**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

PLANT, FOOD, ENVIRONMENTAL AND MICROBIAL

P-463

Metabolomics reveals key chemical responses and susceptibilities in commercial forest species to infestation by pest insects introduced in Chile

PRESENTING AUTHOR: Andy Pérez, Department of Instrumental Analysis, Faculty of Pharmacy, University of Concepcion, Chile

CO-AUTHORS: Valentina Campos, Sebastián Riquelme, Claudia Mardones, Rosa Alzamora, Rafael Rubilar

Forest industry is of great importance for the Chilean economy, with annual exports over US$ 5000 million since 2011 and planted surface around 2.4 million ha. Pinus radiata and Eucalyptus (mainly E. globulus and E. nitens) are the preferred species by forest companies, covering the largest plantation area in Chile. However, P. radiata as Eucalyptus species currently face biotic stressors that significantly threaten profits of this economic activity. One of these is the woodwasp Sirex noctilio F., considered as the most damaging invasive pest in Southern Hemisphere Pinus plantations, and the eucalyptus defoliator, Gonipterus platensis M. These are insects under official control in Chile declared by the Chilean Livestock and Agricultural Service (SAG). Here, we describe the LC-MS based metabolomics analysis on P. radiata wood, bark and needles from resistance and susceptible trees, aimed to distinguish defensive or resistance mechanisms against S. noctilio. Among revealed results, down-regulation for biosynthesis of taxifolin (dihydroquercetin) and derived of citric acid in the bark of infested trees, seems to be the most significant. Similarly, metabolites responsible for the different susceptibility between E. globulus and E. nitens to the defoliator G. platensis were distinguished. A combination of stibenoids and gallate derived metabolites present in the leaves of the less susceptible species was suggested as potentially exerting a feeding deterrent effect, opening a new possibility for the environmental friendly control of this pest. These results may contribute to uncover genetic basis for resistance mechanisms that could be harnessed in a resistance breeding program of such species.

P-464*

Metabolomics profiling of meconium using LC/HRMS

PRESENTING AUTHOR: Nihel BEKHTI, UMR CEA-INRA, France

CO-AUTHORS: Florence Castelli, Estelle Paris, Blanche Guillon, Christophe Junot, François Fenaille, Karine Adel-Patient

Some factors of exposure in pregnant women such as diet, living area, drugs are simultaneously transferred to the fetus and may affect its maturation and later, the child health development. These in utero transmissions could be observed through meconium analysis. Actually, meconium is the earliest newborn stools, it begins forming from the second month of pregnancy and accumulate until birth. This matrix has the advantage to integrate a large period of exposure. In the literature, meconium composition has been mostly studied using targeted methods focusing on a given family of compounds (ex pesticides, illicit drugs or commonly prescribed medicines). In this work, we aim to provide an individual mapping of the meconium composition, using non-targeted metabolomics analysis. We first developed and optimized the analysis workflow thanks to two meconium samples collected days 1 and 2 post-partum. High detection sensitivity and method robustness have been reached thanks to thoroughly optimized sample preparation, involving a pre-step of freeze-drying followed by a solvent-assisted metabolic extraction. This step is monitored by a liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) analysis, using a Q-Exactive instrument. Raw data were further processed using the Workflow4Metabolomics infrastructure. We successfully annotated up to 200 metabolites with a high level of identification, which represents the most exhaustive description of human meconium to date. This workflow is now being applied to a meconium cohort collected from 11 children, from birth to days 2-3 post-partum. This analysis will allow to evaluate the meconium composition evolution, and to assess inter-individual variability.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-465 High Throughput Polar Metabolite Analyte Metabolomic Phenotyping of Large Cohort Epidemiological Studies Using Ion Mobility Enabled LC/MS

PRESENTING AUTHOR: Robert Plumb, Imperial College London, United Kingdom

CO- AUTHORS: Robert Plumb, Adam King

As global life-styles change we are seeing increasing cases of obesity, diabetes, and mental health issues. This not only affects a person's quality of life but also places increased strain on the health-care systems to provide the right treatment whilst managing costs closely. Metabolic Profiling of large cohorts offers a valuable and unique insight into the underlying biochemistry of diseases as well as the patient's individual biochemistry "phenotype", diet, health status, age and stress. To deliver this information the analytical data generated in processed via a variety of chemometric modelling and analysis methodologies to deliver the relevant biochemical information. These chemometric platforms employed vary from simple multivariant analysis to highly complex model based analysis and is presented in a format ready for interpretation by medics. On of the major challenges is the identification of putitive biomarkers. Recent advancements in ion mobility MS and in-silico CCS prediction offers the opportunity to revolutionize biomarker identification both in terms of speed and accuracy. In this presentation we will discuss the development of rapid exploratory LC/IMS/MS analytical platforms as well as a detailed discussion on the workflow, validation, reporting and decision-making process. The presentation will cover the development and validation of the "discovery" screening methods for polar, non-polar metabolites using LC/MS methodology, as well as describe the use of Ion Mobility Mass Spectrometry to enhance data quality.

P-466 Using the Retention Time Prediction Method for Choline Plasmalogens Identification by Liquid Chromatography-Mass Spectrometry

PRESENTING AUTHOR: Dave Lee, National Taiwan University, Taiwan

CO- AUTHORS: Sung-Chun Tang, Ching-Hua Kuo

Choline plasmalogens (P-PC) are special subclass of phosphatidylcholine (PC) which compose of a vinyl-ester bond at the sn-1 position of the glycerol backbone. Abnormal P-PC levels have been found associated with many diseases, such as neurological disorders and oxidative stress. Previous MS methods showed limitations on distinguishing P-PC and alkyl-PC (O-PC), as these PC subclasses share the same molecular formula and MS2 fragment ions. Herein, we developed a UHPLC-QTOF-MS method combined with the dynamic MRM (dMRM) method by UHPLC-QqQ-MS/ MS for measuring PC molecule species. To identify P-PC and O-PC, we developed a retention time (RT) prediction method by establishing the linear correlation between prediction parameters considering the XlogP values and RT. The RT prediction method was applied to predict 205 of the plasma PCs, including 33 P-PC and 51 O-PC, and over 60% of the identified PCs showed less than 0.3 minutes errors compared to experimental RT values confirmed by the fragmentation information. This proposed method was applied to study oxygen-glucose deprived (OGD) induced neuron injury. The results revealed PC(O-16:1/20:3) and PC(P-16:0/20:3) with identical formula, but different in the sn-1 structure were regulated in a distinct direction after OGD treatment. With the power of the RT prediction method to provide more structure information for PCs especially on P-PC and O-PC, the biological functions of these PCs could be uncovered. It is anticipated to apply this RT prediction method to explain unknown physiological mechanisms involving PC dysregulation and discover PCs as disease markers.

P-468 MetaboShiny - identify each mass, en masse

PRESENTING AUTHOR: Joanna Wolthuis, UMC Utrecht, Netherlands

Analyzing untargeted mass spectrometry data involves investigating hundreds to thousands of metabolite peaks. This is a major challenge in direct infusion, where samples are introduced into the spectrometer with no column beforehand. Various analytical tools have been created to stand up to this challenge, but are often limited in identifying compounds. To analyze untargeted metabolomics data, one needs to match molecular candidates to many mass/change (m/z) values. To streamline this process we are developing the R tool MetaboShiny. It simplifies discovering m/z of interest from a pool of m/z values in an attractive and interactive interface. MetaboShiny features interactive plots and tables, with a tight integration of a fast SQLITE database backend. Available databases (16 total) include the HMDB, ChEBI, and KEGG. MetaboShiny generates adduct and isotope variants based on user preference. Altogether, the databases encompass over 100 million m/z values. MetaboShiny does normalization and statistics with the MetaboAnalystR package, such as T-tests, PCA, PLS-DA and fold-change analysis, to heatmaps and over 40 machine learning methods. Venn diagrams summarize results, identifying metabolites that are significant in multiple analyses. Users click through results and link significant m/z values to databases. MetaboShiny displays compound descriptors and PubMed abstract search results to enhance exploration. If no matches are found, MetaboShiny predicts a molecular formula from the m/z value, and searches PubChem for known compounds matching the predicted formula. We are implementing network/pathway-based and cheminformatics-based compound identification and neural network adduct formation prediction.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-469 Using Multi-omics Approaches to Maximize Beef Production

PRESENTING AUTHOR: Aidin Foroutan, Department of Agricultural Food and Nutritional Science, University of Alberta, Canada

CO-AUTHORS: Aidin Foroutan, Carolyn Fitzsimmons, Leluo Guan, Rupasri Mandal, David S. Wishart

Over 40% of the cost of beef production is impacted by feed efficiency. Reproductive performance of beef cattle also has a significant impact on the cost of production. Hence, decreasing the cost of production along with improving carcass yield and quality will benefit the beef industry. Maximizing production efficiency of beef cattle requires not only genetic selection to maximize feeding efficiency (i.e. residual feed intake - RFI) but also adequate nutrition throughout all stages of growth and development - even during gestation. Nutrient restriction during gestation has been shown to negatively affect post-natal growth and development as well as fertility of the offspring. This, when combined with RFI, may affect progeny traits. Therefore, we decided to conduct a comprehensive multi-omics study to investigate the impacts of pre-natal nutrition and RFI selection on the metabolism and fertility parameters in Angus bulls. Four different tissues (Longissimus thoracis muscle, semimembranosus muscle, liver, testis) and three biofluids (serum, semen, rumen content) were analyzed. Epigenetics, transcriptomics, and metabolomics experiments identified 891, 4, and 47 candidate biomarkers, respectively, that were associated with RFI as well as pathways such as cellular growth and proliferation, embryonic development, organ morphology, and connective tissue development. These markers have been integrated through a variety of novel bioinformatic techniques to reveal key biological pathways affected by RFI selection and pre-natal nutrition in Angus bulls. If these gene/metabolite biomarkers and corresponding pathways are validated in a larger animal population, they could potentially be used in breeding programs to select for superior animals.

P-470

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate compounds and genes

PRESENTING AUTHOR: Sven Bergmann, University of Lausanne, Switzerland

CO-AUTHORS: Bita Khalili, Mattia Tomasoni, Mirjam Mattei, Roger Mallol Parera, Reyhan Sonmez, Daniel Krefl, Rico Rueedi

Applying genome-wide association studies (GWAS) to metabolomics associates metabolites with genotypic variants. We observed that the effect of a genetic variant on the concentration of a metabolite often translates into associations with all or many features of the metabolite NMR spectrum. The set of association scores with all measured features provides a pseudospectrum across the full range of ppm covered by 1H NMR spectra. The challenge is then to identify the metabolite underlying the most significant associations. To this end we developed the analysis tool metabomatching, which takes as input a pseudospectrum and a collection of reference spectra for individual metabolites such as found in HMDB. We first demonstrate that metabomatching works well to prioritize the most likely metabolite candidates for pseudospectra derived from metabolic feature association with genotypes (Rueedi et al. PLoS Genetics 2014 and PLoS Computational Biology, 2017). Next we show that the metabomatching methodology can also be used for identifying metabolites that vary across large collection of samples without the need for any external variables associated with this variation. We investigate three methods that identify co-varying spectral features within large-scale NMR data. Specifically, we compare Principal Component Analysis, the Iterative Signature Algorithm and averaging correlation profiles inspired by the STOCSY approach. For each method, we devise a principled way for processing their output into pseudospectra. We then evaluate systematically in which cases metabomatching provides strong evidence for matching metabolites, and assess to what extent the three methods provide consistent or complementary output.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-472 Integration of metabolomics and transcriptomics to assess the effects of environmental pollutants

PRESENTING AUTHOR: Joaquim Jaumot, IDAEA-CSIC, Spain

CO-AUTHORS: Elena Ortiz

In the framework of the omics sciences, there is a current trend towards the integration of information coming from different omics levels. Advanced processing tools and novel data fusion strategies have been developed to gather information allowing a better understanding of the underlying biological processes. However, multiomics data coming from multiple sources is heterogeneous, and the mining of information using these data fusion approaches remains a challenge. Different strategies have been proposed to perform this data fusion. In general, three approaches can be identified depending on the processing phase at which data is fused: 1) low-level fusion combines raw data to generate a new fused data to be analyzed, 2) mid-level fusion involves joining subsets of relevant variables from different datasets, and 3) high-level fusion just merges results from the independent analysis for a combined interpretation. In this work, the effects of bisphenol A (BPA) in regulatory pathways of zebrafish embryos is evaluated by using a non-targeted LC-MS based metabolomics analysis and a high-throughput RNA sequencing (RNA-Seq). Independent metabolomics and transcriptomics results revealed a similar set of altered pathways despite the different number of detected potential biomarkers. Then, different integration approaches have been employed to deepen knowledge about the affected metabolomic pathways (mainly signaling pathways) in an attempt to decipher possible divergences in the information provided by the two omic levels.

P-473 The Power of MS/MSALL Acquisition for High-Throughput Metabolomics Studies

PRESENTING AUTHOR: Mariateresa Maldini, SCIEX, Italy

CO-AUTHORS: Eva Duchoslav, Cyrus Papan, Khatereh Motamedchaboki

Flow Injection Analysis using an electrospray source gives a simple technique maximizing analytical throughput and yet allowing metabolite separation by m/z in complex samples by using a data independent MS/MSALL acquisition approach. This technique combined with tailored data processing methods built from the accurate mass metabolite spectral libraries allows fast qualitative and quantitative sample profiling. The MS/MSALL acquisition, a sequential precursor ion fragmentation acquisition technique, with metabolome-tailored precursor mass defect captured high resolution MS and MS/MS for every mass. MultiQuant processing method was constructed using the metabolite experimental MS/MS spectra from the Accurate Mass Metabolite Spectral Library (SCIEX). To minimize the effect of mixed MS/MS spectra, MS/MS responses for series of up to 5 unique fragments typically including unfragmented molecular ion, combined with a response in TOF MS were extracted for each target metabolite. Calibration curves were constructed for set of 35 representative compounds. For example, in the positive ion mode the % CV for Tryptamine replicate measurements was better than 15% and the linear dynamic range was greater than 200 (coefficient of determination r2 > 0.95) based on groups of 3 independent fragments. More than 70% of spiked compounds in the standard mix and more than 50% of spiked compounds in the urine matrix were identified, and supplement the identifications with additional compound classifications. These preliminary data complemented with statistical analysis illustrate a potential of the MS/MSALL approach for high-throughput metabolomics studies where a fast, semi-quantitative profiling approach is to be combined with a confidence in compound identity and a potential for identification of unexpected secondary metabolites.

P-474 Novel visual analytics platforms for multi-omics integration and network visualization

PRESENTING AUTHOR: Guangyan Zhou, McGill University, Canada

CO-AUTHORS: Huiting Ou, Guangyan Zhou, Jianguo Xia

Multi-omics integration is emerging as a powerful strategy to simultaneously analyze and interpret multiple datasets from different omics technologies. It aims at gaining systems-level understanding of different disease states and other experimental conditions by revealing novel mechanistic insights of molecular pathways involved. There is an urgent need for user-friendly bioinformatics tools dedicated for multi-omics network integration and visualization. To address these challenges, we are actively developing two web-based platforms - OmicsAnalyst and OmicsNet to support statistics-based integration and network-based integration, respectively. OmicsAnalyst aims at facilitating the access to multivariate methods including O2PLS, Procrustes analysis, multiblock PCA and PLS. In addition, it uses advanced visual analytics techniques including 3D scatter plot and interactive heatmap, to deconvolute the complex outputs of these methods. OmicsNet enables users to create and merge different types of biological networks and visualize using an innovative WebGL-based system in 3D space. Users can query known interactions between seven different types of elements including SNPs, genes/proteins, TFs, miRNAs, metabolites, mass peaks and phenotypes, within the context of current knowledge.
<figure><img src="image_78.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-475 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 Hällqvist, 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.

P-476 On-demand construction of deep reference libraries and how they improve compound ID

PRESENTING AUTHOR: Michal Raab, HighChem Ltd., Slovakia(Slovak Republic)

CO-AUTHORS: Jakub Mezey, Samuel Benkovič, Melissa Montoya, Tim Stratton, Robert Mistrík

Compound ID through library searching is arguably the leading application in LC/MS, yet it unconditionally relies on the quality and coverage of available spectral libraries. However, experimental diversity and hard-to-reproduce fragmentation drives the volume of the LC/MS libraries to grow disproportionately compared to GC/MS, posing a challenge for data processing, management and searching applications. Here we describe an integrated pipeline for high-throughput acquisition of reference compounds and semi-supervised curation utilizing dedicated software for automated instrument control and scalable modular microservices-based architecture for quality control, curation, annotation and continuous delivery of library records. This system has been employed to handle the reference standards in the mzCloud library, allowing to expand the coverage of unique compounds by 7.500 in two months. HRAM-MS/MS data was acquired for reference standards by nanoinfusion using an automated tool that acquired fragmentation data at increasing NCE (Normalized Collision Energy) levels of 10-200 in increments of 10. Intelligent controlling software was deployed for batch-like data dependent MSn acquisition of reference compounds. A collection of data processing services has been built using a docker-based microservice platform including feature detection/extraction, noise filtering, annotation, fragment prediction and mass error correction. Additionally, separate quality control services are employed for real-time data inspection. A web-based frontend is used to monitor and control the processing queue, applying a sequence of the processing services from one of the pre-defined profiles individually for each spectral reference dataset, and to provide feedback through quality control indicators.

P-477 Integrating Polygenic Risk Scores and Metabolite Quantitative Trait Loci to Infer Dysregulated Mechanisms in Rheumatoid Arthritis Subtypes in Women

PRESENTING AUTHOR: Su Chu, Brigham and Women's Hospital and Harvard Medical School, United States

CO-AUTHORS: Jing Cui, Jeffrey A. Sparks, Bing Lu, Clary Clish, Jessica Lasky-Su, Elizabeth Karlson, Karen Costenbader

Background: Rheumatoid arthritis polygenic risk scores (RA-PRS) improve RA risk prediction, but the added predictive value over clinical variables is modest. Several human leucocyte antigen (HLA) haplotypes are strongly related to seropositive RA, a severe subtype. Recently, we identified several metabolites associated with RA risk. Integrating RA-PRS and metabolomics may provide insight into RA pathogenesis. Methods: Plasma samples from 254 pre-RA cases in the Nurses' Health Studies were analyzed using untargeted liquid-chromatography mass-spectrometry (360 unique metabolites after quality control). PRS comprised 1) non-HLA 93 single nucleotide variants (PRS93), and 2) HLA haplotypes (HLA-PRS) previously associated with RA risk. Using ordinary least squares, associations between both PRS93 and HLA-PRS and individual metabolites were tested to identify RA-related metabolite quantitative trait loci (metaboQTLs). Interaction models assessed effect modification by RA serostatus. Results: After multiple comparison adjustment using the pooled local index of significance, no PRS93 metaboQTLs were found; however, 27 RA HLA-PRS metaboQTLs were identified, including those involved in branched chain amino acid and polyamine metabolism: C2 carnitine (β=0.120; padj=0.018), C3:carnitine (β=0.122; padj=0.007), C5:1 carnitine (β=0.120; padj=0.016), 4-acetamidobutanoate (β=0.134; padj=0.027), and N-acetylputrescine (β=0.119; padj=0.011). Two findings suggesting effect modification of metabolite levels by seropositivity were observed in RA- HLA metaboQTLs: C5:1 carnitine (p-interaction=0.068) and arecaidine (p-interaction=0.077). Conclusions: We identified several metaboQTLs of HLA haplotypes in pre-RA, but none for PRS93. Evidence to support effect modification of the HLA-PRS and metabolite association by RA serostatus was identified for one acylcarnitine and one alkaloid derivative. However, further validation is required.
<figure><img src="image_79.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-478

Food Metabolome Repository: A database for cross-sample specificity-based peak prioritization in untargeted metabolomics

PRESENTING AUTHOR: Nozomu Sakurai, National Institute of Genetics, Japan

CO-AUTHORS: Kunihiro Suda

The substantial issue in mass spectrometry (MS)-based untargeted metabolomics is a lack of information by which we can prioritize the candidate peaks for further detailed investigation, rather than the accuracy of metabolite prediction/annotation tools. The major bottleneck in untargeted metabolomics is an annotation of compound peaks largely due to limited availability of authentic standards for identification. A lot of computational tools and databases have been developed so far for prediction of metabolites using accurate mass value, pattern of isotopic ions, and MS/MS spectra. However, the results from most of them are the candidate lists with some evaluation scores and the candidate with the highest score is not always true. Therefore, we have to investigate the validity of the prediction results one-by-one based on the other information such as literature knowledge of the occurrence of the candidate compound in nature. However, the occurrence of unknown peaks is not available because of the lack of databases by which peaks from a wide variety of samples can be compared and searched. To tackle this, we developed Food Metabolome Repository (http://metabolites.in/foods). A large variety of compounds is expected in foods because a lot of biological species are included and they can be processed by heating and fermenting. Untargeted metabolome data obtained from 222 foods using reversed-phase liquid chromatography-high resolution MS with electrospray ionization (ESI) positive and negative modes are available. The searching and acquiring functions can be integrated into other computational tools for automatic and large-scale processing via application programming interfaces (APIs).

P-479

Toward a high-quality non-targeted analysis of large metabolomics data

PRESENTING AUTHOR: Masahiro Sugimoto, Keio University, Japan

CO-AUTHORS: Masahiro Sugimoto, Rintaro Saito, Tomoyoshi Soga, Masaru Tomita, Toru Takebayashi

Toward a high-quality non-targeted analysis of large metabolomics data - Masahiro Sugimoto, Rintaro Saito, Tomoyoshi Soga, Masaru Tomita, Toru Takebayashi. Metabolomics cohort studies are becoming more widespread. Data processing and correcting data to remove unexpected biases are important for quality controls (QCs). We have conducted the Tsuruoka Metabolomics Cohort Study enrolling 11,002 community-dwelling adults in Japan. Capillary electrophoresis-mass spectrometry (CE-MS) has been used for the identification and quantification of hydrophilic metabolites in human plasma. Here we developed processing tools to analyze these data. First, we have developed MasterHands, a Java-based GUI software which is capable of versatile data analysis of CE-MS data. We upgraded this software to have an application programmable interface for Python language. We prepared two types of programs, one for non-targeted and the other for targeted analyses. Firstly, we conducted a non-targeted analysis of QC sample and analysts conducted curation of analyzed data in order to optimize analytical parameters for individual peaks. The characteristics of curated peaks were used for training data for the subsequent analysis. The other samples were analyzed using targeted analysis program and the curation of each peak integration are automatically adjusted following to the training data. These two-step analyses enable high-quality data processing even in the large scale datasets. Here, we show the algorithm and the performance of the processed data.

P-480*

A genome-wide association study of circulating plasma metabolite levels identifies differences by sex and suggests that metabolites represent polygenic traits

PRESENTING AUTHOR: Oana Zeleznik, Harvard Medical School and Brigham and Women's Hospital, United States

CO-AUTHORS: Oana A. Zeleznik, Xia Jiang, Rachel Kelly, Marta Guasch, Constance Turman, Jessica Lasky-Su, Clary B. Clish, A. Heather Eliassen, Peter Kraft

**Background:** Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with disease risk but the mechanisms driving these associations remain largely unknown. Understanding the effects of SNPs on the metabolome of healthy individuals may elucidate the mechanisms linking SNPs to disease. We performed a GWAS of circulating plasma metabolomics (metaboQTLs) in 4390 participants of Nurses' Health Studies (NHS), NHS II and Health Professionals Follow-up Study (HPFS). We also performed a genome-wide screen for metaboQTLs showing differences by sex. **Methods:** Linear regression of metabolite levels and genotype, adjusted for age, cohort, fasting status and the first four genotype principal components was used to identify metaboQTLs. Cochran's Q-test was used to identify metaboQTLs showing heterogeneity by sex. **Results:** Seventy-four metabolites were associated with 93 independent SNPs (p<2.3E-10). Individual SNPs were associated with 1-21 metabolites and individual metabolites with 1-6 SNPs. The top identified metaboQTL (p=5.37E-248) was between a SNP next to carbamoyl-phosphate synthase 1 (CPS1) and glycine. CPS1 encodes an enzyme known to directly modify glycine. Fifteen of the 93 independent SNPs represent new metaboQTLs (LDr2<0.5 with published metaboQTLs). Twenty-three metaboQTLs showed genome-wide significant heterogeneity by sex. The metaboQTL of a SNP on the gene fatty acids dehydrogenase 2 (FADS2) and cholesteryl ester C20:4 showed strongest evidence for sex differences (p=4.91E-39). **Conclusion:** Multiple SNPs were associated with circulating metabolite levels. We identified new associations and also validated previously published results. Several metaboQTLs showed heterogeneity by sex. Some metabolites were associated with multiple variants, suggesting that metabolites may represent polygenic traits.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-481

MsCompare: An Untargeted GC/MS Metabolomics Platform for Quality Control, Precise Deconvolution and Data Analysis

PRESENTING AUTHOR: Marco Ruijken, MsMetrix, Netherlands

A GC/MS workflow for Metabolomics includes a number of distinct steps: Experimental Design, Sampling, Sample Preparation, Data Analysis, Identification and Data Interpretation. The MsCompare platform includes all tools to properly control each step in this workflow. One of the key issues in this field is the precise and sensitive detection of all components present in a series of samples. GC/MS deconvolution remains by far the most difficult step for low level components, especially when highly similar co-eluting or nearly co-eluting compounds are present. In these cases, precise GC/MS Peak Detection and Deconvolution is necessary with minimal user interference. Proper deconvolution also allows for correct identification of all components. Another problematic area in GC/MS Metabolomics studies might be the proper alignment of chromatograms before the actual data processing starts. Depending on the application, we often see individual components in GC/MS having bad peaks shapes or bad reproducibility regarding retention times. MsCompare contains a number of alignment algorithms to correct for this behavior. Data analysis in MsCompare comprises both Univariate and Multivariate analysis methods like PCA, PLS-DA, Clustering etc. However, it will be shown that for many cases, due to the high selectivity of GC/MS, univariate analysis methods are adequate in solving the main questions. Examples from a number of different studies (small and large) will be given, showing an overview of the workflow and implemented tools.

P-482

A statistical approach to classification of mechanistic computational models of Parkinson's Disease

PRESENTING AUTHOR: Lalithasushma Chakravadhanula, LIACS, Netherlands

CO-AUTHORS: Agnieszka Wegrzyn, German Preciat, Alissa Schurink, Edinson Lucumi, Michael Emmerich, Ronan M.T. Fleming

Parkinson's Disease (PD) is a progressive neurodegenerative disorder. It is the second most common disease that affects the central nervous system and affects 7-10 million people worldwide. PD manifests with both motor symptoms such as tremors, rigidity, and non-motor symptoms like depression and fatigue. Although the pathogenesis of PD remains a puzzle, several genetic and environmental factors are known to affect the progression of the disease. This project focuses on the discovery of changes in metabolism due to the PINK1 gene mutation. PINK1 is a mitochondrially targeted, serine/threonine protein kinase PTEN-induced kinase 1 (PINK1) that protects cells from stress-induced mitochondrial dysfunction. We compare the effects of mitochondria-targeting inhibitors in patient and gene-corrected PINK1-Q456X cells, with healthy controls. Our data was generated from in vitro cultures of patient-derived human neuroepithelial stem cells, that were differentiated into dopaminergic neurons. A generic human genome-scale metabolic model was used to create a dopaminergic neuron-specific metabolic model. The steady-state solution spaces of constraint-based models of metabolism, in patient and gene-corrected PINK1-Q456X cells, were sampled and the flux distributions for each reaction were compared. Individual flux distributions may be diverse (uniform, normal, truncated, etc.), posing a challenge in statistical analysis. To address this issue, we developed an algorithm that classifies diverse flux distributions and highlights dissimilarities. Our approach allows us to study the differences in the uptake and secretion rates between the patient, gene-corrected, and healthy control models. Thereby, leading to a better understanding of the pathogenesis of PINK1-PD.

P-483

Extended Quality Control for Biocrates' Targeted Metabolomics Kits

PRESENTING AUTHOR: Mathias Kuhring, Max Delbrück Center (MDC) for Molecular Medicine, Germany

CO-AUTHORS: Alina Eisenberger, Raphaela Fritsche, Yoann Gloaguen, Dieter Beule, Jennifer Kirwan

Targeted mass spectrometry profiling methods optimized and validated for defined metabolites enable comprehensive routine metabolomics applications such as the analysis of larger cohorts. However, comprehensive studies require consistent processing and reliable instrumentation to minimize technical variance and interference. Consequently, multiple and reproducible controls are required to verify data quality. While standardized methods such as the Targeted Metabolomics Kits of Biocrates promise consistent and comparable measurements, they are not fully resistant to external influences. These include sample handling and processing errors, contamination, sample carryover, batch effects, intra-batch drift, edge effects, missing values of unknown origin and instrument condition. Here, we present an extensive quality control procedure for targeted data acquired using Biocrates kits designed to be complementary to the Biocrates MetIDQ software. Based on MetIDQ outputs, it combines several visualizations into a comprehensive HTML report using an R Notebook. These include, for instance, visualizations of measured and missing values, of positional irregularities with respect to acquisition sequence or well plate coordinates as well as of sample and metabolite variability and reproducibility. The tool supports Biocrates' AbsoluteIDQ® p400 HR Kit and MxP® Quant 500 Kit, however, most features apply to other Biocrates kits exportable by MetIDQ, with possible future extension to generic targeted metabolomics data. Overall, the report aids in either verifying data consistency and quality or, if necessary, in identifying pattern of interference as well as removing low quality samples or metabolites, thereby increasing confidence in data and subsequent analysis. An R package will be made available under a permissive license.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-484

Blend to avoid mixed results

PRESENTING AUTHOR: Daniel Malmodin, Swedish NMR Centre, Sweden

CO-AUTHORS: Anders Pedersen, Göran Karlsson

By using also blends of urine samples from participants in an NMR study the problem of variation in salt concentrations and pH, causing variation in spectrum shifts, can partly be overcome resulting in increased accuracy of assignments and precision of concentration estimates, as well as giving means to quantify each estimate in a more formal way than today.

P-485

A novel algorithm to improve NMR alignment of urine metabolomics data by spectral reordering and ridge tracing

PRESENTING AUTHOR: Sicong Zhang, The University of Georgia, United States

CO-AUTHORS: Arthur S. Edison

In Nuclear Magnetic Resonance (NMR) spectroscopy, the chemical shifts of some resonances are sensitive to sample properties, such as pH, salt concentration, temperature, and other matrix effects. This phenomenon challenges feature annotation and chemometric analysis in metabolomics research, leading to the necessity of data alignment processing steps. Urine is a widely used sample type in NMR metabolomics because of its simplicity in sample preparation and rich data. However, urine NMR samples are difficult to buffer and lead to large chemical shift variations. Although different alignment algorithms have been developed, none of them perfectly solve this problem. Here we present a new algorithm to improve urine NMR spectra alignment, by reordering the spectra according to the chemical shift of an internal peak then tracing responding peaks. We utilized DBSCAN-clustering to exclude mis-traced peaks, then used confidence intervals of traced peaks to find missed peaks. All traced peaks were aligned and placed at the end of the original spectra for further analysis. Two human urine datasets were used here for algorithm development. Significant improvement of peak alignment and STOCSY performance were observed using this algorithm. We will next compare results from this algorithm with other urine NMR analysis and alignment approaches. This work is expected to be generally useful for NMR processing to improve the accuracy and coverage of urine metabolomics studies.

P-486

An introduction to TameNMR (open source web-server based analysis of 1D NMR datasets)

PRESENTING AUTHOR: Marie Phelan, Technology Directorate, University of Liverpool, United Kingdom

CO-AUTHORS: Arturas Grauslys, Andy Jones

TameNMR is an open source web-server based toolset for analysis of 1D NMR datasets. Incorporating Bruker data and R packages, the University of Liverpool Computational Biology facility has developed a set of free-to-use tools that bridge the gap between raw NMR spectra and quantitative data analysis. Starting from raw or processed spectra, data can be normalised, aligned and scaled interactively prior to bucketing peaks and multiplets for downstream statistical analysis. The software has been developed by Dr Arturas Grauslys under the guidance of Prof Andy Jones who has a proven track record in developing server-based software solutions for MS proteomics community and it is hoped that functionality (as well as integration with other phenomenal tools) will expand with sufficient uptake by the NMR metabolomics community.

P-487

An IROA-based modified MSTUS Normalization corrects non-IROA sample-to-sample metabolite variation

PRESENTING AUTHOR: FELICE DE JONG, IROA TECHNOLOGIES LLC, United States

CO-AUTHORS: Chris Beecher

The IROA TruQuant (TQ) protocol uses a Long-Term Reference Standard (LTRS), a defined chemical mixture containing hundreds of metabolites, plus an Internal Standard (TQ-IS) that is chemically identical but isotopically different, to measure instrument performance and provide verifiable chemical identification. Using these standards we have previously shown the ability to correct for the ion suppression of natural abundance experimental compounds that are paired with compounds in the TQ-IS, and we have further shown that once ion suppression is corrected, sample-to-sample normalization may be achieved using a modified MSTUS algorithm, in which unlike the original MSTUS algorithm, the experimental compounds are normalized to their Internal Standard counterparts. In this poster we provide a comparison of the original MSTUS-based normalization algorithm to this IROA-based modified-MSTUS algorithm to demonstrate that IROA-based normalization is not only significantly more accurate within a single experiment, but by normalizing to a standard mixture, will normalize not only sample-to-sample intraday, but also interday (day-to-day) analyses. In addition, we selected several compounds that were not present in either the LTRS or TQ-IS to demonstrate that the same normalization factor used to normalize compounds for which we had internal standards could be applied to compounds that did not have internal standards. While they did not normalize as accurately without the ability to correct for ion suppression, the ability to normalize was greatly improved compared with that of the original MSTUS algorithm.
<figure><img src="image_80.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-488

**ASCA-fusion of NMR Metabolomics Data Reveals Metabolic Alternations in Response to Bioactive Milk Ingredients in Preterm Piglets with Intra-amniotic Inflammation**

**PRESENTING AUTHOR:** Masoumeh Alinaghi, Department of Food Science, Aarhus University, Denmark

**CO-AUTHORS:** Duc Ninh Nguyen, Per Torp Sangild, Hanne Christine Bertram, Johan A. Westerhuis

**Background:** Analysis of multiple compartments of the same piglets can provide complementary information about the studied biological system. However, in such complicated systems, distinguishing different experimental variations by ANOVA-simultaneous component analysis (ASCA) prior to data fusion could be of importance. In this study, a dietary intervention for prenatal inflammation¹ is investigated by analysis of urine and gut samples from the same piglets using the ASCA-fusion approach. Methods: Preterm pigs (n=17), subjected to intra-amniotic lipopolysaccharide (LPS, 1 mg/fetus), were administered i) standard formula, ii) bovine colostrum or iii) caseinoglycomacropeptide for five days. Collected urine and gut contents were analyzed by ¹H NMR-based metabolomics. ASCA was used to separate the various sources of variation in the data related to experimental factors (time and treatment), while fusion of the ASCA-decomposed matrices was applied by using penalized exponential simultaneous component analysis (P-ESCA)² to distinguish the common and distinct variation associated with the dietary intervention. Results: ASCA-fusion of the urine and gut metabolomes reveals the common alternation of lactate, glucose, acetate levels as well as disaccharides in response to the different dietary interventions. However, common responses related to the sex of piglets are also observed. Conclusion: ASCA-P-ESCA improves the understanding of metabolomics alternations in urine and gut content by separation of induced variations and by finding common and distinct variations in both compartments. 1 Nguyen, D.N., et al., The American journal of pathology, 188.11 (2018), 2629-2643. 2 Song, Y., et al., (2019), arXiv: 1902.06241.

P-489

**AutoTuner - high fidelity, robust, and rapid data processing parameter selection tool for metabolomics data**

**PRESENTING AUTHOR:** Craig Mclean, MIT, United States

**CO-AUTHORS:** Elizabeth B Kujawinski

Untargeted metabolomics experiments have the capability to capture an unbiased snapshot of cellular metabolism but remain challenging due to the computational complexity involved in data processing and analysis. Raw data must be processed to remove noise and to align features across samples through software tools like XCMS or MzMine2, resulting in a table of features with paired mass-to-charge (m/z) and retention-time (RT) values. This processing step requires dataset-specific parameters. Several optimization methods exist, but each design includes undesirable drawbacks. Here, we present a new method, AutoTuner, designed to optimize data processing parameters based on a novel paradigm. Instead of maximizing an optimization function like its predecessors, AutoTuner relies on statistical inferences within the distribution of raw data. We tested the accuracy and the run time of AutoTuner against the most common parameter selection tool, isotopologue parameter optimization (IPO). We also analyzed how parameter selection for AutoTuner and IPO influenced the quality of feature tables after XCMS. In our presentation, we will show that AutoTuner is a desirable alternative to existing tools, with substantially shorter computational times, easy implementation into existing metabolomics pipelines, and openly available to software developers.

P-490

**NonTplus – a new R package for the high throughput processing of high resolution mass spectrometry data**

**PRESENTING AUTHOR:** Tobias Schulze, Helmholtz Centre for Environmental Research - UFZ, Germany

**CO-AUTHORS:** Erik Müller, Caroline Huber, Marc Stöhr, Werner Brack, Martin Krauss

High resolution mass spectrometry is a key analytical technology in the identification of targets, suspects and unknowns for the profiling of environmental or human samples. In larger scale environmental surveys or human cohort studies, hundreds or thousands of samples could emerge. Assuming that these samples are measured at least once in positive and negative mode, a large number of raw mass spectral files is derived. NonTplus is a new pipeline which automatizes the whole processing including peak picking, gap filling, blank peak elimination, peak alignment, annotation, quantification and the export of peak lists for post-analysis. NonTplus implements a new gap filling algorithm which improves missing value imputation with less false positives compared to existing algorithms. The pipeline is tailored to run on a HPC cluster and a future implementation in Galaxy is planned.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-491

On the Interpretability of O-PLS Filtered Models

PRESENTING AUTHOR: *Barry M. Wise, Eigenvector Research, Inc., United States*

CO-AUTHORS: *Federico Marini, Frank Westad*

Orthogonal PLS, introduced originally by Trygg and Wold in 2002 [1], is a patented algorithm that has received much attention for its perceived ability to simplify and thus improve regression and classification model interpretation. Since its introduction, it has been shown by Ergon [2] and Kemsley and Tapp [3] that results identical to the original O-PLS formulation can be obtained by post-processing conventional PLS models in a non-patented way. This demonstrated, unequivocally, that O-PLS models have predictive properties identical to their non-rotated versions. The authors did not, however, consider the interpretability of the models at length. In this poster we explore the issue of interpretability of O-PLS models by applying the method to carefully constructed simple systems and well characterized data. It is demonstrated that O-PLS results match the true underlying (first principle) components under only very specific conditions which are typically not met. O-PLS results are shown to be a strong function of the correlation between the factor of interest and interfering components even under mild conditions, (small correlations). In simulated binary expression data it is shown that O-PLS is actually more sensitive to chance correlation than the conventional PLS regression vector. [1] J. Trygg and S. Wold, "Orthogonal Projections to Latent Structures (O-PLS)," J. Chemo, 16, 119-128, 2002. [2] R. Ergon, "PLS post-processing by similarity transformation (PLS+ST): a simple alternative to OPLS," J. Chemo, 19, 1-4, 2005. [3] E.K. Kemsley and H.S. Tapp, "OPLS filtered data can be obtained directly from non-orthogonalized PLS1," J. Chemo, 23, 263-264, 2009

P-492

An Improved Lipid Profiling Workflow Demonstrates Disrupted Lipogenesis Induced with Drug Treatment in Leukemia Cells

PRESENTING AUTHOR: *MARK SARTAIN, Agilent Technologies, United States*

CO-AUTHORS: *Genevieve Van de Bittner, Xiangdong Li, Jeremy Koelmel, Adithya Murali, Sarah Stow*

While shotgun lipidomics has advanced the field of lipid analysis, it suffers from limitations including the failure to distinguish isobaric species which may be of biological importance. This has led to a shift in the field of lipidomics to chromatographic-based lipid profiling approaches using high performance liquid chromatography coupled to high resolution mass spectrometry. Confident lipid annotation requires data acquisition at the MS/MS level to enable product-ion spectral matching against in silico generated databases. In this study, a novel software tool was employed which uses Bayesian scoring to assign lipid class annotation and a non-negative least squares fit with a theoretical lipid library (LipidBlast) to annotate the iterative mode MS/MS spectra. The tool takes special care not to over annotate lipid entities by only providing the level of structural information confidently informed by the MS/MS spectra. The tool quickly generates an accurate mass-retention time database in an automated fashion, and the resulting database annotates MS1 lipid profiling data. We applied this novel workflow to study lipidome alterations of the acute-myeloid-leukemia K562 cell line in response to a combination of the drug candidates bezafibrate (BEZ) and medroxyprogesterone acetate (MPA). The resulting analysis revealed several cellular changes in response to drug treatment, including a decrease in diacylglycerols, an increase in triacylglycerols, and differences in fatty acyl components. Utilization of the new lipid analysis workflow provided a more comprehensive lipid annotation than can be achieved by traditional approaches, and the results supported that BEZ/MPA may exert anticancer properties through disruption of lipogenesis.

P-493

Developing a systematized and integrated workflow for large-scale LC-MS data processing and cross-study investigations

PRESENTING AUTHOR: *Sajjan Mehta, University of California, Davis, United States*

CO-AUTHORS: *Gert Wohlgemuth, Diego Pedrosa, Sili Fan, Oliver Fiehn*

We present on the development of an enterprise-grade LC-MS data processing pipeline, LC-BinBase, and its integration with existing software to serve as an automated data management strategy for metabolomics and lipidomics studies. LC-BinBase implements MS-DIAL concepts and techniques to perform standardized data processing and feature annotation in a highly scalable capacity by utilizing cloud-based AWS Lambda services and Fargate clusters. Sample and study management is handled by the in-house MiniX and Stasis services and defines all sample preparations and quality controls. Acquired data are pre-processed and converted on the fly to the open mzML data format and are stored on AWS S3 for processing long-term storage. The converted samples are automatically scheduled for data processing by LC-BinBase using the study definition, instrument type and matrix information to tune the processing parameters. Feature identification uses m/z-RT libraries and MS/MS mass spectral libraries that are method-specific to minimize erroneous annotations. Identified features are stored in a dedicated instance of MassBank of North America (MoNA), and later consensus spectra are uploaded to the public MoNA database as metabolites are identified and manually confirmed. Each sample's validated annotations along with its matrix information are aggregated by BinVestigate to provide unique insight into the prevalence of individual metabolites within specific species and organs or components from across all historical studies. In addition, the use of standardized quality controls and retention time normalization enables cross-study investigation utilizing Systematic Error Removal using Random Forest (SERRF) for robust sample normalization.
POSTER SESSIONS 1 AND 2 – Monday and Tuesday – all odd number posters will be on display.

POSTER SESSIONS 3 AND 4 – Wednesday and Thursday – all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-494

BioMagResBank: Database and Tools for NMR Metabolomics Analysis

PRESENTING AUTHOR: Pedro Romero, University of Wisconsin Madison, United States

CO-AUTHORS: Hamid R. Eghbalnia, Hesam Dashti, Naohiro Kobayashi, Jonathan R. Wedell, Kumaran Baskaran, Takeshi Iwata, Masashi Yokochi, Dimitri Maziuk, Hongyang Yao, Toshimichi Fujiwara, Genji Kurusu, Eldon L. Ulrich, Jeffrey C. Hoch, John L. Markley

The Biological Magnetic Resonance Data Bank (BioMagResBank or BMRB), founded in 1988, serves as the archive for data generated by Nuclear Magnetic Resonance (NMR) spectroscopy of biological systems. NMR spectroscopy is unique among biophysical approaches in its ability to provide a broad range of atomic and higher-level information relevant to the structural, dynamic, and chemical properties of biological macromolecules, as well as reporting on metabolite and natural product concentrations in complex mixtures and their chemical structures. BMRB stores experimental and derived data from biomolecular NMR studies on both biopolymers and bioactive small compounds. BMRB supports metabolomics NMR studies through a library of a variety of 1D and 2D NMR spectra of pure compounds (including metabolites, natural products, drugs, and compounds used for screening in drug discovery) and through its adoption of novel analytic tools, like the ALATIS unique atom identifiers, which are universal and based solely on the 3D structure of the compound and the InChl convention, and GISSMO spin matrices, which enable accurate simulation of compound and mixture spectra at any field strength. The combination of unique ALATIS naming and parameterized spectra offers the users of BMRB data a distinctive benefit in terms of robustness and reproducibility, as embodied in the FAIR principles for data resources, which are that data should be Findable, Accessible, Interoperable, and Reusable. Supported by NIH Grants NIH Grants R01GM 109046, P41GM103399, and P41GM111135R01.

P-495

Estimating Partial Correlation Networks Leveraging Prior Information with Applications to Metabolomics Data

PRESENTING AUTHOR: George Michailidis, University of Florida, United States

CO-AUTHORS: Jiahe Lin, Alla Karnovsky, Gayatri Iyer, William Duren

The problem of estimating networks from high-dimensional Omics data has received a lot of attention due to their usefulness in providing insights into interactions amongst biomolecules under different diseases or experimental conditions. Partial correlation networks provide a useful technical framework for the task at hand. However, the limited number of samples available in many studies leads to estimation of very sparse (and fragmented) networks that makes them hard to interpret. To that end, we propose an estimation framework that leverages external information provided in the form of similarities across network edges. We formulate a regularized pseudo-likelihood framework, develop a fast distributed proximal gradient descent algorithm to compute the network structure and discuss selection of tuning parameters. We illustrate the proposed framework and the resulting algorithm by reconstructing and comparing the networks obtained from metabolomic and lipodomic profiling of three groups of samples, one suffering from Crohn's disease, another from ulcerative colitis and the third of normal controls. We further tested for enrichment modules extracted from the estimated networks that identified important alterations in both network structure and expression levels of interacting metabolites/lipids across the three groups of interest.

P-496

Updates to xcms: simplified raw data access and enhanced MS level > 1 capabilities

PRESENTING AUTHOR: Johannes Rainer, Institute for Biomedicine, Eurac Research, Italy

CO-AUTHORS: Laurent Gatto, Steffen Neumann

The xcms Bioconductor package is one of the standard toolboxes for the preprocessing of untargeted metabolomics data. Here we present recent updates to xcms, which re-use and build upon the support for memory-efficient parallel processing capabilities in the MSnbase Bioconductor software package for proteomics and general mass spectrometry data handling. We have improved large-scale experiment data analysis through memory-efficient parallel processing capabilities and simplify raw spectra data access throughout the whole preprocessing task. This comprises also dedicated functionality to extract ion chromatograms/traces from the original files and to perform chromatographic peak detection directly on such chromatographic data. Besides paving the road for MRM/SRM data analysis with xcms, it also allows to evaluate different peak detection settings on selected signals before applying them to the whole data set. Along these lines, we also implemented new visualization capabilities aiding in the definition and evaluation of data set-specific settings for the various preprocessing algorithms. Finally, import of MRM/SRM raw data has been added and a framework for the identification of MS2 spectra for identified chromatographic peaks was implemented.
<figure><img src="image_81.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-497 Prediction, quantification and correction of impaired plasma sample quality induced by pre-analytical errors using LC-MS untargeted metabolomics

PRESENTING AUTHOR: Rui Zheng, Uppsala University, Sweden

CO-AUTHORS: Lin Shi, Rikard Landberg, Huma Zafar, Åsa Torinsson Naluai, Carl Brunius

The quality of biobanked blood samples is of great importance for reliable and accurate determination of metabolites. Pre-analytical handling is one of the most important factors for sample quality. We used untargeted LC-MS metabolomics to evaluate the influence of pre-analytical management on 471 plasma samples from 28 individuals. Random forest modelling accurately predicted pre-centrifuge temperature (classification rate 81%) and time (Q2 = 0.82) as key factors for pre-analytical sample quality. Fasting status, however, did not affect the metabolome reproducibly among individuals. Thirteen and eight metabolites were selected in metabolite panels for highly accurate prediction of temperature and time, respectively. Several metabolites responding to temperature/time interaction in linear regression showed significant difference from 30 to 120 min at 25 or 37 °C compared to 4 °C, whereas temperature could not be accurately predicted at <30 min. Moreover, the changes in plasma metabolome were modelled per cluster at each temperature and pronounced at 4 °C because intensity of lipids-like and organic acids features dramatically declined from 0 until 100 min. Furthermore, only minor to moderate (0 to 25%) correction of data quality could be achieved by normalizing feature data to 0 min based on metadata on time, indicating that the induced variability to a large degree is non-systematic. We conclude that the metabolite profile changes rapidly with pre-centrifugation delay times even at 4 °C. Handling of blood samples from needle to freezer should be completed as soon as possible, preferably at 25 °C with pre-centrifugation delays less than 30 min.

P-498 MetaboAnalystR 2.0: From Raw Spectra to Biological Insights

PRESENTING AUTHOR: Jasmine Chong, McGill University, Canada

CO-AUTHORS: Mai Yamamoto, Jianguo Xia

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpreting these complex datasets have become a key challenge in current computational metabolomics. We therefore present MetaboAnalystR 2.0, an R package to support end-to-end LC-MS based global metabolomics data analysis from spectral processing to biological insights. Compared to its predecessor, this new release integrates XCMS and CAMERA to support raw spectral processing and peak annotation. It also features high-performance implementations of Mummichog and GSEA algorithms to predict pathway activities directly from MS peaks. The application and utility of the MetaboAnalystR 2.0 workflow are demonstrated using a clinical dataset of pediatric inflammatory bowel disease (IBD). Functional analysis identified perturbations in Bile acid biosynthesis and Vitamin D3 metabolism, both of which are well-known mechanisms in IBD. This highlights the ease of which MetaboAnalystR 2.0 can be used to gain biological insights and generate hypotheses for future experimental validation. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment. The R package is freely available from the GitHub repository (https://github.com/xia-lab/MetaboAnalystR).

P-499 Galaxy on Site: A flexible and reliable path to processing metabolomics data reproducibly and collaboratively

PRESENTING AUTHOR: Arthur Eschenlauer, University of Minnesota - Twin Cities, United States

CO-AUTHORS: Mark Esler, Timothy Griffin, Adrian Hegeman

Untargeted metabolomics LC-MS experiments can generate large numbers of large files; before the results can be interpreted, it is necessary to perform many preprocessing, annotation, and statistical analysis steps, each of which may have its own particular parameters. Galaxy provides a web-based interface to capture these parameters into reproducible, reusable, shareable workflows. Getting started can be as simple as using one of the established public Galaxy instances, engaging high-performance computing resources, or running Galaxy on a workstation. A research group may have unique needs in the areas of access, collaboration, or simplified transfer and life-cycle management of sizeable datasets; new, in-house authored tools may also need to be incorporated into Galaxy workflows. A research-group specific Galaxy "appliance" may practically address these needs; however, this requires a sustainable way to administrate a small-scale, high-availability system. We have been running such a system for several years and are encapsulating these functionalities into an "appliance" that can be implemented in a broad spectrum of laboratory settings. Our initial efforts have been focused on scaling the system appropriately, system backup and recovery, balancing cost of storage against available size, and integrating the system with instrument workstations on a laboratory intranet. From the outset, the system was designed to balance security with usability. We have applied this solution to our plant metabolomics research and found that, with minimal instruction, users can work independently and provide feedback on usability and functionality issues in prepublication versions of Galaxy tools.
<figure><img src="image_82.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-500

Which step is the most crucial in sample preparation procedure for GC-MS metabolomics? Design of Experiments approach.

PRESENTING AUTHOR: Julia Jacyna, Dept. of Biopharmaceutics and Pharmacodynamics, Medical University of Gdansk, Poland

CO-AUTHORS: Marta Kordalewska, Joanna Raczak-Gutknecht, Marta Stawiszyńska, Michał Jan Markuszewski

Design of Experiments (DoE) consists of making specific and controlled modifications in a studied system in order to create a mathematical model that will allow to predict how monitored responses are affected by applied modifications. In other words, the use of DoE approach allows to screen the most important factors, predict relationships between them and generate the most optimal settings in order to get the most favorable response while saving the time spent on the optimization and reducing the cost of analysis. Its main advantage is the ability to provide optimal parameters' settings by performing a minimal number of experiments. The objective of the study was to develop and optimize a simple method for preparation of human urine samples for determination of concentration of previously selected metabolites by means of GC-MS/MS analysis. A rapid, simple and reliable method is necessary for targeted metabolomics analysis. Moreover, since sample preparation step for GC-MS/MS is usually very complicated, time-consuming and requires the use of toxic reagents, implementation of Design of Experiments was reasonable. Fractional Factorial Design was implemented as a screening procedure in order to evaluate the significance of variables. The most crucial steps of urine sample preparation procedure were identified with the use of two-step plan, based on the evaluation of more than a dozen of parameters with limited number of experiments. Firstly, time- and temperature-dependent factors were evaluated and subsequently, concentration and volume of reagents used were taken into account.

P-501*

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

PRESENTING AUTHOR: Stephanie Monnerie, Université Clermont Auvergne, INRA, UNH, Mapping, France

CO-AUTHORS: Melanie Petera, Bernard Lyan, Pierrette Gaudreau, Blandine Comte, Estelle Pujos-Guillot

Metabolomics generates complex data that need dedicated workflows to extract the meaningful information. For biological interpretation, experts are mainly focusing on metabolites rather than on the redundant different analytical species. Moreover, the high degree of correlation in datasets is a constraint for the use of statistical methods. In this context, we developed a new tool to detect analytical correlation into datasets. The algorithm principle is to group features from the same analyte and to propose one single representative per group. The user can define grouping criteria with various options including correlation coefficient, retention time, mass defect information. The representative feature can be determined following four methods according to the analytical technology. The present tool was compared to one of the most commonly used free package proposing a grouping method: 'CAMERA', using its Galaxy version 'CAMERA. annotate' available in Workflow4Metabolomics (W4M; http:// workflow4metabolomics.org). To illustrate its functionalities, a published dataset available on W4M (Thevenot et al., 2015) was used as an example. Within the 3,120 ions of the dataset, the tool allowed creating 2,651 groups, meaning that 15% of ions are proposed to be filtered because of analytical redundancies. The proposed tool subdivided more than 20 groups of more than 10 ions into smaller ones corresponding to individual annotated metabolites, thus demonstrating the efficiency and relevance of the present approach. As a key element in metabolomics data analysis, the tool will be available via the web-based galaxy platform W4M with different output files for network vizualisation and for further data analysis within workflows.

P-502

Comets-Analytics: a centralized computational framework for consortia level meta-analyses

PRESENTING AUTHOR: Ewy Mathe, Ohio State University, United States

CO-AUTHORS: Moore, Krista Zanetti, Kai-Ling Chen, Dave Ruggieri, Ella Temprosa

Metabolomics is increasingly applied in large-scale epidemiological studies to uncover metabolites associated with physiological states (e.g. age, disease). The National Cancer Institute-led "Consortium of Metabolomics Studies" (COMETS) includes > 45 international prospective cohorts with serum metabolomics profiles and detailed phenotypic data. To support meta-analysis of these studies at a consortia level, we created a centralized computational infrastructure, Comets-Analytics. Comets-Analytics was built with the following guiding principles in mind: 1) minimal burden on analyst time, 2) reproducibility, 3) data privacy, 4) adherence with FAIR guidelines, 5) usability. The software supports harmonization of metabolite names across different platforms used in COMETS and implements partial correlation modeling. Notably, Comets-Analytics includes "smart analytics", which internally performs extensive data and model checks to ensure that models are valid, and that the model building process is reproducible. Meaningful warnings (e.g. adjustment variable is dropped because it only has one unique value) and errors (e.g. adjustment and stratification variables are the same) are returned to precisely inform users about how to fix their input data and about what models are run. Further, the web application includes interactive tables and plots to empower users to promptly and globally assess results. With Comets-Analytics, cohorts analyze their own data using a common data format and standardized results are sent centrally for meta-analysis. This streamlined approach greatly facilitates large consortia studies and helps ensure integrity, and reproducibility of results. Comets-Analytics, including detailed documentation, is available as an R package at https://github.com/CBIIT/R-cometsAnalytics, and can be run directly from our servers at http://comets-analytics.org/.
<figure><img src="image_83.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-503

Composite Score: A Multivariate Correlation for Comparison of Metabolomics-Based Studies of Complex Mixtures

PRESENTING AUTHOR: Joshua Kellogg, University of North Carolina at Greensboro, United States

CO-AUTHORS: Olav M. Kvalheim, Nadja B. Cech

Untargeted metabolomics analyses, where an entire measurable metabolome is analyzed without defined biomarker compounds to guide analysis, is useful when the study has no a priori chemical or mechanistic hypotheses. However, successful untargeted metabolomics studies rely on effective statistical analysis of the metabolomic dataset to guide interpretation and inform conclusions. A primary statistical tool, principal component analysis (PCA), results in the selection of maximum three components from a larger model to visually represent similarity within the entire dataset. This limits the comprehensiveness of the model and can yield poor discrimination between samples. Other metrics of similarity also have limitations when considering untargeted metabolomics studies. Here we have developed a new statistical metric, the composite score (CS), as a univariate statistic that incorporates multiple principal components to enable quantitative comparisons among metabolomics datasets. By integrating the scores and loadings components of the significant components from the original PCA model, the CS provides an advantageous measure of similarity, enabling more quantitative comparisons than are possible with visual inspection of a PCA scores plot or hierarchical cluster analysis (HCA). Several case studies focusing on complex natural product mixtures – green tea (Camellia sinensis) and goldenseal (Hydrastis canadensis) dietary supplements – highlight the utility of composite scores to evaluate similarity and identify outliers within a sample set.

P-504

Integrating 4D peak picking of LC-TIMS-MS/MS data into GNPS feature based molecular networking for metabolomics and lipidomics analysis

PRESENTING AUTHOR: Florian Zubeil, Bruker Daltonik GmbH, Germany

CO-AUTHORS: Nikolas Kessler, Heiko Neuweger, Sven Meyer, Ulrike Schweiger-Hufnagel, Aiko Barsch

As throughput of metabolomic and lipidomic analyses continuously expands, effective workflows for analyzing the resulting datasets are of increasing importance. Molecular networking in recent years has become a vital tool in the metabolomics community as it quickly allows the identification of compounds with similar fragmentation patterns which are often structurally related. This also allows propagation of annotations from known compounds to related derivatives. While this approach mainly focusses on the fragment spectra, important information can be deduced from the precursor spectra, i.e. intensity, accurate mass and isotopic pattern of the analytes. Herein, we present a workflow to integrate analyte information for untargeted profiling from the software MetaboScape into GNPS feature based molecular networking. The nodes in the resulting molecular network are enriched by useful information about the precursor ions like the intensity in individual samples, molecular formula, annotation, CCS values, group mean and maximum intensity. The latter information are important indicators to assess distribution of a specific analyte between sample groups (by group mean). Likewise, interpretation of the resulting molecular network is greatly simplified by displaying generated molecular formulas instead of precursor masses as node labels. Additionally, this workflow enables the integration of 4D peak picking results from PASEF-MS/MS data into GNPS molecular networking which is demonstrated on a lipid sample. This enables a straight forward processing of TIMS-MS data including the benefit of cleaner MS/MS spectra of co-eluting analytes for molecular networking which are generated by ion mobility separation.

P-505

Hierarchical Bayesian models for Stable Isotope Resolved Metabolomics: a unified framework for testing hypotheses about total abundance and isotopologue distribution

PRESENTING AUTHOR: Patrick J. Trainor, University of Louisville, United States

CO-AUTHORS: Pawel K. Lorkiewicz, Joshua K. Salabei, Bradford G. Hill

The analysis of data from Stable Isotope Resolved Metabolomics (SIRM) experiments presents a unique statistical challenge. First, experimental manipulations (e.g. gene knockout or treatment with an enzymatic inhibitor) may change between phenotypes: the concentration or pool of a metabolite present, the fractional distribution of the metabolite pool within isotopologues, or both. Second, multiplicity concerns arise from conducting univariate statistical tests at the isotopologue level between phenotypes. We propose a hierarchical Bayesian model for testing hypotheses in SIRM experiments that is well-suited for addressing these challenges. This model assumes Gaussian prior distributions for the total abundance of a metabolite, Dirichlet prior distributions for the fractional distribution of abundances within isotopologues, and Gaussian priors to account for within-phenotype variability as well as measurement error. Gibbs sampling, a Markov chain Monte Carlo technique, is utilized to simulate the joint posterior distribution of model parameters given observed experimental data. From the joint posterior distribution hypotheses regarding both total metabolite abundance and isotopologue distribution can be tested. We demonstrate an application of the methodology to a 15N2, 13C5-glutamine labeling experiment conducted in order to evaluate the effect of aminooxyacetic acid (an inhibitor of aminotransferases including aspartate aminotransferase) treatment (AOA) in murine cardiac mesenchymal cells isolated on the basis of c-kit positivity. Metabolites extracted from AOA+ and AOA- treated cells were detected by FTICR-MS and relative abundances were quantified. We report differences in isotopologue distributions between AOA+ and AOA- cells in malate and glutamate; and differences in total abundances of malate, aspartate, and glutamate.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

**P-506**
**Metabolomic profiling identifies a systemic suppression of steroid metabolism among prevalent asthma cases; are inhaled steroids the cause?**

**PRESENTING AUTHOR:** Priyadarshini Kachroo, Brigham & Women's Hospital, United States

**CO-AUTHORS:** Priyadarshini Kachroo, Rachel S. Kelly, Mengna Huang, Isobel Stewart, Claudia Langenberg, Scott T. Weiss, Jessica A. Lasky-Su

Asthma is a complex disease, often effectively treated with the use of inhaled corticosteroids (ICSs). We aimed to identify metabolomic signatures of asthma and evaluate the impact that ICSs may have on the overall plasma metabolomic profile. The current study included 10,754 participants from the population-based EPIC-Norfolk cohort with data available for prevalent physician-diagnosed asthma. Metabolomic profiling of plasma was conducted using ultrahigh-performance liquid chromatography and tandem mass spectrometry. We assessed individual metabolite associations with asthma using multivariable logistic regression models and evaluated the impact of ICS use on those findings. We replicated these findings using data from 613 individuals in the Partners Biobank (PB). After quality control, we identified 858 known metabolites, of which 27 (3.1%) were associated with prevalent asthma after Bonferroni multiple-comparison correction (P<5.8e-5). The top two associations: dehydroisoandrosterone sulfate (DHEA-S) and cortisone were decreased in asthmatics (OR=0.65,P=1.4e-27; OR=0.72,P=7.8e-20 respectively). 15/27 metabolites replicated in Partners Biobank (P<0.05), including strong associations between the top two EPIC findings and asthma: DHEA-S and cortisone (OR=0.36,P=2.7e-4; OR=0.30,P=3.0e-5 respectively). Notably, all 15 metabolites were in the corticosteroid, pregnenolone, and androgenic steroid pathways and were markedly reduced in asthmatics (ORs=0.65-0.81). Further investigation demonstrated that these metabolites had consistent negative associations with ICS use. These findings suggest that significant suppression of multiple steroid pathways in asthmatics could be modulated by ICSs, which merits from further investigation. The consistent negative relationship between ICS use and steroid metabolites further suggests that ICSs may have stronger systemic effects on circulating plasma steroid levels than is currently recognized.

**P-507**
**Hierarchical preprocessing for LC/MS metabolomics data generated in multiple batches**

**PRESENTING AUTHOR:** Tianwei Yu, Emory University, United States

**CO-AUTHORS:** Qin Liu, Douglas Walker, Karan Uppal, Shuzhao Li, ViLinh Tran, Dean P. Jones

With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography – Mass Spectrometry (LC/MS) platform, the samples need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group, which makes it necessary to use larger m/z and retention time tolerance levels in order to allow for between-batch differences. Such an approach is sub-optimal, as it can result in errors in the alignment of peaks, which cannot be corrected by batch effect correction methods applied after preprocessing. To address this issue, we developed a new approach that process the data in a hierarchical manner – first within batch and then between batch. Different parameter settings can be adaptively found for within-batch and between-batch quantification and alignments. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized plasma samples and from real biological studies, the new method resulted in feature matrices with higher consistency. The method can be useful for large studies involving multiple batches.

**P-508**
**Integrated workflow with quality control for large cohort and clinical metabolomics research using robust hardware and signal correction**

**PRESENTING AUTHOR:** Nikolas Kessler, Bruker, Germany

**CO-AUTHORS:** Sebastian Goetz, Ulrike Schweiger-Hufnagel, Matthias Szesny, Aiko Barsch, Sven W. Meyer, Matthew R. Lewis, Nikolas Kessler

Metabolomics research relies on precision measurement of statistically powered sets of hundreds or thousands of samples. First, this requires robust analytical hardware with long term stability, capable of generating high precision data. Second, processing of large datasets may require additional mathematical correction to compensate for systematic changes in observed signals as samples interact with the analytical system affecting its performance. We investigated the long-term stability of an LC-HR-QTOF system by measuring a batch of more than 1000 urine samples and monitoring the effect of data acquisition on MS ion source contamination and detector aging. To address the remaining within-batch intensity drifts we present new software: First, a fully automated software workflow allows for the automated correction of intensity drifts, improving data precision and statistical reliability. Second, an interactive and intuitive visualization provides rapid feature-wise review of intensity drifts (and their corrections) as well as detection of statistical outliers. Run-order signal drift correction effectively reduced the relative standard deviation (RSD) of feature intensities within sample groups measured across replicate quality control sample measurements. This also increased the number of analytes which meet the requirements of an RSD below 20%, a typical cut-off. Visually this improvement was also observed in PCA, with more closely clustering of sample groups. In summary, we present a workflow for population and clinical metabolomics research enabled by robust LC-HRMS hardware and software allowing filtering and correction for signal drift effects.
POSTER SESSIONS 1 AND 2 – Monday and Tuesday – all odd number posters will be on display.

POSTER SESSIONS 3 AND 4 – Wednesday and Thursday – all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-509

Detect and Quantify Sources of Variability in Metabolite Measurement in a Japanese population

PRESENTING AUTHOR: Ayano Takeuchi, Keio University, Japan

CO-AUTHORS: Sei Harada, Taichi Shimazu, Taiki Yamaji, Norie Sawada, Junko Ishihara, Ribeka Takachi, Kazutoshi Nakamura, Junta Tanaka, Manami Inoue, Motoki Iwasaki, Hiroyasu Iso, Masahiro Sugimoto, Akiyoshi Hirayama, Tomoyoshi Soga, Masaru Tomita, Shoichiro Tsugane, Toru Takebayashi

Our study purpose is to quantify sources of variability in urine and plasma metabolite concentrations measured using capillary electrophoresis-mass spectrometry and detect sources of variability in metabolite measurements. We measured metabolite concentrations of using the samples from Japan Public Health Center-Based Prospective Study for the Next Generation (JPHC-NEXT) validation study in Japan (J Epidemiol. 2016;26:420-32.). We used twenty-four-hour urine collections and plasma specimens of 253 men and women aged 40–74 years from five areas in the study, collected at 2 time points, baseline (2012) and same period of next year (2013). We randomly selected 43 samples from study subjects and dispense specimens into 3. We measured some 3 dispensing specimens sequentially to detect the sum of the squared deviations of ‘pure measurement error’. We layout and measured some 3 dispensing specimens in different position of the same batch to detect ‘within batch variation of measurement’. We layout and measured remained 3 dispensing specimens in same position of different batch to detect ‘between batch variation of measurement’. We will show the proportion of these 3 types of variation (pure measurement error, within batch variation, between batch variations) and variation between times (2012 to 2013) for all metabolite (123 metabolites for urine, 102 metabolites for plasma) we measured on our poster.

P-510

Virtual Metabolomics Mass Spectrometer (ViMMS): A Mass Spectrometry Simulator for Comparing Different Fragmentation Strategies in Metabolomics

PRESENTING AUTHOR: Joe Wandy, Glasgow Polyomics, United Kingdom

CO-AUTHORS: Vinny Davies, Justin J.J. van der Hooft, Ronan Daly, Simon Rogers

Liquid-Chromatography (LC) coupled to Tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. However, the development of new MS/MS acquisition strategies is hampered by the lack of simulators that let researchers prototype and compare different fragmentation strategies before validations on real machines. Although some simulators exist, they are typically focused on proteomics and do not include simulation of MS2 acquisition within a chromatographic run. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a modular metabolomics LC-MS/MS simulator framework that allows for real-time scan-level control of the MS2 acquisition process in-silico. ViMMS can generate new data based on kernel density estimates trained on empirical data, or generate data that resembles real data from a list of user-defined chemical formulas. Alternatively, pre-existing data can be re-run in-silico with different fragmentation strategies. Samples can be exported as .mzML files, and different fragmentation controllers compared. ViMMS is also extendable with additional spectra generation processes and noise models. We will show results from experiments that compare different fragmentation strategies. First ViMMS will be used to take the output of a real LC/MS analysis and examine the effect of varying N in Top-N Data Dependent Acquisition protocol. We will also demonstrate how ViMMS can be used to compare published acquisition strategies, e.g. Data-set-Dependent Acquisition (DsDA) and Nested Data-Independent Acquisition (DIA). We expect that ViMMS will save development time by allowing for offline evaluation of novel fragmentation strategies and optimisation of fragmentation strategy for a particular sample.

P-511

Open source software platform for mass spectrometry based non-target screening in the environment

PRESENTING AUTHOR: Rick Helmus, Institute for Biodiversity and Ecosystem Dynamics, Netherlands

CO-AUTHORS: Vittorio Albergamo, Olaf Brock, John Parsons, Pim de Voogt

Chemical analysis has been widely applied over the past decades to characterize both natural and anthropogenic compounds within our environment. A 'non-target' approach becomes increasingly adopted where hundreds to thousands of unknown chemicals are screened simultaneously. At this scale, tools are crucial to automatize extraction, prioritization and identification of chemicals of potential interest. The various software tools now available typically solve only part of the workflow and may lack functionality specifically required for environmental sciences. As a consequence, the need to combine tools may require familiarizing with various software environments, tedious transformation of in-between datasets and complicating reproducible non-target research. We are developing an R based open source software platform which provides a common interface for non-target analysis tailored for environmental sciences. Existing software solutions (e.g. XCMS, OpenMS, CAMERA, and MetFrag) are utilized to provide a typical non-target workflow such as extraction of features, calculation of chemical formulae and tentative compound identification. A common interface to these tools allows easy incorporation of tested algorithms and comparison of their output. Other functionalities include filtering and prioritization of data, interactive and static reporting and interoperability with vendor software. Our software is currently being applied in various non-target studies conducted in our institute. Examples include the chemical characterization of dissolved organic matter to study its stabilization in podzols, the identification of small and polar emerging polar contaminants to study their removal by reverse osmosis during water treatment and elucidation of transformation products of biocides released in constructed wetlands.
POSTER SESSIONS 1 AND 2 – Monday and Tuesday – all odd number posters will be on display.

POSTER SESSIONS 3 AND 4 – Wednesday and Thursday – all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-512

A new method for enhancing gap filling accuracy and specificity in high resolution mass spectrometry data processing

PRESENTING AUTHOR: Erik Mueller, Helmholtz Centre for Environmental Research - UFZ, Germany

CO-AUTHORS: Werner Brack, Martin Krauss, Tobias Schulze

Liquid chromatography high resolution mass spectrometry is a key analytical technology in the identification of targets, suspects and unknowns for profiling in environmental screening and metabolomics. By default, peak picking uses heuristic algorithms for the extraction of individual ion chromatograms (EICs) from chromatographic raw data and identification of peaks in these EICs. But heuristic methods cannot account for all types and shapes of noise occurring in the data. While some algorithms exist to mitigate this problem, most gap filling approaches result in many false positives and negatives. We present here a novel gap filling method that utilizes the similarity between the extracted ion chromatograms of a peak in different samples to deduce where previously undetected peaks can be found while also keeping the number of inaccurately categorized peaks at a minimum. With our new approach, the accuracy of categorized peaks was increased by 51% compared to the peak finder in MZmine.

P-513

A full workflow for machine learning techniques in integrative multiomics studies as part of the COVAIN toolbox

PRESENTING AUTHOR: Xiaoliang Sun, University of Vienna, Austria

CO-AUTHORS: Wolfram Weckwerth

The Vienna Metabolomics Center has established open-source and cross-platform workflows for computational mass spectrometry, integrative multi-omics analysis and predictive modelling for clinical, biochemical, agricultural and ecological studies. All algorithms are implemented in the toolbox COVAIN (1). High resolution mass spectral raw data are processed with an algorithm called mzFun which identifies and aligns thousands of compounds over hundreds of samples from MS/MSn fragmentations, molecular formula, isotopomer pattern and internal and external MS libraries. From this initial annotation biochemical pathways are assigned to unknown m/z features with an algorithm called mzGoupAnalyzer (2). Processed metabolomics, proteomics, transcriptomics, phenotypical and physiological data are imported into COVAIN, which provides rigorous statistical tools for data mining from data cleaning, imputation, uni- and multi-variate statistics including ANOVA, PCA, ICA, PLS, correlation, clustering, Granger causality, multiple regression up to advanced machine learning procedures. These algorithms include multivariate best subset selection by genetic algorithm, classifiers like SVM, DA, KNN and ensemble methods as well as ROC/AUC diagnostics. Further, statistical network inference, visualization, modularity analysis, KEGG pathway enrichment analysis are implemented. COVAIN is also featured with an experimentally validated inverse Jacobian calculation that infers biochemical regulation Jacobian matrix directly from genome-scale metabolomics covariance data (1,3). During this processes high quality editable figures are provided. All these computational mass spectrometry and data mining tools are organized in an All-In-One tool with graphical user interface. 1. Sun X & Weckwerth W (2012) Metabolomics 8(1):81-93. 2. Doerfler et al. (2014) Plos One 9(5):e96188. 3. Doerfler et al. (2013) Metabolomics 9(3): 564-574.

P-514

UMetDIA: Advancing SWATH-MS Based Untargeted Metabolomics

PRESENTING AUTHOR: Ruohong Wang, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, China

CO-AUTHORS: Yandong Yin, Zheng-Jiang Zhu

Advancing SWATH-MS Based Untargeted Metabolomics through High-Coverage Spectral Library Building and Quantitative Ion Selection. Data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. However, in DIA, the direct link between MS1 and MS2 ions in multiplexed MS2 spectra is missing, and there is no universal evaluation approach to decide whether MS1 or MS2 ion should be selected as the quantification ion for one metabolite. To address these current limitations in DIA data analysis, we developed a new strategy integrating DIA spectral library construction, quantitative ion selection with large-scale targeted re-extraction in DIA data itself. First, we proposed a new methodology that enabled us to construct a comprehensive spectral library directly from DIA data (referred as DIA-Lib, usually from pooled samples). The constructed DIA-Lib possessed the strengths including broad spectrum coverage, high quality and high reproducibility. Then, for every feature in DIA-Lib, the suitable quantitative ion was selected by Qscore method we proposed. We demonstrated the advantage of Qscore quantification method such as accuracy, linearity, and reproducibility. Furthermore, the high-coverage DIA-lib was used to re-extract ions in biological samples. Combining with the ion information selected by Qscore method above, large-scale metabolites extraction and accurate quantification in biological sample can be realized. Finally, the strategy was applied on colorectal cancer clinical sample set. Compared with conventional MS1 quantification method, our method performed better on separating cancer and adjacent tissues.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-515 Rapid analytical platforms for biofluid profiling in discovery metabolomics

PRESENTING AUTHOR: Adam King, Waters corporation and Murdoch University, United Kingdom

CO-AUTHORS: Ian D Wilson, Robert S Plumb, Paul D Rainville, Robert D Trengove

Metabolic phenotyping has been widely employed in large epidemiological studies in the effort to discovery more about health and disease. UPLC and hybrid mass spectrometers have been essential tools in analyzing the matrices chosen for these studies. However, long acquisition times per samples (>15 mins) has meant large sets, typically employed in epidemiological and biobanking studies, can take days to analyze, ultimately putting strain on resources. Furthermore, this has led to sample acquisition being performed across multiple runs for the same data set, risking the development of batch effects when attempting to recombine the data. In order to address these issues, a suite of rapid profiling methods have been developed, reducing run time and solvent consumption by 75%. These methods were employed for the analysis of rodent urine and human plasma lipid extracts from breast cancer patients, using a Waters Acquity I-class UPLC system coupled to a Synapt G2-Si QToF mass spectrometer. Standard LC-MS methods for metabolite and lipid profiling using 2.1 mm i.d. columns were geometrically scaled, reducing the i.d., analysis times, injection volumes and mobile phase flow rate, while ultimately increasing the linear velocity. Each scaling demonstrated a preservation of the retention mechanism with relative retention times of probe compounds being maintained. Incorporating the ion mobility schema improved resolution of co-eluting ions, ultimately improving spectral clarity and with the generation of collisional cross section values, increased confidence in identification through database searches.

P-516 Sensomics: understanding flavor properties in food

PRESENTING AUTHOR: Brenda Ammerlaan, DSM Food Specialties, Netherlands

CO-AUTHORS: Raymond Raymaker, Leon Coulier, John Gauvin, Peter Lankhorst, Adriana Carvalho de Souza, Cock Tas, Marjon Kok, Margriet Hendriks, Marieke Nijmeijer, Denise Jacobs, Maurien Olsthoorn

DSM Food Specialties develops and sells process flavors. Understanding flavor at molecular level and the impact on sensory flavor perception, enables steering of the desired product properties during processing. This poster presentation outlines the workflow we have developed to identify mutual relations between metabolites and sensory attributes. Thirty different commercially available DSM process flavors were characterized both in sensory as well as chemically. For sensory evaluation, QDA (Quantitative Descriptive Analyse was done with a trained Savoury panel (n=12) on 46 attributes in odour, flavor and mouthfeel. To cover the broadest possible range of chemical compound classes, an untargeted sensomics approach was developed that combines four different complementary analytical methods to analyze the volatile and non-volatile metabolites that are possibly related to the flavor of our process flavors. The non-volatiles were analyzed by NMR and a GC-FID/MS method in combination with oximation and silylation. All volatiles were analyzed by an SPME-GC-FID/MS method. LC-MS was done for general profiling of peptides up to about 15 amino acids. Sample preparation and method development was done for all four analytical methods. Using these methods, all process flavors were characterized successfully. Multivariate data-analysis was applied to identify possible correlations between the chemical analytical data and sensory data. Visualization tools are optimized and together with DSM colleagues in Savory Ingredients, more detailed data mining is being done to be able to translate findings into answers to business questions. A holistic metabolic profiling was applied to develop models to describe flavor in our process flavors.

P-517 Rapid Evaporative Ionization Mass Spectrometry (REIMS) offers direct from sample mapping of faecal metabolites without sample preparation

PRESENTING AUTHOR: Petra Paizs, Imperial College London, United Kingdom

CO-AUTHORS: Alvaro Perdones-Montero, James Kinross, Simon Cameron, Zoltan Takats

Faecal metabolomics can provide a unique metabolic signature of intestinal function in a non-invasive manner and is crucial for the identification of diagnostic and therapeutic biomarkers in gut-related diseases. However, this relies on homogenous distribution of biomarkers within samples. Mass spectrometric imaging (MSI) is a key analytical tool for biomarker discovery, though most studies have concentrated on tissue analysis rather than faeces. Here, we present a high-throughput analysis pipeline using REIMS imaging, which allows for near-real time analysis and mapping of metabolites in whole fresh human faecal samples. Our strategy allows in-depth analysis of the diversity and complexity of the faecal metabolome by visualizing the relative abundance and spatial distribution of metabolites. This pipeline has been optimized and validated as a high-throughput tool for direct-from-sample analysis with minimal sample preparation: Whole faecal samples (<1 hour of bowel evacuation) were segmented into cross-sectional plates (5mm) and analyzed at 1 mm resolution in negative and positive ionization modes. Pre-processing of data and statistical analysis in R Studio (V1.0.44) allowed for targeted or untargeted analysis. Faeces from control patients with no known gastrointestinal disease were investigated and a detailed analysis of key features was performed. In the fatty acid region, we observed the most abundant peaks at m/z 255.25, 281.25, 279.25, 283.25, and 311.15 to be distributed heterogeneously. We are now looking at performing observational studies in colorectal cancer patients to investigate the spatial distribution of potential biomarkers. The mapping of metabolites through REIMS imaging demonstrates the first MSI technique for faecal sample analysis.
<figure><img src="image_84.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-518

Impact of instrument, detection method, and statistical methods in mouse plasma metabolite profiling

**PRESENTING AUTHOR:** Lukas Kucera, Czech Centre for Phenogenomics, Institute of Molecular Genetics of the ASCR, v.v.i., Prague, Czec, Czech Republic

**CO-AUTHORS:** Ashkan Zareie, Karel Chalupsky, Krystof Klima, Vendula Novosadova, Matthias Witt, Radislav Sedlacek

Changes in the metabolic profile of plasma reflect the metabolic state of an organism and may reveal disease biomarkers or depict the phenotype of an animal model. Fast and reliable methods for high throughput are needed for non-targeted screening of samples. The aim of this study was the comparison of plasma metabolite measurements among a group of wild-type and Klk8 deficient mice across four methods; C18 HPLC column attached to an LC-MS instrument (6550 iFunnel Q-TOF, Agilent), MRMS instrument (7T, scimaX, Bruker) coupled with an ESI source (FIA - direct infusion) or MALDI, and MALDI-TOF-MS instrument (rapifleX, Bruker). Ions in positive polarity mode were acquired. Results were analyzed by Metaboanalyst statistical toolkit (Metaboanalyst 4.0, McGill Univeristy) and linear mixed-effects models (lme4 package, R language). Number of peaks identified by each instrument, detection method and number of significant peaks were scored by different statistical models. In positive polarity the MRMS instrument detected the highest number of peaks (6682 by FIA and 5246 by MALDI). For FIA method, Metaboanalyst toolkit found 366 peaks in positive mode, whereas mixed-effects model analyses reported 677 peaks. In conclusion, flow injection analysis (FIA) on the MRMS instrument followed by mixed-effect model analysis may aptly serve the purpose of fast metabolite profiling of plasma.

P-519

rMSIcleanup: an open-source computational tool for matrix-related peak annotation in MALDI-MSI

**PRESENTING AUTHOR:** Gerard Baquer Gómez, Universitat Rovira i Virgili, Spain

**CO-AUTHORS:** Gerard Baquer, Pere Ràfols, Maria Garcia-Altares, Maria Vinaixa, Xavier Correig

Laser Desorption/Ionization Mass Spectrometry Imaging (LDI-MSI) is a label-free technology that provides spatially resolved molecular information from tissue sections. LDI-MSI has been broadly adopted in proteomics and peptidomics. Its use in metabolomics has received much attention as a novel tool to understand mechanisms underlying complex diseases such as cancer or diabetes. However, conventional organic matrices used in MALDI-MSI cause spectral interferences in the low m/z range which seriously hamper metabolomics data processing. As an alternative, several matrix-free techniques such as sputtered metal nanolayer deposition have been used to significantly reduce such interferences. With the aim of expanding LDI-MSI usage to metabolomics applications we are developing rMSIcleanup, an open-source R package to annotate exogenous signals related to the ionization source (organic matrix or metal layer) based on its chemical composition. The development version of rMSIcleanup is available at https://github.com/gbaquer/rMSIcleanup. As the first validation, rMSIcleanup was challenged using images acquired with a sputtered silver nanolayer. Silver clusters previously reported in literature were consistently identified and chemically unfeasible cluster patterns showed lower similarity scores. A second validation step is in progress aiming to replicate the results using MALDI-MSI images. This includes a dataset containing images acquired using the most common MALDI matrices and both TOF and FTICR spectrometers. In conclusion, we have developed an open-source software tool that confidently annotates silver-related peaks in Ag-LDI-MS which anticipates its applicability to MALDI-MSI. Once demonstrated its performance in MALDI-MSI, rMSIcleanup will pave the path to the use of common organic matrices for metabolomics studies.

P-520

Discriminant analysis and feature selection in hyperspectral imaging using CORRS-CV

**PRESENTING AUTHOR:** Guillermo Quintas, Leitat Technological Center, Spain

**CO-AUTHORS:** Juan Daniel Sanjuan, David Pérez-Guaita, Julia Kuligowski

Biomarker identification through hyperspectral imaging is gaining popularity in the clinical field. Hyperspectral imaging generates large, complex datasets that require high-throughput data processing and data mining for biological analysis. The application of multivariate discriminant analysis using bilinear models such as PLS-DA to hyperspectral images requires to unfold the spatial directions in a two-way matrix, resulting in a loss of spatial information and structure. During model development, internal validation methods such as random k-fold cross-validation (CV) are widely used. However, a selection of the k subsets completely at random results in a loss of spatial structure and pixel-neighbourhood information. Highly correlated, spatially proximate pixels are distributed simultaneously into train and validation sets, leading to overly optimistic performance estimates. The use of biological replicates to define k-fold CV splits provides the most accurate estimation of the generalization performance, but it is inefficient when the number of replicates is scarce. Using simulated data sets as well as real IR and MS imaging data, we show the applicability of COnstrained Repeated Random Subsampling-Cross Validation (CORRS-CV). Results show that CORRS-CV avoids overly optimistic effects due to spectral oversampling in IR imaging. In situations where holding images back for testing is a waste of valuable information, CORRS-CV reduces the overly optimistic bias due to the use of test pixels close to the train set. Besides, the combined use of CORRS-CV and rank products increases the robustness of the selection of candidate biomarkers.
<figure><img src="image_85.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-521 Multi-metabolomics strategy sheds light on monoterpane indole alkaloids in Catharanthus roseus

PRESENTING AUTHOR: Ryo Nakabayashi, RIKEN CSRS, Japan

CO-AUTHORS: Ryo Nakabayashi, Tetsuya Mori, Kei Hashimoto, Kiminori Toyooka, Yutaka Yamada, Hiroshi Tsugawa, Kazuki Saito

Monoterpane indole alkaloids (MIAs) are one of important pharmaceutical resources in medicinal plants. For instance, 13.5% of Apocynaceae plants that produce MIAs were used for medicinal purposes in the world. Exploring known and unknown MIAs in natural plant resources is worthwhile in metabolomics. Here, we established an approach for efficiently finding MIAs in Catharanthus roseus, which is a MIA-producing plant (Apocynaceae). Metabolome data were acquired in an untargeted way using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in 15N (nitrogen)-labeled and non-labeled Catharanthus. Principal component analysis using the 15N- and non-labeled MS data in the flowers, leaves, petioles, stems, and roots paired a set of non-labeled monoisotopic ion and 15N-labeled counterpart using retention time. Mass shift between the ions showed the number of N atom in the monoisotopic ions. In MS/MS analysis, the mass shift of the product ion m/z 144.08 that is derived from indolic skeleton was evaluated. Finally, the elemental composition of 45 MIAs was identified in the plant. Similarity analysis using MS/MS spectra showed the commonality or specificity in the spectra. Detail MS/MS analysis identified a vintage MIA that has been never found in Catharanthus as well as known MIAs such as ajmalicine, catharanthine, perivine, and yohimbine. Combined analysis of microscopy and imaging mass spectrometry characterized the localization of the newly identified MIA, suggesting a function in a certain tissue. The metabolome data will be uploaded to the PlaSMA database (http://plasma.riken.jp/) at an appropriate time.

P-522 Discovery of new cholesteryl esters using a mathematical model-assisted UHPLC-MS/MS method

PRESENTING AUTHOR: Jin-Lan Zhang, Institute of Materia Medica, CAMS & PUMC, China

Cholesteryl esters (CEs) are composed of the 3-hydroxyl group of cholesterol and a fatty acyl chain through an ester bond. Abnormal CE levels are often related to various diseases, particularly hyperlipidemia and atherosclerosis. A mathematical model-assisted ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) method has been developed to profile CEs in plasma, different density lipoprotein samples from humans, rats, and golden hamsters. 81 CE candidates were detected in the above samples, of which 24 CEs were reported in the Human Metabolome Database and 57 CEs were newly identified based on a created database of theoretically possible CEs. Three mathematical models based on the characteristic chromatographic retention behavior related to structural features were established and validated using commercial and synthetic CE standards. The mathematical model-assisted UHPLC-MS/MS strategy was proposed to provide a global profile and identification of CEs. With the efficient strategy, 74 CEs in plasma of golden hamster were identified and then quantified in normal and hyperlipidemic golden hamsters by dynamic multiple reaction monitoring (dMRM). 21 CEs among 35 shared potential biomarkers were newly found for hyperlipidemia. Our study will contribute to the in-depth study of CE functions and the discovery of biomarkers for diseases.

P-524 Using Knime for the analysis of LC-MS/MS metabolic datasets of amitriptyline and verapamil

PRESENTING AUTHOR: Nouf Alourfi, School of Chemistry, University of Bristol, United Kingdom

A method for studying metabolic datasets has been developed by using Knime (Konstanz Information Miner). Knime is free and open-source data analytics, reporting and visualization platform. Our aim is to address the growing variety and complexity of data in this field and thus contribute to improving the analysis process in general. We demonstrate our approach using datasets derived from an ESI-LC-MS/MS analysis of in vitro metabolites of amitriptyline and verapamil using liver microsomes. Knime platform has been used to create, treat, share and store the raw ESI-LC-MS/MS data in a transparent and straightforward way. By using our approach, it was possible to automatically generate standard MS data visualisations. Moreover, we extend Knime by generating putative metabolites and MSMS spectra of our drugs using computational applications such as SyGMa and CFM-ID. SyGMa consists of a set of reaction rules covering a broad range of phase1 and 2 metabolism that has been derived from metabolic reactions demonstrated in the metabolite database which take a place in humans. Next, we used a Competitive Fragmentation Modeling which is used by CFM-ID to produce a probabilistic generative model for the MS/MS fragmentation process and machine learning techniques to adapt the model parameters from data. This generated model can be used to predict the spectra for chemical structure of a given drugs and then computes the predicted spectrum for each candidate and compares it to the input spectrum to rank the candidates metabolites according to how closely they match. Using this workflow, we are able to assign structures to candidate metabolites within our samples.
<figure><img src="image_86.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-525

Supporting reproducibility in metabolomics via a data analysis reporting template and the mzTab-M data standard

PRESENTING AUTHOR: Reza Salek, IARC, France

CO-AUTHORS: 1 Considine EC, Salek R., 2 N Hoffmann, J Rein, T Sachsenberg, J Hartler, K Haug, G Mayer, O Alka, S Dayalan, JTM Pearce, P Rocca-Serra, D Qi, M Eisenacher, Y Perez-Riverol, J Antonio Vizcaíno, RM. Salek, S Neumann, AR. Jones

A significant challenge in metabolomics is result reproducibility and confidence in metabolite identification, which are directly related to the choice of analytical instruments, available evidence extracted through data analysis, and quality of the reporting. Here two approaches are presented to improve reproducibility and reporting in metabolite identification. To enable comprehensive data analysis reporting, we present an R markdown reporting template [1] that guides the production of text and generates workflow diagrams based on user input. This R Markdown template presents a set of minimum information requirements specifically for data pre-treatment and data analysis in biomarker discovery (https://github.com/MSI-Metabolomics-Standards-Initiative/MIDAS). We will also present mzTab-M, a Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics [2]. This standard was developed as a joint effort between the Metabolomics Standards Initiative and Proteomics Standards Initiative organisations over several years. mzTab-M is a simple tab-separated text-format, with highly standardized structures and a detailed specification document, tightly coupled to a validation software API, and mandatory controlled vocabulary terms to populate it. The format represents final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC/GC-MS, DIMS, etc.) and the approaches used to identify molecules. It allows clear communication of identification ambiguity to readers and has widespread adoption potential, for details and implementations see https://github.com/HUPO-PSI/mzTab. Both approaches presented will facilitate more transparent and reproducible metabolomics. Citation and details: [1]Metabolites2019.doi:10.3390/metabo9030043. [2]Anal Chem.2019.doi:10.1021/acs.analchem.8b04310.

P-526

L-homocysteine Sulfinic Acid and Cysteic Acid as Novel Biomarkers of Acute Myocardial Infarction

PRESENTING AUTHOR: Youngja Park, Korea University, South Korea

CO-AUTHORS: Adnan Khan

Identifying changes in serum metabolites before occurrence of acute myocardial infarction (AMI) is an important approach for finding a novel biomarker of AMI. In this retrospective cohort study, serum samples obtained from patients at risk of AMI risk (n = 112) and non-risk control (n = 89) were tested using high resolution metabolomics (HRM), coupled with LC-MS/MS. Partial least-squares discriminant analysis (PLS-DA), along with univariate analysis using false discovery rate (FDR) at q = 0.05, were performed to discriminate metabolic profiles and to determine significantly different metabolites between healthy control and AMI risk groups. PLS-DA significantly separated the AMI risk sera from healthy. Altered metabolic pathways analysis in KEGG online database showed that biosynthesis of amino acid, 2-ococarboxylic acid metabolism, tryptophan metabolism, and amino sugar and nucleotide sugar metabolism were mainly altered in AMI risk sera. The metabolites associated with these pathways were mainly elevated among AMI patients. Further validation and quantifications by MS/MS showed that carnitine, L-homocysteine sulfinic acid (L-HCSA), and cysteic acid (CA) were upregulated, while L-cysteine and L-cysteine sulfinic acid (L-CSA) were downregulated, specifically among AMI risk sera. Additionally these discriminant metabolic profiles and phenotypes among AMI were not related to other factors such as hypertension, smoking or alcoholism. In conclusion our study suggests that detection of upregulated L-HCSA and CA along with carnitine among AMI risk patient could serve as promising non-invasive biomarkers for early detection of AMI.

P-527

Establishing a Spectral Library and Accurate Mass Retention Time (AMRT) Database for Neonatal Metabolomics Analysis

PRESENTING AUTHOR: Anas Kamleh, Thermo Fisher Scientific, Sweden

CO-AUTHORS: Chiara Lavarello, Sebastiano Barco, Igor Fochi, Martina Batolucci, Gino Tripodi, Giuliana Cangemi, Andrea Petretto

Metabolomics is an established discovery tool for biomarker discovery, disease diagnosis and novel mechanistic insights of pathophysiological processes. Liquid-chromatography mass spectrometry (LCMS) is currently the method of choice for metabolome analysis, however, the wide variety of chromatographic columns and conditions is presenting a challenge in sharing and interpretation of metabolite identities. The metabolic standard initiative (MSI) guidelines require identification of metabolites to be based on matching accurate mass, retention time and fragmentation spectra to those from authentic standards. In order to facilitate sharing of information between groups involved in neonatal metabolomics analysis, we have established a spectral library of compounds in the commercial product (IROA) metabolite identification. Data in the spectral library was acquired using flow injection analysis (FIA) which allowed the fast acquisition of spectra at a wide range of collision energies (0-120, normalized collision energy NCE). Additionally, a separate library, with optimized collision energy, was constructed by applying rules of maximal selectivity and structural information. The latter library is the basis of further confirmation of the identity of compounds in future studies. Furthermore, we provide a scheme for obtaining the minimum number of metabolite mixtures that provide full coverage of the IROA kit. These mixtures were used to establish an accurate mass/retention time (AMRT) databases using six different chromatographic columns (three reversed-phase and three HILIC) and chromatographic suitability criteria were applied to select the most appropriate methods. Metabolite identification in matrices (plasma, urine and sweat) were further validated using the established AMRT databases and libraries.
POSTER SESSIONS 1 AND 2 – Monday and Tuesday – all odd number posters will be on display.

POSTER SESSIONS 3 AND 4 – Wednesday and Thursday – all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-528

Metabolite identification from LC-MS/MS spectra using deep learning

PRESENTING AUTHOR: Svetlana Kutuzova, Technical University of Denmark, Denmark

CO-AUTHORS: Douglas McCloskey, Christian Igel

Mass spectrometry is a powerful high-throughput technology for chemical composition assessment. However the data processing of the resulting spectra is a major bottleneck for large studies, and in particular the metabolite identification from the mass spectra. The joint community effort of collecting and maintaining metabolomics spectral databases provides the opportunity to approach the metabolite identification problem with powerful but data-hungry algorithms including deep learning. We present a novel deep learning based algorithm for compound identification that makes a prediction of a structural chemical fingerprint based on a LC-MS/MS spectrum of a compound. Both raw spectra and fragmentation tree predicted by SIRIUS software are used as an input. A Tree-LSTM network is used to process the fragmentation trees alongside with a feed forward neural network that captures patterns in the spectral data. Our method is validated on the CASMI 2017 challenge dataset. While the method does not yet outperform the state-of-the-art approach it is shown to be a proof of concept and a solid base for future developments. The future work would include learning fragmentation rules from the spectrum itself enabling a complete end-to-end spectrum analysis.

P-529

Prediction Models of Retention Indices: Application to Gas Chromatography Coupled with High Resolution Mass Spectrometry for two Column Types: DB-624 and HP-5ms

PRESENTING AUTHOR: Adrian Haiduc, PMI, Switzerland

CO-AUTHORS: E. Dossin, P. Diana, P.A. Guy, N.V. Ivanov, M. Peitsch

Monitoring of volatile and semi-volatile compounds was performed using gas chromatography (GC) coupled to high resolution electron ionization mass spectrometry, using both headspace and liquid injection modes on DB-624 and HP-5ms columns. A total of 1'300 reference compounds (n=400 analyzed on HP-5ms and n=900 on DB-624 columns), including n-alkanes (covering C5 to C30) as reference index markers, were analyzed and experimental linear retention indices (LRI) were determined. These reference compounds were randomly split into training and validation sets. LRI for all 1'300 reference compounds were predicted based upon computational Quantitative Structure-Property Relationship (QSPR) models using calculated 2D descriptors, and based on multiple approaches: PLS, Lasso regression, stepwise MLR, Genetic Evolution Algorithm predictor selection and Neural Networks, with PLS and Lasso providing the fastest calculation and most accurate prediction level. Correlation coefficients for experimental versus predicted LRI values were calculated at 0.96 for DB-624 and 0.98 for HP-5ms for the training sets and at 0.94 and 0.95 for the validation sets, respectively. These models were then used to predict LRI values for several thousand reported metabolite compounds. The predicted LRI values can be used for column type selection as well as increased confidence level in unknown identification by means of the Mahalanobis distance.

P-530

Synthesis of IROA fragmentation scans from recursive DDA fragmentation data

PRESENTING AUTHOR: Chris Beecher, IROA Technologies, United States

CO-AUTHORS: Felice de Jong, Alexander Raskind, Philip L. Lorenzi, Lucas J. Veillon

IROA fragmentation patterns are unique in two significant ways: 1) the ability to discriminate between fragment and artifact peaks in the MS-MS spectra, and 2) the formula for every fragment is known. However, the acquisition of IROA fragment patterns has always required the use of fragmentation windows wide enough to include the entire IROA peak cluster, but ideally no wider. Since the width of each IROA peak cluster is determined by the number of carbons in the molecule, no single window width setting can be used. Using recursive DDA systems such as Thermo's AquireX, or Agilent's iDDA, have made it possible to acquire fragmentation scans for a majority of the individual peaks of an IROA cluster and then sum these fragmentation scans to synthesize what the wide window MS-MS scan would have shown. In this poster we demonstrate the implementation of such a strategy. The synthesized scans may be processed to exclude artifact peaks and determine formulae for all fragments. These two factors provide for better compound identification, and structure elucidation if the identity of a peak is unknown. While dot product calculations can still be performed on the synthetic scans, the availability and use of fragment formulae is cleaner and more accurately verifies compound identity. We believe that this is the first use of the systematic synthesis of complex MS-MS scans from individual MS-MS scans. We expect that many related, derivative applications will soon appear.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-531

Extractive Ratio Analysis NMR Spectroscopy for Improved Metabolite Identification in Complex Biological Mixtures

PRESENTING AUTHOR: *Dan Raftery, University of Washington, United States*

CO-AUTHORS: *G.A. Nagana Gowda, Liladhar Paudel*

The complexity of biological mixtures continues to challenge efforts aimed at unknown metabolite identification in the metabolomics field. To address this challenge, we provide a new method to identify related peaks from individual metabolites in complex NMR spectra. Extractive ratio analysis NMR spectroscopy (E-RANSY) builds on our previously described ratio analysis method which calculates a peak ratio divided by its standard deviation [Anal. Chem. 2011, 83, 7616-7623], such that peaks from the same molecule are enhanced and peaks from other metabolites are diminished. Here, E-RANSY first creates simplified NMR spectra that result from liquid-liquid extraction of metabolites under varied pH conditions. Under such conditions, metabolites from the same biological specimen are extracted differentially such that their signals vary dramatically across the sample set; the resulting NMR spectra exhibit characteristics favorable for unraveling unknown metabolite peaks using ratio analysis. We demonstrate the utility of the E-RANSY method for the analysis of carboxylic acid containing metabolites in human urine. E-RANSY performs better than correlation methods such as STOCSY as well as the original RANSY method, and offers new avenues to identify unknown metabolites in complex biological mixtures. We will also discuss the development and optimization of the algorithm for RANSY and ways to expand its utility, such as using it to deconvolute MS data and for bridging the gap between NMR and MS spectra of the same sample for improved unknown detection.

P-532

Advantages and limitations of Orbitrap GC-MS in metabolomics research

PRESENTING AUTHOR: *Daniel Stettin, Friedrich-Schiller-University Jena, Germany*

CO-AUTHORS: *Georg Pohnert*

The annotation of unknown compounds represents the current bottleneck in MS-based metabolomics research (Viant et al., 2017). LC-MS based metabolomics has received a boost in that regard with the introduction of the Q-Exactive Orbitrap MS (Alvarez-Rivera et al., 2019). The later introduced Q-Exactive Orbitrap GC-MS has remained fairly unexplored in its potential for metabolomics research. In this work, we set out to assess what level of insight into the metabolome of a non-model organism the GC-Orbitrap can provide. We devised a simple comparative metabolomics experiment involving an osmotic stress treatment on a unicellular algae and ran the samples both on a nominal mass GC-MS as well as the GC-Orbitrap. Resulting data was compared in regards to data quality, degree of putative annotation and the potential to identify unknowns. Herein we show that the accurate mass provided by the GC-Orbitrap does not improve compound annotation when combined with traditionally used EI-database matching but does enhance current annotation techniques (Lai et al., 2017). We successfully identify more than 50% of the dysregulated compounds found on both platforms. Still, the majority of unknowns remain unannotated despite the plethora of structural information provided by the GC-Orbitrap. These results show the potential of the Orbitrap mass analyzer for GC-MS based metabolomics but also reveal a lack of computational support currently hampering the rapid annotation of unknowns.

P-534

Differentiation of positional isomers of drug metabolites using infrared ion spectroscopy

PRESENTING AUTHOR: *Rianne van Outersterp, FELIX Laboratory, Netherlands*

CO-AUTHORS: *Giel Berden, Valerie Koppen, Filip Cuyckens, Jos Oomens, Jonathan Martens*

An understanding of the metabolism of drug candidates, including the identification of downstream drug metabolites, is a crucial step in drug development. Due to its high sensitivity and selectivity, mass spectrometry (MS) is often the analytical method of choice. However, closely related compounds may be hard to distinguish using (tandem) MS alone, as these are often isobaric and give identical fragmentation mass spectra. Alternative methods involving expensive and time-consuming purification steps are usually needed to confidently resolve full molecular structures. Infrared ion spectroscopy (IRIS) records an IR spectrum of a mass-selected gas-phase ion directly inside a mass spectrometer. The IR fingerprint is highly sensitive to molecular structure, and can be recorded for each m/z-feature detected in a standard MS experiment. We exploit this technique, which has full MS sensitivity and compatibility, to identify small molecules resulting from untargeted MS-based experiments, including drug metabolites. Here, we explore the use of IRIS for the differentiation of positional isomers resulting from the phase I metabolism reactions (usually oxidation, reduction or hydrolysis) that introduce a reactive or polar group on a phenyl ring in a drug molecule. The chemical modification that occurs can usually be determined from the mass spectrum, but the exact site of biotransformation often remains unknown. We demonstrate how metabolites bearing a ortho-, meta- or para-hydroxylation can be distinguished based on their IR spectra. Also, we show that identification is possible on the basis of spectra predicted by quantum-chemical calculations, opening opportunities for reference-standard free identification.
<figure><img src="image_87.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

**P-535*** DynaStl: a dynamic database for LC-MS annotation in metabolomics

PRESENTING AUTHOR: Santiago Codesido, Université de Genève, Switzerland

CO-AUTHORS: Giuseppe Marco Randazzo, Fabio Lehmann, Víctor González-Ruiz, Arnaud García, Ioannis Xenarios, Robin Liechti, Alan Bridge, Julien Boccard, Serge Rudaz

One of the key problems in metabolomics is identification from few analytical parameters, within the wide range of compounds that comprises the metabolome. Steroidomics is a salient example of this problematic, facing the additional difficulty of dealing with a set of molecules with very similar structures, and so they are the perfect testing ground for new approaches. Liquid chromatography (LC) is often the method of choice for metabolite separation, but such structural similarity can be compromising for this separation due to the consequently similar physico-chemical properties. The problem is usually overcome by careful tuning of the mobile phase gradient, to focus the separation on compounds of interest. However, in a standard workflow this is highly problematic for annotation. It requires characterizing a library of known compounds for every fine-tuned configuration. We present a software solution, DynaStl, capable of annotating LC-MS (liquid chromatography-mass spectrometry) features by dynamically generating the retention times from a database containing intrinsic properties for a library of metabolites. In this way, the chemical characterization of the library only needs to be performed once, and the generated retention times are adapted to the parameters of each gradient on the fly. We study the influence of experimental vs. in-silico compound properties on the quality of the prediction and the annotation, and we introduce a calibration mechanism to increase accuracy and compensate for deviations in the input parameters. We run tests on both standards and real samples, and observe that the algorithm produces reliable predictions, suitable for metabolomics compound annotation.

**P-536** Comparison of the metabolite profiles of human and canine saliva

PRESENTING AUTHOR: Soile Rummukainen, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Finland

CO-AUTHORS: Jenni Puurunen, Olli Kärkkäinen, Seppo Auriola, Arja M Kullaa, Hannes Lohi, Kati Hanhineva

There is a growing interest towards using saliva as a non-invasive sample material for monitoring health and disease status to assist diagnosis or studying molecular mechanisms of disease pathologies. Same applies to domestic dogs (Canis lupus familiaris) which suffer from similar diseases like humans having e.g. diabetes, inflammatory bowel disease and cancer. Human saliva is known to be rich in small molecules, but there is no data available for dog saliva metabolome even though a dog is also a modern model for human diseases. The present study investigated metabolite composition of stimulated saliva samples collected from 13 privately-owned dogs and 14 human individuals. We applied non-targeted metabolomics method based on ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Complementary methods, reverse phase and hydrophilic interaction chromatography were used with both, positive and negative ionization, to cover a broad repertory of compounds. Metabolite identification was accomplished using MS-DIAL software and both in-house and open-source libraries. With this approach, > 200 metabolites were identified revealing distinct metabolic profile in dog and human saliva. The biggest differences between species were found among lipids and small peptides. Other identified metabolites were amino acids, amino acid derivatives, biogenic amines, nucleic acid subunits, organic acids and other metabolites, such as general waste products. In addition, some exogenous compounds were identified. Further studies are needed to evaluate the utility of saliva as a diagnostic material for canine health monitoring with possibilities to benefit dog as well as human health in the future.

**P-537** Quantitative Analysis of Over 600 Metabolites in NIST SRM 1950 Using Multiple Analytical Platforms

PRESENTING AUTHOR: Rupasri Mandal, University of Alberta, Canada

CO-AUTHORS: Jun Han, Paulina de la Mata, Xian Luo, Meera Shanmuganathan, Pascal Mercier, Rene Zahedi, Michael Overduin, James Harynuk, Philip Britz-McKibbon, Liang Li, Christoph H. Borchers, and David S. Wishart

A wide variety of analytical methods have been developed for targeted metabolomics. By combining multiple techniques, it is now possible to achieve much more comprehensive coverage of a sample's metabolome. However, the precision, accuracy, and level of metabolome coverage of different platforms is often not well known. To answer these questions we chose to comprehensively characterize a widely-studied biofluid sample (human pooled plasma, NIST Standard Reference Material, SRM 1950) using 12 different targeted assays conducted on 8 different analytical platforms. The platforms included NMR (700 and 800 MHz), direct injection/liquid chromatography tandem mass (QTrap) spectrometry (DI/LC-MS/MS), LC-MS with isotope-labeled internal standards (LC-MRM-MS), LC-coupled with high-resolution mass (Orbitrap) spectrometry (LC-HRMS), inductively coupled plasma mass spectrometry (ICP-MS), two-dimensional gas chromatography mass (QTOF) spectrometry (GCxGC-TOF MS) and capillary electrophoresis with ultraviolet (CE-UV) and mass spectrometry (CE-MS). A total of 860 quantitative measurements for 628 metabolites were obtained from the 12 different analytical assays. Another 81 metabolites are identified (but not quantified). For metabolites quantified by more than one method, the measured concentrations were compared between methods and against either NIST reference data or known reference ranges from the literature. Most assayed metabolites showed excellent cross-platform agreement (10-15%). Clear differences in platform coverage and sensitivity are evident. This represents the most complete quantitative characterization of SRM 1950. It also provides high-confidence reference values for SRM 1950 that should allow other research labs to calibrate their assays. Furthermore, this work gives important insights into the strengths and weaknesses of different metabolomic platforms/assays for plasma/serum analysis.
<figure><img src="image_88.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-538

Machine-learning based spectral similarity measures to better identify different yet related compounds from large
metabolomic datasets.

PRESENTING AUTHOR: Florian Huber, Netherlands eScience Center, Netherlands

CO-AUTHORS: Justin J.J. van der Hooft, Simon Rogers, Lars Ridder

Extensive high-throughput mass spectrometry has become an important tool in many areas of life sciences and medicine. Analyzing and interpreting the resulting complex mass spectral data remains a challenging task, in particular for mixtures containing large numbers of unidentified compounds. One key challenge in extracting useful information from such data is to determine if spectra belong to identical or similar molecules. This is typically done by deriving spectral similarity scores, currently often based on comparing (intensities for matching) peak positions, for instance by calculating a modified cosine score (as used in GNPS molecular networking). Those measures work well for spectra obtained for very similar compounds, but often perform poorly when used to find similarities between spectra of notably different yet related compounds. This is assessed by using Tanimoto coefficients between molecular fingerprints from a large set (>10,000) of MS/MS reference spectra as benchmark. We here propose a number of alternative approaches for measuring spectral similarity which are based on established machine-learning algorithms including techniques adapted from natural language processing, but also PCA and deep autoencoders. We will present several measures that outperform the modified cosine score in selecting spectra from structurally closely related molecules in datasets containing potentially unknown compounds. We further find that some of the presented measures show complementary characteristics which can either be combined or be used to address different types of similarity. Taken together, we conclude that these novel spectral similarity measures are a promising alternative for established measures.

P-539

SIRIUS 4 - Turning tandem mass spectra into metabolite structure information

PRESENTING AUTHOR: Marcus Ludwig, Friedrich-Schiller-University Jena, Germany

CO-AUTHORS: Kai Dührkop, Markus Fleischauer, Martin A. Hoffmann, Juho Rousu, Sebastian Böcker

Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) is one of the predominant experimental platforms for untargeted metabolomics, but searching acquired tandem spectra in spectral libraries will only identify a small portion of the measured metabolites. Here, we present the new release of the SIRIUS software. SIRIUS 4 is the best-in-class software method for de novo molecular formula annotation and structure elucidation. SIRIUS 4 integrates high-resolution isotope pattern analysis and fragmentation trees for molecular formula identification. CSI:FingerID is seamlessly integrated via a RESTful webservice to search MS/MS spectra in a molecular structure database. SIRIUS 4 has a novel isotope pattern scoring. Using a deep neural network, SIRIUS detects rare elements from the isotope pattern. The running times of SIRIUS were reduced by more than two orders of magnitude. CSI:FingerID is now supporting negative ion mode spectra and integrates new kernels and fingerprints for a better identification performance. Users can search in custom suspect databases or view the substructure recommendations for manual structure elucidation. In evaluation on the Agilent MassHunter database the number of correct molecular formula annotations solely based on the isotope pattern increased by 74.3% compared to predecessor SIRIUS 3. On 208 compounds from the CASMI 2016 challenge, SIRIUS 4 correctly identified the molecular formula in 93.75% of the cases. Out of the 127 compounds in positive ion mode CSI:FingerID correctly identified 57.5% of the structures when searching in PubChem and 74% when searching in a smaller database of 0.5 million structures of biological interest. The best competitor correctly identified 36.22%.

P-540

Integrated NMR and LC-MS based metabolomics approach for biomarker identification for radiation exposure

PRESENTING AUTHOR: Ruchi Baghel, Division of Behavioral Neuroscience, INMAS, DRDO, India

CO-AUTHORS: Kiran Maan, and Poonam Rana

In the event of an intentional or accidental release of ionizing radiation in a densely populated area, timely assessment and triage of the general population for radiation exposure is critical. Despite decades of research, counter measures still lack. In this study, we describe the potential of integrated NMR and LC-MS approaches in evaluating the radiation biomarkers. Untargeted profiling by means of broad-spectrum, highly sensitive, UPLC-ESI-QTOFMS provides a comprehensive list of metabolites at one go in a single biofluid. Present study aims to discover new, as well as validate the previously identified metabolic signatures for whole-body irradiation in mice. The study comprised 33 C57BL6 male (8-10 weeks) mice distributed as 5Gy, 7.5Gy and controls having 11 each and irradiated through60Co gamma source. Urine samples collected post24 hrswere run in both ESI positive and negative mode. All the data were normalized by sum and were than pareto-scaled followed by multivariate analysis including PCA and PLS-DA. Of the total 1514 (positive) and 1764 (negative) peaks univariate analysis(t-test, p<0.05significant) revealed a total of 658 significant (positive) molecules with creatinine (p=9.8x10-5) and L-carnitine (p=3.6x10-8)from 5Gy whereas betaine (p=2.0x10-4), 8-hydroxyquinone (p=9.6x10-5) and L-carnitine (p=3.4x10-5) from 7.5 Gy. Out of 537 significant (negative) molecules taurine (4.7x10-4) and Quinnolinic acid (6.3x10-4) were from 5Gy. Present study thus validates our previously (NMR) reported significant metabolites citric acid, hippuric acid and taurine. The results thus lay foundation for high-throughput triaging by metabolomic biomarkers for effective medical management. Further pathway analysis also reveled results.
<figure><img src="image_89.png" /></figure>

**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-541

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

PRESENTING AUTHOR: Gaëlle Lefort, INRA, France

CO-AUTHORS: Laurence Liaubet, Hélène Quesnel, Cécile Canlet, Nathalie Vialaneix, Rémi Servien

1H Nuclear Magnetic Resonance (NMR) is a high-throughput technology that allows to obtain metabolomic profile from easy-to-obtain fluids (such as blood) at moderate cost. It is thus a promising tool to detect practically usable biomarkers. However, its interpretation can be hard to make, because metabolites present from the 1H NMR spectrum of a complex mixture are often identified and quantified only from expert knowledge. To facilitate the use of such data, we developed a new R package, ASICS, that implements a method for the automatic identification and quantification of metabolites in 1H NMR spectra. The package combines all the steps of the analysis (management of a reference library with pure metabolite spectra, preprocessing, quantification, diagnosis tools to assess the quality of the quantification, post-quantification statistical analyses). The latest developments allow to improve alignment algorithm and quantification using information coming from all spectra of the same study. To assess the performance of ASICS, data from PORCINET (ANR-09-GENM-005) were used. Both the quantification and its impact on a post-quantification differential analysis were evaluated. Correlations between ASICS relative quantifications and biochemical dosages of three metabolites were computed and a similar analysis was performed with other quantification methods like Autofit or batman. These comparisons showed that ASICS allows for a faster and simpler direct biological interpretation than the classical bucket approach and obtains more precisely identified and quantified metabolites than other quantification methods. ASICS is released as an R/Bioconductor package.

P-542

High accuracy of retention time prediction for plant food bioactives using PredRet

PRESENTING AUTHOR: Claudine Manach, INRA, France

CO-AUTHORS: Low D, Micheau P, Abranko L., Bronze M., Hanhineva K., Koistinen V., Stanstrup J, Manach C

Plant food bioactives receive widespread interest for their protective health effects. However, due to their huge chemical diversity and the lack of chemical standards for many of their phase I, -II and microbial metabolites, their identification in untargeted metabolomics profiles of food or biofluids remains a challenging feat. Retention time (RT) is a valuable information for assisting the identification as it helps to narrow the number of hypotheses within an observed RT window to a manageable number of compounds to purchase or synthesize for confirmation. In the framework of the COST Action FA1403 POSITIVe (https://www6.inra.fr/cost-positive, we evaluated the usefulness of PredRet (http://predret.org), an open access RT database, to predict RT of plant food bioactive metabolites in a multi-laboratory test involving 18 laboratories and 24 reversed-phase Chromatographic Systems (CS=column + elution phases and gradient). Participants shared datasets of RT in their own CS for 29 to 104 compounds, covering a total of 471 chemicals, including highly polar to lipophilic aglycones, glycosides, conjugated and microbial metabolites of flavonoids, phenolic acids, alkaloids, and others. Depending on its comparability with other CSs, every platform obtained predicted RTs for 67 to 667 compounds that were not analysed in their conditions. The predictions were very accurate, with a median prediction error ranging from 0.03 to 0.76 min. Such level of prediction allowed distinguishing isomeric compounds. It also provided information in all CSs for rare standards. In conclusion, RT prediction with PredRet has proven very useful to facilitate annotation of plant food bioactives in metabolomics studies.

P-543

Metabolic effects of dietary glycerol supplementation in muscle and liver of European seabass and rainbow trout by
1H NMR metabolomics

PRESENTING AUTHOR: Mariana Palma, Universidade de Coimbra, Portugal

CO-AUTHORS: Ludgero C Tavares, João Rito, Luís F Henriques, Paulo Rema, Leonardo J Magnoni, Ivan Viegas

The sustainable development of aquaculture is still dependent in the substitution of fishmeal for alternative ingredients, especially for carnivorous fish, such as rainbow trout (Oncorhynchus mykiss) and European seabass (Dicentrarchus labrax). Glycerol has been already used as an alternative energy source in diets for farmed animals, sparing amino acids to other functions such as growth. The aim of the work was to evaluate the effects of dietary glycerol supplementation in rainbow trout and European seabass muscle and liver metabolome. Fish were fed diets with 0%, 2.5% and 5% glycerol, muscle and liver were collected, and tissue aqueous fraction was extracted. 1H-Presat and CPMG spectra were acquired for each muscle and liver sample, respectively, on a Varian VNMRS 600 MHz spectrometer. Both untargeted and targeted approached were followed applying Principal Component Analysis (PCA) and univariate statistical analysis, respectively. PCA plot scores showed scarce differences in muscle and liver metabolite composition in both species, regardless of the treatment. Regarding univariate analysis, European seabass had more variations in the muscle and liver metabolome than rainbow trout. European seabass presented changes generally related with protein biosynthesis pathways, while in rainbow trout variations were associated with choline-related metabolism. Albeit rainbow trout seems to be more suitable to be fed with these dietary glycerol percentages, the tested diets have the potential to be used in aquaculture production. NMR-Metabolomics approach proved to be adequate to be applied in these studies, providing a quick global overview of the results and also enabling the general tissue metabolite profiling.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-544

Accelerating Substructure Annotations in Complex Metabolite Mixtures

**PRESENTING AUTHOR:** Justin J.J. van der Hooft, Bioinformatics Group, Dept of Plant Sciences, Wageningen University, Netherlands

**CO-AUTHORS:** Madeleine Ernst, Sam Stokman, Cher Wei Ong, Lars Ridder, Stefan Verhoeven, Ricardo da Silva, Mingxun Wang, Kyo Bin Kang, Joe Wandy, Pieter C. Dorrestein, Marnix H. Medema, Simon Rogers

Deciphering complex metabolite mixtures remains a challenging task. Key reasons for this are the complexity and sheer number of information-rich mass fragmentation spectra hampering quick dereplication of known molecules and prioritization of novel chemistry. Here, however, we will take advantage of this information-richness by using computational approaches that discover spectral similarities and mass spectral patterns corresponding to the biochemical building blocks of molecules here termed substructures. We highlight recent advances that integrate existing metabolome mining tools with annotation tools such as Network Annotation Propagation as well as tools that specifically annotate possible peptidic spectra. This eases the interpretation of large mass spectral molecular networks. For example, we used the ClassyFire chemical classification ontology to annotate molecular families (MFs) observed in large MS/MS data collections. This could help to prioritize relevant MFs. In bacterial data, we could quickly assess how many peptidic MFs were present. New structural variants of the cyclic peptide Xenoamicin were found and Xenoamicin-related MFs were discovered by the substructures (spectral patterns) they shared. Additionally, an aminosugar substructure motif revealed many potentially novel natural products. Furthermore, integration of the discovery of mass spectral motifs by MS2LDA with automatic substructure annotation using MAGMa supports and accelerates substructure annotations. Finally, the introduction of MotifDB, a repository for annotated spectral patterns (Mass2Motifs) from MS2LDA, reinforces substructure discovery by capturing community knowledge that will be reused by seeding MS2LDA with structurally annotated Mass2Motifs. In conclusion, substructure annotation is a promising avenue to overcome challenges of metabolite annotation in complex metabolite mixtures.

P-545

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

**PRESENTING AUTHOR:** Bita Khalili, University of Lausanne, Switzerland

**CO-AUTHORS:** Mattia Tomasoni, Mirjam Mattei, Roger Mallol Parera, Reyhan Sonmez, Daniel Krefl, Rico Rueedi, Sven Bergmann

Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the Statistical total correlation spectroscopy (STOCSY) approach. We used our previously developed metabomatching (Rueedi et al. 2017) method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. We concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.

P-546

Learning from Experience: Non Trivial Issues and Solutions in LC-MS-Based Metabolomics Analysis

**PRESENTING AUTHOR:** Ilana Rogachev, The Weizmann Institute of Science, Israel

**CO-AUTHORS:** Maxim Itkin, Adam Jozwiak

It is likely that most if not all metabolomics specialists employing LC-MS-based technology encounter samples displaying un-predictable behavior in the LC column or during MS ionization. While plenty of troubleshooting information is provided by instrument vendors they mostly concern with common LC-MS problems. Quite the reverse, solutions for uncommon problems originating from specific extracts or metabolites is barely accessible. In this presentation, I have composed a set of non-standard problems, engaged in our lab following more than 15 years of research in the HR LCMS-based metabolomics field. Examples of such challenges include retention time shifts of metabolites in the presence of highly abundant compounds, absence of molecular ion in the MS chromatogram, and more. Based on our past experience, I will provide several solutions and furthermore tips for predicting potential technical hitches prior to experimentation.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-547 Investigation of Microalgae Polar Lipidome by Optimized Chromatographic Separation, High-Resolution Mass Spectrometry, and Comprehensive Identification with Lipostar

PRESENTING AUTHOR: Giorgia La Barbera, Department of Nutrition Exercise and Sports, University of Copenhagen, Denmark

CO-AUTHORS: Michela Antonelli, Anna Laura Capriotti, Chiara Cavaliere, Andrea Cerrato, Gabriele Cruciani, Laura Goracci, Susy Piovesana, Aldo Laganà

The polar lipidome of freshwater microalgae has been already investigated [1], mostly by liquid-chromatography (LC) coupled to high-resolution mass spectrometry (HRMS) [2]. However, studies focused on method development for the chromatographic separation of glycosylglycerolipids, i.e. sulfoquinovosyldiacyl- and sulfoquinovosyl-monoacyl-glycerols, (SQDG, SQMG), monogalactosyldiacyl- and monogalactosylmonoacyl-glycerols, (MGDG, MGMG), digalactosyldiacyl- and digalactosylmonoacyl- glycerols (DGDG, DGMG), are lacking. Due to glycosylglycerolipids high abundance in microalgae [1], in this study, a chromatographic optimization of both glycosylglycerolipids and phospholipids was carried out for the comprehensive characterization of spirulina microalgae lipidome. A Kinetex-EVO C18 column was used on a ultrahigh-performance LC system coupled by electrospray ionization source to a hybrid quadrupole orbitrap HRMS. Firstly, different chromatographic conditions were evaluated based on resolution, peak capacity, and peak shape, on a mixture of lipid standards. The optimized conditions were used for the analysis of spirulina microalgae. HRMS and MS/MS spectra were analyzed by Lipostar software [3]. Lipostar was implemented in order to both improve the identification of phospholipids and to allow the identification of glycosylglycerols, resulting in the identification of 205 lipids [4]. Finally, a targeted method was developed for the selective enrichment of SQDG and SQMG from spirulina microalgae, increasing the number of identified compounds. References: [1] E.Da Costa, J.Silva, S.H.Mendonça, M.H.Abreu and M.R.Domingues. Mar. Drugs 2016, 14(5), 101; [2] Y.H.Rustam, G.E.Reid. Anal. Chem. 2018, 90(1), 374-397; [3] L.Goracci, S.Tortorella, P.Tiberi, R.M.Pellegrino, A.Di Veroli, A.Valeri, G.Cruciani. Anal. Chem. 2017, 89(11), 6257-6264; [4] G.La Barbera, M.Antonelli, C.Cavaliere, G.Cruciani, L.Goracci, C.M.Montone, S.Piovesana, A.Laganà and A.L.Capriotti. Anal. Chem. 2018, 90, 12230-12238

P-548 Engineering candidate sets for annotation using Extended Metabolic Models (EMMs)

PRESENTING AUTHOR: Soha Hassoun, TUFTS UNIVERSITY, United States

CO-AUTHORS: Neda Hassanpour, Alden, Nicholas, Kyongbum Lee

A major challenge in metabolomics analysis lies in resolving the chemical identities of detected features. Computational annotation tools recommend one or more candidate chemical structure that best explains the spectral signature. These approaches often rely on a candidate set that is typically culled from a large database such as PubChem. However, not all such compounds are biologically relevant to the sample under study and there are many biologically relevant yet unknown compounds ("dark matter") that are not catalogued at all. We propose a novel method for systematically engineering candidate datasets that can be used during annotation. Our central premise is that promiscuity of enzymes towards substrates within the sample is responsible for a large number of detected measurements. To create an EMM, a reference metabolic model, cataloged in KEGG, BIGG, or BioCyc, for the organism(s) associated with the sample, is augmented with promiscuous metabolic products. Such products are derived using PROXIMAL, a tool for predicting promiscuous products. EMM metabolites with masses that match to measurements, within a small ppm, are considered for the candidate set. We demonstrate the utility of our approach in annotation using an untargeted LC-MS dataset for Chinese hamster ovary (CHO) cells. Our test case shows that our EMMA (EMM Annotation) workflow creates a candidate set that is enzymatically relevant to the sample and that includes metabolites beyond what is already catalogued in PubChem. Further, we experimentally confirm the identification of 4-hydroxyphenyllactate, a metabolite that is currently not part of the known catalog of CHO cell metabolites.

P-549 WiPP: Workflow for improved Peak Picking

PRESENTING AUTHOR: Yoann Gloaguen, Berlin Institute of Health, Max Delbrück Center, Germany

CO-AUTHORS: Nico Borgsmüller, Tobias Opialla, Eric Blanc, Emilie Sicard, Anne-Lise Royer, Bruno Le Bizec, Stéphanie Durand, Carole Migné, Mélanie Pétéra, Estelle Pujos-Guillot, Franck Giacomoni, Yann Guitton, Dieter Beule, Jennifer Kirwan

High false positive rates in GC-MS metabolomics peak detection is a common issue that impedes automated analysis of large-scale data. Many algorithms are available for peak detection but performance differs depending on algorithmic approach and data acquisition method. This makes it difficult to compare between algorithms without extensive manual intervention. We present a parameter optimizing, multi-algorithm peak detection workflow for GC-MS metabolomics which automatically evaluates the detected peaks' quality using machine learning-based classification. First, the classifier is trained to distinguish between real compound-related and false positive peaks. Then the algorithm parameters are scored based on detected peaks quality and optimized accordingly. This procedure is repeated for two peak detection algorithms and both algorithms are run in parallel on the entire data set with the optimized parameters. The qualitative information returned by the classifier for every peak is used to merge individual algorithm results into one final high-confidence peakset. Using this approach, we show that automated detection and evaluation of peak quality is possible with sensitivity of 90% on a standard compound mixture. The information generated by the classifier allows: Optimization of individual peak detection algorithms parameters to increase performances, an objective way to assess peak detection algorithm performance, optional exclusion of peaks which are of intermediate to low quality. We demonstrate that this workflow automatically recovers over 90% of the compounds identified in biological data by manual curation, so less than 10% of the peaks need user attention. The workflow is suitable for large-scale studies.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-550

A Metabolite Identification Guide for the Assignment of Unknown Signals in NMR Metabolomics

PRESENTING AUTHOR: Silke Heinzmann, Helmholtz Center Munich, Germany

CO-AUTHORS: Philippe Schmitt-Kopplin

Identification of molecular features is still a bottleneck in non-targeted metabolomics. Generally, only signals that are highlighted in a study in response to an intervention or associated to a (disease) phenotype are identified. However, when aiming to name and count the metabolites that are visible by NMR spectroscopy, an answer is difficult. Which and how many metabolites do we see in a given sample? Is there a set of metabolites that is present in most samples? How can we organize a straightforward workflow for the identification of metabolites? Here, we show a workflow for the identification of metabolites based on two-dimensional NMR analyses and structural correlation analyses. First, we collect as much information as possible via NMR spectroscopy by acquisition of 1- and 2-dimensional experiments. This information is then submitted to in-house and online databases (e.g. HMDB). In a second step, the CLASSY (i.e. cluster analysis statistical spectroscopy) method is used to cluster all peaks of the NMR spectrum, first locally (i.e. peaks that derive from the same molecule) and then globally (i.e. clusters that are biologically related). Thereby, known metabolites do cluster with unassigned peaks and facilitate structure elucidation by giving a suggestion on pathway relationships or chemical classes. With this approach, we have identified several signatures from host-microbial co-metabolism and food metabolites that require microbial putrefaction processes. The cluster analysis method gave valuable hints towards identification of previously not reported metabolites.

P-551

Multi-feature based data processing of Data Independent acquisition metabolomics data without retention time information

PRESENTING AUTHOR: Stephen Tate, SCIEX, Canada

CO-AUTHORS: Pradeep Narayanaswamy, Adam Lau, Lyle Burton

Mass spectrometry coupled with liquid chromatography is the most popular method for untargeted metabolomics that detects thousands of features from complex samples, but only a fraction of them are annotated. Each analyte can give rise to multiple features like in-source fragments (ISF), adducts, and different charge states. Chromatography retention times (RT) of the metabolites is important orthogonal information required for avoiding pitfalls due to many features having similar mass-to-charge (m/z) ratios which can interfere with known compounds. Recent advances in MS instrumentation with data-independent acquisition (DIA) mode like SWATH® acquisition gave unbiased MS/MS data capturing of all the features that are generated from metabolites. Using multiple features, we developed a scoring model for confident identification of the metabolites. LC-MS often produces multiple features along with due to adducts [Na, K, Li, NH4, Acetate, Formate, Cl etc.], multiply charged, dimers and in-source fragments. We developed a prediction model to generate in-silico MS/MS spectra for other features (ISF) based on the MS/MS of known features of M+H/M-H and then matched the observed fragments of in-silico spectra for sequence coverage against MS/MS spectra of molecular ion (M+H/M-H) to enhance the scoring of metabolites. In complex mixtures, two or more peak groups that represent product ions are located at different RT intervals along with the expected peak group. To address this challenge, we developed a model that correlates the peak groups across the SWATH windows for multiple features to identify right metabolite and reduce the false positive identifications.

P-552

Targeted metabolomics – analytical method development and validation of free fatty acids in mammal's plasma and tissue by gas chromatography mass spectrometry analysis

PRESENTING AUTHOR: Anna Bauer, Max Delbrück Center, Germany

CO-AUTHORS: Raphaela Fritsche, Jennifer Kirwan

Plasma free fatty acids (FFA) are lipolysis byproducts primarily derived from adipose tissue. High levels of plasma FFA have toxic effects on organs related to cardio metabolic disease. Furthermore, lifelong exposure of populations to dietary fatty acids has raised concerns about the potential links between cognitive disorders and nutrition. However, the association of FFAs with cognitive impairments or dementia is still unknown but there is obviously great interest in this field of research given the potential implications for human health. In order that this area of research can be better studied, a well validated, quantitative analytical method is required. A short and simple method by gas chromatography coupled to a mass spectrometer was developed to determine 21 FFA. The optimized methodology entailed the formation of trimethylsilyl derivates using N-Methyl-N-(trimethylsilyl) trifluoroacetamide after a one-step derivatization at 37 °C. Compared to existing methods, the aim of this method was to develop a rapid qualitative and quantitative method for individual FFA suitable for both plasma and tissues of various types. The analytical method is currently being fully validated according to European medicine guideline using external calibration curves. All standard parameters important for a well validated method have been measured and optimized including precision (intra and inter-day), stability and reproducibility, limit of detection and lower and upper limits of quantification. This is designed to work on an automated derivatization system, including the use of pooled quality control samples to both measure and reduce the technical variation in the analyses to a minimum.
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**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

**P-553** A complete workflow for improved untargeted metabolome annotation and identification using ultra high-resolution accurate mass and LC-MSn Orbitrap-based mass spectrometry

PRESENTING AUTHOR: David Peake, Thermo Fisher Scientific, United States

CO-AUTHORS: Reiko Kiyonami, Ioanna Ntai, Amanda Souza, Ralf Tautenhahn

Compound identification is the most significant bottlenecks faced by researchers in untargeted metabolomics today. The gold standard for structure elucidation in organic synthesis is a consensus of information from multiple analytical methods (2D-NMR, FTIR, UV-VIS and other methods) to characterize milligrams of purified material. For identification of nanogram levels of metabolites in human plasma using mass spectrometric methods, confident annotation of known metabolites requires very high resolution, accurate mass measurement coupled with LC-MS and MS/MS analysis. SRM1950 (NIST) human pooled plasma extract was separated on a reversed-phase C18 column and LC-MS analyses were performed using an Orbitrap mass spectrometer. Mass spectral data were acquired using 120K resolution MS and data dependent MS2 at 30K resolution. Identifications were confirmed by spiking standards into the plasma extract and neat standard run under the same conditions. Orbitrap-based mass analyzers offer many analytical advantages including ultra-high resolution, very high mass accuracy and precision, and multiple methods of ion activation with MSn capabilities for structure elucidation. By combining accurate mass measurement, isotopic pattern, fine isotope structure and high quality mass spectra including fragmentation data with multiple structure database and mass spectral library searches one can achieve high confidence in metabolite annotations. These concepts are illustrated with the analysis of NIST SRM1950 human plasma. Using an automated and flexible data processing pipeline, it is possible to obtain consensus between all data sources giving higher confidence in metabolite annotation. Ultimately, proof of identification requires comparison of a reference standard with the analyte under the same analytical conditions.

**P-554** Detecting Low Abundant Endogenous Cardiac Steroids from Biological Fluids Using a Structure-Based MSn Approach on an Orbitrap Tribrid MS

PRESENTING AUTHOR: Sally Webb, Thermo Fisher Scientific, United States

CO-AUTHORS: Reiko Kiyonami, Michael G. Harrington, Alfred N. Fonteh, Roger Biringer, Andreas Huhmer

Endogenous cardiac steroids are specific inhibitors of the sodium pump (Na+/K+-ATPase) and play important biological roles such as regulating cell growth, differentiation, apoptosis, fibrosis, immunity, carbohydrate metabolism, and nervous and mental functions. Detecting endogenous cardiac steroids from biological fluids is very challenging because of their low concentration (ng/ml) ranges and lack of authentic standards. We have developed a novel structure-based MSn discovery approach which enables rapid annotation of cardiac steroid class compounds by identifying basic steroid sub-structure shared by cardiac steroid class compounds using MS2 and MS3 spectral tree data. We applied this approach to cerebrospinal fluid (CSF) samples and were able to detect some endogenous cardiac steroids using complementary information of MS2 and MS3 data. A Thermo Scientific™ Orbitrap ID-X™ Tribrid™ mass spectrometer was used and MSn data was collected using an MS2 data directed data dependent approach. Thermo Scientific™ Mass Frontier™ 8.0 software and Thermo Scientific™ Compound Discoverer™ 3.0 software was used for data processing with mzCloud™ spectral library and ChemSpiderTM database employed for the structural annotation of detected cardiac steroid class compounds. Three cardiac steroid-like compounds were detected from a CSF sample using the structure-based approach. Although the signals of these steroid class compounds are relatively low, both MS2 and MS3 data were being collected on these compounds, improving the sensitivity and selectivity of our workflow. A targeted PRM assay of three detected putative cardiac steroids was used to multiple CSF samples for verifying they could be detected reproducibly from other CSF samples.

**P-555** Innovations in Unknown Identification for Metabolomics and Lipidomics

PRESENTING AUTHOR: Richard Yost, University of Florida, United States

CO-AUTHORS: Robin H.J. Kemperman, Jeremy P. Koelmel, Allison J. Levy, Russell Lewis, Nicholas R. Oranzi, Jiajun Lei, Matthew E. Merritt, Timothy J. Garrett

NIH has funded a new consortium charged with "catalyzing the field of compound identification to dramatically increase the number of identified biomedically-relevant metabolites in untargeted metabolomics". This presentation will explore innovations in identifying unknown metabolites and lipids; the primary focus will be on innovations at UF, but will include a perspective on innovations from the twelve other centers within the consortium. Innovations for identifying unknown metabolites and lipids include two promising strategies, ion mobility and LC/MS/MS/NMR. Ion mobility/mass spectrometry can separate isomeric metabolites, lipids (including eicosanoids and steroids) that are not resolved by chromatography and not readily differentiated by MS/MS. Furthermore, determining an unknown's collision cross section can help determine its structure. We are developing software for processing LC/MS/MS data (including ion mobility data) that provides a wide coverage of lipids and improved lipid annotations. Integrating LC/MS/MS with microflow NMR could provide the ideal platform for identification of unknown metabolites and lipids. Although MS/MS is more sensitive and functions on the chromatographic timescale, the structural information provided by 1H NMR is invaluable, despite its lower sensitivity and longer timescale. By digitizing the LC effluent using segmented flow, the timescale of LC and NMR is decoupled and "smart" selection of LC segments (based on MS/MS data) allows targeted NMR analysis. Initial experiments are focused on differentiating lipid isomers, with extension to structure elucidation of unknown metabolites. Experimental details and recent application of these strategies will be highlighted, along with a perspective on their impact in untargeted metabolomics and lipidomics.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-556

Combining Mass Difference Networks with Spectral Meta Data Analysis for Structural Characterization

PRESENTING AUTHOR: Kris Morreel, Plant Systems Biology, VIB, Belgium

CO-AUTHORS: Sandrien Desmet, Atsushi Fukushima, Rebecca Dauwe, Geert Goeminne, Yvan Saeys, Wout Boerjan

Mass difference networks, such as Candidate Substrate Product Pair (CSPP) networks, implement knowledge on biochemical conversions for the structural characterization of unknowns. This typically occurs via network propagation in which nodes and edges represent m/z features and enzymatic conversions. Including Collision-Induced Dissociation (CID)-spectral similarities enhances both the efficiency and the reliability of this approach. Here, we present RDynLib, an R package that (i) associates different types of CID spectra, i.e. MS/MS and MSn, to the CSPP network, and (ii) provides a set of functions to improve the elucidation of especially the unknown unknowns via a combined CSPP / CID spectral meta data analysis. RDynLib is based on the alignment of chromatograms from different MS instruments following the prior archiving of all recorded CID spectra into the DynLib database. The benefit of a CID spectral meta data analysis is demonstrated by matching the CID spectra against several freely available CID spectral databases.

P-557

Assessment of in silico NMR chemical shift library errors and a new method to automate explicit and implicit solvent conditions

PRESENTING AUTHOR: Yasemin Yesiltepe, Washington State University, United States

CO-AUTHORS: Sean Colby, Jamie Nunez, Niranjan Govind, Mark Borkum, Nancy Washton, John Cort, Thomas O. Metz, Justin Teeguarden, Ryan Renslow

The majority of metabolites are not available as authentic reference material, making confident identification of most molecules in complex samples unachievable. Also, the cost of synthesizing/purchasing standards and collecting experimental data on each individually, represents a major roadblock. A promising approach to expand publicly available databases is through in silico calculation of molecular attributes. We developed the in silico Chemical Library Engine (ISiCLE), a Python-based pipeline, designed to calculate chemical properties, including nuclear magnetic resonance (NMR) chemical shifts, by employing density functional theory (DFT). Using ISiCLE, we investigated the accuracy required for identification of 11,000 chloroform and water-soluble metabolites found in the Human Metabolome Database (HMDB). Of these, 90% can be correctly identified in a pure sample when errors of 1H and 13C chemical shifts reach at least 0.7 ppm and 6 ppm, respectively. Furthermore, to decrease the calculated chemical shift error, it is possible to consider explicit solvation of metabolites. This is especially critical for polar molecules, due to their strong interaction with polar solvents. We built an automated solvation module of ISiCLE to evaluate explicit and implicit solvation at varying levels of theory. This method generates a large solvation box, filled with a solvent of interest, surrounding the analyte, and subsequently optimizes the whole system. Then, an optimum finite number of solvent shells is determined, and the remaining system is simulated with an implicit solvation model (i.e., COSMO). We anticipate this automated solvation pipeline will enable efficient and rapid identification and delineation of metabolites in custom solvent conditions.

P-558

Automated Screening of GC×GC-TOFMS Metabolomics Data Using Scripting

PRESENTING AUTHOR: Seo Lin Nam, University of Alberta, Canada

CO-AUTHORS: Seo Lin Nam, A. Paulina de la Mata, James Harynuk

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry is an analytical platform with high separation efficiency and is a powerful tool for the hybrid target/non-target analysis of complex mixtures. However, the amount of data generated from such comprehensive techniques is massive and nearly impossible to handle manually. The difficulty and complexity of data analysis is the major bottleneck in GC×GC-TOFMS analysis, rather than the usual culprits of sample preparation or instrumental time. The challenge of data processing is holding GC×GC-TOFMS back from more widespread use. In order to speed up and simplify data analysis, script-based filtering of peaks is a promising tool. Scripting is a programming language of logic rules using mass spectrometric and/or retention properties for target compounds such as fragmentation patterns and/or the presence/absence of specific ions. The use of scripting can help to quickly find and visualize compounds of interest and particular chemical classes in a sample of thousands of peaks. For environmental and petroleum work, there are numerous scripts that have been published to aid with, for example, the identification of halogenated species. Presented herein is the recent progress towards the development of a suite of scripts that classifying peaks in GC×GC-TOFMS data from metabolomics studies. This includes the identification of fatty acid methyl esters, free fatty acids, aldehydes, alcohols, amino acids, ketones, sterols and sugars.
POSTER SESSIONS 1 AND 2 – Monday and Tuesday – all odd number posters will be on display.

POSTER SESSIONS 3 AND 4 – Wednesday and Thursday – all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-559

Automated annotation of untargeted LC-MS metabolomics data using all-ion LC-MS/MS and in-silico fragmentation information

PRESENTING AUTHOR: Gonçalo Graça, Imperial College London, United Kingdom

CO-AUTHORS: Yuheng Cai, ChungHo Lau, Panagiotis Vorkas, Elizabeth J. Want, David Herrington, Timothy M. D. Ebbels

Untargeted metabolomics LC-MS experiments produce complex datasets containing tens to hundreds of thousands of features (m/zRetention time pairs) from thousands of molecules. The annotation of such features is a bottleneck, even for known compounds, requiring additional MS/MS experiments and expert knowledge of molecular fragmentation [1]. In all-ion LC-MS/MS schemes such as alternated low and high collision energy acquisitions (MSe or MSall) and sequential window acquisition of all theoretical mass spectra (SWATH) provide MS fragmentation data at no additional experimental time cost [2]. However, reconstruction of parent-fragment relationships is a difficult task, particularly for wide fragmentation windows experiments (MSe or MSall) which generate spectra composed of fragments from multiple parent ions. In the presented work we propose a novel approach for automated annotation of LC-MS using all-ion fragmentation data, which combines correlation-based parent-fragment ion reconstruction with in-silico molecular fragmentation and retention-time information. This strategy allowed the correct annotation of 155 of 207 features (75%) from XCMS outputs [3] in a set of human serum samples. These corresponded to different isotopologues, adducts and fragments from lipid and other small metabolites. This approach provides a useful framework for the annotation and interpretation of untargeted LC-MS metabolomics data and can be easily adapted to other samples and experimental settings. An R package, including dedicated visualisation tools, is planned to be released in the near future. References: [1] Domingo-Almenara et al. Anal. Chem. (2018), 90, 480–489. [2] Broeckling et al. Anal. Chem. (2014), 86, 6812–6817. [3] Smith et al. Anal. Chem. (2006), 78, 779–787.

P-560

Facilitating interpretation of metabolomics data using KEGG and HMDB databases

PRESENTING AUTHOR: Cédric Bovet, Bern University Hospital, Switzerland

CO-AUTHORS: Zhaoyue He, Stefan Zahnd, Katrin Freiburghaus, Carlo R. Largiadèr, Martin G. Fiedler

The annotation of metabolic ions and their assignment to pathways remains the main challenge in non-targeted metabolomics by high-resolution mass spectrometry (HRMS). To gain annotation confidence, laboratories are often measuring hundreds of metabolite standards and generate in-house spectral libraries. Usually, only a small proportion of the detected ions is annotated with this strategy. To enhance the annotation of unknown ions, we developed R and Python packages to extract the most probable metabolites and chemical taxonomy (i.e., class, sub-class, etc.) from HMDB and KEGG databases based on measured m/z ratios. After tentative annotation of ions and pathways using Mummichog (http://mummichog.org/), our developed R package assigns the generated list of empirical compounds to the corresponding fold changes of the input file, downloads the corresponding KEGG maps and generates a comprehensive overview of impaired metabolic pathways. Potentially identified metabolites are shown as nodes colored and sized according to their fold change and p-value, respectively. The Python package extracts the potential metabolite identities for each measured m/z ratio and the chemical taxonomy from HMDB. An annotation tolerance for the taxonomy of interest can be specified and the filtrated annotation is used to reveal collectively regulated classes. To test our approach, we reanalyzed a dataset obtained from male adults with type-1-diabetes undergoing exercises [1] and successfully annotated unknown ions and isolated regulated pathways (i.e., purines, steroids and amino acids). In conclusion, our approach facilitates the annotation of ions and the interpretation of complex non-targeted HRMS data. [1] L. Bally et al. Metabolomics, 13, 78.

P-561

A novel UHPLC-MS method for global profiling of cholesteryl esters and potential biomarkers discovery of hyperlipidemic golden hamsters

PRESENTING AUTHOR: Miao Lin, Institute of Materia Medica, CAMS & PUMC, China

CO-AUTHORS: Zhe Wang, Dongmei Wang, Xiong Chen, Jin-Lan Zhang

Cholesteryl esters (CEs) play crucial roles in cholesterol homeostasis, and abnormal levels of CEs result in various diseases, such as Wolman disease, hyperlipidemia, atherosclerosis, and cancer. Global profiling of CEs in biosamples would be beneficial to the discovery of disease biomarkers. In our study, we reported the global profiling of CEs in plasma and three different lipoproteins (VLDL, LDL, and HDL) of golden hamsters by a mathematical model-assisted ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) method. Seventy-four CEs (50 newly reported CEs) were identified in golden hamster with the validated mathematical strategy based on the chromatographic retention behavior, structural features and CE standards. Then, 74 CEs were quantified by dynamic multiple reaction monitoring (dMRM) for the discovery of potential biomarkers in hyperlipidemic golden hamsters. A total of 57, 52, 42, and 41 CEs were indicated as potential biomarkers in the plasma, VLDL, LDL, and HDL of hyperlipidemic golden hamsters, respectively. We found that 24 CEs were shared in plasma and lipoproteins. And 11 CEs were reported to be closely related to metabolic disorders and heart diseases, while 13 CEs were novel potential biomarkers of hyperlipidemia. Our study expands the scope of CE compound analysis in biosamples and can be applied for the discovery of biomarkers for human diseases.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

P-562

Metabolomics analysis in rat plasma during short-term abstinence following methamphetamine self-administration

PRESENTING AUTHOR: Suji Kim, College of Pharmacy, Keimyung University, South Korea

CO-AUTHORS: Suji Kim, Hyerim Yu, In Soo Ryu, Sooyeun Lee, Sangkil Lee

Drug addiction is a chronic, relapsing brain disease that is strengthened by a dynamic process of repeating drug reward and withdrawal. Drug withdrawal, usually starting within hours of the last dose, is a critical step in the transition from reward to addiction. Methamphetamine (MA) is a highly addictive central nervous system stimulant and its abuse has become a serious global public health concern. In the present study, a time-series metabolomics analysis was performed to uncover metabolic changes associated with MA short-term abstinence. Metabolic alterations were investigated in rat plasma collected immediately after 16 d of MA self-administration (SA) and after 12 and 24 h of abstinence using both non-targeted UPLC-QTOF-ESI-MS and targeted MS of amino acids, biogenic amines, acylcarnitines, glycerophospholipids, and sphingolipids. Principal component analysis revealed that the highest level of separation occurred between the 24 h and control groups from the UPLC-QTOF-ESI-MS data. We detected 358/445/453 and 521/618/584 significantly different ion features in the SA/12 h/24 h groups in positive and negative ESI, respectively, including 5-methylcytosine, deoxycytidine, glycocholate, nicotinamide, corticosterone, hydroxyisobutyric acid, linolenic acid, hippurate, 3-(4-hydroxyphenyl) lactate, taurine, urate, and uridine. We also found that, during the short-term abstinence from MA self-administration, the levels of many amino acids, biogenic amines, and glycerophospholipids were significantly altered. Specifically, distinct changes were observed in the metabolic pathways involved in energy metabolism, the nervous system, and membrane lipid metabolism. These findings provide essential knowledge of the dynamic metabolic effects associated with short-term MA abstinence and may help identify early warning signs of MA dependence.

P-563

Improved Metabolite Identification in a Single Injection with SWATH Acquisition for Untargeted Metabolomics Workflow

PRESENTING AUTHOR: Robert Proos, SCIEX, United States

Comprehensive metabolite identification with MS/MS library spectral matching can be problematic for data dependent acquisition (DDA) workflows as it often requires multiple injections for each sample. SWATH Acquisition, a Data Independent Acquisition (DIA) method, with optimized variable windows, provides a powerful workflow requiring only a single injection per sample for each polarity. In addition to capturing product ion spectra for all ionizing analytes, SWATH also provides the option of quantitation at either the MS or MS/MS level allowing a comprehensive qualitative and quantitative analysis of metabolites in complex biological samples like plasma.

P-564

The GERSTEL Metabolomics Prepstation

PRESENTING AUTHOR: Nathan Hawkins, GERSTEL GmbH & Co.KG, Germany

Robust, reproducible sample preparation methods are fundamental to the delivery of high-quality metabolomics and metabolic phenotyping datasets. Whilst the last decade has seen great improvements in instrumental precision, sensitivity and robustness, most sample preparation is still done manually, and is a significant bottleneck to sample throughput and the principal source of experimental error. Whilst some labs have automated sample preparation to reduce human error and improve analytical precision, most laboratory robotics systems are either dedicated liquid handling systems or expensive bespoke systems designed and programmed to automate a single protocol in batches. Dedicated liquid handling robots have limited sample preparation capability (cannot automate evaporation, vortexing, centrifugation and solid phase extraction) and bespoke systems lack the flexibility to modify the protocol to deal with different samples/matrices to meet changing laboratory needs or improve methods to meet quality or regulatory requirements. Furthermore, whilst preparing samples in batches works for molecular biology and proteomics, metabolites (or their derivatives) that are not stable are excluded during data pre-processing prior to statistical analysis. We have therefore developed a flexible, modular, user-configurable and programmable Prepstation that can fully automate the most commonly used LC/LC-MS, GC/GC-MS and NMR protocols for lipidomics, metabolomics and metabolic phenotyping including: The Fiehn Protocol (MOX-TMS). Biomedical research into inborn errors or metabolism (tBDMS). Amino Acid analysis as their Alkyl Chloroformates.Metabolic phenotyping of microbial, plant, and animal FAMEs. The GERSTEL Metabolomics Prepstation can be configured for a wide range of metabolomics and lipidomics protocols including those for commercially available kits.
**POSTER SESSIONS 1 AND 2 – Monday and Tuesday –** all odd number posters will be on display.

**POSTER SESSIONS 3 AND 4 – Wednesday and Thursday –** all even number posters will be on display.

*AWARD WINNERS

TECHNOLOGY

**P-565** Untargeted metabolomics application to monitor urine samples by ultra-high-pressure liquid chromatography coupled to high-resolution mass spectrometry

PRESENTING AUTHOR: Csaba Laszlo, Philip Morris International, Switzerland

CO-AUTHORS: A. Haiduc, E. Dossin, F. de Jong, C. Beecher, A. Kamleh, P.A. Guy, N.V. Ivanov, M. Peitsch

In order to meet the demands of high-throughput metabolomics analysis, we established urine sample preparation method on 96-well plates and an efficient liquid chromatography (LC) coupled to high-resolution mass spectrometry (MS) method using the latest model of the Vanquish™ Tandem LC system. The separation of urine metabolites was performed using two Hypersil GOLD™ C18 columns (150 x 2.1 mm, 1.9 µm) operating in parallel and running with a fast linear gradient of acetonitrile containing 0.1% formic acid, ramping from 5% to 95% in 10 minutes, alternating between the two columns. Dual-column operation with column reconditioning maintains full occupancy of the MS instrument (MS acquisition of column A during re-equilibration of column B). MS detection was realized on a Q Exactive™ HF mass spectrometer operating in positive and negative electrospray ionization acquisition modes. Data quality consistency was assessed through various pooled urine quality control samples injected on both columns processed by principal component analysis. In order to prevent possible drift in retention time across the columns, various reference index markers eluting across the gradient were analyzed at the beginning and the end of the 96-well plate series on each column. Retention index values were calculated to provide reproducible results over time. In addition, we have assessed the 13C yeast extract (TruQuant IQQ Workflow Kit, IROA®) to obtain a more robust method for metabolite identification and quantification.

**P-566** Electromembrane extraction for the analysis of betaine, carnitine, choline, deoxycarnitine and TMAO in plasma

PRESENTING AUTHOR: Drouin Nicolas, Leiden University, Netherlands

CO-AUTHORS: Amy Harms, Tim Kloots, Julie Schappler, Serge Rudaz, Isabelle Kohler, Petrus Wilhelmus Lindenburg, Thomas Hankemeier

Choline, trimethylamine N-oxide (TMAO), carnitine, deoxycarnitine and betaine are now routinely quantified as potential biomarkers of cardiovascular disease. The common analytical workflow typically relies on protein precipitation and flow injection combined with the use of deuterated internal standards for high-throughput analysis. However, this approach leads to significant matrix effects and up to a 80% sensitivity decrease due to the presence of salts and phospholipids. In this context, electromembrane extraction (EME) appears to be a promising alternative to selectively extract polar ionizable compounds from complex matrices such as plasma. In this study, EMEs were performed in parallel using an in house Parallel-EME device and experimental parameters were optimized. Nitrophenyl pentyl ether (NPPE) was selected as supported liquid membrane based on the physico-chemical properties of the targeted compounds. Intrinsic parameters such as voltage and or applied current evaluated as well as sample composition (i.e., simple plasma dilution, MeOH addition, and protein precipitation) were. Optimal conditions, namely 400 µA/well, 1400 rpm, 15 min, protein precipitation using trichloroacetic acid to reduce the ionic strength and the buffer capacity of plasma, lead to repeatable extraction as the observed process efficiency as lower than 20%, and further improved to <5% using internal standard correction. The developped sample preparation method allowed to reduce the matrix effect up to a 2-fold factor, leading to a sensitivity improvement of 3-fold for compounds such as TMAO. In addition, this new approach was successfully applied to a cohort of 40 plasma samples, demonstrating the potential of Pa-EME in targeted metabolomics.

**P-567** Development of a neurochemical profiling method in rat plasma using liquid chromatography-tandem mass spectrometry

PRESENTING AUTHOR: Seungju Kim, Keimyung University, South Korea

CO-AUTHORS: Byoungduck Park, Sooyeun Lee

Tyrosine and tryptophan are precursors of major neurotransmitters including dopamine and serotonin, which are key in modulating brain functions and neurological diseases. Comprehensive measurement of not only neurotransmitters but also their precursors and metabolites is thus essential to understand the relationship between the pathophysiological states and the neurological actions. However, accurate measurement of those neurochemicals remains challenging due to very low basal concentrations and diverse chemical properties (e.g. polarity). In the present study, a metabolic profiling method for 16 neurochemicals derived from tyrosine and tryptophan was developed and validated in rat plasma using liquid chromatography-tandem mass spectrometry combined with chemical derivatization. The validation results proved the method to be selective, sensitive, accurate and precise, with acceptable linearity within calibration ranges. The lower limits of quantification ranged from 0.05 to 50 ng/mL. This is a powerful neurochemical profiling method for monitoring the metabolic pathways of dopamine, norepinephrine, serotonin and kynurenine in rat plasma and will be very useful to further studies on the regulation of the neurotransmitter metabolic pathways in vivo.