast cancer.

<footnote n="1">¹ Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía. Parque Tecnológico de Ciencias de la Salud. Avda. del Conocimiento, 34, 18016, Granada, Granada, Spain</footnote>

<footnote n="2">² Jaén Hospital Complex, Medical Oncology Unit. Avda. Del Ejército Español, 10, 23007, Jaén, Spain</footnote>
**Poster 003: P003 Metabolomic investigation of immune cell activation by Liquid Chromatography coupled with High Resolution Mass Spectrometry (LC-HRMS)**

Jessica MICHIELETTO1, Aurélie DELVAUX1, Amir LOUAAR1, Emeline CHU-VAN1, Evelyne CORREIA2, Bernard MAILLERE2, Benoit COLSCH1, Christophe JUNOT1, François FENAILLE1 and Florence Anne CASTELLII

1 Service de Pharmacologie et d'Immunoanalyse, Laboratoire d'Etude du Métabolisme des Médicaments, CEA-INRA, université Paris Saclay, MetaboHUB, Gif-sur-Yvette France

2 Service d'Ingénierie Moléculaire des PROtéines, Laboratoire chimie du vivant, CEA, Gif-sur-Yvette, France

**Introduction**

In recent years, it has been shown that some specific metabolites could be involved in immune cell polarization during infection, thus shaping the immune response [1]. Within the adaptive immune system, dendritic cells (DCs) play a central role, orienting the immune response according to the perceived danger signal [2]. While metabolism seems to significantly impact DC activation, the underlying mechanisms are still poorly described and understood.

**Technological and methodological innovation**

We aimed to develop a comprehensive method to get deeper insight into DC metabolic reprogramming. Monocyte-derived DCs were *in vitro* activated either with lipopolysaccharide (LPS, bacterial signal) or poly (I:C) (viral signal) and their corresponding metabolic responses studied by an untargeted LC/HRMS-based metabolomics approach [3]. Statistical data analyses were used for thoroughly assessing specific metabolic differences.

**Results and impact**

We described the first complete metabolic map of DCs (150 metabolites robustly monitored) and identified central metabolic changes following activation by LPS or poly(I:C). Two distinct signatures were obtained with alterations of four main metabolic pathways such as Krebs cycle. Our approach represents a powerful and simple way to robustly monitor the metabolic activation of immune cells that would facilitate the discovery of biomarkers that specifically sign a bacterial or viral infection.

**References**

[1] O'Neill, Luke AJ, and Edward J. Pearce. 2016, *Journal of Experimental Medicine* 213, 15-23.

[2] Pearce, Edward J., and Bart Everts. 2015, *Nature Reviews Immunology* 15, 18-29.

[3] Junot Christophe, et al. 2014, *Mass spectrometry reviews* 33, 471-500.
# Poster 004: P004 GH-OMICs: applying metabolomics to the fight against hormonal doping

Luca NARDUZZI¹, Gaud DERVILLY¹, Magnus ERICSSON2, Bruno LE BIZEC1, Corinne BUISSON2

1) Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAE, Nantes F-44307, France

2) Département des analyses, Agence Française de Lutte contre le Dopage (AFLD), Châtenay-Malabry, France

## Introduction

Growth hormone (GH), an endogenous protein regulating anabolism and lipolysis in humans, is known to be abused by the athletes to improve their performances. GH detectability is very limited due to its quick turnover and its variability across humans [1]. Given the several effects of GH on metabolism [2], the aim of this project is to i) develop a metabolomics strategy to identify GH users, ii) validate it and iii) determine its performance to propose its application as official anti-doping method [3].

## Technological and methodological innovation

The project GH-OMICs uses metabolomics to study metabolic changes induced by GH, identifying “markers of effect” to track GH administration in athletes. Given the several and long-lasting effects of GH on metabolism, we expect to find markers exhibiting disrupted patterns longer than the GH turnover. In the method development step, urine and plasma samples of two groups of athletes treated either with EPO or EPO + GH [4] have been collected and analyzed using metabolomics and lipidomics approaches.

## Results and impact

The results of the method development show that a longitudinal metabolomics screening of the athletes is able to identify correctly the GH users in spite of the confusing presence of EPO. Several biomarkers have been identified in both metabolomics and lipidomics [5]. Further validation with new experimental and real anti-doping samples is required to confirm the biomarkers, complete the biological interpretation and implement the official method.

## References

[1] M. Lehtihet et al, **2018 Drug Test. Anal.**, 11, 411–421.

[2] L. Narduzzi et al. **2019 Submitted**, 1–20.

[3] G. Dervilly-Pinel et al. **2018 Food Addit. Contam. Part A**, 35, 1948–1958.

[4] A. Marchand et al. **2019 Drug Test. Anal.**, 33, 1–18.

[5] L. Narduzzi et al. **2020 Submitted**, 1-22.
**Poster 005: P005 NMR metabolomics for understanding the role of alkaline phosphatase in neurotransmission and inflammation processes**

Stéphane BALAYSSAC<fnref n="1" />, Thomas CRUZ<fnref n="1" />, Marie GLEIZES<fnref n="2" />, Gary LUTIN<fnref n="1" />, Juana COMO<fnref n="3" />, Anne BRIOLAY<fnref n="3" />, Lionel G NOWAK<fnref n="2" />, Myriam MALET-MARTINO<fnref n="1" />, David MAGNE<fnref n="3" />, Caroline FONTA<fnref n="2" />, Véronique GILARD<fnref n="1" />

**Introduction**

Tissue Non-specific Alkaline Phosphatase (TNAP) is a ubiquitous ectoenzyme located in different body tissues. Mutations of the TNAP gene are responsible for hypophosphatasia, a rare disease characterized mainly by defective bone mineralization caused by pyrophosphate accumulation in bone tissue and accompanied by neurological symptoms (pain, seizure...) [1]. We aimed in identifying metabolic pathways associated with TNAP activity in brain and in exploring its role in the inflammatory process.

**Technological and methodological innovation**

Untargeted ¹H NMR-based metabolomics was used on tissue extracts from two original models: (i) Akp2 mice, which is the rodent model of hypophosphatasia by invalidation of the TNAP gene and (ii) a model of postprandial inflammation based on food intake [2].

**Results and impact**

Eight metabolites differentiated TNAP-deficient mouse brain (in 7 day-old animals), among which GABA, adenosine and cystathionine were the most discriminant [3]. Metabolic changes were also observed in both liver and brain in the postprandial inflammation model. These metabolic changes suggest that TNAP is (i) involved in the production of antiinflammatory agents (adenosine) or their precursor (cystathionine, precursor of H₂S), (ii) regulated by pro-inflammatory cytokines.

**References**

[1] Buchet et al. 2013. Methods Mol Biol. 1053, 27-51

[2] Herieka et al. 2014. Mol Nutr Food Res. 58, 136-146

[3] Cruz et al. 2017. J. Neurochem. 140, 919-940

<footnote n="1">¹Laboratoire SPCMIB CNRS-Université de Toulouse, France</footnote>

<footnote n="2">²Centre de recherche Cerveau et Cognition (CerCo), CNRS-Université de Toulouse, France</footnote>

<footnote n="3">³Institut de Chimie et de Biochimie Moléculaires et Supramoléculaires (ICBMS), CNRS-Université Lyon 1, France</footnote>
**Poster 006: P006 A multiplatform approach: HPLC, GC and NMR for urinary
metabolomic analysis of muscle-invasive bladder cancer**

Julia JACYNA<fnref n="1" />, Renata WAWRZYNIAK<fnref n="1" />, Stéphane BALAYSSAC<fnref n="2" />, Véronique GILARD<fnref n="2" />, Myriam MALET-MARTINO<fnref n="2" />,
Aleksandra SAWICKA<fnref n="1" />, Marta KORDALEWSKA<fnref n="1" />, Łukasz NOWICKI<fnref n="3" />, Eliza KUREK<fnref n="4" />, Ewa BULSKA<fnref n="4" />,
Małgorzata PATEJKO<fnref n="1" />, Marcin MARKUSZEWSKI<fnref n="5" />, Piotr GUTKNECHT<fnref n="6" />, Marcin MATUSZEWSKI<fnref n="5" />, Janusz SIEBERT<fnref n="6" />,
Roman KALISZAN<fnref n="1" />, Michał J. MARKUSZEWSKI<fnref n="1" />

**Introduction**

Bladder cancer (BCa) constitutes one of the most common cancers and the second most common malignancy of the urinary tract in the human population worldwide [1]. The main known risk factors of BCa include tobacco smoking, age, obesity, lack of physical activity, gender, genetic and occupational exposure [2]. This study aimed to perform and analyze the urinary metabolomic signatures of high-grade muscle-invasive BCa patients and healthy volunteers thanks to a multiplatform analytical approach.

**Technological and methodological innovation**

Three complementary analytical techniques were used: (i) high performance liquid chromatography (reversed phase and hydrophilic interaction) coupled with mass spectrometry (MS) detection in two ionization modes, (ii) gas chromatography with MS detection in full scan mode and (iii) proton nuclear magnetic resonance spectroscopy. Datasets were submitted to multivariate statistical analyses and discriminating compounds between patients and healthy urinary samples were identified.

**Results and impact**

The multiplatform analysis allowed to identify 17 urinary metabolites that differentiate bladder cancer patients and healthy volunteers at a statistically significant level. For the two metabolites hippuric acid and lactate, concentration changes were observed with the different analytical techniques. The present research showed that a combination of several techniques is better to maximize metabolite coverage and validate their variation, particularly for a biological fluid such as urine.

**References**

[1] World Health Organization, International Agency for Research on Cancer, GLOBOCAN 2012: Estimated Cancer Incidence, Mortality and Prevalence. Worldwide in 2012, (2014) http://globocan.iarc.fr. [2] Stewart and Wild, World cancer report 2014, pp. 444–452.

<footnote n="1">¹Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gdańsk, Poland</footnote>

<footnote n="2">²Groupe de RMN Biomédicale, Laboratoire SPCMIB CNRS-Université de Toulouse, Toulouse, France</footnote>

<footnote n="3">³Perlan Technologies Polska, Warszawa, Poland</footnote>

<footnote n="4">⁴Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warszawa, Poland</footnote>

<footnote n="5">⁵Department of Urology, Medical University of Gdańsk, Gdańsk, Poland</footnote>

<footnote n="6">⁶Department of Family Medicine, Medical University of Gdańsk, Gdańsk, Poland</footnote>
**Poster 007: P007 NMR metabolic profiling can help discriminate between normal primary hepatocytes and diverging hepatic cancer cell lines**

Corentine Goossens*, Valérie-Ann Raymond, Pascal Lapierre and Marc Bilodeau.

CRCHUM, Laboratoire d'hépatologie cellulaire, Montréal, Canada.

**Introduction**

Metabolic alterations are a hallmark of cancer cells. Recent studies suggest that the metabolic reprogramming of cancer cells is essential to support their growth in nutrient-restricted environments. We previously showed that Dt81Hepa1-6 cells, a highly tumorigenic derivative of Hepa1-6 murine cells, display increased metabolic plasticity in vitro. Recently, novel omics technologies have been shown to perform multi-parametric quantification of metabolic responses in normal and pathological cells. To characterize the metabolic pathways active in primary hepatocytes and these two diverging cancer cell lines under proliferative conditions, we examined production and consumption rates of metabolites *in vitro* using a nuclear magnetic resonance (NMR) metabolomic approach.

**Technological and methodological innovation**

Primary hepatocytes were isolated using the two-step collagenase perfusion method from C57BL/6 mice. Dt81hepa1-6 cells have been isolated after an in vivo passage of parental Hepa1-6 cells. Cells were cultured in vitro in high glucose DMEM (25mM) with 10% FBS. After 48 hours, medium was collected and cells were snap-frozen. The extraction of intracellular metabolites was performed using an ice-cold MeOH/CHCl3/H2O solution. Cell extracts and culture medium samples were analyzed using a cryoprobe Bruker 700 MHz AVANCE NMR spectrometer for both structure elucidation and quantification.

**Results and impact**

More than 30 metabolites were identified and quantified in both cellular extracts and extracellular culture medium. Metabolomic analysis revealed distinct metabolic profiles between primary hepatocytes, Hepa1-6 and Dt81-hepa1-6 cell lines. We identified several metabolites that were differentially regulated between normal and cancer cells (that both displayed increased glycolytic activity) as well as metabolites that were specifically associated with the highly tumorigenic Dt81Hepa1-6 cell line. Hence, a significant decrease in glucose, glutamine as well as a specific increase in lactate, alanine, acetate were found in the extracellular medium of both cancer cell lines. Interestingly, ketoacids by-products originating from the valine, leucine and isoleucine degradation pathway were found in the Dt81-hepa1-6 medium. Lactate, alanine, creatine and ATP were also increased in cancer cell compared to primary cells extracts.

**References**

Cassim S. et al. 2018. Oncotarget. 9:26868-26883
**Poster 008: P008** *Quantitative metabolomics analysis reveals altered lipid profile in women with endometriosis: a pilot case-control study*

Komodo MATTA1 ; Stéphane PLOTEAU2 ; Yann GUITTON1 ; Sadia OUZIA1; Bruno LE BIZEC1 ; Jean-Philippe ANTIGNAC1 ; German CANO-SANCHO1

¹ LABERCA, Oniris, INRA, 44307, Nantes, France

² Service de gynécologie-obstétrique, CIC FEA, Hôpital Mère Enfant, CHU Hôtel Dieu, Nantes,

**Introduction**

Endometriosis is a gynaecological disease characterised by the presence of endometrial tissues outside of the uterus. Presently, the gold standard for diagnosis is laparoscopy with an average delay of 8-10 years between symptom onset and surgical confirmation¹. Thus, exploring early biomarkers of endometriosis is a clinical priority to shorten diagnostic delays. Most biomarker research to date has focused on proteomics of inflammation with few studies exploring the application of metabolomics.

**Technological and methodological innovation**

A novel quantitative mass spectrometry (MS) metabolomics approach was piloted in serum from 66 women with and without endometriosis from a case-control study. Serum samples were processed with the quantitative metabolomics kit MxP®Quant 500 and the extracts analysed by liquid chromatography (LC-MS/MS) and flow injection analysis (FIA-MS/MS), for small molecules and lipids, respectively. Multiple reaction monitoring in combination with internal standards served quantification of metabolites.

**Results and Impact**

The tested kit was able to quantify 102 small metabolites and 454 lipids in the serum samples. Statistical analyses revealed significant differences in the lipid profiles between women with vs without endometriosis (94% classification accuracy) but not in the profiles of small metabolites. 11 lipids in particular were as significant biomarkers through multivariate regression analysis. Quantitative metabolomics profiling offers a promising contribution to aetiological research on endometriosis.

**References**

1. Rier SE et al. 1993. Fundamental and Applied Toxicology. 21 (4): 433-441.
**Poster 009: P009** *Steroid profile in human seminal fluid: is it linked to sperm quality?*

Eulalia OLESTI<fnref n="1" />,<fnref n="2" />; Santiago CODESIDO<fnref n="1" />; Arnaud GARCIA<fnref n="1" />,<fnref n="2" />; Rita RAHBAN<fnref n="2" />,<fnref n="3" />; Michel F. ROSSIER<fnref n="2" />,<fnref n="4" />,<fnref n="5" />; Julien BOCCARD<fnref n="1" />,<fnref n="2" />; Serge NEF<fnref n="2" />,<fnref n="3" />; Víctor GONZÁLEZ-RUIZ<fnref n="1" />,<fnref n="2" />, Serge RUDAZ<fnref n="1" />,<fnref n="2" />

**Introduction**

Steroids are essential hormones that play a crucial role in homeostasis of many biological processes including sexual development, spermatogenesis, sperm physiology and fertility [1]. Although steroids have been largely studied in common biological matrices such as plasma or urine, there is a lack of knowledge about the presence of steroids in the seminal fluid, as well as of their potential as indicators of sperm quality. In this study, we aimed to firstly develop a robust LC-HRMS (liquid chromatography coupled with high resolution mass spectrometry) strategy to map the steroidome in seminal fluid and study the relationship between the steroid profile and the seminal quality.

**Technological and methodological innovation**

In this study, a novel LC-HRMS strategy has been developed for obtaining the extended steroid profile in human seminal fluid. An optimized supported solid-phase extraction (SPE) with an automatic annotation (by DynaStI, a publicly available retention time prediction tool developed in house) were carried out for the steroidome method analysis. Then, the relationship between the steroid profile and the sperm quality was evaluated on samples from the FABER Swiss nationwide crosssectional epidemiological study [2] by choosing 200 seminal fluid samples [50 with high total sperm count (TS) and good morphology (M), 50 with low TS and good M, 50 with high TS and poor M and 50 with low TS and poor M].

**Results and impact**

Preliminary results showed different patterns of steroids altered. We observed that patterns of androgens were increased in subjects with high total sperm counts while the production of cortisol appears to be higher in subjects with lower sperm counts. The variations on the steroid profile provided insights for further understanding the biochemistry behind male infertility diseases.

**References**

[1] Jeanneret F et al. 2016. Journal of Chromatography A. 1430; 97-112.

[2] Rahban R et al. 2019. Andrology. 6; 818-826.

<footnote n="1">¹Analytical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel-Servet 1, 1206 Geneva, Switzerland.</footnote>

<footnote n="2">²Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland.</footnote>

<footnote n="3">³Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Rue Michel-Servet 1, 1206 Geneva, Switzerland.</footnote>

<footnote n="4">⁴Service of Clinical Chemistry & Toxicology, Central Institute of Hospitals, Hospital of Valais, Sion, Switzerland.</footnote>

<footnote n="5">⁵Department of Internal Medicine, Geneva University Faculty of Medicine, Geneva, Switzerland.</footnote>
**Poster 010: P010** *Combination of two semi-targeted approaches to detect toxicological biomarkers as a tool to understand the relation between red meat consumption and colorectal cancer promotion*

L.Mervant¹,², E.Jamin¹,², S.Chevolleau¹,², J-F.Martin¹,², I.Jouanin¹,², M.Beslay¹, E.Fouche¹
R.Costantino¹,²,
L.Debrauwer¹,², F.Gueraud¹

¹ Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France. ² Metatoul-AXIOM platform, MetaboHUB, Toxalim, INRAE, Toulouse, France

**Introduction**

Among existing hypotheses to explain the link between red meat consumption and colorectal cancer promotion, heme iron is highly suspected as the key element of this mechanism. It catalyses reactive oxygen species production that subsequently attack dietary poly-unsaturated fatty acids giving rise to lipid peroxidation products (LPPs) that are known to be highly reactive towards biomolecules. Thus, monitoring our exposure to these compounds could be highly informative in a toxicological context.

**Technological and methodological innovation**

LPPs present in free form in the colon lumen can be detoxified via glutathione conjugation and elimination in urine as mercapturate conjugates. Two LC-MS strategies were set up to analyse these compounds. Urinary mercapturate conjugates were screened without a priori by neutral loss monitoring using DIA HRMS. In addition, free reactive aldehydes were analysed using a selective derivatization introducing a bromine atom enabling their detection via the specific isotopic pattern of bromine.

**Results and impact**

Analyses of urine and feces samples obtained from rats fed various diets supplemented or not with heme iron showed specific modulation in LPPs formation. Interestingly statistical analyses performed only on features associated to LPPs showed a much better segregation. These two approaches allowed highlighting the diversity of LPPs formed from various fatty acid precursors and evidencing new compounds linked to lipid peroxidation which exhibited cytotoxic properties as other known LPPs.
**Poster 011: P011 Mre11 nuclease activity removes the replication blocking
nucleoside analogue Gemcitabine from the nascent strand during DNA replication**

Andrea Keszthelyi<fnref n="1" />, Rolf Kraehenbuehl<fnref n="2" />, Ellen Vernon<fnref n="3" />, Marie-Fabrice Gasasira Uwamahoro<fnref n="1" />, Lennart Boeckemeier
<fnref n="3" />, Daniel Chaplin<fnref n="2" />, Hans E. Krokan<fnref n="4" />, Sarah Lambert<fnref n="5" />, Bela Paizs<fnref n="2" />, Edgar Hartsuiker<fnref n="3" />,*

**Introduction**

Nucleoside analogues are clinically important anticancer drugs<fnref n="1" />,<fnref n="2" />. They exert their cytotoxicity by inhibiting nucleotide synthesis and through incorporation into nascent DNA during replication, causing chain termination<fnref n="2" />,<fnref n="3" />. A candidate for the excision of nucleoside analogues from DNA is Mre11. Using eukaryotic model organisms, we demonstrate that Mre11 nuclease activity is required for the removal of Gemcitabine from the nascent DNA strand during replication.

**Technological and methodological innovation**

MRE11-dependent removal of Gemcitabine from newly synthesized DNA during replication was assessed by LC-MS/MS based measurement of Gemcitabine content in wild-type and mutant cells and in cells treated with a nuclease inhibitor.

**Results and impact**

Our data show that the *Schizosaccharomyces pombe* nuclease mutants are sensitive to the nucleoside analogue Gemcitabine and show a higher amount of Gemcitabine in genomic DNA. Chicken lymphoblast DT40 cells treated with an MRE11 3'-5' exonuclease inhibitor also showed increased genomic Gemcitabine content. This suggests that Mre11 nuclease activity contributes to the removal of chain-terminating Gemcitabine from the nascent DNA strand in these cells.

**References**

1. Jordheim et al. (2013). *Nat. Rev. Drug Discov.* **12**, 447–64.

2. Galmarini et al. (2002). *Lancet Oncol.* **3**, 415–24.

3. Sampath et al. (2020). *Oncogene* **22**, 9063–74.

4. Chottiner et al. (1991). *Proc. Natl. Acad. Sci. U. S. A.* **88**, 1531–5.

5. Sivakumar et al. (2020). *Methods* **33**, 213–9.

<footnote n="1">¹ Genome Damage and Stability Centre, University of Sussex, Brighton BN1 9RR, UK.</footnote>

<footnote n="2">² Centre for Environmental Biotechnology, Bangor University, Bangor, LL57 2UW, UK.</footnote>

<footnote n="3">³ School of Medical Sciences, Bangor University, Bangor, LL57 2UW, UK.</footnote>

<footnote n="4">⁴ Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, NO-7491, Norway.</footnote>

<footnote n="5">⁵ Institut Curie, UMR3348, CNRS, Orsay F-91405, France.</footnote>

<footnote>*Corresponding author</footnote>
# Poster 012: P012 Metabolic characterization of *Leishmania mexicana*-infected macrophages

Clement REGNAULT<fnref n="1" />, Michael P. BARRETT<fnref n="1" />,<fnref n="2" />

## Introduction

Leishmaniasis is a Neglected Tropical Disease representing a major burden across the world, with an estimated incidence of 12 million cases and 350 million people at risk of developing the cutaneous or visceral form of the disease. In humans, Leishmania parasites invade macrophages and replicate within those cells as infection progresses. Thus, characterizing the macrophage-leishmania interaction at the metabolic level could lead to the identification of new drug targets to treat leishmaniasis.

## Technological and methodological innovation

We employed untargeted and stable isotope labelling metabolomics approaches using [U-¹³C]-D-glucose to study the effect of infection with *L. mexicana* on the metabolism of murine bone marrow-derived macrophages.

## Results and impact

TCA cycle intermediates in infected macrophages incorporated higher levels of ¹³C₃, ¹³C₄ and ¹³C₅ labelling from D-glucose indicating higher TCA cycle activity, which has been associated with an anti-inflammatory phenotype (Mills & O'Neill, 2016). Our results also point to the existence of previously unreported metabolic routes in *Leishmania* parasites, including a novel degradation route for the immunomodulatory metabolite itaconate as well as a propionate detoxification pathway.

## References

[1] Mills et al. 2016. European Journal of Immunology. 46, 13–21.

<footnote n="1">¹ Glasgow Polyomics, College of Medical, Veterinary & Life Sciences, University of Glasgow, Garscube Estate, Bearsden, Glasgow, United Kingdom</footnote>

<footnote n="2">² Wellcome Centre for Molecular Parasitology, Institute of Infection, Immunity & Inflammation, University of Glasgow, Glasgow, United Kingdom</footnote>
**Poster 013: P013** *A comprehensive analysis of metabolome and lipidome in different sleep deprivation models using a multiplatform technique of chromatography-mass spectrometry*

Sun Jo KIM<fnref n="1" />; Sang Jun YOON<fnref n="1" />; Phuoc Long NGUYEN<fnref n="1" />; Kyung-Hee JUNG<fnref n="2" />; Hyung Min KIM<fnref n="1" />; Yu Jin HONG<fnref n="1" />; Zhenghuan FANG<fnref n="2" />; Tae Joon KIM<fnref n="1" />; Hoang Anh NGUYEN<fnref n="1" />; Soon-Sun HONG<fnref n="2" />; and Sung Won KWON<fnref n="1" />,*

**Introduction**

Sleep deprivation (SD) has been investigated for its association with chronic diseases and metabolic disorders. Until now, several metabolic changes were reported in various SD models. However, comprehensive investigation on the systemic and sleep-related regional effect were more to be explored.

**Technological and methodological innovation**

The multiple platform technique was used to induce SD and two different circadian-phase intervention was adopted for acute and chronic SD models. A multiplatform of chromatography-mass spectrometry, including untargeted and semi-targeted metabolomics and untargeted lipidomics, was conducted to exclusively profile and assess the biomolecules in serum, hypothalamus, and hippocampus for biochemical investigation from acute and chronic SD models.

**Results and impact**

From the annotated features in metabolomics and lipidomics results in acute SD model, 37 metabolites and 28 lipids were selected based on VIP score (≥1) from multivariate statistical analysis for validation of chronic SD model. Systemical metabolic alteration showed similar aspect between two SD models, whereas lipid alteration was model-dependent. Local metabolic perturbation was also observed in hypothalamus. As a result, our finding may extend the understanding biochemical perturbation in SD.

**References**

[1] Yoon, Sang Jun, et al. 2019. Journal of proteome research. 18.9: 3295-3304.

<footnote n="1">¹ College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea</footnote>

<footnote n="2">² Department of Biomedical Sciences, College of Medicine, Inha University, Incheon 22212, Republic of Korea</footnote>