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

Stéphanie Monnerie, Mélanie Pétéra, Bernard Lyan, Pierrette Gaudreau, Blandine Comte, Estelle Pujos-Guillot

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## To cite this version:

Stéphanie Monnerie, Mélanie Pétéra, Bernard Lyan, Pierrette Gaudreau, Blandine Comte, et al.. Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets. 15th Annual Conference of the Metabolomics Society (Metabolomics 2019), Sep 2019, La Haye, Netherlands. 300 p., 2019. hal-02291478

HAL Id: hal-02291478

https://hal.science/hal-02291478

Submitted on 2 Jun 2020

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15th Annual Conference of the Metabolomics Society

METABOLOMICS 2019

JUNE 23-27

THE HAGUE

ORAL AND POSTER ABSTRACTS

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# AGENDA AT A GLANCE

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Biomedical

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Technology

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Plant, Food, Environmental and Microbial

New Frontiers

## SUNDAY, JUNE 23

<table><thead><tr><th></th><th>Atlantic</th><th>Alexia</th><th>Ariane</th><th>Oceania Foyer</th></tr></thead><tbody><tr><td>11:00 a.m.</td><td colspan="4">REGISTRATION OPEN</td></tr><tr><td>12:30 p.m. – 2:15 p.m.</td><td>W1: EMN – Data Fusion</td><td>W2: Mining the Metabolome</td><td>W3: Multi-Omics Integration &amp; Systems Metabolomics</td><td>Intro to the Field 1: Data Acquisition</td></tr><tr><td>2:30 p.m. – 4:15 p.m.</td><td>W4: Application of Graphical Models to Metabolomics</td><td>W5: Plant Metabolomes: Natural &amp; Generated Variability</td><td>W6: How to Link Metabolome &amp; Genome Mining</td><td>Intro to the Field 1: Data Acquisition</td></tr><tr><td>4:30 p.m. – 6:15 p.m.</td><td>W7: EMN – Professional Career Development</td><td>W8: Putting Metabolomic Data into Context</td><td>W9: An Open-Source Pipeline for Appraisal of NMR Datasets</td><td>Intro to the Field 1: Data Acquisition</td></tr><tr><td>6:30 p.m. – 8:30 p.m.</td><td colspan="4">Career Night – Pacific</td></tr></tbody></table>

## MONDAY, JUNE 24

<table><thead><tr><th></th><th>Atlantic</th><th>Alexia</th><th>Ariane</th><th>Oceania Foyer</th></tr></thead><tbody><tr><td>7:30 a.m.</td><td colspan="4">REGISTRATION / INFO DESK OPEN</td></tr><tr><td>8:30 a.m. – 10:15 a.m.</td><td>W10: The Importance of Quality Assurance &amp; Quality Control</td><td>W11: Towards FAIR Spectral Libraries</td><td>W12: EMN – Stable Isotope-Resolved Metabolomics</td><td>Intro to the Field 2: Data (pre) Processing and Biostatistics</td></tr><tr><td>10:30 a.m. – 12:15 p.m.</td><td>W13: Beyond pathway mapping</td><td>W14: Standardizing the Fluxomics Workflows</td><td>W15: Tools to Study the Microbiome-Metabolome Interplay</td><td>Intro to the Field 2: Data (pre) Processing and Biostatistics</td></tr><tr><td>12:15 p.m. – 1:30 p.m.</td><td colspan="4">LUNCH BREAK – ON YOUR OWN</td></tr><tr><td>1:30 p.m. – 3:15 p.m.</td><td>W16: Application Metabolomics in Industry</td><td>W17: Dynamic Modeling of Human Metabolism</td><td>W18: EMN – Volatomics in Human Health</td><td>Intro to the Field 2: Data (pre) Processing and Biostatistics</td></tr><tr><td>3:30 p.m. – 5:00 p.m.</td><td colspan="4">Opening Ceremony | Plenary Session 1 – Joshua Rabinowitz – King Willem-Alexander Hall</td></tr><tr><td>5:15 p.m. – 6:45 p.m.</td><td colspan="4">Welcome Reception – Poster Session 1 – Odd Numbers – Exhibit Foyer</td></tr><tr><td>7:00 p.m. – 8:00 p.m.</td><td colspan="4">Metabolomics Society Town Hall Meeting – Atlantic</td></tr></tbody></table>

## TUESDAY, JUNE 25

<table><thead><tr><th></th><th>Atlantic</th><th>King Willem-Alexander</th><th>Alexia</th><th>Ariane</th></tr></thead><tbody><tr><td>7:45 a.m.</td><td colspan="4">REGISTRATION / INFO DESK OPEN</td></tr><tr><td>8:30 a.m. – 9:30 a.m.</td><td colspan="4">Plenary Session 2 – Dorret Boomsma – King Willem-Alexander Hall</td></tr><tr><td>9:30 a.m. – 10:15 a.m.</td><td colspan="4">BREAK – EXHIBIT FOYER</td></tr><tr><td>10:15 a.m. – 12:00 p.m.</td><td>1. Cancer</td><td>2. Plant Applications 1</td><td>3. Data Integration &amp; Data Basing 1</td><td>4. Novel Technologies</td></tr><tr><td>12:00 p.m. – 1:30 p.m.</td><td colspan="4">LUNCH – IN FOYER WITH EXHIBITS – PLATINUM SPONSOR PRESENTATIONS</td></tr><tr><td>12:20 p.m. – 1:20 p.m.</td><td colspan="2">Sponsor Pres: SCIEX</td><td colspan="2">Sponsor Pres: Waters Corporation</td></tr><tr><td>1:30 p.m. – 3:15 p.m.</td><td>5. Metabolic Disease</td><td>6. Food Applications 1</td><td>7. Flux Studies</td><td>8. Novel Instruments, Tools and Services</td></tr><tr><td>3:15 p.m. – 3:45 p.m.</td><td colspan="4">BREAK – EXHIBIT FOYER</td></tr><tr><td>3:45 p.m. – 5:30 p.m.</td><td>9. Epidemiology</td><td>10. Microbial Applications</td><td>11. Metabolite Identification 1</td><td>12. Stem Cells, Organoids</td></tr><tr><td>5:30 p.m. – 7:00 p.m.</td><td colspan="4">Poster Session 2 – Odd Numbers – Exhibit Foyer</td></tr><tr><td>7:00 p.m. – 8:30 p.m.</td><td colspan="4">EMN Reception – Pacific</td></tr></tbody></table>

## WEDNESDAY, JUNE 26

<table><thead><tr><th></th><th>Atlantic</th><th>King Willem-Alexander</th><th>Alexia</th><th>Ariane</th></tr></thead><tbody><tr><td>8:00 a.m.</td><td colspan="4">REGISTRATION / INFO DESK OPEN</td></tr><tr><td>8:30 a.m. – 9:30 a.m.</td><td colspan="4">Plenary Session 3 – Cathie Martin – King Willem-Alexander Hall</td></tr><tr><td>9:30 a.m. – 10:15 a.m.</td><td colspan="4">BREAK – EXHIBIT FOYER</td></tr><tr><td>10:15 a.m. – 12:00 p.m.</td><td>13. Lipidomics and Cardiovascular Disease</td><td>14. Plant Defense</td><td>15. Data Analysis &amp; Statistics</td><td>16. Single Cell</td></tr><tr><td>12:00 p.m. – 1:30 p.m.</td><td colspan="4">LUNCH – IN FOYER WITH EXHIBITS – PLATINUM SPONSOR PRESENTATIONS</td></tr><tr><td>12:20 p.m. – 1:20 p.m.</td><td colspan="2">Sponsor Pres: Thermo Fisher Scientific</td><td colspan="2">Sponsor Pres: Shimadzu Europa GmbH</td></tr><tr><td>1:30 p.m. – 3:15 p.m.</td><td>17. Ageing and Disease</td><td>18. Food Applications 2</td><td>19. Data Integration &amp; Data Basing 2</td><td>20. Regulatory Session</td></tr><tr><td>3:15 p.m. – 3:45 p.m.</td><td colspan="4">BREAK – EXHIBIT FOYER</td></tr><tr><td>3:45 p.m. – 5:30 p.m.</td><td>21. Infection and Immunity</td><td>22. Environment &amp; Toxicology</td><td>23. New Instrumentation</td><td>24. Novel Applications</td></tr><tr><td>5:30 p.m. – 7:00 p.m.</td><td colspan="4">Poster Session 3 – Even Numbers – Exhibit Foyer</td></tr><tr><td>7:30 p.m. – 10:30 p.m.</td><td colspan="4">Conference Dinner – Xiringuito</td></tr></tbody></table>

## THURSDAY, JUNE 27

<table><thead><tr><th></th><th>Atlantic</th><th>King Willem-Alexander</th><th>Alexia</th><th>Ariane</th></tr></thead><tbody><tr><td>8:30 a.m.</td><td colspan="4">REGISTRATION / INFO DESK OPEN</td></tr><tr><td>8:45 a.m. – 10:00 a.m.</td><td>25. Respiratory Diseases</td><td>26. Model Organisms</td><td>27. Genome-scale Modeling</td><td></td></tr><tr><td>10:00 a.m. – 10:45 a.m.</td><td colspan="4">Poster Session 4 – Even Numbers – Exhibit Foyer</td></tr><tr><td>10:45 a.m. – 12:00 p.m.</td><td>28. Microbiome</td><td>29. Plant Applications 2</td><td>30. Metabolite Identification 2</td><td></td></tr><tr><td>12:00 p.m. – 1:30 p.m.</td><td colspan="4">LUNCH – IN FOYER WITH EXHIBITS – PLATINUM SPONSOR PRESENTATIONS</td></tr><tr><td>12:20 p.m. – 1:20 p.m.</td><td colspan="2">Sponsor Pres: Bruker Daltonics/Bruker Biospin</td><td colspan="2">Sponsor Pres: Agilent Technologies</td></tr><tr><td>1:30 p.m. – 3:30 p.m.</td><td colspan="4">Plenary Session 4 – Jean-Charles Portais – Closing Ceremony – King Willem-Alexander Hall</td></tr></tbody></table>
Plenary Abstracts

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# The marriage of technology development and biological discovery

Monday, June 24, 4:00 p.m. – 5:00 p.m.

**PRESENTING AUTHOR:** Joshua Rabinowitz, Lewis-Sigler Institute, United States

Today, 20 years after sequencing of the first human chromosome, we now have powerful tools for measuring all of the fundamental classes of biomolecules, including metabolites. While the data gathering capacity of these tools is inspiring, the complexity of biology remains humbling. The failure of these tools to substantially eliminate major diseases, including metabolic disease, is self-evident. In this lecture, I will describe our past and ongoing efforts to bridge metabolomic technology development and biological discovery. I will provide examples of how intensive biological focus improved our metabolomic pipelines, and of how isotope tracing illuminated otherwise obscure aspects of metabolism. Examples will be drawn from microbiology, cell biology, and cancer. A major focus will be on our recent efforts to revisit quantitatively mammalian metabolism, with an eye towards better understanding of both basic metabolic principles and the intersection of diet, metabolism, and disease.

# Twin and SNP-based studies of metabolomics traits

Tuesday, June 25, 8:30 a.m. – 9:30 a.m.

**PRESENTING AUTHOR:** Dorret I. Boomsma, Netherlands Twin Register, Vrije Universiteit, Netherlands

Metabolite profiling allows for an increased understanding of behavioral and psychiatric phenotypes. We hypothesized that promising metabolites in this respect include those for which a genetic component is present estimated heritability (h2: standardized estimate of genetic variance) based on the different genetic relatedness of mono- and dizygotic twin pairs.

Metabolomics data from 4 different platforms for up to 5,117 individuals, including some family members of twins. We estimated that ~50% of variation is due to genetic variation, but heritability estimates differed across metabolite classes and lipid species.

Heritability also can be estimated from genotype data (SNPs: Single Nucleotide Polymorphisms) by quantifying the genetic similarity of 'unrelated' individuals and comparing it to their trait similarity (h2SNP). To elucidate the genomic architecture of blood metabolites, we simultaneously estimated total heritability (h2total), SNP heritability (h2SNP) and the proportion of heritability captured by known metabolite loci (h2GW-loci). We reviewed all association studies of SNPs and blood metabolites, retrieving 241,965 associations for which all associated metabolites were classified. For the estimation of h2GW-loci we aggregated > 800 class-specific metabolite loci for 309 lipids and 52 organic acids. This revealed significant differences in h2SNP and h2GW-loci among different classes of lipids and organic acids. Phosphatidylcholines with a higher degree of unsaturation had higher h2GW-loci estimates.

This study highlights the genetic architecture of metabolite classes and lipid species, with genes explaining on average 50% of variation in metabolites, and common genetic variants influencing metabolites. This knowledge can be used in construction of polygenic scores and causal modeling.
# The future for food may be in our past: the importance of plants in our diets.

Wednesday, June 26, 8:30 a.m. – 9:30 a.m.

PRESENTING AUTHOR: Cathie Martin, John Innes Centre, Colney Research Park, UK

The appreciation of the challenges of achieving global food security has matured to include nutritional security, as scientists have realised that not only calorie content but food and colonic microbial composition impact our health and well-being, dramatically. The ways that the nutrients we consume affect our health are highly complex due to the diversity of what we eat, the varying digestibility of what we eat, the changing composition and functioning of each individual's gut microbiota, the differences in absorption and bioavailability of the nutrients we eat, the differences in responses between individuals to what they eat and the multi-fold mechanisms of action that nutrients have on our health. It has been accepted for more than 50 years that diets rich in plants, particularly fruit and vegetables, protect health, and yet diets have declined, with lower fruit and vegetable content replaced by more cheap, sugary, oily processed foods. These dietary shifts have had a marked impact on the incidence of chronic diseases; obesity, metabolic diseases, type2 diabetes and cardiovascular diseases.

Only by understanding how phytonutrients improve our health by reducing risks of chronic disease and which phytonutrients confer greatest benefits in protecting against specific diseases (a process termed comparative nutrition) can we hope to achieve dietary improvements at all levels in society. By describing examples of preclinical comparative nutritional analyses, I hope to illustrate the potential of dietary improvement using plant-based foods to improve our health and quality of life and to reduce the economic burden on our health-care systems.

PLENARY SESSION 4

# Comprehensive investigations of cancer cell metabolism using 13C-fluxomics

Thursday, June 27, 1:30 p.m. – 2:30 p.m.

PRESENTING AUTHOR: Jean-Charles Portais, University Toulouse, France

Metabolism is a basic cellular function that sustains survival, growth and adaptation of living organisms. At the cellular level, metabolism is organized as a network, i.e. a complex set of biochemical reactions that are tightly interconnected. To investigate such complex systems, powerful tools are required, and a broad range of approaches (metabolomics, fluxomics) have been developed over the past decade to provide detailed and quantitative measurement of the operation of metabolic networks. These methods can be combined with other omics tools (e.g. transcriptomics, proteomics, etc.) and to metabolic modelling to provide systems biology approaches for in-depth, comprehensive understanding of the organization, functioning and adaptation of cellular metabolism. This understanding can provide the rationale for efficient and cost-effective optimization of production systems (knowledge-based metabolic optimization) in biotechnology, or to characterize the metabolic dysfunctions associated with pathologies, or to identify and validate new therapeutic targets or strategies. In this lecture, particular emphasis will be made on 13C-fluxomics, with selected illustrations in the field of cancer metabolism, and how this can help in designing improved therapeutic strategies.
# VENDOR SESSION

**SESSION 8: NOVEL INSTRUMENTATIONS, TOOLS AND SERVICES** *Presented by Platinum and Gold Sponsors*

Tuesday, June 25 | 1:30 p.m. – 3:15 p.m.

Princess Ariane

By participating in this session you will get an overview of the latest developments in instrumentation and tools (from our 6 platinum sponsors) and services (from our 4 gold sponsors). Each company will highlight their latest developments in a 7-minute flash presentation. The session is scheduled at the start of the conference, allowing you to determine which exhibitors will provide the most valuable follow-up visit at their booth during the event. The interactive session also has 2 panel discussions, moderated by Thomas Hankemeier and David Wishart.

1:30 p.m. – 2:30 p.m.

**PLATINUM PRESENTERS AND PANEL DISCUSSION**

**Agilent Technologies**

Christine Miller, Omics Market Manager, USA

**A New LC/Q-TOF Platform for Metabolomics Analysis**

Innovations in LC/MS and GC/MS have driven the field of metabolomics. Speed, sensitivity, resolution, dynamic range, and isotopic fidelity are all important MS attributes for metabolomics research. This presentation will describe some of the innovative technologies that are in the new Agilent 6546 LC/Q-TOF platform which is designed with metabolomics research in mind.

**Bruker Daltonics/Bruker BioSpin**

Lucy Woods, phD, Product Manager QTOF, Germany

**New ground-breaking releases to advance 4D Metabolomics and Lipidomics**

Get to know Brukers' latest innovations for 4D metabolomics and lipidomics research. We cordially invite you learn more about our newest developments for SpatialOMx and NMR-based metabolic fingerprinting. Be ready to get surprised!

**SCIEX**

Baljit Ubhi, Market Manager - Metabolomics Business, USA

**Technology Innovations for Advancing Metabolomics at SCIEX**

Technology advancements will be presented highlighting SCIEX's innovation in improving healthcare and wellness. Analytical hardware and software tools for metabolomics and lipidomics highlight their utility for enabling precision medicine.

**Shimadzu**

Emily Armitage, Research Scientist, UK

**Driving data-driven decision making just a little bit quicker...**

For many metabolomics workflows, there is a need for a smarter path to data-driven decision-making and to help find actionable data. This headline overview will highlight the way in which high resolution accurate mass with data independent acquisition methods can make a difference to metabolomic workflows. The talk will focus on the Shimadzu LCMS-9030 QTOF platform and software tools to help accelerate data-driven decision making in metabolomics using DIA and a research application to find components and help verify identification.

**Thermo Fisher Scientific**

Amanda Souza, Manager, Product Marketing, USA

**7 Minutes of mzCloud Spectral Library: We'll Leave You Begging for More**

Unknown annotation is one of the most difficult challenges for untargeted metabolomics researchers. Learn how the mzCloud spectral library accelerates the unknown annotation process using more spectral information across diverse chemical classes to ultimately annotate more with greater confidence.

**Waters Corporation**

David Heywood, Senior Manager Omics Business Development, United Kingdom

**From out of the box methods to structural characterization. Solutions for metabolomics research**

To fully understand the complex relationship between biology and the compounds we measure, multiple approaches and workflows need to be considered. Discovery workflows provide indications of metabolic perturbation often needing supplemental measurements across large cohorts, measurement of metabolic flux or even clarification of compound structure. This presentation will discuss how Waters innovates with purpose to address the challenges in Metabolomics research.
# VENDOR SESSION CONTINUED

## SESSION 8: NOVEL INSTRUMENTATIONS, TOOLS AND SERVICES Presented by Platinum and Gold Sponsors

Tuesday, June 25 | 1:30 p.m. – 3:15 p.m.

Princess Ariane

By participating in this session you will get an overview of the latest developments in instrumentation and tools (from our 6 platinum sponsors) and services (from our 4 gold sponsors). Each company will highlight their latest developments in a 7-minute flash presentation. The session is scheduled at the start of the conference, allowing you to determine which exhibitors will provide the most valuable follow-up visit at their booth during the event.
The interactive session also has 2 panel discussions, moderated by Thomas Hankemeier and David Wishart.

2:30 p.m. – 3:15 p.m.

## GOLD PRESENTERS AND PANEL DISCUSSION

### Biocrates Life Sciences AG

Therese Koal, PhD, Head of Research & Development, Austria

### Unlock Nutrition-Microbiome-Host Interaction with Metabolomics

Intestinal bacteria influence a vast range of biological processes and have been associated with the pathogenesis and the course of many diseases. However, establishing associations is not sufficient to understand the functional interaction between nutrition, microbiota and the host organism. As metabolic processes are a key element of this interaction, metabolomics has evolved as an important tool for functional microbiomics studies.

This talk will be discussing the relevance of selected metabolic pathways in this context. It will also present the new MxP® Quant 500 Kit as an analytical tool to assess a variety of microbiota-derived metabolites, including bile acids, indoles and TMAO. The MxP® Quant 500 Kit is the most comprehensive solution within Biocrates' portfolio of kits for Targeted Metabolic Profiling, covering up to 630 compounds from 26 metabolite and lipid classes. It enables researchers to obtain detailed information about endogenous metabolism as well as the systemic impact of microbial activity.

### Cambridge Isotope Laboratories, Inc.

Krista Backiel, Marketing Manager and Metabolomics Manager, USA

### Stable Isotope-Labeled Mixes for Multiplexed Metabolomics: Production and Application

One of the aims of metabolomics research is to quantitatively measure metabolites in biosamples. The impetus is to better understand disease mechanisms and to identify or validate candidate biomarkers toward improved personalized medicine. The field has made significant progress over the past decade in addressing these aims. Efforts of which have been aided by advancements in analytical methodology, technology, software, as well as reagents (e.g., stable isotope-labeled standards). To assist in the high throughput and multiplexed analysis of metabolites, we have formulated (and are in the process of formulating) an array of isotope-enriched, multi-component mixtures. This presentation will chronicle the process of producing multi-component mixtures, from standard preparation at our production facility through to the assembly of the isotope-enriched mix at our chemical formulations laboratory. Additionally to be discussed is the application of our isotopically labeled mix in targeted or untargeted MS metabolomic studies.

### Human Metabolome Technologies

Tom Hoshiya, Managing Director, Netherlands

### HMT Europe

Japanese biotech company Human Metabolome Technologies (HMT) opened an European Office in Leiden, Netherlands.

### Metabolon

Alex Forrest-Hay, VP Population Health, USA

### Delivering Actionable Insights with Metabolomics

There are 4 CORE capabilities essential to insight-deriving metabolomics.
## SESSION 20: REGULATORY SESSION

### Translating metabolomics from academic science into regulatory practice: challenges and progress in pharmacology and toxicology

Wednesday, June 26 | 1:30 p.m. – 3:15 p.m.

While metabolomics has become a mature and widely used technology in academic research, its application to regulatory science has been limited to date. Several factors contribute to this slow uptake, the most commonly cited roadblocks are the lack of standardisation, validation and reporting formats for metabolomics. Recently, progress has been made in translating 'omics technologies (including metabolomics) from academic science towards regulatory practice in the fields of pharmacology and toxicology, specifically for drug and chemical safety, respectively. This timely session at Metabolomics-2019 will introduce and review some of the opportunities, challenges and recent progress in this translational activity. While focused on drug and chemical safety legislation in Europe, the progress reported will have much wider implications for metabolomics in international regulatory practice. Talks will be presented by regulators, industry and academic scientists to provide a balanced perspective, and include a panel discussion chaired by Dr. Pim Leonards (Vrije Universiteit Amsterdam).

## An Overview of the 21st Century Cures Act: Opportunities for Biomarkers and Precision Medicine

### Dr. Rick Beger, National Center for Toxicological Research, US FDA, United States

A biomarker is defined as a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure, or therapeutic intervention. There are seven BEST biomarker categories; diagnostic, monitoring, pharmacodynamic response, predictive, prognostic, safety, and susceptibility/risk. The 21st Century Cures Act establishes a biomarker "qualification" process for drug development tools that can then be used for particular context of use (COU) in a regulatory submission. The submission of a biomarker for regulatory use under the 21st Century Cures Act, is a three-stage submission process and builds on the existing biomarker submission process. The first Stage is a letter of intent (LOI), that includes the drug development need the biomarker is intended to address, biomarker information. The intended context of use (COU), and information on how the biomarker will be measured. If the FDA accepts the LOI, a qualification plan (QP) describing the information including the analytical method and performance characteristics that will qualify the biomarker for the proposed COU in drug development is submitted. The third step is a full qualification package (FQP) that contains all accumulated information on the biomarker, organized by topic area. The FDA will make a final decision about whether the biomarker is qualified based on the FQP. The 21st Century Cures Act and metabolomics ability to capture phenotype information on a patient provides opportunities and for biomarkers and precision medicine. Examples of potential metabolomics biomarkers will be provided.

## Big Data “Omics” – Challenges and Opportunities in Regulation

### Dr. Renate König, Paul-Ehrlich-Institut, Federal Institute for Vaccines and Biomedicines, Germany

A new Joint HMA/EMA Task Force was established, initiated by EU Heads of Medicines Agencies (HMA) in collaboration with the European Medicines Agency (EMA), to explore how medicines regulators can use Big Data to support research, innovation and robust medicines development to benefit human health.

This talk will focus on the identification of the emerging challenges from the use of Big Data, with the focus on Bioanalytical Omics, to prepare the European regulatory system for the future, on establishing recommendations for use of “Omics” big data in assessment of medicines as well highlight the opportunities for “Omics” in the regulation of medicines.

## Various Applications of Metabolomics in Toxicology

### Prof Hennicke Kamp, BASF, Germany

BASF and its daughter company metanomics established the database MetaMap®Tox containing the plasma metabolome of more than 1000 compounds derived from 28-day studies in rats. Approximately 250 different endogenous metabolites are measured and more than 100 patterns for different toxicological modes of action are available. MetaMap®Tox is nowadays routinely used at BASF to support systemic toxicity testing in terms of identification of target organs and modes of action or for read-across applications (Kamp at al., 2012; van Ravenzwaay et al., 2016). Over the last years, we have broadened this approach to identify maternal toxicity in reproductive toxicity studies (Keller et al., 2018). Additionally, a highly stable and reproducible liver in vitro model was established, in which the intracellular metabolome of HepG2 cells can be specifically altered through treatment with different hepatotoxicants (Ramirez et al., 2017). So far more than 90 different treatments have been analysed with this setup. In this presentation, examples will be shown how metabolomics can be used for the purposes mentioned above. Since the acceptance of such approaches in the regulatory community is somehow limited, also concepts for increasing regulatory acceptance will be discussed.
**SESSION 20: REGULATORY SESSION**

*Translating metabolomics from academic science into regulatory practice: challenges and progress in pharmacology and toxicology*

Wednesday, June 26 | 1:30 p.m. – 3:15 p.m.

**Best Practice Guidelines and Reporting Standards for Applications of Metabolomics in Regulatory Toxicology: the International MERIT Project**

**Prof. Mark Viant, University of Birmingham, UK**

During the last two decades metabolomics has become a very widely used technology in academic research, yet its application to regulatory science, including regulatory toxicology, has been limited. While several factors contribute to this slow translation into regulatory practice, the most commonly cited roadblock by chemical regulators, government and industry scientists is the lack of standardisation, best practice guidelines and data and metadata reporting formats. Here we will describe the final report of the MEtabolomics standaRds Initiative in Toxicology (MERIT) project, which brought together a team of international experts from multiple sectors to address this need. MERIT sought to define best practice guidelines and minimal reporting standards for designing, undertaking, reporting and assessing the quality of both untargeted metabolomics and targeted metabolite analyses in the context of regulatory toxicology. To provide context, the most relevant use cases for metabolomics in toxicology were also identified, including for example chemical grouping based upon metabolic phenotypes. The MERIT guidelines included experimental design; QA/QC; sampling and extraction; data acquisition and processing of mass spectrometry and NMR spectroscopy assays; data processing and statistical analysis; and metabolite identification. A strategy and architecture for integrated data management, sharing and exploitation in regulatory toxicology was also developed. We concluded that most steps in the application of untargeted metabolomics and targeted metabolite assays to regulatory toxicology are well established, including significant recent progress in QA/QC. We recommend that the MERIT minimal reporting standards and data management strategy are tested as multi-stakeholder use cases.
Oral Abstracts

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## SESSION 1: CANCER

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

**1A Session Keynote**
**10:15 a.m. – 10:45 a.m.**

Metabolomics Applications for Oncology and Immunology in Drug Discovery

**PRESENTING AUTHOR:** Thomas Roddy, Agios Pharmaceuticals, United States

Metabolomics data from in vitro and in vivo studies is more comprehensive and in depth than ever. Large data sets from metabolomics and flux studies are often daunting to analyze and interpret, especially when projects move quickly. A major focus of our lab has been in developing workflows and data analytics to allow rapid turnaround of data and fast iteration of biological experiments. Our goal is to quickly learn from one experiment, to design the next, and to iterate this process in a way that effectively impacts our projects, allowing us to identify novel biological mechanisms and pharmacodynamic biomarkers for our oncology and rare genetic disease programs. In this presentation, I will briefly discuss our multidisciplinary MS lab (metabolomics, lipidomics, fluxomics, and proteomics) and data systems. I will also present examples when metabolomics gave us unique insights into biological processes, including the development of a pharmacodynamic marker for a cholesterol pathway oncology target, an analysis of the tumor microenvironment across several models, and the modulation of metabolism in immunological models (in vitro and in vivo).

**1B 10:45 a.m. – 11:05 a.m.**

Multi-omic discovery of metabolic rewiring in triple-negative breast cancer following mitochondrial folate transport ablation

**PRESENTING AUTHOR:** Steven Gross, Weill Cornell Medicine, United States

**CO-AUTHORS:** Qiuying Chen, Joshua B. Zuk, Chris A. Miller, Steve M. Fischer, Steven S. Gross

Mitochondrial tetrahydrofolate (THF)-mediated reactions provide one-carbon units for synthesis of purines, thymidine, and methylation of DNA, RNA, proteins, and lipids. Inhibition of THF-mediated reactions (i.e., antifolates) have been a staple of cancer chemotherapy, including triple-negative breast cancer (TNBC). The present study sought to broadly discover how TNBC cells retaliate to knockout of the mitochondrial THF transporter (MFT/ slc25a32), and consequent loss of mitochondria-dependent one-carbon trafficking reactions. Using untargeted metabolomics, discovery proteomics and untargeted stable isotope tracing strategy, we discover that mitochondria folate transporter (slc25a32) gene deletion in TNBC cells elicits a profound rewiring of glucose to polyol pathway, concomitant with delayed cell cycle and a 6-10-fold increase in several aldo-keto reductases (AKRs) - enzymes that consume NADPH to facilitate cytosolic one carbon reaction that generates NADPH. Activation of polyol pathway and switch of serine-derived one carbon metabolism from mitochondria to cytosol were similarly observed in methotrexate treated TNBC cells. Our findings nominate these upregulated AKR enzymes as chemotherapeutic targets, for inhibition in combination with antifolate drugs. Additionally, multi-omics studies revealed that loss of slc25a32 increases TNBC cell dependence on purine salvage, and TGF-β signaling with evidence of epithelial-mesenchymal-transition (i.e., increased cell migration/invasion). Dependence of upregulated polyol pathway, purine salvage, cytosolic one carbon reaction and TGF-β signaling was supported by downregulation in slc25a32 protein-restored KO cells. Taken together, multi-omic findings provide unexpected insights into how TNBC cells adapt to disabled mitochondrial folate-dependent one-carbon metabolism, and suggest novel therapeutic targets for combined intervention with antifolate drugs.

**1C 11:05 a.m. – 11:20 a.m.**

Investigating the metabolic response of cancer cells to indisulam

**PRESENTING AUTHOR:** Lili Herendi, PhD student, United Kingdom

**CO-AUTHORS:** Anke Nijhuis, Arti Sikka, Eirini Koulura, Orli Yogev, Louis Chesler, Gerald Larrouy-Maumus, Hector Keun

Neuroblastoma (NB) is one of the most common type of solid tumour in infants. Currently there are no reliable biomarkers identified predicting disease outcome in patients within the high-risk group highlighting the acute need for novel therapies. Indisulam (E7070) an aryl sulphonamide, a carbonic anhydrase IX (CAIX) inhibitor currently in Phase II clinical trials for the treatment of patients with solid tumours has recently been connected with pre-mRNA splicing factor RNA binding protein 39 (RBM39). Recruitment of RBM39 by indisulam to the DCAF15/CUL4/E3 complex promotes proteasomal degradation. These findings connect cancer cell line sensitivity to indisulam with stability of RBM39 and high expression of DCAF15. Our data confirmed sensitivity of IMR32 human NB cells to indisulam both in vitro and in vivo. In IMR32, the DCAF15 dependent RBM39 degradation and mis-splicing were necessary, but not sufficient for the full cytotoxic response to indisulam. Using LC-MS and GC-MS metabolomics analysis together with stable isotope labelling we have identified the activity of indisulam to be associated with alterations in metabolite pools connected with NADH production and changes in redox balance. Furthermore, as mitochondrial respiration was also impaired after indisulam treatment, these metabolic alterations observed imply that neither CAIX nor RBM39 are the sole targets of indisulam. Overall, these results provide insights into the contribution of metabolism to the mechanism of action of indisulam and suggest that metabolic subtypes of tumours may respond differently to therapy. Such knowledge will improve our understanding of which patients would benefit from indisulam treatment.
## SESSION 1: CANCER

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

**1D 11:20 a.m. – 11:40 a.m.**

Rewiring of energy metabolism drives resistance to the proteasome inhibitor bortezomib

**PRESENTING AUTHOR:** Esther Zaal, Biomolecular Mass Spectrometry and Proteomics, Utrecht University, Netherlands

**CO-AUTHORS:** Harm-Jan de Grooth, Pieter Langerhorst, Haley Baptist, Wei Wu, Celia R. Berkers

The proteasome inhibitor bortezomib is successfully applied in the treatment of multiple myeloma, but its efficacy is restricted by the wide-spread occurrence of resistance. Metabolic alterations play an important role in cancer development and aid in the cellular adaptation to pharmacologically changed environments. Metabolic changes may therefore also play an essential role in the development of drug resistance. Interestingly, cells could become reliant on such drug-induced metabolic reprogramming, a vulnerability that could be exploited for therapy. Here, the metabolic pathways involved in resistance to bortezomib were elucidated using a mass spectrometry-based metabolomics approach. To this end, bortezomib-sensitive and -resistant cell lines were profiled using a combination of steady-state metabolomics experiments and stable isotope labelling approaches. These metabolomics studies were complemented with a metabolism-oriented targeted proteomics approach. We demonstrate that BTZ-resistant cells extensively rewire their mitochondrial energy metabolism and identify metabolic drugs that could overcome bortezomib resistance. By investigating gene expression patterns of metabolic genes in genome-wide data of MM patient samples, we show that rewiring of mitochondrial metabolism correlates to drug response and survival. In conclusion, we provide novel mechanistic insights in the role of mitochondrial energy production in mediating bortezomib resistance. Our data indicate that this metabolic rewiring correlates to drug response in MM patients and provide rationale for combining bortezomib with metabolic drugs to increase treatment efficacy.

**1E 11:40 a.m. – 11:55 a.m.**

Understanding metabolite heterogeneity in pancreatic ductal adenocarcinoma, and the role of adaptive metabolic reprogramming in chemotherapy resistance

**PRESENTING AUTHOR:** Sarah Hancock, University of New South Wales, Australia

**CO-AUTHORS:** Jesse Estoque, Nigel Turner

Pancreatic ductal adenocarcinoma (PDAC) has one of the most dismal prognoses in modern medicine with a 5-year survival of just 7.7%. Contributing to the poor survivability of PDAC is its highly aggressive and chemoresistant nature, which is driven by extreme genetic and phenotypic heterogeneity. Mutations in oncogenes result in wide-ranging effects on cell metabolism, allowing the cells to acquire metabolites necessary for rapid growth and proliferation. Cellular heterogeneity can aid the cancer cells in acquiring chemoresistance through adaptive metabolic reprogramming, in which chemotherapy action is circumvented by engaging pro-survival metabolic pathways. In this study, we use an untargeted metabolomics approach to determine the breadth of metabolite heterogeneity across four PDAC cells lines (MiaPaCa2, Panc1, BxPC3 and AsPC1) and one non-cancerous immortalised human pancreatic ductal epithelial (HPDE) cell line. Approximately 800 features (i.e. unique combinations of retention time and mass-to-charge, m/z) were detected across these four pancreatic cell lines and a number of metabolic pathways were found to be altered compared with noncancerous pancreatic cells, including metabolites involved in central carbon metabolism, glycolysis, glutaminolysis, nucleotide synthesis, and lipid synthesis. Metabolite profiling of gemcitabine-treated PDAC cells has uncovered several potential adaptive metabolic pathways, many of which centre around non-essential amino acid metabolism. Work is ongoing to elucidate mechanisms of chemoresistance in PDAC, including the use of stably labelled isotope tracing to further characterise mechanisms of chemoresistance in these cells.
## SESSION 2: PLANT APPLICATIONS 1

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

**2A Session Keynote**
**10:15 a.m. – 10:45 a.m.**

The volatile flavor composition network in tomato and its modification

**PRESENTING AUTHOR:** Antonio Granell, CSIC, Spain

Tomato is a model for fruit ripening and more recently also for the identification of the molecular genetic basis of what makes fruit healthy and palatable. Organoleptic and nutritional quality in this climacteric fruit is regulated at different levels and metabolites associated with organoleptic and nutritional composition are part of the ripening process and increasingly the target/focus of geneticists and breeders. The nutritional value of tomato fruit is not based on calories but on the healthy compounds they provide to our diet (main source of antioxidants and vitC in Western diets). Many of the plant/ tomato nutritional and healthy compounds are often precursors of compounds that contribute to flavour what supports the contention that good flavour and healthy compounds are part of the mechanism plants use to reward frugivors for dispersing the seed contained in their fruits. Breeding or Biotech approaches aimed to higher nutritional content should go hand with hand with good flavour in order to really have an impact on consumers. In my presentation I will illustrate examples were the use of genetic resources in combination with high throughput genotyping and phenotyping and biotech approaches is contributing to identifying genomic regions, markers and genes associated /underlying the variation in compounds of organoleptic or nutritional value. I will also present how Plant Biotechnology and Synthetic Biology approaches are used in our lab to further expand the natural variation in metabolites either to increase the levels of nutritional or good flavor compounds or to eliminate anti nutritional compounds.

**2B 10:45 a.m. – 11:05 a.m.**

Developing Advanced and Integrated Metabolomics Technologies to Address the Grand Challenges of Metabolite Identification and Depth of Coverage

**PRESENTING AUTHOR:** Lloyd Sumner, University of Missouri, United States

**CO-AUTHORS:** Lloyd W. Sumner, Feng Qiu, Dennis Fine, Daniel Wherritt, Zhentian Lei, Anil Bhatia, Mark Schroeder, Aiko Barsch, Sven Meyer

The vast utility of metabolomics is well documented in the literature; however, its full scientific promise has not yet been realized due to multiple technical challenges. These grand challenges include large-scale confident chemical identification of metabolites and greater depth of coverage. We have developed sophisticated spectral, computational and integrated experimental metabolomics tools for the systematic and biologically directed annotation of plant metabolomes and for greater metabolome depth of coverage. UHPLC-QTOF-MS/MS metabolite profiling was first performed using Medicago truncatula methanol extracts, and the data processed by peak deconvolution and formula prediction. Metabolite identifications were first attempted through spectral matching with custom MS and MS/MS libraries of 222 plant specialized metabolites. Additional identifications were predicted using novel software entitled 'Plant Metabolite Annotation Toolbox' (PlantMAT) that generates in-silico prediction of metabolite structures based upon orthogonal empirical data. PlantMAT includes both GC-MS and LC-MS modules. The data was imported into PlantMAT and structures for approximately 100 saponins and polyphenolic glycosides were predicted. Approximately 80 of these were isolated, purified and concentrated by UHPLC-MS-SPE. The SPE isolated compounds were eluted and 1D and 2D NMR spectra acquired. The results demonstrated that the cumulative platforms allow for higher-throughput and high confidence metabolite identifications. UHPLC-timsTOF-MS/MS analyses were performed to discover potentially coeluting compounds and to increase our metabolomics depth of coverage for isobaric compounds not readily separated by UHPLC. Examples are provided for hydroxylated flavonoids. A library of CCS values are being measured and compiled for added confidence in metabolite identification.

**2C 11:05 a.m. – 11:20 a.m.**

From MS peak to unambiguous metabolite identification using the WeizMass spectral library and LC-MS-SPE-NMR system

**PRESENTING AUTHOR:** Adam Jozwiak, Weizmann Institute of Science, Israel

**CO-AUTHORS:** Nir Shahaf, Prashant D. Sonawane, Tali Scherf, Ilana Rogachev, Asaph Aharoni

Despite rapid advances in MS-based methods, unambiguous and high-confidence metabolite annotation is hitherto a significant concern in metabolomics experiments. One of several complementary approaches that might assist in tackling this intricate issue is the usage of a comprehensive, reference compound-based spectral library. We recently generated the WeizMass LC-MS based library composed of more than 6,000 spectra and representing diverse chemicals structures. We also developed the MatchWeiz software that allows matching of experimental data to the WeizMass library. Using such approach we significantly raised our confidence in metabolite identification, carrying out de-replication of known metabolites in diverse plant species and identifying in high confidence metabolites not reported previously from plants. Direct coupling of LC-MS with Solid Phase Extraction (SPE) and NMR spectroscopy represents a complementary approach. The main advantage of this set-up is its simplicity and low amount of material necessary for metabolite identification. It is especially advantageous in case of amphipathic substances, e.g. saponins, requiring high quantities for NMR analysis. In my presentation, I will demonstrate how hyphenated analytical techniques (i.e. LC-MS-SPE-NMR) facilitate saponin pathway discovery by providing structural data of complex metabolites and generating substrates for enzyme- and bio- activity assays. Combination of high-resolution MS and several 2D-NMR techniques allowed unambiguous identification of novel acetylated medicagenic acid derived saponins from spinach. Finally, we utilized the LC-MS-SPE-NMR system for an unequivocal annotation of biosynthetic intermediates produced by partial reconstruction of the biosynthetic pathway in heterologous system.
## SESSION 2: PLANT APPLICATIONS 1

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

2D 11:20 a.m. – 11:40 a.m.

Chocolate metabolomics

**PRESENTING AUTHOR:** Robert Hall, Wageningen UR, Netherlands

**CO-AUTHORS:** Ric CH de Vos, Isabelle Privat, Maud Lepelley, Roland Mumm, Marco CAM Bink, Jos A Hageman, Jwanro Husson, Veronique Maffone, Philippe Pollien, Jean-Claude Spadone, Susan Strickler, Dominique Crouzillat, Robert D Hall

Optimising and securing sustainable cacao production is hampered by a lack of knowledge of the chemical and genetic factors controlling the yield and quality of the beans used for preparing chocolate liquor. We have used an integrated, multi-omics approach to genotype and chemically phenotype a dedicated F2 segregating population of a high yielding and a high quality Theobroma cacao parental cross. Fermented beans and cocoa liquors obtained following standard industrial procedures were subjected to comprehensive metabolite profiling (LCMS pos/neg modes, GCTOFMS of (derivatised) polar compounds, and GCMS of volatile components). In parallel, sensory (taste) panel evaluations were performed on the chocolate liquors. Dedicated statistics was applied to model sensory scores based on metabolite profiles and to select a small set of marker compounds predicting sensory quality scores based on either liquor or bean metabolites. Subsequently, genetic mapping of the population was performed using co-dominant molecular markers and this map was used for large-scale Quantitative Trait Loci (QTL) analysis. We identified 1760 traits (metabolites) in beans or liquors and these metabolites concerned many different chemical groups including amino acids, sugars, polyphenols and aromatic components. 484 QTLs were also identified controlling either cocoa quality traits or individual metabolites indicating that 27.5% of the cocoa traits analysed is genetically controlled. Further use of cacao fruit transcriptome data and gene mining of potential candidate genes co-localizing with QTLs controlling key cocoa quality parameters has given us for the first time deep insights into the genetic basis of cocoa (chocolate) quality.

2E 11:40 a.m. – 11:55 a.m.

A tissue specific metabolomic study in hybrid aspen

**PRESENTING AUTHOR:** Ilara Budzinski, Swedish University of Agricultural Sciences (SLU), Sweden

**CO-AUTHORS:** Ilka N Abreu, Thomas Moritz

Hybrid aspen is a commercially fast-growing tree, used in short rotation wood production in many countries. As a renewable natural resource, providing timber, fibers and energy, wood is important for the environmental and economic perspective. Wood formation is a complex process, subject to multiple levels of regulation (from cell division through programmed cell death). Despite its importance, the metabolic profile underlying wood formation still poorly understood. To overcome this, we present a tissue specific metabolomics study in hybrid aspen (Populus tremula x P. tremuloides), using cryosectioning. We used four-month-old trees cultivated under greenhouse conditions and supplied with different levels of fertilization solution (low, adequate and high) for controlling growth. The stems were 20 µm thick tangential cryo-sectioned in: bark, inner bark, phloem, expanding phloem, cambium, expanding xylem, xylem and mature xylem. Target and untargeted metabolomics (GC and LC-MS) was performed, including lipidomics analysis. Trees treated with higher fertilization doses showed higher diameter and biomass, compared to the lower treatment. By OPLS-DA we discriminate tissues and treatments. Differentially abundant metabolites (VIP ≥1.0 and P ≤ 0.05) were identified from bark towards xylem. As an example, proline, rhamnose, trehalose, sucrose exhibited a similar distribution pattern along the tissues, when comparing trees with contrasting stem diameter. However, other metabolites were significant only in trees with smaller (fructose and glucose) or higher (arginine, methionine, salicin, stearyl acid) stem diameter. The results obtained here represent the metabolic overview from bark towards wood-forming tissues, and highlighted the powerful combination of cryosectioning and metabolomics analysis
## SESSION 3: DATA INTEGRATION & DATA BASING 1

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

**3A Session Keynote**
10:15 a.m. – 10:45 a.m.

The Internet for Social Machines

**PRESENTING AUTHOR:** Barend Mons, GO FAIR, Netherlands

We are in a transition phase of science, where machines (mainly computers) have become our major research assistants. Humans and computers increasingly work together as 'social machines' to makes sense of complex natural phenomena. However, computers need a very different input as compared to people and the way we adapt the communication and reuse of our research results is adopting to this new situation only at glacial speed. Still, the 15 FAIR Principles, publsihed in 2016, dealing with machine actionable data and services, have found unusually rapid uptake among a broad spectrum of stakeholders, from research scientists who create and reuse data, to publishers who distribute data, to science funders who track impact of data. Barend will describe the FAIR Principles and show examples of how they have been implemented. He will also present a set of core FAIR Metrics that can help gauge the level of FAIRness of any digital resource. Of particular interest is how additional FAIR Metrics can (and should) be defined to address community-specific data structures and analytic requirements. This discussion, and these examples will be presented in the context of the International GO FAIR Initiative. GO FAIR is a voluntary community of stakeholders devoted to implementation solutions of an emerging Internet of FAIR Data and Services.

**3B 10:45 a.m. – 11:05 a.m.**

Matching Untargeted Liquid Chromatography – Mass Spectrometry Features Across Multiple Cohorts: Finding the Same Needles in Several Haystacks Via Networks

**PRESENTING AUTHOR:** Rui Climaco Pinto, Imperial College London, United Kingdom

**CO-AUTHORS:** Ibrahim Karaman, Matt R. Lewis, Jenny Höllqvist, Manuja Kaluarachchi, Gonçalo Graça, Elena Chekmeneva, Mohammad Arfan Ikram, Abbas Dehghan, Paul Elliott, Ioanna Tzoulaki, David Herrington, Timothy Ebbels

In untargeted metabolomics, accurate within-dataset feature matching is aided by accessing large amounts of chromatographic/spectral information in each sample. In contrast, when matching features from multiple cohorts/batches that used the same analytical method but were acquired and peak-picked separately, only limited information is available - often only retention time and m/z (RT-MZ) medians. We propose a new cross-cohort matching method which uses the RT-MZ median values of each feature (plus optionally feature intensity and other feature-quality measures). Initially matches within manually-defined RT-MZ thresholds between all one-to-one dataset combinations are detected. In the next - key - step it builds a network of features (nodes) based on those matches (edges). Only matches in subnetworks (connected components) in which all nodes are connected to all other nodes (maximal cliques) are accepted, which removes features that matched by chance. As some features may present multiple matching possibilities, a third step using RT-MZ differences to define a match-quality score is then used to decide the best single matches. We illustrate the method's performance by matching thousands of features from large studies of serum samples and inspecting the matching accuracy of hundreds of manually annotated metabolites, and by looking at several feature-quality metrics. The method is not computationally intensive – e.g. matching 3 cohorts, each with 1000s of features, takes less than a minute on a typical desktop machine. The new approach addresses a key problem in metabolomics studies and promises to make analysis of large untargeted multi-cohort data sets a viable option.

**3C 11:05 a.m. – 11:20 a.m.**

Visualizing metabolomics data in directed biological networks

**PRESENTING AUTHOR:** Denise Slenter, Maastricht University, Netherlands

**CO-AUTHORS:** Martina Kutmon, Jonathan Mélius, Ryan Miller, Georg Summer, Chris T. Evelo, Egon L. Willighagen

Metabolomics data describes the state of a biological system at a phenotypic level. Unfortunately, not all measured metabolites can be linked to metabolite identities present in biological pathway models. The resulting sparseness makes it more complicated to use metabolomics data in pathway and network analysis. By creating networks from existing pathways, the metabolic data sparseness can be overcome, by calculating the shortest path between metabolites. To upscale this approach, we need to be able to combine different pathway knowledge bases and introduce detailed directionality information, ensuring the shortest paths follow one-directional biological cause-and-effect paths. The presented work creates subnetworks of shortest, directed pathways between active metabolites. First, with the WikiPathways RDF, we created a directed network of all metabolic reactions from the WikiPathways and Reactome pathway knowledgebase. In the next step, we identified the location(s) of the active metabolites in the network, in which we match data with nodes in the network using knowledge from Wikidata. Finally, using the cyNeo4j app for Cytoscape we extracted the smallest connected subnetwork between the changed metabolites using the functionality of the graph database Neo4j. We developed a new solution to visualize the biological pathways involved in sparse metabolomics data. Using knowledge from two pathway resources, we can show the directed networks between active metabolites from metabolomics data. By using Neo4j and Cytoscape, we ensure the computational calculation environment for larger networks as well as advanced visualization functionality to investigate the identified subnetworks. This approach can be extended with proteomics and transcriptomics data.
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## SESSION 3: DATA INTEGRATION & DATA BASING 1

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

**3D 11:20 a.m. – 11:40 a.m.**

High-throughput metabolomics identifies substrate-enzyme relationships in a metabolism-wide CRISPR interference library

**PRESENTING AUTHOR:** Hannes Link, Max Planck Institute for Terrestrial Microbiology, Germany

**CO-AUTHORS:** Stefano Donati, Vanessa Pahl, Timo Glatter

Construction of large genetic variant libraries has become very fast and versatile. The current challenge is measuring molecular phenotypes of large strain libraries with high-throughput. Here, we show that high-throughput metabolomics identifies metabolic phenotypes in a CRISPR interference library in Escherichia coli. We have recently created a metabolism-wide CRISPR interference (CRISPRi) library that targets all genes in the metabolic network of E. coli and characterized growth of the strains with next generation sequencing (1). Now we measured the metabolome of 118 CRISPRi strains, which have targets in central metabolism, and in biosynthesis pathways of cofactors, nucleotides and amino acids. By using a quantitative and fast (2 min) isotope ratio LC-MS/MS method (2), we quantified about 150 primary metabolites in the CRISPRi strains. In many strains we observed specific increases of substrates of the targeted enzymes. Especially downregulation of enzymes in biosynthetic pathways resulted in an accumulation of intermediates, which are under normal conditions undetectable in E. coli. These substrate-enzyme relationships suggest that biosynthetic enzymes in wild-type cells operate far away from their maximal capacity, and therefore biosynthetic intermediates are often very low abundant. Finally, by integrating the metabolome data with proteome data we show how metabolite concentration changes affect enzyme-level regulation and the global proteome of CRISPRi strains.

**3E 11:40 a.m. – 11:55 a.m.**

Weighting strategies for the analysis of secondary outcomes in nested case-control metabolomics data

**PRESENTING AUTHOR:** Gerard Gonzales, Ghent University, Belgium

**CO-AUTHORS:** Vladimer Kobayashi

Freely available metabolomics datasets in public repositories allow us to investigate other biological questions without necessarily recruiting new cohorts of human volunteers. However, considering that the data was selected for a different purpose, it may potentially introduce bias when analysing secondary outcomes of interest. We propose to remedy this problem by introducing weights to observations. In this study, we tested various weighting strategies namely, inverse probability weighting and the ones derived from boosting and neural network. We then illustrate their uses to investigate the urinary metabolomic signature specifically associated with smoking by using available data from a case-control urinary metabolomics study where the participants were originally selected for presence or absence of lung cancer (MTBLS28). We show that directly using the dataset without weighting resulted to biomarkers of lung cancer being strongly associated to smoking. This distorts the biological interpretation of metabolites and pathways associated with smoking and does not generalize to new population of non-cancer patients. The combination of weighting strategies with popular analytical models (i.e. PLS-DA, generalized linear models with regularization, and random forest) has the effect of not only leading to statistically valid models with good predictive ability, but also, the extracted biomarkers are independent of the primary outcome. In short, the extracted urinary biomarkers were more specifically associated with smoking than with lung cancer status. Consequently, we developed a framework for introducing weights to guide researchers in choosing an appropriate weighting method. Finally, we discuss the application of the framework to metabolomics studies using secondary data.
## SESSION 4: NOVEL TECHNOLOGIES

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

**4A Session Keynote**
10:15 a.m. – 10:45 a.m.

Improving Technologies for High Throughput and Miniaturized Metabolomics for Precision Medicine

**PRESENTING AUTHOR:** Thomas Hankemeier, Leiden University, Netherlands

Where genomics has proven to be successful to predict disease risk, metabolomics can assess the actual health state and monitor disease development and treatment response. Large-scale metabolomics studies are necessary for better and more specific prognosis of chronic diseases. For this efficient and quantitative metabolomics methods are necessary. Novel technologies to enable this will be discussed in this lecture. We have developed a hanging droplet evaporator that allows innovative workflows for high throughput (HT) and miniaturized sample preparation. We have developed electrodriven sample preparation technologies for metabolomics. Next, the use of microfluidic-based advanced in-vitro models with organotypic characteristics will be introduced, and how this platform can be used to study disease mechanisms and disease pathways identified using metabolomics. This requires miniaturized metabolomics methods and technologies. Actually, similar innovations for HT metabolomics can be used for miniaturized metabolomics. An outlook will be given how metabolomics will impact clinical research and ultimately clinical decision support.

**4B 10:45 a.m. – 11:05 a.m.**

Investigation of host-microbiota co-metabolism as a new strategy for biomarker discovery –
New Chemical Biology tools for Metabolomics analysis

**PRESENTING AUTHOR:** Daniel Globisch, Uppsala University, Sweden

**CO-AUTHORS:** Mário S.P. Correia, Louis P. Conway, Weifeng Lin, Abhishek Jain, Caroline Ballet, Neeraj Garg

The detailed investigation of metabolites in human samples has been termed metabolomics and carries a great potential for the discovery of unknown biomarkers. Metabolomics still requires advanced chemical tools compared to other 'omics areas. One of the most exciting scientific developments in the past decade has been the understanding that gut microbiota profoundly impact human physiology. The complex consortium of trillions of microbes possesses a wide range of metabolic activity. Only limited information on this interspecies co-metabolism has been elucidated on a molecular level. We have developed new state-of-the-art Chemical Biology techniques for an enhanced metabolomics analysis using liquid chromatography-coupled with tandem mass spectrometry (UPLC-MS/MS). A unique chemoselective probe immobilized to magnetic beads was prepared for analysis of human fecal samples. This complex probe allows for facile extraction of metabolites and led to increased mass spectrometric sensitivity by a factor of 2000. In another new methods, we utilized selective enzymatic treatment of metabolites in human samples to easily identify converted metabolites and elucidate their chemical formula using mass spectrometry. We chemically synthesized each identified molecule to unequivocally validate the molecular structure. Using this specific workflow, we have successfully identified three times as many sulfated metabolites than reported in the Human Metabolome Database. Our unique metabolite-analyzing methodologies at the interface of Chemistry and Biology are aimed at overcoming limitations in mass spectrometry-based metabolomics research. We are applying these methods for the discovery of unknown metabolites in medical relevant samples to evaluate their potential as biomarkers for pancreatic cancer.

**4C 11:05 a.m. – 11:20 a.m.**

GC×GC-TOFMS and SIFT-MS approaches for clinical breath-based asthma phenotyping

**PRESENTING AUTHOR:** Pierre-Hugues Stefanuto, Liège University, Belgium

**CO-AUTHORS:** Delphine Zanella, Joeri Vercammen, Florence Schleich, Renaud Louis, Jean-François Focant

The ballistic rise of analytical technologies has opened a large playground for all type of "omics" screening. On one side, separation science based on multidimensional methods such as comprehensive two-dimensional gas chromatography (GC×GC) appeared as one of the methods of choice for the characterization complex mixtures. On the other side, direct introduction instruments such as single ion flow tube mass spectrometry (SIFT-MS) offered the capacity to perform both targeted and non-targeted analyses within a few minutes. At the price of high cost equipment and limited adaptability to routine medical usage, GC×GC-HRTOFMS offers the possibility to almost completely characterize a sample composition. This is of prime importance when systems biology are considered. For large scale screening, SIFT-MS can generate compositional patterns from direct sample introduction at the same time than other routine medical actions. These two orthogonal approaches for pathology screening should ideally conduct to identical sample classifications but have never been directly compared over an identical set of patients. In this study, breath from 50 asthmatic patients were analyzed by both techniques. As a reference, asthma phenotypes were established using sputum analysis. Breath samples were collected using Tedlar® bags. For GC×GC-HRTOFMS analyses, the bags were transferred onto thermal desorption tubes prior to injection. For SIFT-MS, the bags were directly emptied into the instrument. Next, data were analyzed using identical processing workflow. We observed that both approaches offered similar classification capacities. GC×GC-HRTOFMS allowed identifying the putative markers for comparison with previous studies and metabolic interpretation while SIFT-MS offered a faster screening-capacity.
SESSION 4: NOVEL TECHNOLOGIES

Tuesday, June 25

10:15 a.m. – 12:00 p.m.

4D 11:20 a.m. – 11:40 a.m.

Mathematical modelling of metabolism: a driver for developing personalized and precision medicine

PRESENTING AUTHOR: Natal van Riel, Eindhoven University of Technology, Netherlands

Metabolic derailments associated with metabolic syndrome and type 2 diabetes can be studied with mixed meal tests (MTT's). The plasma metabolome enriched with measurements of hormones and cytokines, provide valuable information about the physiological state of an individual. An increasingly important, but complex task is to extract biomedical parameters with diagnostic value from large and multivariate datasets. We applied model-based data processing and analysis, combining computer simulation models, stochastic models of uncertainties and machine learning of time-series data obtained from repeated blood sampling during MTT's. In a mouse model of metabolic syndrome we identified differences in lipid metabolism to be associated with variation in weight gain and development of NAFLD (fatty liver disease). The computational model predicted the progression of dyslipidemia to be linked to bile acids, which was confirmed in a validation study. To investigate the role of bile acids in humans with metabolic syndrome a detailed simulation model of bile acid metabolism and physiology was developed. Model-based analysis of plasma bile acids provides a metabolic 'window' on the gut microbiome and other digestive processes in the gastrointestinal tract. The model was applied to simulate bariatric surgery in patients with metabolic syndrome. The model predicts changes in dynamics in the small intestine to result in a stronger and faster GLP-1 response, hence insulin secretion, explaining observations of rapid glycemic improvement after surgery. The simulation model turned out to be sufficiently robust that personalized variants for individual patients could be made, using MTT plasma bile acid metabolomics as input.

4E 11:40 a.m. – 11:55 a.m.

TIMS and PASEF multiply speed and sensitivity in lipidomics

PRESENTING AUTHOR: Catherine G. Vasilopoulou, Max Planck Institute of Biochemistry, Germany

CO-AUTHORS: Karolina Sulek, Andreas-David Brunner, Dmitry Voytik, Aiko Barsch, Sven Meyer, Ulrike Schweiger-Hufnagel, Ningombam Sanjib Meitei, Matthias Mann, Florian Meier

We have recently introduced a novel scan mode termed 'parallel accumulation – serial fragmentation' (PASEF), which promises to overcome longstanding limitations in throughput and sensitivity of mass spectrometry-based proteomics (Meier et al., PMID:26538118; Meier et al., PMID:6283298). PASEF synchronizes precursor selection with trapped ion mobility spectrometry (TIMS) and achieves over ten-fold increased sequencing rates without loss in sensitivity. Here we explore the benefits of PASEF for lipidomics. We analyzed lipid extracts from human plasma (SRM 1950), mouse liver and HeLa cells via nanoflow LC coupled to a high resolution TIMS-QTOF mass spectrometer (Bruker timsTOF Pro). Raw data were processed with MetaboScape (Bruker) and MS/MS spectra were annotated with SimLipid (PREMIER Biosoft). As compared with conventional TIMS-MS/MS, we acquired six times more MS/MS scans with PASEF, which translated into about twice as many lipid identifications. The PASEF speed allowed us to reduce the LC analysis time from 90 to 30 min, while still covering over 90% of the lipids identified with the longer gradient. Comparison to community efforts reveals an excellent coverage of all major lipid classes from just 1 µL plasma. The ion mobility dimension further allows separation of isomeric species and provides a high-precision measure of lipid collisional cross sections (CCS) with CVs <1%. In a single experiment, we compiled a CCS library of over 1000 lipids and show high correlation with literature as well as predictions based on machine learning (R2>0.98). We anticipate that accurate CCS measurements in conjunction with PASEF acquisition will greatly benefit metabolomics and lipidomics.
## SESSION 5: METABOLIC DISEASE

Tuesday, June 25

1:30 p.m. – 3:15 p.m.

**5A Session Keynote**
1:30 p.m. – 2:00 p.m.

Integrating epidemiologic, pharmacologic, genetic and gut microbiome data in the BBMRI-NL drug-metabolome atlas

**PRESENTING AUTHOR:** Cornelia van Duijn, University of Oxford, United Kingdom

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands (BBMRI-NL) has constructed an atlas of drug-metabolome associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance (1H-NMR). The atlas involves a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolome associations after evaluating confounding including age, sex, weight, smoking and co-treatment. Data integration with genetic and pharmaceutical intervention studies on statins shows that cross-sectional data of the BBMRI-NL atlas can yield information of future drug effects. Further data integration involving epidemiological data links proton pump inhibitors to circulating metabolites, biochemical liver-parameters, hepatic steatosis and the gut microbiome. Our atlas provides a tool and starting point for targeted experimental pharmaceutical research to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualisation of the atlas (http://bbmri.researchlumc.nl/atlas/).

**5B 2:00 p.m. – 2:20 p.m.**

Metabolic profiling of tissue-specific insulin resistance in human obesity: Results from the Diogenes Study and The Maastricht Study

**PRESENTING AUTHOR:** Ilja Arts, Epidemiology & MaCSBio, Maastricht University, Netherlands

**CO-AUTHORS:** Nicole Vogelzangs, Carla J van der Kallen, Marleen M van Greevenbroek, Birgitta W van der Kolk, Johan WE Jocken, Gijs H Goossens, Nicolaas Schaper, Ronald Henry, Simone J Eussen, Armand Valsesia, Thomas Hankemeier, Arne Astrup, Wim HM Saris, Coen DA Stehouwer, Ellen E Blaak

Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies towards type-2 diabetes and other cardiometabolic diseases. The aim of this study was to investigate whether non-diabetic IR in the liver and in the skeletal muscle are associated with distinct metabolic profiles. Our study included 634 overweight/obese (BMI≥27 kg/m2) adults without diabetes (≤65 years; 63% women) of the European multicenter Diogenes Study. Hepatic insulin resistance index and muscle insulin sensitivity index were derived from a 5-point oral glucose tolerance test (OGTT). Seventeen plasma metabolites were quantified by nuclear-magnetic-resonance spectroscopy. In an independent sample of 540 overweight/obese participants without diabetes (BMI≥27 kg/m2; 40-65 years; 46% women) of The Maastricht Study, 11 metabolites and a 7-point OGTT were available for validation. Replicated results indicate that both liver and muscle IR are associated with elevated levels of (branched-chain) amino acids (isoleucine, alanine), lactate and triglycerides and lower glycine levels, but only liver IR associates with lower ketone body levels (acetoacetate, 3-OH-butyrate) and elevated levels of ketogenic amino acids (leucine, tyrosine), suggestive of decreased ketogenesis. These findings suggest that in early stages of cardiometabolic disease development, distinct metabolic profiles of liver IR and skeletal muscle IR can be observed. This knowledge might enhance developments of more targeted tissue-specific interventions to prevent progression to more severe disease stages.

**5C 2:20 p.m. – 2:35 p.m.**

Identification of novel metabolites in alkaptonuria by LC-QTOF-MS profiling and flux analysis of a targeted HGD-/- mouse model

**PRESENTING AUTHOR:** Brendan Norman, Institute of Ageing & Chronic Disease, University of Liverpool, United Kingdom

**CO-AUTHORS:** Brendan P Norman, Juliette H Hughes, Andrew S Davison, Hazel Sutherland, Peter J Wilson, Norman B Roberts, Lakshminarayan R Ranganath, George Bou-Gharios, James A Gallagher

Alkaptonuria (AKU) is a rare disorder of tyrosine metabolism caused by congenital lack of homogentisate 1,2-dioxygenase (HGD). The primary biochemical consequence of HGD-deficiency is elevated circulating homogentisic acid (HGA), which accumulates in cartilage as 'ochronotic' pigment causing severe early-onset osteoarthropathy. Metabolic profiling was performed to investigate for the first time the wider metabolic consequences of HGD-deficiency in a new targeted HGD-/- mouse model which recapitulates human AKU. Urine from 15 HGD-/- and 14 HGD+/- male age-matched mice was analysed by LC-QTOF-MS. Data were mined using a published accurate-mass/retention-time database developed in-house from metabolite standards with additional compounds of potential interest from wider tyrosine metabolism. Comparing profiles of HGD-/- and HGD+/- urine in negative and positive polarity revealed that the clearest differences (FDR-adjusted p<0.05, log2 FC>1.5) were increases in HGA and 7 previously unreported HGA-derived biotransformation products. Other metabolites showing clear alteration in HGD-/- included thymidine-5'-diphospho-alpha-D-glucose, DL-3,4-dihydroxymandelic acid, 2-aminophenol, p-hydroxyphenylacetic acid (increased) malic acid, isocitrate and inosine 5'-monophosphate (decreased). Dehydroxymethylene-HGA, HGA-decarboxylate, HGA-glucuronide and HGA-sulfate were subsequently confirmed as derived from HGA in a flux experiment in which HGD-/- mice were injected with 13C6-labelled HGA; analysis of plasma taken 2-60 min post-injection showed clear evidence of M+6 isotopologues attributable to the 13C6 label. The data indicate previously uncharacterised clearance mechanisms for elevated HGA; the central biochemical cause of morbidity in AKU. Further alteration to urinary metabolites from other pathways including catecholamine, citrate and tryptophan indicate that targeted HGD enzyme disruption has wide-ranging and previously unreported metabolic consequences beyond the tyrosine pathway.
## SESSION 5: METABOLIC DISEASE

Tuesday, June 25

1:30 p.m. – 3:15 p.m.

**5D 2:35 p.m. – 2:55 p.m.**

Combining untargeted metabolomics, human genetics, causal inference, and pathway enrichment to define the obesity metabolome

**PRESENTING AUTHOR:** Yu-Han Hsu, Harvard Medical School, United States

**CO-AUTHORS:** Christina M. Astley, Joanne B. Cole, Sailaja Vedantam, Josep M. Mercader, Andres Metspalu, Krista Fischer, Kristen Fortney, Eric K. Morgen, Clicerio Gonzalez, Maria E. Gonzalez, Tonu Esko, Joel N. Hirschhorn

Obesity is a major health problem associated with extensive metabolic disturbances. Identifying the causal connections between obesity and its associated metabolites can uncover relevant biology and inform intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable analyses to infer causality between metabolites, obesity, and related diseases. In this study, we expand upon previous research by using genetic instruments to classify body mass index (BMI)-associated metabolites identified in multiple untargeted metabolomics datasets. Meta-analyses and pathway analyses of both known and unknown metabolites were enabled by our recently developed bioinformatics tool, PAIRUP-MS. We identified several known metabolites that are more likely to be the cause (e.g. alpha-hydroxybutyrate) or the effect (e.g. valine) of higher BMI, or may have a more complex bidirectional cause-effect relationship with BMI (e.g. glycine). Importantly, we also identified about 5 times more unknown than known metabolites in each of these three groups. Pathway analysis including both known and unknown metabolites identified different enriched metabolic pathways in the cause and effect groups, providing further evidence that these metabolite groups are associated with obesity via distinct biological mechanisms. These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining causal inference using genetic data with the ability to match unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity phenotypes, which will be required to generate sufficient power for the robust discovery and replication of causal biological relationships between obesity and metabolites.

**5E 2:55 p.m. – 3:10 p.m.**

Metabolomics of Ndufs4-/- skeletal muscle: adaptive mechanisms converge at the ubiquinone-cycle

**PRESENTING AUTHOR:** Karin Terburgh, North-West University, South Africa

**CO-AUTHORS:** Jeremie Zander Lindeque, Shayne William Mason, Francois Hendrikus van der Westhuizen, Roan Louw

Complex I (CI) deficiency impedes the most efficient mechanism providing electrons to the respiratory chain (RC) — a system that, together with ATP-synthase, produces most cellular energy. Although CI deficiency is the most common mitochondrial defect, it is among the most complex, poorly understood disorders currently lacking an effective treatment. Current research on the whole-body Ndufs4 knockout (Ndufs4-/-) mouse model aims to better understand this disease. Although the model’s neurological phenotype has been studied, the effect of CI deficiency on skeletal muscle metabolism remains elusive and despite this tissue’s high energetic demand, the phenotype lacks strong muscle involvement. We combined hypothesis-generating metabolic profiling and enzyme assays to gain insight into the metabolism of glycolytic and oxidative skeletal muscles from Ndufs4-/- mice. Multi-platform metabolomics was employed, comprising of liquid chromatography-tandem mass spectrometry, gas chromatography time-of-flight mass spectrometry and proton nuclear magnetic resonance spectroscopy. Enzyme assays revealed an 80% reduction in CI activity in both Ndufs4-/- muscle types, which would greatly reduce electron flux to the RC. As an adaptive response, metabolomics identified that several non-classical pathways participate in an attempt to restore electron flux to CIII via the ubiquinone (Q)-cycle. Among the numerous discriminatory metabolites identified between Ndufs4-/- and wild-type muscles, the most prominent alterations indicate the involvement of the glycerol-3-phosphate shuttle, electron transfer flavoprotein system, CII, and the proline cycle in fuelling the Q-cycle. These adaptive mechanisms could maintain adequate ATP production, despite CI deficiency — providing a possible explanation for the lack of muscle involvement in the Ndufs4-/- phenotype.
# New from Bruker
at Metabolomics 2019

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

## Visit us at Booth #P2

Don't miss our Bruker Lunch Seminar „A new approach to Metabolomics & Lipidomics“ with special guest speakers

Prof. Gary Frost/ Dr. Isabel Garcia Perez (Imperial College London, UK)
and Dr. Catherine Vasilopoulou (Max-Planck-Institute Munich, Germany)
on Thursday, June 27th, 2019 at 12:20 pm at room: Princess Alexia

### Bridging OMICS and function

timsTOF fleX is the newest member of the timsTOF portfolio enabling SpatialOMx workflows to be acquired on one platform for the first time. By adding Bruker's proven MALDI technology to the ultra-high resolution timsTOF platform, in-depth coverage using novel PASEF acquisition can be combined with spatial information from high speed imaging.

### IVDr by NMR, the Platform for Metabolic Profiling

The accurate determination of even small changes in profiles related to a disease, therapeutic intervention, genetic modification or environmental variation is central to all metabolomics studies. The B.I.Methods 2.0, available on the newest Avance Neo technology, are standardized and automated methods for bodyfluid analysis that support metabolic profiling, be it on a personal or population wide basis.

<figure><img src="image_15.png" /></figure>

For more information please scan the QR-Code
or visit www.bruker.com/metabolomics
## SESSION 6: FOOD APPLICATIONS 1

Tuesday, June 25

1:30 p.m. – 3:15 p.m.

**6A Session Keynote**
1:30 p.m. – 2:00 p.m.

A metabolomic study on coffee – From coffee brews to liver cancer risk

**PRESENTING AUTHOR:** Augustin Scalbert, International Agency for Research on Cancer (IARC), France

**CO-AUTHORS:** Joseph A Rothwell, Erikka Loftfield, Pekka Keski-Rahkonen, Nivonirina Robinot, Roland Wedekind, Pietro Ferrari, Callie Kambanis, Neal Freedman, Rashmi Sinha, Augustin Scalbert

Coffee drinking has been associated with lower risk of certain chronic diseases and overall mortality. In this work, we used a mass spectrometry-based untargeted metabolomic approach (i) to characterize variations in the chemical profiles of 76 coffee brew samples, (ii) to identify biomarkers of coffee intake in 451 participants of the European Prospective Investigation on Cancer and Nutrition (EPIC) study, and (iii) to explore association of coffee-associated metabolites in 1:1 matched case-control studies on liver cancer (n=221 cases) and fatal liver disease (n=242 cases) nested in the ATBC cohort. PCA analyses run on 18 identified coffee compounds showed that the first three principal components were driven respectively by roasting intensity, type of coffee beans and caffeineation. In the EPIC study, 8 coffee metabolites identified in serum samples were highly correlated with coffee intake, with trigonelline showing the highest correlation. Differences in the magnitude of correlations were observed between countries, possibly explained by preferences in the types of coffee consumed. In the ATBC cohort, 21 metabolites were jointly associated with coffee intake, liver cancer and liver disease mortality. Trigonelline and six other metabolites (serotonin, leucyl-valyl, 3 glycerophospholipids and hypoxanthine) were inversely associated with liver cancer and liver disease mortality; tyrosine and two bile acids were positively associated with both outcomes. This work adds further support to possible hepatoprotective effects of coffee consumption. It also provides new data on variations of coffee composition and optimal biomarkers of coffee intake important to consider in future epidemiological studies.

**6B 2:00 p.m. – 2:20 p.m.**

Integrated analysis of metabolomics and microbiome data showing additional dietary effects

**PRESENTING AUTHOR:** Jildau Bouwman, TNO, Netherlands

**CO-AUTHORS:** Femke Hoevenaars, Everton Souto Lima, Lydia Afman, Martin Beaumont, François Blachier, Sandrine Claus, Annick Hartstra, Louise Kjølbæk, Lesli Hingstrup Larsen, Max Nieuwdorp, Yolanda Sanz, Hans Schött, Kevin Portune, Alfonso Benítez, Bart Keijser, Ben van Ommen

The burden of life-style related diseases has been increasing since the 70's. Disease prevention through improvement of human health is attracting more and more attention. The microbiome is an important new target in these approaches. Our microbiome affects the metabolism of nutrients and thereby influences host physiology. Currently there is a general lack of understanding about the importance of the role of the gut microbiome in health and well-being. Within the MyNewGut project research is performed on the gut microbiome and its relation to dietary interventions. By integration of the data of separate MyNewGut studies we are expanding the understanding for diet-microbiome-host interaction. Clinical, NMR and microbiome data of 5 different dietary intervention studies were integrated. First all measured parameters (clinical, LC-MS, NMR and microbiome) were aligned to discover overlap between studies. Next, overlapping parameters were selected for further integration. A workflow was developed to align and integrate NMR data the data of the different studies. Also for the microbiome data of all studies a workflow was designed. We show that metabolism is improved by butyrate/ FT and fibre/PUFA, but becomes more unhealthy after a high protein intervention. Traditionally microbiome, LC-MS and NMR data is evaluated separately. Integration of all these datasets from the different studies into a model which visualizes treatment effects is useful for detection of new diet-microbiome-host interactions.

**6C 2:20 p.m. – 2:35 p.m.**

Quantitative Dietary Fingerprinting (QDF)—A Novel Tool for Comprehensive Dietary Assessment Based on Urinary Nutrimetabolomics

**PRESENTING AUTHOR:** Raúl González-Domínguez, Biomarkers and Nutrimetabolomics Laboratory, University of Barcelona, Spain

**CO-AUTHORS:** Raúl González-Domínguez, Mireia Urpi-Sarda, Olga Jáuregui, Paul W. Needs, Paul A. Kroon, Cristina Andrés-Lacueva

Accurate dietary assessment is a great challenge in nutritional research, needing powerful and robust tools for the reliable measurement of food intake and monitoring dietary habits. To address this issue, we optimized a novel Quantitative Dietary Fingerprinting (QDF) approach, based on the combination of solid phase extraction and subsequent analysis by ultra-high performance liquid chromatography coupled to tandem mass spectrometry (t). This method was validated for the quantitation of about 350 biomarkers, including (poly)phenolic aglycones, phase II metabolites and microbial-transformed compounds, as well as other dietary components. The simultaneous extraction of the complex urinary food metabolome was accomplished by using Oasis HLB solid-phase extraction plates. To improve the recovery of sulfate conjugates, ammonium formate was added to elution solvents as modifier. Then, chromatographic separation was achieved by reversed-phase UHPLC in very short run times, which facilitates the method application to large sample sizes. Tandem mass spectrometry, by using the multiple reaction monitoring acquisition mode, enabled the selective and sensitive detection of these 350 dietary markers at concentrations usually found in real urine samples. The applicability of this QDF method was assessed by analyzing urine samples collected after acute dietary interventions with various foods, including orange, apple, grapefruit, red wine, beer, coffee, green tea, soy, walnuts and wholegrain bread. Therefore, it could be concluded that the optimized methodology presents a huge potential for the assessment of dietary patterns by means of high-throughput analysis of the urinary metabolomic pattern.
SESSION 6: FOOD APPLICATIONS 1

Tuesday, June 25

1:30 p.m. – 3:15 p.m.

6D 2:35 p.m. – 2:55 p.m.

Grass, beef and human. Unlocking the value of the food chain in New Zealand

PRESENTING AUTHOR: Arvind Subbaraj, AgResearch Ltd., New Zealand

We recently published the ryegrass metabolome and a chemotaxonomic classification of 715 ryegrass genotypes, representing 118 populations from 21 different countries. Ryegrass is integral to temperate pastoral agriculture which contributes to most of the meat and milk production worldwide. Pasture-fed beef is claimed to have several health benefits compared to its grain-fed counterparts. Lipids are central to this debate, and phospholipids are predominantly implicated in the health benefits of meat consumption. We therefore conducted a non-targeted lipidomics analysis of 38 different cuts of pasture-fed Wagyu to account for differences in the lipid profile across the beef carcass. Ten cuts with high phospholipid content were identified, and breed (Wagyu vs Angus) and feed (Pasture vs Grain) effects were evaluated in a separate study. Significant differences in phospholipid content was not observed between pasture-fed Wagyu and Angus. However, phospholipids were in higher concentrations in pasture-fed compared to grain-fed Wagyu beef.
Finally, we aim to track the effect of consumption of pasture-fed Wagyu beef, grain-fed beef, and a vegetable-based meat alternative, on circulating lipids and biomarkers of cardiovascular disease risk in humans. An eight-week dietary intervention study is currently underway, and a non-targeted lipidomics analysis of blood plasma will be conducted. This talk will summarise recent metabolomics studies conducted in New Zealand to elucidate and decipher the critical components of the food chain from grass and animal, through to human consumption. Missing links in our understanding e.g. the rumen metabolome, a key component of the grass to animal continuum, will be discussed.

6E 2:55 p.m. – 3:10 p.m.

NMR metabolomics revealed different impact of non-acylated and acylated anthocyanins on plasma metabolic profiles in obese diabetic Zucker rats

PRESENTING AUTHOR: Kang Chen, University of Turku, Finland

CO-AUTHORS: Kang Chen, Xuetao Wei, Raghunath Paryjani, Maaria Kortesniemi, Jari Heinonen, Yumei Zhang, Jian Zhang, Tuomo Vainio, Kaisa Linderborg, Baoru Yang

Anthocyanins are present in food as non-acylated (NAAs) and/or acylated (AAs) and known to possess potent anti-diabetic properties. AAs have been reported to have higher stability, antioxidant activity, and bioavailability compared to NAAs. Currently, no research has been reported comparing the effects of these two forms of anthocyanins on metabolic profile of diabetic models. Here, we aimed to compare the modulatory effects of NAAs (from bilberry) and AAs (from purple potato) on metabolic changes in obese Zucker diabetic rat (fa/fa). Experimental rats were fed with NAAs and AAs at two doses for 8 weeks. 1H NMR metabolomics was used to reveal changes in plasma metabolites. NAAs supplementation reduced the plasma glucose level, but similar effect was not seen in the group fed with AAs. All treatment groups decreased levels of branch chain amino acids (BCAAs) and improved lipid profiles, although no significance was found in the group fed with NAAs at the low dose. AAs fed groups showed improved secretion and sensitivity of insulin, as indicated by the positive change in glutamine/glutamate ratio. In AAs fed groups, decreased levels of lactate, serine, threonine, and glycine might be associated with improved oxidative status and shift in energy production from glycolysis and TCA cycle towards lipid catabolism. Our data demonstrate that NAAs improved glucose level through modulating insulin resistance and lipid profiles. AAs reversed most of the changed levels of metabolites in type 2 diabetes to the normal state by modifying insulin resistance and secretion, oxidative stress, energy production, and lipid profiles.
## SESSION 7: FLUX STUDIES

Tuesday, June 25

1:30 p.m. – 3:15 p.m.

**7A Session Keynote**
1:30 p.m. – 2:00 p.m.

A systems medicine approach to identify new drug targets: Model-driven discovery of metabolic reprogramming in metastatic prostate cancer

**PRESENTING AUTHOR:** Marta Cascante, University of Barcelona, Spain

**CO-AUTHORS:** Cristina Balcells, Igor marin de Mas, Esther Aguilar, Erika Zodda, Pedro de Atauri, Josep J. Centelles, Balázs Papp, Francesc Mas, Timothy Thomson, Silvia Marin, Marta Cascante

Tumors harbor combinations of heterogeneous neoplastic cells. In this complex ecosystem, all modalities of mutual cell interactions can take place within the context of environmental cues that exert selective pressures. In spite of the underlying heterogeneity, two broad operational categories of neoplastic cells, namely cancer stem cell (CSC) and non-CSC, are most relevant with regards to two key properties of evolving tumor cells: survival to stress and metastatic colonization. Using a dual model of two clonal subpopulations isolated from an established prostate cancer cell line (PC-3), we have applied a systems biology approach including experimental data integration into genome-scale metabolic models to unveil metabolic differences and potential vulnerabilities to be exploited as putative drug targets. The dual model consists of a CSC-subpopulation (PC-3M-high metastatic potential and low invasiveness) and a non-CSC-subpopulation (PC-3S-expressing EMT markers with high invasiveness and low metastatic potential). Results show that EMT and metastasis programs display different metabolic traits. The main differences observed have been at the level of differential use of glucose and glutamine to fuel TCA cycle, mitochondrial respiration, one-carbon metabolism, beta-oxidation and eicosanoids metabolism. We applied the same approach to identify the metabolic reprogramming associated to platinum resistance and we have also identified metabolic alterations emerging from platinum resistance to be used in combined drug therapies. Acknowledgments: MINECO-European Commission FEDER funds- "Una manera de hacer Europa" (SAF2017-89673-R), AGAUR-Generalitat de Catalunya (2017SGR1033) and Icrea Academia award 2015 granted to MC.

**7B 2:00 p.m. – 2:20 p.m.**

Using stable-isotope labelled metabolomics to explore cell-cell communication between malaria parasites and host cells

**PRESENTING AUTHOR:** Darren Creek, Monash University, Australia

**CO-AUTHORS:** Anna Sexton, Christian Doerig, Teresa Carvalho

Malaria is caused by infection of host red blood cells (RBCs) with the Plasmodium parasite and leads to a range of clinical presentations including fever, anaemia and metabolic acidosis. Characterising the interplay between host and parasite factors is critical for understanding the molecular basis of these clinical symptoms. RBCs are essential for the lifecycle of P. falciparum; however, biochemical changes in uninfected, bystander RBCs have not been studied in detail. We performed an untargeted metabolomics study to investigate metabolic changes in uninfected RBCs when exposed to media from P. falciparum culture ("conditioned RBCs"). We observed significant perturbations to central carbon metabolism in conditioned RBCs. Stable isotope labelled U-13C-glucose was used as a carbon source to differentiate active RBC metabolism from parasite-derived metabolites, and this showed that flux through glycolysis and the pentose phosphate pathway was significantly perturbed in conditioned RBCs. Notably, the level of U-13C-lactate secreted by conditioned RBCs was higher than control cells, indicating increased glycolytic flux. To identify factors driving this effect, P. falciparum culture medium was filtered (to remove protein and extracellular vesicles) and incubated with RBCs. Metabolomic analysis showed that the remaining parasite small molecules, alone, were capable of inducing metabolic perturbations in RBCs. Metabolomic profiling of media from P. falciparum cultures has provided a candidate list of small molecules that may be responsible. Altogether these data demonstrate the modulation of RBC metabolism by P. falciparum, which may contribute to the metabolic symptoms of clinical infection.

**7C 2:20 p.m. – 2:35 p.m.**

Using deuterium labelled glucose to quantify redox metabolism in plants

**PRESENTING AUTHOR:** Edward Smith, University of Oxford, United Kingdom

**CO-AUTHORS:** James S O McCullagh, R George Ratcliffe, Nicholas J Kruger

A complete picture of plant metabolism is essential for understanding plant productivity and successful metabolic engineering. However, a lack of quantitative information about sources of metabolic reductant limits the ability to engineer plants to exploit desirable traits. NADPH is the primary source of cellular reductant, required for biosynthesis and protection against oxidative stress. Whilst the oxidative pentose phosphate pathway represents the most direct route for NADPH production from sugars, there is increasing evidence that other pathways make significant contributions to redox balance. Deuterium based isotopic tracers have recently been developed to quantify the production of NADPH from different pathways in mammalian cells, but the application of these tracers to plants has not previously been explored. In this study LC-MS was used to measure deuterium incorporation into metabolites extracted from heterotrophic Arabidopsis cell cultures grown on [1-²H]glucose or ²H₂O. The results highlight features of plant metabolism that can confound analysis, such as the interconnectivity of the plant metabolic network, the duplication of pathways in subcellular compartments and the high abundance of enzymes that can catalyse the loss of isotopic label to solvent. Understanding these features is a prerequisite for developing more effective strategies for flux determination in plant metabolism and ensuring data are both quantitative and representative of biological processes in vivo.
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

## SESSION 7: FLUX STUDIES

Tuesday, June 25

1:30 p.m. – 3:15 p.m.

**7D 2:35 p.m. – 2:55 p.m.**

Modelling Cancer Lipogenesis using REIMS Metabolic Flux analysis in breast cancer cell lines

**PRESENTING AUTHOR:** Seyma Turkseven, Imperial College London, United Kingdom

**CO-AUTHORS:** Alvaro Perdones-Montero, Simon Cameron, Nikolaos Koundouros, George Poulogiannis, Zoltan Takats

Rapid evaporative ionisation mass spectrometry (REIMS) offers a novel opportunity for understanding the alterations in lipid metabolism that plays an important role during cancer progression. REIMS is an ambient technique which allows sample analysis without preparative steps which hold the potential to alter the chemical composition of a sample. Here, REIMS was utilised to monitor metabolic flux during cell proliferation using U-13C-labelled palmitic acid to provide a tracer for lipogenesis in in vitro studies. For labelling experiments, SKBR3, MCF7 and MCF10A cells were seeded into culture dishes using DMEM and DMEM/F12 and were then changed to a fatty acid free-BSA media overnight. Following this, cells were treated with [U-13C]-palmitic acid under normoxic conditions for 6h, detached using a cell scraper, and then analysed using REIMS utilising a CO2 laser for sample heating. The ratio of the labelled palmitate taken up by cells to the production of unlabelled palmitate through de novo lipogenesis showed a similarity in cancer cells and nontumorigenic cells. However, palmitic acid incorporation into complex lipids resulted in specific lipidomic profiles for the different breast cancer cell lines. The detected labelled phospholipids after the supplementation cells with the nutrient might help to further interpretation of the lipid signalling process as the cancer cell lines were related to PI3K/Akt/mTOR mediated regulation of the cell functions. This study demonstrates the applicability of LA-REIMS for the characterization of metabolic flux experiments using labelled metabolites in human cell lines – with wider applications to metabolic flux analysis.

**7E 2:55 p.m. – 3:10 p.m.**

Exploring the use of GC-CI-MS for stable isotope labeling in metabolomics

**PRESENTING AUTHOR:** Jordi Capellades Tomàs, IISPV, Spain

**CO-AUTHORS:** Alexandra Junza, Oscar Yanes

Isotopic labeling experiments have been incredibly valuable to monitor metabolic reactions in biological systems, which is crucial to understand homeostasis alterations that occur in disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry. Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected by both gas chromatography (GC) coupled to electron ionization (EI) MS and liquid chromatography (LC) coupled to electrospray (ESI) MS approaches. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode or monitoring selected ion clusters using optimized narrow-windows ("10 Da) in scan mode, and standard full scan methods using high resolution GC-TOF and GC-Orbitrap systems. The GC-Orbitrap MS in full scan showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called IsoScan that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway and urea cycle.

## SESSION 8: NOVEL INSTRUMENTATIONS, TOOLS AND SERVICES

Presented by Platinum and Gold Sponsors See Page 7
## SESSION 9: EPIDEMIOLOGY

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

**9A Session Keynote**
**3:45 p.m. – 4:15 p.m.**

Integrating metabolomics with genomics, proteomics, and other omics for health and drug research

**PRESENTING AUTHOR:** Karsten Suhre, Weill Cornell Medicine - Qatar, Qatar

Deep molecular phenotyping of population-based studies allows to investigate disease relevant biological pathway by studying their natural variation in large numbers of individuals. Genome-wide association studies with metabolic endpoints already revealed hundreds of genetically influenced metabolotypes that define human metabolic individuality. Recently, these large scale omics studies have been extended by including proteomics and other omics phenotypes, and also by linking these multiomics readouts to epigenetic regulation. In this keynote I will review recent developments in the field and discuss potential applications for health and drug research from a precision medicine's perspective.

**9B 4:15 p.m. – 4:35 p.m.**

Application of 1H NMR metabolomics in ~7,000 people to investigate potential molecular mechanisms of genetic risk variants for coronary artery diseases

**PRESENTING AUTHOR:** Ibrahim Karaman, Imperial College London, United Kingdom

**CO-AUTHORS:** Ibrahim Karaman, Gonçalo Graça, Claire Boulangé, Rui Climaco Pinto, Georgia Saylor, Eliana Portilla, He Gao, Nicholette Palmer, Timothy Howard, Chunhong Mao, Donald Bowden, Arfan Ikram, Abbas Dehghan, Ioanna Tzoulaki, Timothy Ebbels, Paul Elliott, David Herrington

Numerous common genetic variants have been identified for association with the risk of coronary artery disease (CAD) using genome-wide association studies (GWAS). For the vast majority, the molecular mechanisms leading to the disease phenotype are not well understood or are completely unknown. We conducted untargeted and targeted 1H NMR metabolomics to characterize the metabolomic signatures associated with the risk variants to generate new knowledge about possible mechanisms involved in the pathogenesis of CAD. Both CPMG and conventional 1D 1H NMR spectra acquired from serum of participants in the Multi-Ethnic Study of Atherosclerosis (MESA, N=~4,000), the Rotterdam Study (RS, N=~2,000), and the Airwave Health Monitoring Study (Airwave, N=3,000) were used in the untargeted metabolomics analysis. For targeted analysis, we used Bruker Lipoprotein Subclass Analysis (B.I.-LISA) in MESA and Airwave cohorts. The current analyses included 7,141 individuals for CPMG, 7,149 for 1D NMR and 5,794 for B.I.-LISA data. We found that variants in 37 loci out of 365 reported loci for CAD were associated with metabolites at the 1% metabolome-wide significance level. These metabolites mainly originated from lipoprotein subclasses, lipids from several lipoprotein particles, and several amino acids potentially involved in protein synthesis. This information could produce new knowledge on the causal pathways linking genes to cardiovascular diseases and identify novel preventive targets.

**9C 4:35 p.m. – 4:50 p.m.**

Untargeted metabolomics in a prospective cohort to identify diet-related metabolites associated with age-related cognitive decline

**PRESENTING AUTHOR:** Dorrain Low, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore

**CO-AUTHORS:** Sophie Lefèvre-Arbogast, Raúl González-Domínguez, Mireia Urpi-Sarda, Pierre Micheau, Melanie Petera, Delphine Centeno, Stephanie Durand, Estelle Pujos-Guillot, Aniko Korosi, Paul Lucassen, Ludwig Aigner, Cécile Proust-Lima, Boris Hejblum, Catherine Helmer, Cristina Andres-Lacueva, Sandrine Thuret, Cécilia Samieri, Claudine Manach

With a global rise in ageing population and age-associated diseases, understanding how diet modifies cognitive ageing represents key revenues for prevention. In this discovery (D-CogPlast#) study, we aimed to identify a combination of diet-derived metabolites associated with accelerated cognitive decline using untargeted metabolomics. We leveraged the French Three-City cohort of elderly people and using an exploratory approach, designed a nested case-control study contrasting the metabolic profiles of 209 cases of cognitive decline over 13 years against 209 controls (matched for age, gender and educational level) over 12 years following baseline blood draw. Serum samples were profiled using high-resolution UHPLC-QTOF. Validated PLS-DA of the baseline serum profiles clearly distinguished between case and control populations. A signature of 22 serum metabolites were associated with cognitive decline, using bootstrap-enhanced least absolute shrinkage and selection operator (LASSO) regression. Seven metabolites derived from the food metabolome included coffee-derived metabolites (atractyligenin glucuronide, cyclo(leucyl-prolyl) and caffeine), a biomarker of citrus intake (proline betaine), 3-carboxy-4-methyl-5-pentyl-2-furanpropionic acid (CMPFP), a cocoa-derived metabolite (cyclo(prolyl-valyl)) and an unknown compound putatively linked to wine intake. The other endogenous metabolites included three acylcarnitines, glycodeoxycholic acid, a glycerophospholipid, trimethyllysine, glucose, cortisol, creatinine and arginine. The 22 metabolite-signature increased the predictive performance of cognitive decline from a cross-validated Area Under the Receiver Operating Curve of 62% (95% CI 56-67%) to 75% (95% CI 70-80%). Our untargeted metabolomics study supports a role for the food metabolome (e.g., metabolites from coffee, citrus fruits, cocoa and wine) and various alterations in endogenous metabolism in cognitive aging.
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## SESSION 9: EPIDEMIOLOGY

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

9D 4:50 p.m. – 5:10 p.m.

Metabolic profile and the risk of developing prostate cancer - A nested-case control study

**PRESENTING AUTHOR:** Ali Moazzami, Department of Molecular Sciences, SLU, Sweden

**CO-AUTHORS:** Hanna E Röhnisch, Cecilie Kyrø, Anja Olsen, Elin Thysell, Göran Hallmans, Ali A. Moazzami

Prostate cancer is the most frequently diagnosed cancer and the second cause of cancer-related death in men. Identifying modifiable risk factors and markers for disease risk are therefore important. Our aim was to identify metabolites associated with risk of prostate cancer using a nested case-control study design i.e. 777 pairs of prostate cancer cases and their matched controls (n = 1554) recruited from Northern Sweden Health and Disease Study Cohort (NSHDC). Metabolites were quantified with targeted MS and NMR-based metabolomics in fasting plasma samples. Association to disease risk was examined using conditional logistic regression conditioned on matching factors (BMI, age and sample storage time), followed with correcting for multiple testing. Statistical analyses were also done after restriction to non-aggressive and aggressive cases and stratification by baseline age. After correction for multiple testing, we identified a positive association between overall disease risk and plasma levels of two lysophosphatidylcholines (i.e., LPC C17:0 and LPC C18:0). The associations were more pronounced in older subjects (60 years), where the association for LPC C17:0 was significant even after Bonferroni correction. For younger subjects (40 and 50 years), glycine levels positively, and pyruvate levels negatively, associated with risk of overall disease. We also identified positive association between aggressive disease risk and plasma levels of six glycerophospholipids, whereas levels of acylcarnitine C18:2 displayed a negative association. A strong association was found for LPC 17:0 and aggressive disease in older individuals, where individuals in the top quartile had a 3.9 fold higher odds of developing aggressive disease.

9E 5:10 p.m. – 5:25 p.m.

The Metabolome of BMI: A COnsortium of METabolomics Studies (COMETS) Meta-analysis of 85,000 adults

**PRESENTING AUTHOR:** Rachel Kelly, HMS and BWH, United States

**CO-AUTHORS:** Steven Moore, Krista Zanetti, Ella Temprosa, Ewy Mathe, Jessica Lasky-Su

Metabolomics is ideally suited to explore the drivers and consequences of body mass index (BMI) on a mechanistic and metabolic level. Here, we present the largest meta-analysis to date exploring the metabolome of BMI including >85,000 adults from 34 cohorts worldwide; within the context of the COnsortium of METabolomics Studies (COMETS). Of 436 plasma metabolites which could be harmonized across at least ten cohorts; a random-effects meta-analysis identified 186 (47.2%) metabolites as significantly associated with BMI after adjustment for gender, age, race and Bonferroni correction. Of these, 123 increased with increasing BMI, including valine (p=2.4x10-78); tyrosine (p=6.8x10-74) and lactate (p=8.7x10-55); among the top hits. The remaining 63, including cortisol (p=1.3x10-63); and glutamate (p=3.0x10-47) decreased. These results were largely unchanged with additional adjustment for fasting-status, smoking, alcohol consumption, and diabetes status. However, several other BMI-associated metabolites demonstrated significant heterogeneity across the cohorts that was primarily driven by sex and fasting-status. A sub-study of 2000 children (<10years) from three COMETS cohorts additionally determined that different metabolic changes may accompany BMI prior to puberty. These findings confirm the feasibility of large-scale meta-analyses of metabolomics data utilizing the COMETS Analytics platform. Our analyses suggest an important role for branched chained amino acids, cortisol and glutamate metabolism in BMI, which may act through hypothalamic regulation of appetite, insulin sensitivity or dyslipidemia. The results also suggest that the BMI metabolome may differ by sex and between childhood and adulthood, and that BMI-plasma metabolite relationships are sensitive to fasting status at the time of blood draw.
## SESSION 10: MICROBIAL APPLICATIONS

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

**10A Session Keynote**
**3:45 p.m. – 4:15 p.m.**

Functional characterization of Escherichia coli lipid genes

**PRESENTING AUTHOR:** Jos Brouwers, Utrecht University, Netherlands

**CO-AUTHORS:** Aike Jeucken, Chris van de Lest

The lipidome of a cell is controlled by the intricate actions of lipid gene encoded enzymes. We aimed to increase tolerance of E. coli membranes for hydrophobic compounds by engineering the lipid composition of its membrane. To characterize the contribution of individual genes, we analyzed over 140 strains that either over-express or are knock-out for each individual lipid gene. To this end, we developed a high-throughput procedure that allows for the analysis of over threehundred cell cultures per day. We demonstrate the plasticity of the phospholipidome and discover novel activities of lipid enzymes thanks to the unbiased high-throughput method. We visualize the existence of interesting relationships between lipid classes, lipid species and cell growth. We show that we can reliably model the lipidomic response to exposure to short chain alcohols. Taken together, we present a complete high-throughput lipidomic platform, including growth data, lipid extraction, LC-MS analysis, data processing and visualization.

**10B 4:15 p.m. – 4:35 p.m.**

Metabolomics-based engineering biology of microorganisms

**PRESENTING AUTHOR:** Tomohisa Hasunuma, Engineering Biology Research Center, Kobe University, Japan

**CO-AUTHORS:** Christopher J. Vavricka

Recent remarkable progresses in DNA sequencing, bioinformatics and synthetic biology have brought various innovations in biotechnology. In constructing microorganisms that can produce functional molecules with high titer, yield and productivity, suitable metabolic pathways and genes can be designed using data stored in database. Variety of microorganisms can be built and then tested by high throughput technologies to learn the relationship between the design and reality with computational approach, which can lead to better design of recombinant microorganisms. The state-of-art Design-Build-Test-Learn (DBTL) cycle would contribute to develop industrial microorganisms in a short time. In the present study, an apparatus for automating pretreatment process of microbial metabolome analysis is developed to enable high throughput and precise analysis of metabolites in Escherichia coli and Sacchayomyces cerevisiae. Our group has recently implemented computationally-designed novel benzylisoquinoline pathways in E. coli, and used metabolomics to search for clues to overcome loss of key aldehyde intermediates, and a benzylisoquinoline alkaloids (BIA) methylation bottleneck. LC-MS based metabolomics of 115 central metabolites, in addition to BIA related secondary metabolites, resulted in the identification of intermediates related to the recycling of S-adenosyl methionine cofactor as contributors to the methylation bottleneck. In addition, our group has used a CE-MS to understand tolerance of yeast to organic acids, and this could be successfully applied to the optimization of ethanol production from xylose. A later 13C flux and temperature-dependent analysis enabled the optimization of a glycolysis to TCA bottleneck, leading to the highest reported cyanobacteria succinate titer.

**10C 4:35 p.m. – 4:50 p.m.**

Expanding the Applications of Rapid Evaporative Ionisation Mass Spectrometry (REIMS) to the Pharmaceutical Product Development Workflow

**PRESENTING AUTHOR:** Toma Ramonaite, Imperial College London, United Kingdom

**CO-AUTHORS:** Alvaro Perdones-Montero, Kate Alexander-Hardiman, Adam Burke, Andy Ray, Miriam Guest, Simon Cameron, Zoltan Takats

Rapid evaporative ionization mass spectrometry (REIMS) yields highly specific metabolic profiles of bacteria and fungi which can be used to provide species level taxonomic identification. REIMS offers a novel opportunity for the development of metabolite-based applications for industrial microbiology screening: providing fast and accurate results without the need for pre-analysis preparative steps. One of the focuses of the REIMS applications for pharmaceutical companies is to develop a technique for identifying isolates from environmental monitoring plates which would be an enabler for future real time release (as part of a wider control strategy). Here, we present work on developing REIMS as an automated and high-throughput tool for industrial microbiology with a focus on measuring the metabolomic effect of various growth conditions. Firstly, the effect on bacteria speciation of using refrigerated microbial plates was tested. Samples used for speciation were a mixture of eight frequently found pharmaceutical environmental microorganisms and seven infrequent but concerning clinical isolates. The effect of refrigeration on speciation was tested for 24h, 48h, and 72h incubation at 4°C. Achieved speciation accuracy for regular incubation samples was 97.6% and a measurable effects was only detected after 72 hours of 4°C storage. Secondly, 1 m resolution REIMS imaging was used to identify fungal metabolite distribution over five days growth on live cultures directly from agar culture. Thus, in contrast with other existing techniques (e.g. MALDI) and methods in pharmaceutical companies, REIMS technology could be a technique with substantial savings for business (e.g. time and money, increasing sustainability, integrity and robustness).
SESSION 10: MICROBIAL APPLICATIONS

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

10D 4:50 p.m. – 5:10 p.m.

Linking metabolic function to member taxa in complex microbial communities using
metabolome-guided multi-omics

PRESENTING AUTHOR: Nay Min Min Thaw Saw, Singapore Center for Environmental Life Science Engineering, Singapore

CO-AUTHORS: Rogelio E. Zuniga Montanez, Irina Bessarab, Krithika Arumugam, Pipob Suwanchaikasem, Uma Shankari, Mindia A. S. Haryono, Benjamin Kaehler, Lachlan Speirs, Daniela I Drautz-Moses, Yuguang F. Ipsen, Stefan Wuertz, Rohan B. H. Williams

Whole community metabolic activity is an important phenotypic measure in complex microbial communities (microbiomes) but resolving the identity of taxa who are contributing remains challenging to the presence of multiple taxa that can contribute a given metabolic function. To investigate the feasibility of identifying such inter-relationships, we conducted replicated longitudinal metabolome studies in continuous-culture bioreactors inoculated with activated sludge from a wastewater treatment plant performing enhanced biological phosphorus removal (EBPR) over 19 time points during the P-release/uptake cycle. Extra- and intra-cellular metabolites were extracted by chloroform/methanol/water (2:2:1 v/v) solvent mixture and untargeted metabolomics data were analyzed by ultraperformance liquid chromatography mass spectrometry (UPLC-MS). The community was characterized to the level of metagenome-assembled genomes (Illumina and Nanopore MinION sequencing) and ribosomal-depleted RNA-Seq from the mid-epoch of each phase of the EPBR cycle. We describe a statistical framework, using a functional data analysis approach, for estimating time-varying metabolic profiles associated with physiochemical conditions during EBPR, and from which we can identify known metabolic functions (KEGG reactions) that are 1) plausibly linked to the physico-chemical phenotypes of community and 2) we can link to expressed genes encoding relevant enzymes. For example, in the case of glutamate fed to the reactor, we can identify specific members of phyla Chloroflexi and Actinobacteria that are expressing glutamate related pathways. Our approach enables the systematic analysis and interpretation of multi-omics studies of complex microbial communities using perturbation studies and permits prioritization of targets for testing using NanoSIMS-FISH or related methods.

10E 5:10 p.m. – 5:25 p.m.

Microbial metabolic networks: the hidden key to resilience of coral algal endosymbionts

PRESENTING AUTHOR: Jennifer Matthews, University of Technology Sydney, Australia

CO-AUTHORS: Jennifer L. Matthews, Jean-Baptiste Raina, Justin R. Seymour, David J. Suggett

The intimate relationship between reef-building corals and their associated microorganisms is fundamental to healthy coral reef ecosystems. Endosymbiotic microalgae (Family: Symbiodiniaceae) and coral-associated microbes support coral health and resilience through metabolite transfer, interpartner signalling, and genetic exchange. Much of our understanding of the coral holobiont relationship has come from studies that investigated either coral-Symbiodiniaceae or coral-microbial interactions. However, the inherent ecological and metabolic interactions occurring between Symbiodiniaceae and other microbes has been almost entirely overlooked. Recent evidence of intimate ecological coupling between phytoplankton and bacteria has demonstrated that obligate resource exchange with microbes underpins the ecological success of marine microalgae. We hypothesize that similar associations with microbial consortia regulate Symbiodiniaceae fitness and that this in turn will govern the health of corals. Using gas chromatography–mass spectrometry (GCMS), we compared the metabolite profiles and physiology of Symbiodiniaceae cultures at increasing levels of axenisation to investigate how associated microbes regulate Symbiodiniaceae fitness. We then applied 13C stable-isotope labelling coupled to GC-MS to characterise carbon translocation from associated microbes to Symbiodiniaceae. Our data indicate that microbes act as resource surrogates for Symbiodiniaceae when they live transiently as free-living cells outside of their host corals. Mapping carbon fate reveals vital functions that associated microbes contribute to Symbiodiniaceae hosts, including molecular signalling pathways and amino acid metabolism. Unravelling the significance of the microbial consortium on Symbiodiniaceae fitness provides a step-change in thinking for the resilience of coral reef organisms to survive in a changing ocean, with wider applications for understanding algal-bacterial interactions in extreme environments.
## SESSION 11: METABOLITE IDENTIFICATION 1

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

**11A Session Keynote**
3:45 p.m. – 4:15 p.m.

Infrared ion spectroscopy: new opportunities for molecular structure identification in MS-based metabolomics

**PRESENTING AUTHOR:** Jos Oomens, Radboud University, Netherlands

**CO-AUTHORS:** Jonathan Martens, Giel Berden, Rianne van Outersterp, Kas Houthuijs, Karlien Koene, Leo Kluijtmans, Udo Engelke, Ron Wevers

Studies in metabolomics often rely strongly on mass spectrometry (MS), enabling the sensitive, mass-resolved detection of metabolites in complex mixtures, such as patient samples. The primary weakness of MS-based methods is the limited molecular structure information that is provided even by accurate mass values. Despite the availability of extensive MS/MS and LCMS databases, it remains challenging to distinguish between closely related structures (e.g. positional or stereo isomers), let alone to identify unknowns not present in databases. We have developed methods that allow one to record an infrared (IR) spectrum for species in situ isolated in an ion trap mass spectrometer. Infrared ion spectroscopy (IRIS) is based on the coupling between a frequency-tunable IR laser source and the mass spectrometer. The IR spectrum provides structural information on the connectivity between the constituent atoms of a molecule that can be derived on the basis of reference IR spectra, either experimental or theoretical. IRIS thus combines the selectivity of MS with the structural diagnostics of IR spectroscopy. We will present proof-of-principle experiments, but also actual identification of previously unknown metabolites that can serve as new biomarkers for an inborn error of metabolism. We will show that IRIS is fully compatible with existing MS workflows in metabolomics and especially with liquid chromatography mass spectrometry (LCMS). Furthermore, the ability to accurately predict IR spectra on the basis of quantum-chemical calculations – in strong contrast to the situation for MS/MS spectra – opens new venues towards efficient reference-free identification.

**11B 4:15 p.m. – 4:35 p.m.**

mFam – prediction of metabolite families based on spectral data

**PRESENTING AUTHOR:** Hendrik Treutler, Dept. of Biochemistry of Plant Interactions, Leibniz Institute of Plant Biochemistry, Germany

**CO-AUTHORS:** Asaph Aharoni, Pierre-Marie Allard, Megan Augustin, Ulschan Bathe, David Broadhurst, Corey Broeckling, Joerg Buescher, Stefanie Doell, Oliver Fiehn, Maximilian Frey, Andrej Frolov, Emmanuel Gaquerel, Geert Goeminne, Alain Goossens, Jérémy Grosjean, Maria Halabalaki, Stephanie Herman, Justin van der Hooft, Kim Kultima, Toni Kutschan, Romain Larbat, Tytus Mak, Jacob Pollier, Stacey Reinke, Nir Shahaf, Amy Sheflin, Alena Soboleva, Otmar Spring, Lloyd Sumner, Ric De Vos, Jean-Luc Wolfender, Gerd Balcke, Steffen Neumann

Using untargeted approaches, modern hyphenated MS instrumentation is capable of generating thousands of mass to retention time features at the MS1 and the MS/MS level. The full structure elucidation of all features from a biological sample set is unfeasible and annotations at the level of metabolite families help to interpret spectral data sets and thus answer biological questions (van der Hooft et al. 2016, Wang et al. 2016). We have developed mFam to identify conserved MS/MS fragmentation patterns of metabolite families. Analogously, pFam automatically classifies protein families based on conserved sequence domains in proteomics. mFam aims at the automatic annotation of MS/MS spectra of unknown compounds at the metabolite family level. We mined >500,000 spectra of >25,000 compounds from different HR MS/MS spectral libraries. We classified these compounds into >1,000 metabolite families based on the ChemOnt chemical taxonomy (Feunang et al. 2016). For hundreds of metabolite families we extracted characteristic MS/MS fragmentation patterns and built a library of predictive classifiers. Using MetFamily we demonstrate the application of mFam to predict metabolite families in untargeted metabolomics experiments with promising results (Treutler et al. 2016). Since important metabolite families such as sesquiterpene lactones are still sparsely populated in available spectral libraries the predictivity of those classifiers can be limited. We launched the mFam initiative to broaden the coverage of those metabolite families. By now we jointly collected >6,000 HR MS/MS spectra including spectra for ~100 sesquiterpene lactones. These spectra will be added to MassBank in due course and we welcome further contributions.

**11C 4:35 p.m. – 4:50 p.m.**

Supercharging Comparative Metabolomics with METABOseek

**PRESENTING AUTHOR:** Maximilian Helf, Cornell University / Boyce Thompson Institute, United States

**CO-AUTHORS:** Frank C. Schroeder

Comparative analysis of high resolution mass spectrometry (HRMS) data plays a central role for our understanding of living systems, including elucidation of metabolic networks and small molecule biosynthetic routes. However, data processing is presenting a productivity bottleneck, preventing widespread adoption of comparative metabolomics as a routine tool to complement transcriptomics. To break this barrier, we developed METABOseek, an R/shiny- based, discovery-oriented platform to extract differential features from HPLC-HRMS data. Integrated analyses include xcms-based feature detection, statistical analysis, prediction of molecular formulas, annotation of MS2 spectra, MS2 molecular networking and chemical compound database searches. By making metabolomics tools more accessible, METABOseek enables high-throughput identification of differential molecular features and facilitates structure elucidation of novel compounds, addressing an urgent need to explore the vast universe of unknown compounds revealed by HPLC-HRMS. METABOseek also serves as a launch pad for innovative data analysis strategies developed by the metabolomics community. Many new software tools do not include a graphical user interface and require users to write scripts for their data analysis. This generates a growing disconnect between the available data analysis tools and their potential user base: getting to know the functions and data standards used in a tool takes time and keeps users from integrating them into their workflows, especially when programming experience is limited. The open-source, modular architecture of METABOseek allows developers to dramatically expand their audience by integrating script-based analysis tools into its graphical user interface with minimal effort. METABOseek is available for download and as an online preview version at https://metaboseek.com.
## SESSION 11: METABOLITE IDENTIFICATION 1

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

11D 4:50 p.m. – 5:10 p.m.

Ion mobility – mass spectrometry based multi-dimensional metabolite annotation

**PRESENTING AUTHOR:** Zhiwei Zhou, IRCBC, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, China

**CO-AUTHORS:** Zhengjiang Zhu

Unambiguous identification of metabolites is the long-standing challenge for untargeted metabolomics. Ion mobility – mass spectrometry (IM-MS) shows great potential for metabolomics application due to its excellent capability for both separation and metabolite annotation. Although the collision cross section (CCS) value derived from IM-MS has been widely proved to improve the metabolite annotation, it still lacks effective and universal ways to integrate CCS values with other available descriptors (e.g. MS/MS spectra) to provide confident metabolite annotation. To address this challenge, we present the first strategy to integrate CCS values with in-silico MS/MS spectra to perform accurate metabolite annotation. Here, we first optimized the CCS value prediction algorithm with a large and complex training set (containing ~2000 CCS values). It significantly expanded the applicable coverage for prediction, and enabled to generate large-scale CCS values for metabolites. These predicted CCS values were further integrated with in-silico MS/MS spectra (generated by CFM-ID) to perform confident metabolite annotation. Comparing with using exact mass match, this strategy was validated to effectively reduce ~30-40% false candidates for different database match (e.g. KEGG, MINE) using several benchmark datasets. It also supported to improve the rank of correct metabolite comparing with directly using in-silico MS/MS spectra. In addition, we would demonstrate the discrimination power of integrating CCS and MS/MS spectra in the whole metabolome level. In conclusion, the strategy of integrating multi-dimensional properties (i.e. m/z CCS and MS/MS spectrum) effectively promoted metabolite annotation.

11E 5:10 p.m. – 5:25 p.m.

Recommending substructures for unknown tandem mass spectra

**PRESENTING AUTHOR:** Youzhong LIU, University of Antwerp, Belgium

**CO-AUTHORS:** Liu Youzhong, Mrzic Aida, Meysman Pieter, De Vijlder Thomas, Edwin P. Romijn, Valkenborg Dirk, Bitremieux Wout, Laukens Kris

Structural elucidation of unknown metabolites from LC-MS/MS spectra remains a challenging task. However, these metabolites may share common substructures, which may result in common spectral features (product ions and mass differences). 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. We present a method that mine patterns containing both spectral features and substructures from spectral libraries. These novel patterns are represented as "rules" that associate a substructure with single or several spectral features. Most rules represent high sensitivity for substructure prediction, and they have two important characteristics: i) Spectral features are error-free theoretical masses and mass difference; ii) Predicted substructures are maximum common substructures (MCSs) between biological molecules – non-informative substructures are minimized. From GNPS spectral library, we generated 8378 rules that associate spectral features to 712 substructures, thus recommending substructures in an automated fashion. 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. Using non-targeted MS/MS data of yeast, we implemented a system biology approach that clusters metabolites sharing substructure, revealing substructure enrichment in two yeast phenotypes. Our goal is to achieve system biology interpretation of massive MS/MS data without identifying every metabolite. Our substructure recommendation system was developed into an openly-available web-tool at messar.biodatamining.be.
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SESSION 12: STEM CELLS/ORGANOIDS/ORGAN-ON-A-CHIP

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

**12A Session Keynote**
**3:45 p.m. – 4:15 p.m.**

No transporters means no transport – and assessment of the ‘real’ (natural) substrates of xenobiotic transporters.

**PRESENTING AUTHOR:** Douglas Kell, University of Liverpool, United Kingdom

**CO-AUTHORS:** Steve O’Hagan

Despite what it says in textbooks, bilayer flux through intact cell membranes is negligible [1] and hydrophobicity is often a poor guide to permeability. Often their ‘natural’ substrates are unknown. We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their rank order varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies very greatly depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity. However, for a subset of such drugs, amounting to some 10% of the set and a Tanimoto similarity of ~0.8 or greater, the similarity coefficient is relatively robust to the encoding used. This leads to a metric that, while arbitrary, suggests that a Tanimoto similarity of 0.75-0.8 or greater genuinely does imply a considerable structural similarity of two molecules in the drug-endogenite space. However, the majority are in fact similar to natural products [2]

**12B 4:15 p.m. – 4:35 p.m.**

Spatial Isotope tracer Metabolomics to study 13C labelled Metabolite Distribution in 3D Tumor Spheroid Cell culture

**PRESENTING AUTHOR:** Prasad Phapale, EMBL, Germany

**CO-AUTHORS:** Mariia Naumenko, Karin Mitosch, Theodore Alexandrov

Tracer-based studies when performed in 2D cell culture do not provide information about spatial aspects of metabolism neglecting spatio-molecular gradients and 3D microenvironment manifested in tissues and tumors. Multicellular tumor spheroids in 3D cell cultures are established in vitro models to mimic the primary tumors. Study of the distribution of isotope-labeled metabolites in spheroids can give us a valuable spatial understanding of the 3D spatial aspects of tumor metabolism. Here we present a method combining using 13C6-glucose as a tracer in 3D cell culture and untargeted LC-MS/MS analysis to map central carbon metabolism spatially across different layers of spheroids. Tumor spheroids were cultured using HCT116 human colon carcinoma cells for 10 days in Nunclon SpheraTM plates. Metabolites and lipids were extracted from each layer as well as from respective culturing medium to perform untargeted metabolomic profiling using our high-resolution LC-MS/MS-based EMBL-MCF platform (protocols available online https://www.embl.de/mcf/metabolomics-core-facility/protocols). Preliminary results of the metabolite profiling show the highest incorporation of the 13C-label in layer 1 and the lowest in layer 3. The numbers of carbons labeled for pyruvate, succinate, sugar phosphates, other glycolytic metabolites, and amino acids were found to be decreasing from layer 1 to layer 3. The necrotic core with dead cells had the least incorporation of the 13C label which suggests diffusion of labeled metabolites across layers. Our results suggest that the developed method is useful for investigating spatial aspects of metabolic fluxes in a tumor spheroid model.

**12C 4:35 p.m. – 4:50 p.m.**

Microengineered Human Blood Vessel for Next Generation Drug Discovery

**PRESENTING AUTHOR:** Abidemi Junaid, Systems Biomedicine and Pharmacology, LACDR, Leiden University, Netherlands

**CO-AUTHORS:** W. Stam, J.M. van Gils, C. van Kooten, S.C. Dólleman, H.C. de Boer, V. van Duinen, A. Mashaghi, A. J. van Zonneveld, T. Hankemeier

Chronic kidney disease (CKD) is associated with cardiovascular complications including heart failure. To identify early prognostic factors for heart failure in CKD, we aim to explore the association of circulating plasma factors with microvascular integrity. As current human 2D models with cultured endothelial cells lack sufficient complexity to assess the functionality of microvascular endothelial-pericyte interactions, research on microvascular loss largely depends on pre-clinical animal models for ischemia/reperfusion injury. We recently developed a microfluidics-based, 3D ‘microvessel-on-a-chip’ platform that models patient specific human microvessels and allows quantitative and parallel testing of microvascular leakage. Human umbilical vein endothelial cells (HUVECs) were cultured to generate microvessels-on-a-chip. To assess microvascular destabilization, the layout of the microfluidics platform was modified to measure microvascular leakage of fluorescently labelled albumin by means of high-resolution time-lapse fluorescent microscopy. In this small volume platform, we were able to measure metabolites that are critical in disease progression by using liquid chromatography-tandem mass spectrometry metabolomics. Additionally, we developed an approach to test plasma samples of patients suffering from vascular diseases for the presence of destabilizing factors such as thrombin or TNF-alpha. Our platform may serve as a unique tool for microvascular destabilization studies as well as for the development of novel therapeutic strategies to combat cardiovascular complications. Plasma samples of patients with a destabilizing profile may well predict ongoing microvascular rarefaction and risk of cardiovascular complications.
SESSION 12: STEM CELLS/ORGANOIDS/ORGAN-ON-A-CHIP

Tuesday, June 25

3:45 p.m. – 5:30 p.m.

12D 4:50 p.m. – 5:10 p.m.

Stable Isotope-Resolved Metabolomics (SIRM) Defines DEK Oncogene Driven Metabolic Reprogramming in 3D Epidermal Organoids

PRESENTING AUTHOR: Sara Vicente-Muñoz, NMR-Based Metabolomics Core Facility, Division of Pathology and Laboratory Medicine, United States

CO-AUTHORS: Sara Vicente-Muñoz, Marie C Matrka, Miki Watanabe, Marion G Brusadelli, Kaylin Earnest, Andrew N Lane, Lindsey E Romick-Rosendale, Susanne I Wells

The DEK oncoprotein is amplified and overexpressed in many malignancies, including squamous cell carcinomas (SCCs) originating in keratinocytes of the human epidermis. Our previous work on 2D cell models demonstrated that DEK overexpression reprogrammed metabolism at the level of aerobic glycolysis and oxidative phosphorylation in the absence of proliferative gains, indicating that DEK drives metabolic reprogramming. Engineered 3D epidermal organoids, which mimic stratified human epidermis, show induction of hyperplasia by DEK overexpression and a significant shift in steady state metabolism. To reconstruct global metabolic networks in 3D models of human epidermis, we report the application of stable isotope-resolved metabolomics (SIRM) on epidermal organoids. The goal was to unequivocally define DEK-driven metabolic pathways and specific enzymes, and their respective roles in enabling and sustaining oncogenic phenotypes. Normal immortalized keratinocytes were transduced with either control or DEK overexpression vector, placed into 3D organoid culture conditions for 13 days, and then incubated with 13C6-glucose for an additional 24 hrs prior to collection and metabolite extraction. NMR spectroscopy was used to determine positional isotopomers in downstream metabolites. Media aliquots were collected post isotope addition to quantify 13C6-glucose consumption and de novo production of labelled metabolites. Incorporation of 13C6-glucose validated SIRM feasibility in this system; we observed numerous 13C-labeled metabolites including lactate, alanine, glutamine and glutamate. Comprehensive analyses of DEK-overexpressing versus control organoids defined a number of reprogrammed pathways including glycolysis, the TCA cycle and glutathione metabolism. Current experiments aim to target therapeutically relevant enzymes for the prevention of DEK-dependent SCC development and progression.

12E 5:10 p.m. – 5:25 p.m.

Metabolomics as a quality control for the production of chondrogenic microtissues towards characterized endochondral bone regeneration

PRESENTING AUTHOR: Niki Loverdou, Prometheus, Division of Skeletal Tissue Engineering, KULeuven, Belgium

CO-AUTHORS: G. Nilsson Hall, K.Bernaerts, B.Ghesquiere, G.Carmeliet, I.Papantoniou, L.Geris

The use of 3D microtissues is becoming a standard for bone tissue engineering approaches, as this format allows cell-cell and cell-extracellular matrix interactions. Considering the role of metabolism as a key regulator of stem cell fate and the high sensitivity of metabolomics, this study aims to identify metabolic quality attributes indicative of a functional cartilage intermediate TE construct. LC-MS (liquid chromatography-mass spectrometry) tracer analysis was conducted to investigate metabolic alterations during chondrogenic differentiation of spheroids of hPDCs (human periosteum derived stem cells). 13C labeled glucose, glutamine but also serine and aspartate have been used, as these metabolites showed significant differences between the time points of interest in a prior exometabolomics study. Samples were analyzed at day 0, day 14 and day 21, time points indicative of the proliferating, prehypertrophic and hypertrophic state. Our tracer analysis results showed progressive 13C glucose enrichment in palmitate from 0% at day 0 to 8% at day 14 and 22% at day 21, suggesting activation of fatty acid synthesis. Furthermore, we observed 13C glutamine enrichment in proline from 0% at day 0 to 20% at day 14 and 42,5% at day 21 and a similar trend of 13C glutamine contribution to hydroxyproline (from 0% at day 0 to 35% at day 14 and 38% at day 21). The consecutive stages of chondrogenic differentiation of hPDCs are characterized by specific metabolic adaptations and highlight the importance of unexplored metabolic pathways such as fatty acid and glutamine metabolism for chondrogenic differentiation.
SESSION 13: LIPIDOMICS AND CARDIOVASCULAR DISEASE

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

13A Session Keynote
10:15 a.m. – 10:45 a.m.

High-throughput lipidomic quantitation of human blood in cancer screening

PRESENTING AUTHOR: Michal Holčapek, University of Pardubice, Czech Republic

CO-AUTHORS: Michal Holčapek, Denise Wolrab, Robert Jirásko, Michaela Chocholoušková, Ondřej Peterka, David Vrána, Bohuslav Melichar, Roman Hrstka

A large diversity of lipids is found in eukaryotic cells, where they fulfill important physiological functions. The dysregulation of lipids is often related to serious diseases, e.g., various types of cancer. The robust, high-throughput, and validated analytical methods for the lipidomic quantitation can be applied for biomarker discovery research and also for monitoring the progress of disease therapy. MS and its coupling with the liquid-phase separation techniques together with exogenous internal standards is the most common approach for the lipidomic quantitation. We have developed the following MS based methods for the high-throughput clinical lipidomic quantitation: 1/ shotgun ESI-MS using characteristic NL and PI scans on QqQ, 2/ UHPSFC/MS, 3/ UHPLC/ MS, and 3/ MALDI coupled to Orbitrap mass analyzer. Shotgun MS, UHPSFC/MS, and UHPLC/MS techniques are applied mainly for glycerophospholipids, sphingolipids, and glycerolipids using positive-ion ESI, while MALDI is used in the negative-ion mode to obtain complementary information on sulfatides and other anionic lipid subclasses. About 300 – 500 lipid species are typically quantified in studied biological samples from over 1000 human subjects, mainly plasma or serum from healthy volunteers and cancer patients. All mentioned methods follow the basic rule of reliable lipidomic quantitation that IS should be coionized with analytes from the same lipid subclass. Finally, MDA methods are applied for building the statistical models to differentiate cancer patients and healthy controls with over 90% accuracy for samples with known classification and also for blinded samples. This work was supported by project No. 18-12204S sponsored by the Czech Science Foundation.

13B 10:45 a.m. – 11:05 a.m.

Changes in plasma lipids predict pravastatin efficacy in secondary prevention

PRESENTING AUTHOR: Peter Meikle, Baker Heart and Diabetes Institute, Australia

CO-AUTHORS: Kaushala S Jayawardana, Piyushkumar A Mundra, Corey Giles, Christopher K Barlow, Paul J Nestel, Elizabeth H Barnes, Adrienne Kirby, Peter Thompson, David R Sullivan, Zahir H Alshehry, Natalie A Mellett, Kevin Huynh, Malcolm J McConville, Sophia Zoungas, Graham S Hillis, John Chalmers, Mark Woodward, Ian C Marschner, Gerard Wong, Bronwyn A Kingwell, John Simes, Andrew M Tonkin

Background- Statins have pleotropic effects on lipid metabolism. However, the relationship between these effects and future cardiovascular events is unknown. We sought to characterise the changes in lipid species upon pravastatin treatment and define the relationship with risk reduction for future cardiovascular events. Methods - Plasma lipids (n=342) were measured on baseline and one-year follow-up samples from a LIPID study sub-cohort (n=4991). The associations of changes in lipids with treatment and cardiovascular outcomes were investigated using linear regression and cox regression. The effect of treatment on future cardiovascular outcomes was examined by the relative risk reduction (RRR). Results - Pravastatin treatment was associated with changes in 206 lipids. Species containing arachidonic acid were positively associated while phosphatidylinositol species were negatively associated with pravastatin treatment. Findings were replicated in an ADVANCE case-cohort. A greater decrease and increase in the phosphatidylinositol (PI(36:2)) and phosphatidylcholine (PC(38:4)) respectively, were associated with fewer events. The RRR from pravastatin treatment for cardiovascular events reduced from 23.5% to 16.6% after adjustment for clinical risk factors and change in LDL-C, and to 3.0% after further adjustment for the change in the lipid ratio PI(36:2)/PC(38:4). 58% of the treatment effect was mediated by the change in PI(36:2)/PC(38:4), as indicated by causal mediation analysis. Stratification of patients into quartiles of change in PI(36:2)/PC(38:4) indicated no benefit of pravastatin in Q4. Conclusion-The change in PI(36:2)/PC(38:4) predicts benefit from pravastatin, independent of change in LDL-C, demonstrating its potential as a biomarker for monitoring the clinical benefit of statin treatment in secondary prevention.

13C 11:05 a.m. – 11:20 a.m.

Metabolomics analysis of human atherosclerotic plaques reveals a potential novel pathway of macrophage foam cell apoptosis in advanced atherosclerosis

PRESENTING AUTHOR: Panagiotis Vorkas, Imperial College London, United Kingdom

CO-AUTHORS: Sarah Onida, Kevin Woollard, Alun H Davies, Elaine Holmes

Atherosclerosis remains a leading worldwide cause of mortality and morbidity. In this study, ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS)-based metabolomics were utilized for the analysis of human advanced atherosclerotic tissue. From 78 patients, a total of 52 carotid and 26 femoral plaques were compared to 16 adjacent arterial non-plaque tissue (intimal thickening). Tissue samples were homogenised and extracted consecutively for aqueous and organic extracts. Aqueous extracts were analysed using hydrophilic interaction chromatography (HILIC)-UHPLC-MS, whilst organic extracts by reversed-phase (RP)-UHPLC-MS. A panel of established as well as novel molecules, from several biological pathways, were identified as being dysregulated. These included free unesterified cholesterol (FUEC), oxidized cholesteryl esters, purines, pyrimidines, sphingolipids and acylcarnitines. A previously unassociated sphingolipid, namely phosphatidylethanolamine-ceramide (PE-Cer), was detected with high statistical significance (p=9.8x10-12) and 2-fold reduction in plaque tissue. PE-Cer also demonstrated the highest (inverse) correlation to FUEC (p=-0.76). In pilot validation studies, human primary monocyte-derived macrophages (MDM) were treated with vehicle (naïve), acetylated-LDL (acLDL) (foam cells) and acLDL/FUEC (FUEC-loaded foam cells). The acLDL/FUEC-treated MDM demonstrated elevated apoptosis, and a 2-fold reduction in PE-Cer, in concordance with the findings in human tissue. This was accompanied by a reduction of SAMD8 RNA, the enzyme responsible for PE-Cer synthesis. Finally, a comprehensive examination of the sphingolipid pathway demonstrated an increase in de novo ceramide synthesis, further to the recognised in apoptosis hydrolysis of sphingomyelin (to ceramide). This provides insight for the role of PE-Cer and sphingolipids in advanced atherosclerosis and specifically in FUEC-loaded MDM foam cell apoptosis.
SESSION 13: LIPIDOMICS AND CARDIOVASCULAR DISEASE

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

13D 11:20 a.m. – 11:40 a.m.

Plasma metabolomics profiles associated with endothelial health and dysfunction impose changes to endothelial glycan biosynthesis and reflect endothelial catecholamine response

PRESENTING AUTHOR: Öttar Rolfsson, University of Iceland, Iceland

CO-AUTHORS: Sarah McGarrity, Hanne H. Henriksen, Per Johannsson

Endothelial dysfunction (ED) contributes to diseases of the vasculature by influencing blood pressure, clotting and transport of fluids, nutrients and immune cells. Metabolic phenotypes associated with ED are not well characterised due to difficulties in assessing endothelial metabolism in situ. To address this, we recently built a cell scale metabolic network model of endothelial metabolism (iEC2812) and applied it to infer endothelial metabotypes from sepsis patient plasma metabolomics data. These highlighted changes to endothelial glycan metabolism. We subsequently hypothesized that endothelial glycocalyx maintenance contributes to endothelial dysfunction and updated our model (iEC2997) to more accurately account for endothelial glycocalyx synthesis and biomass. We then analysed, i) plasma metabolomics data from 20 trauma patients vs. 20 controls and ii) ASGR1del12 carriers vs. controls using iEC2997 to identify reactions associated with both dysfunctional and above normally healthy vasculature, respectively. Flux into the hexosamine biosynthetic pathway was altered in both cases. To verify this effect, we titrated HUVEC monolayers with physiological concentrations of catecholamines. Increased permeability and glycocalyx loss was verified by TEM, immunostaining and by permeability assays. UPLC-MS metabolomics analysis showed a drop in the intracellular concentrations of the glycan precursors UDP-glucose and N-acetyl-glucosamine. Extracellular measurements along with 13C-UL-glucose and 15N2-glutamine flux analysis supported lower turnover of glycocalyx intermediates and lower glycolytic and TCA cycle flux. In summary, metabolic network analysis of three independent plasma metabolomics datasets highlighted the importance of glycan synthesis to endothelial health. Induction of endothelial dysfunction in vitro is accompanied by compromised glycan synthesis.

13E 11:40 a.m. – 11:55 a.m.

Sphingolipidomics investigation of the ischemic brain injury

PRESENTING AUTHOR: Ching-Hua Kuo, National Taiwan University, Taiwan

CO-AUTHORS: Hsi-Chun Chao, Tsung-Heng Lee, Sung-Chun Tang

Stroke is among the three leading causes of death worldwide and is the most frequent cause of permanent disability. Significant changes of sphingolipid (SPL) levels have been observed after stroke. We conducted sphingolipidomic profiling of mouse brain tissue by liquid chromatography-electrospray ionization tandem mass spectrometry at 3 hour (hr) and 24 hr after 1 hr of middle cerebral artery occlusion (MCAO), and SPL were compared with those of the Sham control group. At 3 hr post MCAO, ceramides (Cers) exhibited increase in levels of long-chain Cers but decrease in very-long-chain Cers. Moreover, sphingosine, was decreased and S1P was increased at 3 hr after MCAO. Both long-chain and very-long-chain Cers showed an increased trend at 24 hr post MCAO. The administration of atorvastatin improved the neurological function of the mice and significantly reversed the SPL changes resulting from the ischemic injury. To further investigate the prognostic roles of SPL in patients with acute ischemic stroke, we collected plasma samples from acute ischemic stroke patients at 24 hr and 72 hr (n=90; prognosis: 40%) post stroke and non-stroke controls (n=64). The association study between the plasma ceramide concentrations and 3 months post stroke outcome revealed that the levels of long chain ceramides at 72 hr were significantly higher in patients with poor outcome. Long chain ceramides represented as potential prognostic markers for patients with acute ischemic stroke, and the network of SPL components that change upon ischemic damage may provide novel therapeutic targets for ischemic stroke.
## SESSION 14: PLANT DEFENSE

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

**14A Session Keynote**
10:15 a.m. – 10:45 a.m.

Elucidating insect resistance in tomato through genetical metabolomics

**PRESENTING AUTHOR:** Ric de Vos, Bioscience, Wageningen Plant Research, Wageningen University and Research, Netherlands

**CO-AUTHORS:** Robert D. Hall, Roland Mumm, Roeland E. Voorrips, Ben Vosman

Host plant pest resistance is becoming increasingly important in agriculture as more and more insecticides are being banned due to environmental concerns. In tomato, resistance towards several pests has been found in wild relatives (Solanum section Lycopersicon). To identify quantitative traits loci (QTLs) regulating pest resistance, we combined untargeted metabolomics using LC-PDA-Orbitrap FTMS with insect resistance assays, trichome phenotyping and genetic mapping. Screening a set of 14 accessions from close relatives of the cultivated tomato (S. lycopersicum, cv. Moneymaker) for resistance against different pest insects, including whitefly, thrips, caterpillar and aphids, we identified broad spectrum insect resistance in both S. galapagense and a few accessions of S. pimpinellifolium. An untargeted metabolomics comparison of leaves of resistant S. galapagense with those of its closest relative but sensitive S. cheesmaniae, containing and lacking type-IV trichomes respectively, indicated significantly higher levels of both a range of acylated sucroses and methylated flavonols in S. galapagense. Subsequent whitefly resistance screening and metabolomics comparison of a recombinant inbred line population and near-isogenic lines derived from a cross between S. lycopersicum and S. galapagense indicated a major QTL on chromosome 2 that controls the presence of both whitefly resistance, trichome types IV and V, and the accumulation of a specific set of phytochemicals including all identified acyl sugars and the methyl flavonols. We hypothesize that this QTL regulates the formation of glandular trichome type IV on the leaf epidermis, thus enabling the production and accumulation of the bioactive metabolites in this specific trichome type.

**14B 10:45 a.m. – 11:05 a.m.**

Dissecting the Genetic Basis of Variation in Tomato Fruit Metabolism and Pathogen Resistance through Multimodal Investigation of Wild Species Introgressions

**PRESENTING AUTHOR:** Jedrzej Szymanski, Leibniz Institute of Plant Genetics and Crop Plant Research, Germany

**CO-AUTHORS:** Samuel Bocobza, Sayantan Panda, Prashant Sonawane, Pablo Cardenas, Justin Lashbrooke, Avinash Kamble, Nir Shachaf, Sagit Meir, Arnaud Bovy, Jules Beekwilder, Yury Tikunov, Irene Romero de la Fuente, Dani Zamir, Ilana Rogachev, Asaph Aharoni

Wild tomato species represent a rich gene pool for numerous desirable traits lost during domestication. Here, we exploited an introgression population representing wild desert-adapted species and a domesticated cultivar to establish the genetic basis of gene expression and chemical variation accompanying the transfer of wild species-associated fruit traits. Transcriptome and metabolome analysis of 580 lines coupled to pathogen sensitivity assays resulted in the identification of genomic loci significantly linked with levels of hundreds of transcripts and metabolites. These associations occurred in functionally coherent hotspots that represent coordinated perturbation of metabolic pathways and ripening-related processes. We demonstrated the efficacy of our approach by discovering yet undisclosed components of the renowned Solanum alkaloids pathway and showing that fruit fungal resistance is associated with changes in the ripening regulatory network. Together, our results outline a framework for understanding metabolism and pathogen resistance underlying ripening and provide insights into key fruit quality traits.

**14C 11:05 a.m. – 11:20 a.m.**

Metabolomics application to unravel the biochemistry underlying enhancement of drought stress tolerance in crops by plant growth promoting rhizobacteria

**PRESENTING AUTHOR:** Fidele Tugizimana, University of Johannesburg, South Africa

**CO-AUTHORS:** Lerato Nephali, Johan Huyser, Ian Dubery

Drought conditions pose a growing threat to food security, affecting agronomically important crop plants. Although improved adaptation to drought has long been a pursuit of crop breeders, it has been difficult to achieve, since drought resistance is a quantitative trait controlled by multilayered cellular and molecular events. Hence, innovative and efficient strategies are imperatively required, such as the use of plant growth promoting rhizobacteria (PGPR). However, to devise these novel PGPR-based agricultural strategies, there is a necessity to firstly understand the physiology and biochemistry governing the interactions between beneficial bacteria and plants, and the resultant enhanced plant defences against abiotic stresses. Thus, we present the application of LC-MS-based metabolomics to elucidate the biochemical events and molecular mechanisms underlying PGPRs–maize interactions and the subsequent potentiation against drought conditions. The results of this study showed that the application of PGPRs induced alterations in different pathways of primary and secondary metabolism such as phenylpropanoid, flavonoid biosynthesis, fatty acid metabolism, amino acids and phytohormones pathways. Metabolic network analysis allowed the characterization of key metabolic ‘hubs’ that define the observed metabolic reprogramming. These changes were found to be related to enhanced drought resistance traits. Thus, our results suggest that selected PGPRs act as priming agents, enhancing plant resistance to abiotic stresses. Furthermore, the study contributes to ongoing efforts towards a comprehensive understanding of the PGPR-related defence priming phenomenon, which could play a significant role in alleviation of drought stress in plants, providing sustainable and economically favorable solutions to improve agricultural practices and crop productivity.
## SESSION 14: PLANT DEFENSE

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

14D 11:20 a.m. – 11:40 a.m.

A strategy for the discovery and characterisation of health and decline biomarkers in British Oak trees using non-targeted metabolomics

**PRESENTING AUTHOR:** Jasen Finch, Aberystwyth University, United Kingdom

**CO-AUTHORS:** Manfred Beckmann, Sandra Denman, John Draper

Decline syndromes in British Oak trees are becoming increasingly important across the UK with the onset of climate change. These syndromes exhibit a multitude of complex disease phenotypes to which biotic and predisposing abiotic factors can contribute to over many growing seasons. A minimally destructive strategy for reproducibly sampling the metabolome content of living cell types in woody tissues has been developed that has allowed samples to be collected from trees at a number of sites across the UK. Trees have been extensively phenotyped for attributes reflecting aspects of decline status including; canopy condition, tree size and presence of biotic agents. Consideration of all of these phenotypic data has led to the development of a novel "tree health index", which describes the trees over a continuous decline spectrum. Sampled woody tissues have been analysed using untargeted flow infusion electrospray ionisation high-resolution mass spectrometry (FIE-HRMS), normal and reverse phase liquid chromatography high-resolution mass spectrometry (LC-HRMS) and gas chromatography-mass spectrometry (GC-MS) to provide comprehensive metabolome coverage. Using the machine learning algorithm random forest, metabolome features have been integrated with the tree health indexes revealing strong associations with a number of explanatory metabolites including amino acids involved in nitrogen metabolism/transport. These markers have been shown to be robust over 2 growing seasons and can adequately predict decline status in an independent set of trees. Identified markers have the potential to provide future diagnostic tools to monitor health status and even as a predictive tool for long term management strategies for oak decline.

14E 11:40 a.m. – 11:55 a.m.

Linking the volatilome and metabolome via plant defenses: stimulation of the salicylic acid pathway recruits natural enemies below ground

**PRESENTING AUTHOR:** Camila Filgueiras, Cornell University, United States

**CO-AUTHORS:** Denis S Willett

The metabolome and volitalome can be considered linked, overlapping sets. Traditionally, studies have looked at each in isolation with the implicit understanding that one influences the other. In plant-insect interactions this understanding is becoming more than implicit. Here we show that exogenous aboveground stimulation of the salicylic acid pathway induces release of volatiles by plant roots that recruit entomopathogenic nematode natural enemies belowground. This is one of the first examples of aboveground effects propagating belowground and points to a role of the salicylic acid pathway in mediating secondary plant defenses reliant upon volatile recruitment of natural enemies. Understanding the relationship between internal plant defense metabolites and volatiles released by plants that recruit natural enemies enhances not only our knowledge of the connection between metabolome and volitalome, but also our ability to control pests in the field.
<figure><img src="image_25.png" /></figure>

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## SESSION 15: DATA ANALYSIS & STATISTICS

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

**15A Session Keynote**
10:15 a.m. – 10:45 a.m.

Is Metabolomics ready for the return of Artificial Neural Networks?

**PRESENTING AUTHOR:** David Broadhurst, Edith Cowan University, Australia

Artificial Neural Networks (ANNs) were first applied to metabolomics data in the late 1990's as part of a research programme spearheaded by Kell and Goodacre at Aberystwyth University. It was from this nexus that my PhD was published in 1998 titled 'Genetic Algorithms, Artificial Neural Networks and their Application to Chemometrics'. Even though the methodology proved effective for modelling metabolomics data, ANNs were generally perceived as a 'black box', and considered too difficult to interpret. By the early 2000's the popularity of ANNs had dwindled to zero, with a more transparent linear machine learning method, partial least squares regression, rapidly becoming the dominant mode of interrogating multivariate metabolite data. Over the last ~5 years there has been a resurgence in the use of ANNs in many areas of systems biology under the guise of Artificial Intelligence (AI) and Deep Learning. Most of the successful applications have been in image analysis, but there is increasing interest in applying these methods to biomarker discovery in systems biology. In this presentation I will map out the current and future challenges in applying ANNs to metabolomics, given the changing landscape of acceptance and increased computational resources. It is clear that for any AI techniques to be adopted without cynicism, all code and applications must be open, transparent and detailed scrutiny. As such, I will introduce the use of experiential learning through open-source Jupyter Notebooks, operating via cloud computing resources such as Binder and Microsoft Azure.

**15B 10:45 a.m. – 11:05 a.m.**

**MS-DIAL 4.0: a computational workflow for ion mobility tandem mass spectrometry data in metabolomics**

**PRESENTING AUTHOR:** Hiroshi Tsugawa, RIKEN, Japan

**CO- AUTHORS:** Yoshifumi Mori, Yasuhiro Higashi, Aya Satoh, Sven Meyer, Kazuki Saito, Masanori Arita

Ion mobility mass spectrometry provides the robust physiochemical measurement (or property) called collision cross section (CCS) of metabolites by increasing the peak capacities in mobility dimension. Although metabolite identification can be substantially improved in terms of accuracy and coverage, few programs can handle its raw data for peak picking, metabolite annotation, peak alignment, and statistical analysis. Here, we release a new software program, MS-DIAL 4.0, for data dependent MS/MS acquisition in liquid chromatography coupled with ion mobility tandem mass spectrometry (LC-IM-MS/MS). The program facilitates the metabolite annotation by integrating five-dimensional (5D) physicochemical properties, i.e., ion abundance, retention time (RT), CCS, m/z, and MS/MS spectrum. In addition, we developed the enriched mass spectral database for lipidomics containing the 5D properties for more than 200,000 molecules of 90 lipid classes. We showcase our workflow for plant lipidomics where the metabolic changes in heat stress are grasped by the 5D diagnostic criteria.

**15C 11:05 a.m. – 11:20 a.m.**

Chemically informed distance metrics for tandem mass spectrometry data

**PRESENTING AUTHOR:** Madeleine Ernst, Department of Congenital Disorders, Statens Serum Institut, Denmark

**CO- AUTHORS:** Justin J.J. van der Hooft, Kyo Bin Kang, Asker Brejnrod, Louis-Félix Nothias, Ricardo R. da Silva, Pieter C. Dorrestein

Untargeted mass spectrometry-based metabolomics has become a method of choice to evaluate differences and similarities in metabolite profiles in a multitude of applications and sample types. Pairwise dissimilarities across samples are often summarized using a distance metric followed by multivariate statistics such as principal coordinate analysis (PCoA) for comparative analyses. However, conventional distance metrics ignore the chemical structural relatedness between molecules. Thus, whilst two samples could contain chemically similar yet distinct molecules, they would be regarded as very different. Here, we propose two chemically informed distance metrics inspired by the UniFrac metric, a metric used to compare microbial communities by incorporating phylogenetic relatedness between observed organisms. Similar to microbes, molecules are also related to each other as they share biosynthetic pathways and consequently similar chemical substructures. We implement two versions of calculating chemical relatedness, one based on shared chemical class annotations, and one based on shared substructure patterns. Using datasets from diverse sources, we show how these metrics can improve multivariate statistics and subsequent data interpretation. For example, in the cosmopolitan plant family Rhamnaceae we observe high chemical dissimilarity across two phylogenetic clades producing chemically very distantly related compounds, such as flavonoids and terpenoids, a trend that could not be revealed using conventional methods. We expect that the proposed chemically-informed distance metrics will be of great benefit to any metabolomics fields studying complex mixtures.
## SESSION 15: DATA ANALYSIS & STATISTICS

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

**15D 11:20 a.m. – 11:40 a.m.**

Identifying biologically relevant modules in metabolomics and lipidomics data with Differential Network-based Enrichment Analysis (DNEA)

**PRESENTING AUTHOR:** Alla Karnovsky, University of Michigan, United States

**CO-AUTHORS:** Gayatri Iyer, Janis Wigginton, William Duren, Marci Brandenburg, George Michailidis

Metabolomics and lipidomics datasets are becoming increasingly large and complex, requiring powerful statistical and bioinformatics tools. A well-established approach to linking alterations in metabolite levels to specific biological processes is to map experimentally measured metabolites to known biochemical pathways and to identify the pathways that are significantly enriched with those. However, traditional enrichment analysis techniques have limited utility for the analysis of untargeted metabolomics and lipidomics data. We developed an alternative approach, which relies on extracting meaningful associations between metabolites/lipids directly from the experimental data. Our Differential Network Enrichment Analysis method (DNEA) uses joint structural sparsity estimation to build partial correlation networks from the data, performs consensus clustering to identify highly connected subnetworks, and uses Network-based Gene Set Analysis (NetGSA) to identify the differentially enriched subnetworks. We have extended the method, improving its versatility in situations such as unbalanced designs and low sample size relative to number of features. The program is implemented in Java as a client-server application with a GUI. The input to the program is a comma-separated file with metabolite measurements and the information about experimental groups. The output contains the list of enriched subnetworks ranked by significance and can be viewed as an HTML file, or exported into a network visualization software (e.g. Cytoscape). We tested DNEA in a number of publicly available metabolomics and lipidomics datasets from a variety of diseases and will demonstrate that DNEA can identify alterations in both network structure and expression levels of interacting biomolecules that impact disease phenotypes.

**15E 11:40 a.m. – 11:55 a.m.**

Signature Mapping (SigMa): A new automatic tool for rapid processing of complex urine NMR spectra

**PRESENTING AUTHOR:** Bekzod Khakimov, University of Copenhagen, Denmark

**CO-AUTHORS:** Bekzod Khakimov*, Nabiollah Mobaraki, Alessia Trimigno, Violetta Aru, Søren Balling Engelsen

1D 1H NMR analysis of urine generates rich but complex data. Retrieving metabolite information from complex NMR spectra is a main bottleneck due to signal overlapping and chemical shift changes. This study illustrates a new method; Signature Mapping (SigMa), for identification and quantification of signature signals (SS) and allows rapid conversion of complex human urine NMR spectra into an informative metabolite table. SigMa compiles advanced multivariate data analysis algorithms for NMR data alignment, baseline correction, peak finding and peak deconvolution into user friendly software. The method utilizes an experimental data on unique chemical shift ranges of 166 SS representing 100 urine metabolites, enabling unambiguous metabolite identification in thousands of spectra simultaneously. In addition, an automatic peak finding option of SigMa allows detection of spectral regions (SR) of any unknown spin systems, and complex regions representing overlapped signals of several metabolites. This makes SigMa suitable for targeted and untargeted metabolomics. SigMa applies an individual MCR modelling for relative quantification of each SS, while complex SR are quantified using a binning approach. The performance of SigMa was evaluated using three NMR datasets and proved to be more efficient than the most often used binning approach. Processing of 40 spectra measured on urine of seven healthy individuals using SigMa showed an increase of the between individual variation while decreasing the within individual variation. SigMa also depicted the highest quantification accuracy and reproducibility of urine metabolites in large datasets containing several thousands of human urine NMR spectra.
## SESSION 16: SINGLE CELL

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

**16A Session Keynote**
**10:15 a.m. – 10:45 a.m.**

Spatial metabolomics in tissues and single cells

**PRESENTING AUTHOR:** Theodore Alexandrov, EMBL, Germany

Metabolites, lipids, and other small molecules exhibit complex and cell-specific spatial localization in tissues supporting tissue homeostasis in health and metabolism reprogramming in disease. Our team develops novel tools for spatial metabolomics to detect and interpret the roles of these molecules in tissues and single cells. I will present a recently developed cloud platform METASPACE for spatial metabolomics in tissues which provides a comprehensive community-populated resource for metabolism research and for spatial systems biology. I will also present a spatial single-cell metabolomics method which, by correlative in situ imaging of cell monolayers, provides for each cell its metabolic profile and assesses its optical, morphological, and fluorescent phenotype. These tools open novel avenues for understanding metabolism in tissues and on the single-cell level.

**16B 10:45 a.m. – 11:05 a.m.**

Comprehensive Single Cell Multi Omics

**PRESENTING AUTHOR:** Christian Berchtold, Institute for Chemistry and Bioinformatics, Switzerland

**CO-AUTHORS:** Luca Rima, Stefan Arnold, Götz Schlotterbeck, Thomas Braun

The comprehensive combination of various analytical methods is a key feature for the future of biochemical investigations. Analyzing metabolites and proteins from the same sample, especially from the same cell, might be extremely supportive of gaining a fundamental understanding of cellular processes. A recently developed single-cell picker [1, 2] allows the sampling and lysis of individual cells. This enables the picking of cells according to phenotypical appearance. In this proof of concept study, we used this system to deposit the lysate from one or a few cells on dedicated slides. Using a modified CAMAG TLC-MS interface, the cell lysate was transferred into a standard LC-MS system including chromatographic separation. Highly abundant metabolites such as glutamine or glutamic acid were detected to a level of single cells. Proteins remained on the microscope slide and were detected by antibody staining after LC-MS analysis of the same cell. Finally, we could demonstrate a strategy to combine several techniques to detect metabolites and proteins from the same sample. However, the concept needs further optimization according to sensitivity, robustness, and speed for quantitative high-throughput measurements.

**16C 11:05 a.m. – 11:20 a.m.**

Quantifying heterogeneity in drug uptake, metabolism and effect using Raman spectroscopy and mass spectrometry on the single-cell level

**PRESENTING AUTHOR:** Ahmed Ali, Leiden University, The Netherlands | RIKEN, Japan, Japan

**CO-AUTHORS:** Yasmine Abouleila, Arno Germond, Mashaghi Tabari A, Thomas Hankemeier, Toshio Yanagida

In the drug discovery field, monitoring drug uptake, its metabolism and response on the single-cell level has been extremely challenging. In this study, we show the potential of label-free single-cell analysis in drug discovery by utilizing a bi-modal analytical platform that combines Raman spectroscopy and mass spectrometry (MS). Raman spectroscopy was used to monitor the effect of tamoxifen on single-cells treated with the drug in a high-throughput and non-invasive manner. The same cells were then sampled and analyzed by mass spectrometry, in which, the concentration of the drug and its metabolite 4-hydroxy tamoxifen (4-OHT) were measured in each cell. MS measurements revealed a strong heterogeneity in the drug concentration across single-cells. This phenomenon was even more pronounced in the case of its metabolite. Despite this, the concentrations of both the drug and its metabolite correlated positively, confirming the drug uptake and drug metabolism in the cell. Moreover, Raman could not only detect the cellular metabolic changes caused by the drug, but also predict whether a given cell is affected by the drug or not with 97% accuracy using a PLS-DA model. Finally, we succeeded in correlating the drug effect (measured by Raman spectroscopy) and the concentration of the drug or its metabolite (measured by MS) on the single-cell level. The Raman peaks used in this correlation could be used as biomarkers for the drug activity in the cell. In conclusion, our integrated Raman-MS platform proved to be a powerful tool that could aid future drug discovery efforts.
SESSION 16: SINGLE CELL

Wednesday, June 26

10:15 a.m. – 12:00 p.m.

16D 4:35 p.m. – 4:50 p.m.

Relationships between cellular metabolite and drug uptake and transporter expression profiles

PRESENTING AUTHOR: Marina Muelas, University of Liverpool, United Kingdom

CO-AUTHORS: Farah Mughal, Steve O'Hagan, Philip J. Day, Douglas B. Kell

It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. We present untargeted metabolomics time course analyses of the uptake and secretion of metabolites in human serum by a number of human cell lines. We show how distinct the metabolic footprints of different cell lines are from one another. We subsequently compare the transcriptomes (by RT-qPCR or using published RNA-Seq datasets) and published proteomic expression profiles of cell lines with the uptake of substances (by LC-MS/MS). By employing mathematical methods we infer the variations in SLC and ABC membrane transporter expression to best explain the variation in metabolite uptake and the relationship to the function of the tissues from which these cell lines are derived. This analyses will also admit the production of quantitative structure-activity relationship (QSAR) models that will further aid in the prediction (and testing) of transporters responsible for the uptake and secretion a number of pharmaceutical drugs. We also utilise the Gini index (coefficient) as a novel means of characterising the variation in individual transporter distributions between these cell lines. Our results show that many transporters exhibit extremely high Gini coefficients, indicating a much higher degree of specialisation than is usually assumed.

16E 11:40 a.m. – 11:55 a.m.

A Single-Cell Look at Biological Nitrogen Fixation: Direct Determination of Metabolite Formulas from Isotopic Fine Structures in Heterogeneous Cell Populations

PRESENTING AUTHOR: Tina Tran, The George Washington University, United States

CO-AUTHORS: Laith Z. Samarah, Rikkita Khattar, Sylwia A. Stopka, Christine A. Brantner, Paola Parlanti, Dušan Veličković, Jared B. Shaw, Beverly J. Agtuca, David W. Koppenaal, Christopher R. Anderton, Nikola Tolić, Gary Stacey, Ljiljana Paša-Tolić, Akos Vertes

Many legumes, like soybean, do not depend on synthetic fertilizers for nitrogen uptake. Instead, they create mutualistic relationships with bacteria capable of biological nitrogen fixation. Specialized plant organs are created from these interactions (i.e., root nodules), were bacteria infected plant cells are intimately mixed with uninfected cells. Exploring the metabolic profiles for these heterogeneous systems requires single-cell analysis techniques. Here, we present the combination of fiber-based laser ablation electrospray ionization (f-LAESI), with 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (21T FTICR-MS) for the direct analysis of single cells. Direct determination of metabolite formulas from isotopic fine structures (IFS) was attained from our single cell data. Over 100 compounds were tentatively assigned based on ultra-high mass accuracy, and elemental formulas for 47 of these were verified by IFS. Comparing the calculated IFS patterns for possible metabolite ions with the experimental data enabled the identification of close-to-isobaric compounds with different elemental compositions. Infected cells showed higher abundances for nitrogen-containing compounds and lipids compared to uninfected cells. Compositional heterogeneity among infected cells was observed by determining the metabolic noise (η²). Within a population of 124 infected cells, several primary metabolites essential for cellular growth and maintenance, and plant-specific secondary metabolites, exhibited relatively low η² (≤ 0.50). Conversely, lipids associated with plant and bacteroid membranes showed greater noise levels (η² ≥ 0.50), indicating a larger variance in their amounts. Metabolic noise measurements can provide insight on how tightly the metabolite levels are regulated, and how that relates to their biological function.
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## SESSION 17: AGEING AND DISEASE

Wednesday, June 26

1:30 p.m. – 3:15 p.m.

**17A Session Keynote**

1:30 p.m. – 2:00 p.m.

Multiomic data integration using machine learning and data-driven inverse metabolic modelling -
from diabetes to immune system modulation

**PRESENTING AUTHOR:** Wolfram Weckwerth, University of Vienna, Austria

An integrated proteomics/phosphoproteomics/metabolomics platform is presented. This platform serves also as the basis for the Vienna Metabolomics Center (VIME) (https://metabolomics.univie.ac.at/). The data mining strategy is based on the integrative toolbox COVAIN (COVAriance INverse) for data processing, integration, multivariate statistical analysis, machine learning and data-drive inverse metabolic modelling [1]. We applied this integrative workflow to gestational diabetes mellitus (GDM) [2] and to the analysis of mTOR-dependent immune system modulation [3]. Activation of immune cells is accompanied by a metabolic reconfiguration of their cellular energy metabolism including shifts in glycolysis and mitochondrial respiration that critically regulate functional effector responses. However, while current mass spectrometry strategies identify overall or flux-dependent metabolite profiles of cells or tissues, they fail to comprehensively identify the checkpoint nodes and enzymes that are responsible for different metabolic outputs. Here, we demonstrate that a data-driven inverse modelling approach from mass spectrometry metabolomics data can be used to identify causal biochemical nodes that influence overall metabolic profiles and reactions. Using multiomics metabolomics, proteomics, phosphoproteomics, transcriptomics analysis as well as enzymatic activity measurements we identified metabolic signatures of energy signaling and macrophage differentiation. The presented concept of multiomics analysis, machine learning and data-driven inverse modelling allows for systematic integration of genome-scale metabolic reconstruction, prediction and analysis of causal biochemical regulation in microbes, plants, animals, human and their interactions.

**17B 2:00 p.m. – 2:20 p.m.**

Mass spectrometric analysis of sebum contents for classification of Parkinson's disease

**PRESENTING AUTHOR:** Drupad Trivedi, University of Manchester, United Kingdom

**CO-AUTHORS:** Eleanor Sinclair, Depanjan Sarkar, Joy Milne, Monty Silverdale, Tilo Kunath, Roy Goodacre, Perdita Barran

Parkinson's disease is a progressive neurodegenerative disorder that affects ageing population and around 1 in every 350 adults is diagnosed in the UK with Parkinson's disease (PD). Currently there are no definite diagnostic tests that can detect PD at early stages. This research is an effort towards early diagnosis of PD using biomarkers on skin, which anecdotally is known to smell differently in PD than in control. Sebum swabs from upper back of 276 participants consisting of PD on medication (n=139), drug naive PD (n=81) and control (n = 56) participants were collected across 28 different NHS sites in the UK. High-performance liquid chromatography was couple to time-of-flight mass spectrometry for separation and detection of metabolites extracted from sebum samples. After pre-processing, the resultant 700 features were carried forward for statistical analyses. Machine learning techniques consisting of support vector machines (SVM) and random forests (RF) were used for classification of data into two groups by using two subsets: (i) control and drug naive (ii) control and PD on medication. The metabolome measured was able to achieve classification accuracy by SVM and RF. Pathway enrichment analysis using all detected and identified metabolites showed significant enrichment (p < 0.05) of primary bile acid biosynthesis, sphingolipid metabolism, D-Arginine and D-ornithine metabolism as well as arginine and proline metabolism. Our results indicate a significant shift of endogenous metabolome in PD, causing an enhanced effect reflected on lipid-like molecules that are captured on the skin sebum.

**17C 2:20 p.m. – 2:35 p.m.**

Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism

**PRESENTING AUTHOR:** Ronan Fleming, Leiden University, Netherlands

**CO-AUTHORS:** Jennifer Modamio, Can Gulersonmez, Fatima L. Monteiro, Diana El Assal, Edinson L. Moreno, Miguel A.P. Oliveira, Cornelius C.W. Willacey, Laurent Heirendt, Zhi Zhang, Ben Cousins, Hulda S. Haraldsdóttir, Susanne Zach, Averina Nicolae, Siham Hachi, Amy Harms, Santosh Vempala, Bastian Hengerer, Jens C. Schwamborn, Ines Thiele, Enrico Glaab, Thomas Hankemeier, Ronan M.T. Fleming, German Andres Preciat Gonzalez

Patient-derived cellular models are a powerful approach to study human disease, especially neurodegenerative diseases, such as Parkinson's Disease, where affected primary neurons, e.g., substantia nigra dopaminergic neurons, are almost inaccessible. Induced pluripotent stem cell-derived models of midbrain-specific dopaminergic neurons are increasingly used to investigate Parkinson's Disease. Starting with the comprehensive generic reconstruction of human metabolism, Recon3D, we generated the first constraint-based, genome-scale, in silico model of human dopaminergic neuronal metabolism (iNESC2DN). Transcriptomic data, obtained by RNA sequencing, and quantitative exometabolomic data, obtained by targeted mass spectrometry-based metabolomics were generated for in vitro neuroepithelial stem cell-derived cultures and supplemented by extensive manual curation of the literature on dopaminergic neurons. The predictions of the iNESC2DN model are consistent with neurobiochemical prior information and in concordance with measured fluxes of uptake and secretion of many extracellular metabolites by dopaminergic neurons in vitro. We leverage it to rank order the most important metabolite concentrations to quantify to maximally reduce the uncertainty associated with current predictions of normal dopaminergic neuronal metabolism in vitro, as well as optimally design experiments to measure metabolic perturbations associated with Parkinson's Disease. Finally, the iNESC2DN model provides a foundation for future targeted metabolomic and tracer-based metabolomic analyses of dopaminergic neurons. This illustrates the synergy between constraint-based computational modelling of metabolism and biology-driven quantitative bioanalytical chemistry.
## SESSION 17: AGEING AND DISEASE

Wednesday, June 26

1:30 p.m. – 3:15 p.m.

17D 2:35 p.m. – 2:55 p.m.

Interleaving metabolic effects of sleep and aging

**PRESENTING AUTHOR:** Arjun Sengupta, University of Pennsylvania, United States

**CO-AUTHORS:** Jennifer Choi Tudor, Joseph Baur, Ted Abel, Aalim Weljie

Decreased sleep is a hallmark of modern society. Environmental factors such as stress as well as biological factors such as aging contributes to decreased sleep quantity and quality. Sleep deprivation (SD) has been linked to life threatening complications including learning and memory related neurodegenerative diseases and cardiometabolic disorders. The role of metabolic disruption in physiological effects of SD is emerging, but not completely understood. The nature of the interaction of SD with aging phenotypes is also unclear. Here we used a mouse model of 5 h acute sleep deprivation in young adult (2-4 months) and aged (20-22 months) mice to capture metabolic shifts as function of SD. Remarkably, numerous metabolites were perturbed post SD in young (24, 61, 53 metabolites in hippocampi, liver, and plasma, respectively) and aged animals (17, 42, 38 metabolites). Young animals demonstrated greater metabolic susceptibility post acute SD than aged animals. Specific SD metabolites in young animals (5, 19, 15 metabolites in hippocampi, plasma and liver, respectively) recapitulated a portion of the aging signature, thus demonstrating how SD makes the 'young seem old'. Hepatic signatures of ketosis were common to both groups, with greater prominence in the young animals. Altered hepatic NAD metabolism and urea cycle was also common signature of SD. Choline and acetylcholine pool in the young animals was specifically depleted in hippocampi, potentially linking metabolism to SD induced alteration in memory consolidation. These results form a foundation for understanding the systemic metabolic effects of sleep deprivation and aging.

17E 2:55 p.m. – 3:10 p.m.

Analysis of changes in the eye lens and aqueous humor under cataract development using quantitative metabolomics

**PRESENTING AUTHOR:** Vadim Yanshole, ITC SB RAS / NSU, Russian Federation

**CO-AUTHORS:** Lyudmila Yanshole, Olga Snytnikova, Yuri Tsentalovich

**Introduction:** The most common vision impairment of older people is caused by the formation of cataract (clouding of the eye lens). The protection of the lens is almost solely provided by metabolites; most of them are synthesized in the lens epithelium or enter the lens through the epithelial layer from the surrounding aqueous humor (AH). Therefore, changes in the metabolome of the lens and AH may help to establish the molecular mechanisms of the cataract onset. **Methods:** Quantitative metabolomic profiles of eye tissue extracts (lens, AH) were obtained with the combination of three methods – high-frequency 1H NMR and ion-pairing HPLC with optical (LC-OD) and high-resolution ESI-q-TOF MS detection (LC-MS) methods. **Results:** The concentrations of more than 80 metabolites were determined for four groups of samples: lenses and AH from cataract patients and lenses and AH from human cadavers. **Conclusion:** Our metabolomic data confirm the hypothesis that although the age-related cataract usually manifests itself as the opacification of the lens nucleus, the initial site of the cataract onset might be the lens epithelial layer. The most important for the lens protection metabolites – antioxidants, UV filters, osmolytes – are synthesized in the lens epithelial cells. The reduced levels of these metabolites were found in the cataractous lenses; that indicates that the cataract development may originate from the dysfunction of the lens epithelial cells. The increase in the concentrations in non-cataractous post-mortem tissues for other metabolites corresponds to the post-mortem processes. **Acknowledgements:** Supported by the RFBR (projects 18-33-20097, 18-34-00137, 17-03-00656).