“Cracking” Cellular Metabolism: from Novel Sample Prep to Powerful Insights with Discovery-based LC/MS Workflows

Presenters:

Genevieve Van de Bittner, PhD

Scientist, Agilent Research Laboratories,Agilent Technologies, Inc.

Mark Sartain, PhD

LC/MS Applications Scientist, Agilent Technologies, Inc.

Abstract

In this Webinar, we will demonstrate comprehensive untargeted metabolomics and lipidomics workflows from sample prep to data analysis within the context of cancer cell biology. We describe an innovative cellular extraction method that lyses cells and quenches metabolism under room temperature conditions. Additionally, we explore extending and automating this method with the Agilent Bravo Liquid Handling Platform to sequentially extract both polar metabolites and lipids from a single sample of harvested cells. Extracts are analyzed with an Agilent1260 Infinity II Prime LC and 6546 LC/Q-TOF system that offers simultaneous high mass resolution (>30k for 118 m/z) and wide linear dynamic range (~ 4 orders). Finally, we use a full suite of recent software introductions (Lipid Annotator) and enhancements (Profinder, MPP, Quant) to elucidate the molecular response to perturbations in a cancer cell model.

"Leveraging Global Metabolomics at the Intersection of Physiology and Behavior"

Moderator:

Samuel Bohney, Ph.D.

Business Development Executive, Midwest

Presenters:

Nicole Vike, Ph.D.

Post-Doctoral Fellow, Northwestern University - Feinberg School of Medicine, Department of Psychiatry and Behavioral Sciences

Sumra Bari, Ph D.

Post-Doctoral Fellow, Northwestern University-Feinberg School of Medicine, Department of Psychiatry & Behavioral Sciences

Abstract

Metabolomics offers researchers the ability to get closer to the phenotype by identifying small molecules and combining these results with bioinformatic and biochemical expertise, producing actionable insights. Metabolon’s Precision Metabolomics™ LC-MS global metabolomics platform provides a high-fidelity, reproducible analysis of the current-state of a biological system to reveal changes in key biological pathways.

As the interest in and understanding of sports-related head injuries continues to grow, Nicole Vike and Sumra Bari will share small molecule insights to better characterize these injuries. Drs. Vike and Bari have integrated metabolomic data with other technologies such as resting state fMRI, transcriptomics and computational behavior using a virtual reality task, to assess effects of exposure to head acceleration events (HAEs). These findings not only provide exciting insights into the mechanism and monitoring of HAEs, but also provide examples of integration of metabolomics with other data streams to fuel physiological discoveries.

Webinar attendees will have a chance to win a $100 Amazon Gift Card (three card giveaway)

"Measuring the metabolome: Using Cyclic IMS, Targeted pathway analysis, and novel chemistries to increase coverage, precision, and accuracy"

Presenters:

David Heywood

Senior Manager, Discovery Omics

Andrew Peck

Senior Manager, Targeted Omics

Abstract

Metabolomics and lipidomics continue to gain interest as differentiated methodologies in revealing the complexities of normal and pathological biology. The broad number and diversity of metabolites, and the interactions amongst them, demands the highest fidelity, accuracy, and precision in analytical measurements. Whether you are analyzing the global metabolome or specific classes or pathways, Waters has tools to ensure rigorous and reproducible quantitation. In this workshop, we will present on the game-changing SELECT Series Cyclic IMS mass spectrometer and the use of variable ion mobility resolution for metabolomics research, a suite of plug-and-play tools for targeted workflows, and new column chemistries developed for difficult to chromatograph metabolites and lipids.

We plan to have an expert panel discussion with successful women in metabolomics to discuss the challenges they overcame in their career trajectories.

Featured speakers:

Leila Pirhaji - Founder and CEO, ReviveMed

Erin Baker - Associate Professor, North Carolina State University

Susan Sumner - Professor, Nutrition Research Institute Department of Nutrition & Director, of Metabolomics Core & Nutrition Obesity Resource Core, UNC Chapel Hill. Director, Untargeted Analysis, NIEHS Human Health Exposure Analysis Resource Program

Ruth Welti - Professor & Director of Kansas Lipidomics Research Center, Kansas State University

Contact: Arpana Vaniya (avaniya@ucdavis.edu)

Authors

Djawed Bennouna, (1) Department of Human Sciences, Human Nutrition Program, The Ohio State University, Columbus, OH (Primary Presenter)

Tonya S. Orchard, Department of Human Sciences, Human Nutrition Program, The Ohio State University, Columbus, OH

Courtney DeVries, (2) Department of Neuroscience, College of Medicine, The Ohio State University, Columbus, OH. (3) Rockefeller Neuroscience Institute, West Virginia University, WV

Maryam Lustberg, (4) Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus,OH

Rachel Kopec, (1) Department of Human Sciences, Human Nutrition Program, The Ohio State University, Columbus, OH. (5) Foods for Health Discovery Theme, The Ohio State University, Columbus, OH

Abstract

Breast cancer chemotherapy negatively affects long-term brain functioning, known as chemotherapy-induced cognitive impairment (CICI), in approximately one third of breast cancer survivors. Our goal was to determine which metabolites in the brain cortex are modified following doxorubicin-based chemotherapy (DOX), in both the immediate and short term. Ovariectomized mice received an AIN-76A diet for 4 weeks, followed by two injections of either DOX (9 mg/kg) + cyclophosphamide (90 mg/kg), or vehicle (n ≈ 10 per treatment group). Animals were sacrificed at 4 and 14 days after the last injection. Samples were defatted using MTBE, then extracted with methanol/water. Extracts were analyzed by UHPLC-QTof using a C18 column, with the ion source operated in both positive and negative mode. Pooled quality control samples (QC) were analyzed, as well as process blanks. Data collected was processed by MZmine 2.26, followed by post-processing filters to eliminate features present in the blank and those with a standard deviation higher than 30% in the QC samples, leaving 4743 features in positive mode and 570 features in negative mode in the final dataset. Principle component analysis (PCA) demonstrated QC sample grouping, and partial least square-discriminant analysis (PLS-DA) was assessed using a permutation test and cross-validation ANOVA (CV-ANOVA) after features selection based on a VIP score (VIP > 1) to highlight differences between the studied group. A 2-way ANOVA was used to determine the significance of treatment and sacrifice day on feature intensities (P < 0.05). Chemotherapy significantly increased inosine, hypoxanthine and tryptophan and decreased adenosine, relative to vehicle injection. Xanthine and kynurenine were both significantly increased in chemo group. No significant effect was observed for sacrifice day, nor the interaction of sacrifice day x treatment. Many of these metabolites have previously been correlated with diseases of cognitive impairment/decline including schizophrenia and bipolar disorder and Alzheimer’s disease, in human cortex and plasma. Investigations underway are determining if these metabolites indicative of cortex chemotherapy damage can be measured in the plasma of human breast cancer survivors, and whether or not they are associated with symptoms of CICI.

Authors

Raquel Cumeras, NIH-West Coast Metabolomics Center, Univeristy of California, Davis (Primary Presenter)

Luis M. Valdiviez, NIH-West Coast Metabolomics Center, University of California, Davis, United States

Jeremiah Wells, NIH-West Coast Metabolomics Center, Univeristy of California, Davis

Alice Dalo, NIH-West Coast Metabolomics Center, UC Davis

Tobias Kind, UC Davis Genome Center - Metabolomics

Oliver Fiehn, UC Davis

Abstract

Urea is by far the most abundant compound (after water) in urine (9.3-23.3mg/mL). Removal of urea metabolite with urease enzyme is a general practice in GC metabolomics, however, its effects in the whole urine metabolome have never been studied in detail. In this study we explored changes in metabolite profile of urine due to urease treatment, not limiting to GC-MS, but also including HILIC and CSH in both modes. BioIVT urine was used for all measurements, with the addition of 20uL of Urease Enzyme at 2mg/mL dissolved in LCMS grade water, incubated at 37ºC for 30minutes. We included three different treatment groups: urine with urease, urine with water and urine. Each treatment group consisted of 6 biological replicates. Data was acquired with a Leco Pegasus IV for GC-MS, and an Agilent 6560 QTOF for HILIC and CSH. As expected, urea was removed enzymatically by Urease treatment which was confirmed in GC-MS measurements. Also, lactose was introduced as a contaminant derived from the stabilizing agent in urease. However, other compounds showed an increased or decrease in concentration. One-way ANOVA showed up to 200 compounds significantly altered with an FDR=0.05. From those, 42% are altered in GC, but the 68% are altered in HILIC and CSH. With this study, we broaden the knowledge of urease enzyme effect in the removal of urea in urine as it is the first time that a full metabolomics coverage of the effect of urea removal is studied.

Authors

Nicholas Paul Lesner, University of Texas Southwestern Medical Center (Primary Presenter)

Abstract

Mitochondrial DNA (mtDNA) diseases are genetic disorders of energy production with an occurrence rate of approximately 1:5000 and no effective treatment options. Our current understanding is limited due to the heterogeneous nature of this disease, as well as the lack of a good model system. We utilized isogeneic cell lines harboring patient derived mtDNA mutations to assess protein and metabolic changes as a result of genetically dysfunctional mitochondria. Understanding metabolic reprogramming in response to clinically relevant mtDNA mutations may provide insight into strategies for metabolic flexibility. Isotope tracing mass spectrometry and metabolic flux analysis was used to quantitatively determine alterations in intracellular fluxes in central carbon and amino acid metabolism. Changes in glutamine and cystine transport were identified, which indirectly regulate metabolism and oxidative phosphorylation. Specifically, cystine metabolism promoted glucose oxidation via the transsulfuration pathway and the production of a-ketobuytate (aKB). Intriguingly, promoting or inhibiting aKB production was sufficient to modulate glucose oxidation and respiratory activity in these mtDNA disease cell lines, indicating that cystine-stimulated transsulfuration is an adaptive mechanism that links glucose oxidation with respiratory status in patient cells. Therefore, we propose that the precise nature of metabolic perturbations in response to mtDNA mutations contributes to the heterogeneity of disease pathophysiology by coregulating respiratory status with nutrient utilization.

Authors

Joseph Alaimo, Center for Pediatric Genomic Medicine, Children’s Mercy Kansas City

Kevin Glinton, Baylor College of Medicine (Primary Presenter)

Ning Liu, Baylor College of Medicine

Jing Xiao, Baylor College of Medicine

Yaping Yang, Baylor College of Medicine

V. Reid Sutton, Baylor College of Medicine

Sarah Elsea, Baylor College of Medicine

Abstract

Background: A primary barrier to improving exome sequencing diagnostic rates is the interpretation of variants of uncertain clinical significance. The American College of Medical Genetics and Genomics (ACMG), the Association for Molecular Pathology (AMP) and Clinical Genome Resource (ClinGen) have attempted to streamline and standardize interpretation processes however inter-laboratory variation continues to exist. Many of these discrepancies are due to a lack of compelling and validated “functional” evidence as outlined in the ACMG framework. While in some cases such evidence can be easily obtained through complementary targeted biochemical testing or model organism studies, this is not always feasible or easily accomplished in a time-sensitive manner. One approach to address this limitation would be to integrate untargeted metabolomic profiling with genomic analysis. We therefore aimed to determine the contribution of integrated untargeted metabolomics in the analysis of exome sequencing data by retrospective analysis of patients evaluated by both whole exome sequencing and untargeted metabolomics within the same clinical laboratory.

Methods: Exome sequencing and untargeted metabolomic data were collected and analyzed for 170 patients. Pathogenic variants, likely pathogenic variants, and variants of uncertain significance in genes associated with a biochemical phenotype were extracted. Metabolomic data were evaluated to determine if these variants resulted in biochemical abnormalities which could be used to support their interpretation using current ACMG guidelines.

Results: Metabolomic data contributed to the interpretation variants in 74 individuals (43.5%) over 73 different genes. The data allowed for the re-classification of 9 variants as likely benign, 15 variants as likely pathogenic, and 3 variants as pathogenic. Metabolomic data confirmed a clinical diagnosis in 21 cases, for a diagnostic rate of 12.3% in this population.

Conclusion: Untargeted metabolomics can serve as a useful adjunct to exome sequencing by providing valuable functional data that may not otherwise be clinically available, resulting in improved variant classification.

Authors

Yamile Lopez, CONACYT (Primary Presenter)

Ana Sofia Herrera, Facultad de mEDICINA, Departamento de Bioquímica, Universidad Autónoma de San Luis Potosí

Abstract

Gestational Diabetes Mellitus (GDM) is the most frequent medical complication of pregnancy and is associated with multiple adverse pregnancy outcomes, affecting around 7–10% of all pregnancies worldwide. GDM is a severe and neglected threat to maternal and child health: women with GDM are at subsequent high risk of type 2 diabetes (T2D), especially three to six years after delivery; exposure to hyperglycaemia in the womb predisposes children to a high risk of becoming overweight or obese, associated with the posterior development of T2D. Recently, Perrone et al. reported that urine metabolomic profiles of newborns at birth mirrors that of their mothers in the last phase of pregnancy. Despite certain proportion of newborns from diabetic mothers do not present perinatal complications, it has been established that these neonates are the best metabolic example of the morbidity that may exist due to maternal disease. Studies comparing the metabolic profile of newborns whose mothers had been previously diagnosed with GDM are scarce. In the present work we aimed to investigate the metabolic alterations occurring throughout the second half of pregnancies complicated with GDM, with trimestral sampling of maternal urine. We used targeted MS/MS-based metabolomic assays to quantify 136 metabolites in the urine from 73 pregnant (33 GDM and 40 healthy controls) collected in the second and third trimester of gestation, and from 48 healthy, full-term term neonates collected in the first 24 hours of life. By comparing the urinary samples of healthy and GDM pregnant, we found that metabolites derived from the gut microbiota metabolism are closely related to the dysregulation of lipid metabolism in GDM pregnant. Moreover, we investigated the impact of the metabolic changes taking place in the 3rd trimester of GDM pregnant on the urinary metabolome of the newborns. Despite all newborns were considered as healthy, a differential urinary profile was found in those born to GDM pregnant, characterized also by a deregulation of lipid metabolism. By a logistic regression model, we built a panel of metabolites (glutamic acid, LysoPC aa 28:0 and C16:1) able to early discriminate those babies coming from GDM mothers, with an AUC of 0.91. Finally, we described the differential expression for the measured urinary metabolites from the second half of pregnancies to the birth, showing that in the first hours of life and consistently with a transient period of starvation, the neonates become highly dependent on lipid metabolism.

Authors

Mengna Huang, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital (Primary Presenter)

Antonio Checa, Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Sweden

Pei Zhang, Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Sweden; Gunma University Initiative for Advanced Research (GIAR), Gunma University, Japan

Rachel Sabine Kelly, Channing Division of Network Medicine, Brigham and Womens Hospital

Su Chu, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Priyadarshini Kachroo, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Bo Chawes, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark

Klaus Bønnelykke, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark

Augusto Litonjua, Division of Pediatric Pulmonary Medicine, Department of Pediatrics, Golisano Children's Hospital at Strong, University of Rochester Medical Center, Rochester, NY, USA

Scott Weiss, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Hans Bisgaard, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark

Craig Wheelock, Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Sweden; Gunma University Initiative for Advanced Research (GIAR), Gunma University, Japan

Jessica Lasky-Su, Brigham and Womens Hospital

Abstract

Sphingolipids play important roles in influencing the risk of childhood asthma. Chromosome region 17q21 (ORMDL3) is a widely replicated asthma-susceptibility locus for childhood-onset asthma with several identified polymorphisms, and the encoded protein suppresses serine palmitoyl-CoA transferase (rate-limiting enzyme in de novo sphingolipid synthesis). Additionally, experimental studies have shown that vitamin D metabolites can activate the sphingolipid pathway, and 17q21 genotype may modify the effect of vitamin D in preventing childhood asthma/recurrent wheeze in early life. In this study, we aim to investigate associations between vitamin D and sphingolipid concentrations in plasma, and disentangle the impact of single nucleotide polymorphisms (SNPs) in the 17q21 region on these relationships.

Sphingolipid concentrations were measured in plasma samples from children participating in the Vitamin D Antenatal Asthma Reduction Trial (VDAART) at age 6 years, using a targeted liquid chromatography coupled tandem mass spectrometry platform quantified with internal standards. The standardized levels of 77 sphingolipids were associated with concurrent 25-hydroxyvitamin D [25(OH)D] (considered most reliable biomarker of vitamin D status) levels using linear regression models adjusted for child sex, race, ethnicity, study site, sample quality (color indication of hemolysis), and concurrent body mass index, with subgroup analyses conducted within categories of four pre-selected 17q21 SNPs. The same sphingolipids were also tested for associations with the SNPs using linear regression models adjusted for child sex, race, ethnicity, and sample quality. Replication analysis was conducted in the similarly-designed Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010), with targeted sphingolipids data from the same platform.

Data from 421 children in VDAART with targeted sphingolipid measurements were analyzed as the discovery population, including 366 with genetic data; 501 children from COPSAC2010 were included as the replication population (442 with genetic data). Account for multiple comparisons at a significance threshold (p-value<4.17x10-4) derived from an effective number of independent tests accounting for 80% variance in sphingolipids, two species of sphingosine-1-phosphate (S1P) and four species of ceramides were associated with plasma 25(OH)D levels. Among these, with a replication threshold of p-value<0.05, S1P (d16:1) was positively associated with 25(OH)D consistently in VDAART (p-value=3.64x10-4) and COPSAC2010 (p-value=8.27x10-3), while ceramide (d18:2/16:0) had inverse associations with 25(OH)D in both cohorts (p-value=3.62x10-3 in VDAART, p-value=2.51x10-4 in COPSAC2010). Additionally, three ceramides and one dihydroceramide were negatively associated with 25(OH)D at nominal significance in both cohorts: ceramide (d18:1/20:0), ceramide (d18:1/22:0), ceramide (d18:1/24:0), and dihydroceramide (d18:0/16:0). In general, the negative associations between 25(OH)D and plasma ceramides levels were only observed in children with at least one copy of the high-asthma-risk C allele for rs12936231 and rs12603332 within the 17q21 region. Both SNPs by themselves were positively associated with ceramide (d16:1/24:1), ceramide (d18:1/22:0), and ceramide (d18:1/26:0) at nominal significance in VDAART.

Consistent with previous experimental evidence, plasma 25(OH)D was positively associated with S1Ps, and negatively associated with multiple ceramides. Given the complexity of the sphingolipid synthesis and recycling pathways, our analyses provided novel insight into how vitamin D and 17q21 genotype may influence plasma levels of different species of sphingolipids, which may in turn alter the risk of childhood asthma.

Authors

Monee McGrady, Pacific Northwest National Laboratory (Primary Presenter)

Sean Colby, Pacific Northwest National Laboratory

Jamie Nunez, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Thomas Metz, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Ryan Renslow, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Abstract

When considering metabolomics data or large sets of molecules in general, it is helpful to place them in the context of a “chemical space” – a multidimensional space defined by a set of descriptors (e.g. molecular properties) that can be used to visualize and analyze compound grouping, as well as regions that might be devoid of structures. In processing and analysis of metabolomics data sets, chemical space analyses could be used to lower false discovery rates through creating better sets of decoy molecules and to contribute to a more in-depth understanding of how likely it is that a specific compound would be in a given sample based on where the chemical composition of the sample generally lies. The chemical space of all possible molecules in a given sample can be vast and largely unexplored, mainly due to current limitations on processing of ‘big data’ required by brute force methods (e.g. enumeration of all possible compounds in a space). Recent advancements in artificial intelligence (AI) have led to multiple new chemical software tools that incorporate AI techniques to characterize and learn the structure and properties of molecules in order to generate plausible compounds, and thereby contribute to more accessible and explorable regions of chemical space without the need for brute force. We have used one such tool, a deep-learning software called DarkChem, which learns a representation of the molecular structure of compounds by compressing them into a latent space representation where similar data points are closer together in space, to target current underpopulated areas of small molecule chemical space to generate potentially novel molecules and to learn more about the spaces themselves. By creating a 1 million compound sample representative of the chemical space of small molecules (less than 1000 Da) and plotting it in two different chemical spaces – one defined by molecular properties and one by DarkChem’s latent space – we were able to ascertain regions with few or no compounds and select a sample of compounds from the original sample that bordered the region of interest to be used as input for DarkChem generation. DarkChem’s ability to target a specific region of chemical space rather than generate molecules at random or with specific properties allowed for the generation of novel molecules that filled previously empty areas. These novel molecules are structurally valid, and while they are chemically similar to compounds in neighboring regions of chemical space, over 99% of them represent molecules not found in any chemical database to date (i.e., comparing to 2.5B known molecules in public databases). AI tools such as DarkChem can be used to explore areas of chemical space more quickly than a brute force method, and because DarkChem uniquely targets specific regions, novel compounds in the same neighborhood as compounds of interest can be generated for uses in drug discovery and metabolomics/forensics applications.

Authors

Andrew Christopher Patt, The Ohio State University (Primary Presenter)

Ewy Mathe, National Center for Advancing Translational Science

Kevin Coombes, The Ohio State University

Abstract

A common goal of lipidomic and metabolomic studies is to identify predictive biomarkers that are associated with prognosis, disease progression or treatment response in patients. However, reproducibility of findings at the metabolite level across experiments have limited the number of metabolite biomarkers that have influenced clinical practice. One strategy for overcoming this lack of reproducibility is to look for differences between samples on a pathway level, which can be more reproducible than individual metabolite differences. The most popular strategy in 'omic settings is to perform overreprentation analyses (ORA) to identify enriched pathways. Current ORA tools mainly rely on the Fisher’s test for statistical analysis and assume biological processes are independent. However in reality, metabolite/lipid relationships and their biological annotations are interdependent and redundant. We thus posit that modeling interdependencies between multiple information types better reflects the biology underlying experimental data. Other pathway analysis methods, such as MSEA, may better model pathway interdependencies, but do not integrate annotations from multiple sources. Combining multiple annotation types from multiple sources maximizes coverage of metabolitess mapping to annotations.

We developed a novel network method for pathway/chemical enrichment analysis of a list of metabolites/lipids of interest. In our method, metabolites/lipids that share pathway annotations with the set of interest are extracted from our RaMP database, which harmonizes metabolic pathway annotations from multiple metabolic pathway databases. We then quantify the proportion of shared annotations (pathways, chemical class, chemical similarity, disease association, etc.) the database metabolites share with the list of interest and with each other. These are represented as separate similarity network models per annotation type. These models are combined into a consensus model using a similarity network fusion algorithm. To identify subnetworks enriched in the metabolites of interest (“seed” set), we run random walks with restarts algorithm that uses the seed set as starting points in the network. We then compare individual node scores to an empirical score (calculated for each node) distribution generated by evaluating the random walks algorithm on random seed sets that are the same size as the actual seed set. Lastly, we extract metabolites that were highly proximal to the seed set (97th percentile or higher). This procedure generates a list of metabolites that are highly similar to the seed set that are evaluated for enrichment analysis.

For preliminary validation, we applied this method to an in-house metabolomics data set taken in liposarcoma cell lines of varying MDM2 copy number amplification. In vitro experiments had demonstrated that cell lines of lower MDM2 copy number exhibited slower growth when treated with atorvastatin (a cholesterol synthesis inhibitor), while cells with higher MDM2 copy number were unaffected. We identified a list of 17 metabolites that were significantly different between MDM2 higher and lower cell lines. Using our novel method, we identified bile acid synthesis as an altered pathway based on this metabolite list, which is a downstream product of cholesterol synthesis. Conventional Fisher’s enrichment using our RaMP package did not detect this pathway. Further computational and biological validation of this method is ongoing.

Authors

Christopher Brydges, West Coast Metabolomics Center, UC Davis, United States

Abstract

Background: Frequentist statistical techniques, such as p values, are almost exclusively used in metabolomics research. However, these techniques are limited in several important ways: First, they do not answer the question that researchers often want answered (“is my hypothesis true?”); second, they do not provide any indication of the strength of the finding (e.g., a p value of 0.0001 is not ‘more significant’ than a p value of 0.049); and third, a non-significant finding does not distinguish between a true null effect and insensitive/underpowered data. Bayes factors address all three of these problems, as they provide a measure of the relative likelihood of a hypothesis being true given the data, rather than the likelihood of the data occurring given the null hypothesis (i.e., a p value). Through the use of Bayes factors, researchers can quantify and easily interpret the strength of evidence in favor of one hypothesis over another, including the relative likelihood of the null hypothesis over the alternative, which p values cannot do. Bayes factors can also distinguish between a null effect and insensitive/underpowered data.

Method and Results: The current study uses Bayes factors on metabolomics data from the Alzheimer’s Disease Neuroimaging Initiative (http://adni.loni.usc.edu/) to demonstrate these advantages. Metabolites from plasma samples of 808 older adults who were typically aging, diagnosed with likely Mild Cognitive Impairment (MCI), or diagnosed with Alzheimer’s Disease (AD) were profiled using gas chromatography time of flight mass spectrometry. Using Bayes factors to compare identified metabolite abundance between groups, we can correctly conclude that, for example, a) there is at least moderate evidence of differences between the typically aging and AD groups in five organic acids and derivatives; b) that homogenous non-metal compounds, organoheterocyclic compounds, and lipids and lipid-like molecules generally show evidence of no effect of MCI or AD; and c) all but one identified metabolite (pseudo-uridine) either show sufficient evidence of no difference or insufficient evidence of a difference between the typically aging and MCI groups. None of these conclusions could be correctly inferred from the use of p values, and can be used to shed further light on potential biomarkers of Alzheimer’s Disease, as well as provide evidence against other potential candidates.

Conclusions: A Bayes factor is a simple, intuitive statistic that allows researchers to quantify the strength of evidence for or against a hypothesis of interest. Given that the primary question asked by a study is commonly “is this hypothesis correct?”, it is logical to use statistical techniques that directly answer this question. By including Bayes factors in their analyses and results, researchers can glean richer inferences from their data, thereby increasing knowledge in the field.

Authors

Peter Karp, SRI International (Primary Presenter)

Abstract

BioCyc.org is an extensive web portal containing 17,000 genomes and associated metabolic pathways. Most databases are prokaryotic and archaeal; eukaryotic databases are available for yeast, fly, mouse, and human. BioCyc databases provide extensive encyclopedias that connect the genomes and metabolomes of thousands of organisms, and provide a variety of tools for metabolomics data analysis.

BioCyc databases are created through a process that combines computational inferences with imported and curated data from multiple sources. The first step in the creation of BioCyc databases is prediction of metabolic pathways, operons, PFam domains, and orthologs. We next run programs that import data from related databases including regulatory network data, protein features, subcellular locations, and Gene Ontology assignments. Sixty curated databases have received intensive review and updating by a Ph.D. biologist that includes review of the computationally predicted metabolic pathways, entering new gene functions and metabolic pathways from the experimental literature, and defining protein complexes. The resulting databases are high-quality reference sources for the latest gene and pathway information. Overall the BioCyc databases have been curated from 95,000 publications.

The BioCyc website provides extensive bioinformatics tools for searching and analyzing these databases, and for metabolomics analysis. The user can search for metabolites interactively and via web services; searches include monoisotopic mass, chemical formula, SMILES, InChI, and InChI key. A metabolite translation service converts metabolite identifiers among different databases. SmartTables enable users to import, view, and analyze a set of metabolites, such as transforming the metabolite set to a set of all pathways containing those metabolites. Metabolite sets can be painted onto individual metabolic pathway diagrams and onto zoomable organism-specific metabolic networks. The Omics Dashboard enables interactive hierarchical analysis of metabolite sets. Enrichment analysis of metabolite sets is provided. New tools include (1) Computation of pathway covering sets for metabolites, i.e., the smallest set of pathways that will account for a given set of metabolites. (2) Computation of pathway activation levels for all pathways from metabolomics data and depiction of which pathways have changed their activation levels most significantly.

Authors

Hani Habra, University of Michigan (Primary Presenter)

Alla Karnovsky, University of Michigan

Charles R Evans, University of Michigan

Maureen Kachman, University of Michigan

Abstract

A key step in the analysis of untargeted LC-MS metabolomics data is the alignment of features (characterized by mass-to-charge-ratio (m/z) and retention time (rt)), detected in individual samples. Most existing software focus on aligning features detected in samples analyzed under roughly identical conditions. As experimental protocols vary across institutions and could be modified within the same lab, an alternative approach is needed to achieve a correspondence between identical features in disparate assays.

We present metabCombiner, an R package that implements a pipeline for matching known and unknown features in a pair of untargeted LC-MS metabolomics datasets. The key steps in this pipeline are: 1) separate preprocessing and filtering of input feature lists; 2) grouping of dataset features by m/z values; 3) retention time spline fitting through selected ordered pairs; 4) pairwise similarity scoring of paired features using differences in m/z, retention time (projected vs observed), and relative abundance. metabCombiner provides a list of possible feature pair alignments. metabCombiner outputs a data frame containing an overlap of two feature lists with concatenated measurements, providing increased power to statistical analyses and orthogonal information for curation of metabolite identities.

We demonstrate metabCombiner on metabolomics data acquired from different sample types analyzed with varied experimental parameters by different institutions. First, we evaluate metabCombiner's performance on a pair of plasma datasets run with different RPLC-POS gradient elution methods on a QTOF-MS instrument, determining a per-compound matching accuracy of 91% for 136 shared identified compounds. We then align a published HILIC-POS urine metabolomics dataset (ST001122, Blazenovic et al., 2019) with a urine dataset generated using our instruments under largely replicated conditions, determining 41-44 named compounds in overlap and 170 further compound IDs suggested in our dataset through alignment with the published study. Finally, we demonstrate the efficacy of metabCombiner using a pair of muscle datasets acquired by separate institutions using varied experimental protocols (varied column type, mobile solvents, m/z range, mass spectrometer, and sample prep methods). metabCombiner is a versatile method that may be used to bridge the gap between otherwise incompatible LC-HRMS metabolomics datasets and is available for download at https://github.com/hhabra/metabCombiner.

Authors

Sean Colby, Pacific Northwest National Laboratory (Primary Presenter)

Jamie Nunez, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Christine Chang, Pacific Northwest National Laboratory

Madison Blumer, Pacific Northwest National Laboratory

Ryan Renslow, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Thomas Metz, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Abstract

Background: Critical to any molecular profiling assay is the ability to reliably and accurately process raw instrumentation data. While software solutions provided by vendors with mass spectrometry instrumentation offer critical functionality for data analysis workflows, they lack the flexibility required to rapidly adapt to evolving community needs. Lack of open-source, community-driven development has motivated researchers to pursue alternatives across instrument platforms: liquid or gas chromatography and/or ion mobility spectrometry coupled to tandem mass spectrometry. Implementations vary in their instrument focus -- e.g. LC-MS/MS, GC-MS -- and offered functionality -- data input/output, alignment, peak detection, MS/MS spectral deconvolution, and fragment extraction. However, an open-source, truly platform-agnostic solution implementing all core functionality has yet to exist, limiting applications such as liquid chromatography coupled to ion mobility spectrometry-mass spectrometry. Toward this end, we have developed DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface and command-line tool for mass spectrometry data analysis workflows, offering ease of development and access to efficient algorithmic implementations.

Methods: DEIMoS operates on data of arbitrary dimension, regardless of acquisition instrumentation. We demonstrate DEIMoS with LC-IMS-MS/MS data, highlighting multidimensional peak detection, alignment, and MS/MS spectral deconvolution, with standardized datasets of known content for validation and verification.

Results: Here, we discuss the design and implementation of this tool, as well as an initial evaluation of this suite on a set of brain total lipid extracts and blood plasma samples from a sleep-cycle study. For each, we demonstrate the advantages of our multidimensional approach to each data processing step. Use of all dimensions simultaneously (i) offers greater separation between features, improving detection sensitivity, (ii) increases alignment/feature matching confidence among datasets, and (iii) mitigates convolution artifacts in mass fragmentation patterns.

Conclusion: Metabolomics/exposomics data processing tools offer immense value for disease diagnosis, evaluation of environmental exposures, and discovery of novel molecules. However, black-box and/or instrument-specific offerings are not necessarily positioned to take full advantage of the latest instrumentation. The successful application of our burgeoning data analysis package signals a paradigm shift in the processing of multidimensional spectrometry data.

Authors

Tomas Cajka, Institute of Physiology CAS (Primary Presenter)

Jiri Hricko, Institute of Physiology CAS

Michaela Novakova, Institute of Physiology CAS

Michaela Paucova, Institute of Physiology CAS

Ondrej Kuda, Institute of Physiology CAS

Abstract

During untargeted analysis of biological samples, many known metabolites are expected to be detected, and thus, can be quantified. Reporting absolute concentrations of particular metabolites is crucial to enable direct comparisons of the results between different laboratories and studies. Absolute metabolite quantities also immediately distinguish major from minor species, allowing biological interpretations of the results in the context of other analytes.

Liquid chromatography−mass spectrometry (LC−MS) is the preferred technique in metabolomics, lipidomics, and exposomics permitting effective compound separation and detection. However, the true breadth of a metabolome, lipidome, and exposome cannot be captured by a single extraction method or instrumental platform. Hence, the main task is to cover polar metabolites, lipids, and exposome compounds using as few platforms as possible while maintaining the requisite precision and accuracy for the metabolite classes detected by the chosen platforms.

We have addressed these challenges and introduce an LC−MS workflow LIMeX for the simultaneous extraction of complex LIpids, polar Metabolites, and eXposome compounds in human, mouse, and rat plasma that combines targeted and untargeted analysis. The sub-groups of compounds are isolated using an ‘all-in-one’ extraction with a methanol/methyl tert-butyl ether mixture and water. These extraction solvents contain over 60 internal standards covering main lipid classes, selected polar metabolites, and exposome compounds (drugs and food components). Analysis of complex lipids is conducted using reversed-phase LC (RPLC) in positive and negative electrospray (ESI) mode while polar metabolites and exposome compounds are separated using hydrophilic interaction chromatography (HILIC) in ESI(+) and RPLC in ESI(–). Simultaneous acquisition of MS1 and MS/MS spectra in data-dependent mode is used for each platform. The acquired raw data files are processed using user-friendly MS-DIAL software including also MS/MS library search.

For NIST SRM 1950 human plasma, calculated concentrations for selected polar metabolites (amino acids, glucose) were in agreement with the NIST certificate. In case of complex lipids, we compared our results with LIPID MAPS (Quehenberger O. et al., J. Lipid Res. 51 (2010) 3299) and NIST Inter-lab comparison study (Bowden J. et al., J. Lipid Res. 58 (2017) 2275). A good agreement between calculated concentrations and consensus location from these studies was achieved for most of the reported lipids demonstrating that even a single point calibration can provide accurate data. The key point was to avoid ion saturation by carefully evaluating linear dynamic range and adjusting the resuspension volume and injection volume before running actual samples. Overall, 500+ complex lipids, 100+ polar metabolites, and tens of exposome compounds (mainly food components and pharmaceutical drugs) for human cohort studies can be reported. Our workflow shows that untargeted metabolomic methods can be extended to include targeted analysis of selected metabolites and exposome compounds.

Acknowledgments: Projects 20-21114S (Czech Science Foundation, Czech Republic), LTAUSA19124 (Ministry of Education, Youth and Sports, Czech Republic), and NU20-01-00186 (Ministry of Health, Czech Republic)

Authors

Jiamin Zheng, University of Alberta (Primary Presenter)

Lun Zhang, University of Alberta

Rupasri Mandal, University of Alberta

Mathew Johnson, University of Alberta

David Wishart, University of Alberta

Abstract

Among all the human biological fluids used for disease biomarker discovery or clinical chemistry, urine stands out. It can be collected easily and non-invasively, it is readily available in large volumes, it is typically free from protein contamination and it is chemically complex – reflecting a wide range of physiological states and functions. However, the comprehensive metabolomic analysis of urine has been somewhat less studied compared to blood. Indeed, most published metabolomic assays are specifically optimized for serum or plasma. In an effort to improve this situation we have developed a comprehensive, quantitative MS-based assay for urine analysis. The assay we describe here uses two different derivatization methods, with one (Phenyl isothiocyanate) targeting primary- and secondary-amines and the other (3-Nitrophenylhydrazine) targeting keto-acids and carboxylic acids. The assay robustly detects and quantifies 142 urinary metabolites including 28 amino acids and derivatives, 17 organic acids, 22 biogenic amines and derivatives, 40 acylcarnitines, 34 lipids and glucose/hexose, among which 67 metabolites are absolutely quantified and 75 metabolites are semi-quantified. All the analysis methods in this assay are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) using both positive and negative-mode multiple reaction monitoring (MRM). The recovery rates of spiked urine samples at three different concentration levels, i.e., low, medium and high, are in the range of 80% to 120% with satisfactory precision values of less than 20%. This assay permits a diverse range of urinary metabolites to be detected and quantified over a wide range of concentrations (from 2.31 nM to 24 mM), and has been extensively validated on authentic urine samples and comparisons with metabolite concentrations measured by NMR show excellent agreement, while all the measured values are within expected physiological ranges reported in literature. The assay was specifically developed in a 96-well plate format, which enables automated, high-throughput sample analysis. The assay has already been used to analyze more than 1800 urine samples in our laboratory since early 2019. Moreover, the use of two different derivatization methods made the expansion of targeted metabolite list relatively easy. Efforts are now underway to convert this assay into a simple and inexpensive kit format.

Authors

Rupasri Mandal, University of Alberta (Primary Presenter)

Jiamin Zheng, University of Alberta

Lun Zhang, University of Alberta

Mark Berjanskii, University of Alberta

Shirin Zahraei, University of Alberta

David Wishart, University of Alberta

Abstract

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 given sample’s metabolome. However, the precision, accuracy, level of metabolome coverage and limit of quantification 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 22 different targeted assays conducted on 9 different types of analytical platforms that included high resolution NMR (700 and 800 MHz), direct injection/liquid chromatography tandem mass (QTrap) spectrometry (DI/LC-MS/MS), liquid chromatography tandem mass (Qtrap) spectrometry with isotope-labeled internal standards (LC-MRM-MS), liquid chromatography coupled with high-resolution mass (Orbitrap) spectrometry (LC-HRMS), inductively coupled plasma mass spectrometry (ICP-MS), two-dimensional gas chromatography mass spectrometry (GCxGC-TOF MS) and capillary electrophoresis with ultraviolet (UV) (CE-UV) and mass spectrometry (CE-MS). To avoid bias, the assays were conducted in isolation, without prior knowledge of the published data on SRM 1950 or prior knowledge of the results from any of the other assays or platforms. A total of 733 quantitative measurements for 613 metabolites were obtained from the 22 different analytical assays. 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. Of the 613 metabolites quantified, 82 had previously been identified in SRM 1950 and 52 had been previously quantified. A total of 531 metabolites were identified/quantified in SRM 1950 for the first time. Most assayed metabolites showed excellent cross-platform agreement (10%). Clear differences in platform coverage and sensitivity are evident. The assays exhibiting the best breadth of coverage were the LC-MS assays (with 473 metabolites detected and quantified), followed by the CE-MS assays (with 104 metabolites detected and quantified). In terms of quantitative accuracy (relative to the NIST/literature-derived values), a number of inconsistencies were identified. These included inconsistencies across different platforms, across similar platforms and between NIST-reported values and TMIC-reported values. Metabolites at the lower limit of detection for all assays typically showed the greatest inconsistency or lowest level of quantitative accuracy relative to NIST-reported values. Generally the most accurate platform was ICP-MS, followed by NMR followed by various kit-based LC-MS assays. This represents the most complete quantitative characterization of the world’s most used metabolomic reference standard (SRM 1950). In addition, it 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 (and assays) for plasma/serum analysis.

Authors

Wenyun Lu, Princeton University (Primary Presenter)

Xi Xing, Princeton University

Lin Wang, Princeton University

Li Chen, Princeton University

Melanie R. McReynolds, Princeton University

Joshua Rabinowitz, Princeton University

Abstract

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, we developed two complementary approaches for different sample types. One approach that is suitable for microorganisms such as E. coli and S. cerevisiae is to grow them in unlabeled, 13C, 15N, and dual 13C/15N-labeled media. Biological peaks are differentiated from nonbiological peaks based on whether the peaks exhibit mass shifts between unlabeled and labeled samples. Moreover, the adduct ions and the metabolite [M+H]+/[M-H]- ions share the same labeling pattern with carbon or nitrogen atoms coming from adducting remaining unlabeled, facilitating their annotation. The other approach is to modify the LC-MS running buffer without labeling the sample itself. We demonstrated that modifying the regular 14NH3-acetate buffer with 15NH3-formate allows accurate annotation of NH4+ and CH3COO- adducts. For example, ammonia adduct ions now appear as a pair of similar intensity peaks separated by 0.997 amu when equal amount of 14NH4+ and 15NH4+ are present in LC buffer. Incorporation this information allowed us to develop a Buffer Modification Workflow (BMW), which is suitable for the untargeted analysis of samples that are not readily isotope labeled, including plants, mammalian tissues, and tumors. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Overall, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match.

Authors

Meera Shanmuganathan, McMaster University (Primary Presenter)

Abstract

Human hepatitis C virus (HCV) is a pathogen that cause of liver fibrosis and cirrhosis, which could subsequently result in Hepatocellular Carcinoma (HCC). Recent data indicate that an estimated total of 170 million individuals worldwide are chronically infected with HCV and 700, 000 deaths are due to HCV related liver diseases annually. Currently, a liver biopsy is the gold standard technique for measuring the progression of liver fibrosis caused by HCV using a METAVIR scoring system. However, liver biopsies require invasive sampling procedures that are prone to bias and high variability due to reliance on histopathological examination. Alternatively, metabolomics is an emerging field which can offer a new direction for better understanding of complex molecular networks associated with human health and disease pathogenesis by taking a snapshot of the metabolic state of a biological sample.

Metabolomic analyses were performed on fasting serum samples from HCV patients at different stages of disease progression (n=20) as well as healthy controls (n=14) in this pilot study using two complementary instrumental platforms based on proton nuclear magnetic resonance spectroscopy (1H-NMR) and capillary electrophoresis coupled to mass spectrometry (CE-MS). The serum samples were pre-processed using a standardized method for both analytical platforms; the data were acquired on Bruker Avance III 70 mHz NMR spectrometer, and Agilent G7100A CE module interfaced with a coaxial sheath liquid Jetstream electrospray ion source to an Agilent 6230 time of flight mass spectrometry (TOF-MS). A multisegment-injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) technique was introduced to allow for improved sample throughput at lower cost that yet retains data quality. MSI-CE-MS uses a serial injection of thirteen samples in a single CE run as compared to the traditional single sample injection method. Forty serum metabolites were quantified by 1H-NMR while sixty polar metabolites and twenty non-esterified fatty acids were quantified by MSI-CE-MS. To our best knowledge, this is the first study rigorously validated the serum metabolome using CE-MS and 1H-NMR.

An inter-method comparison analysis was achieved using a Passing-Bablok regression and Bland-Altman % difference plots for the common metabolites measured by MSI-CE-MS and 1H-NMR. The Passing-Bablok regression plot highlighted good correlation for twenty-three common metabolites from thirty-four HCV patients and healthy controls (n = 23) measured by MSI-CE-MS and 1H-NMR reflected by a linear slope of 0.898. In addition, the Bland-Altman % difference plot showed good mutual agreement between common metabolites with a mean bias of 6% with few outliers exceeding agreement limit. Overall, the serum metabolites level measured by MSI-CE-MS was in good mutual agreement with 1H-NMR. Moreover, serum choline concentrations were consistently elevated in late stage fibrosis as compared to early stage HCV patients as measured independently in 1H-NMR and MSI-CE-MS after adjusted for covariates including age, gender and BMI. Notably, biopsy results and standard blood tests (GGT or ALT) do not differentiate between these two subgroups (early and late) of fibrosis patients. Therefore, MSI-CE-MS may be suitable for high throughput monitoring of early detection liver fibrosis without invasive tissue biopsies.

Authors

Shannon Cornett, Bruker Daltonics (Primary Presenter)

Simeon Vens-Cappell, Bruker Daltonik

Annika Koch, Bruker Daltonik

Henning Peise, Bruker Daltonik

Andreas Haase, Bruker Daltonik

Jens Hoehndorf, Bruker Daltonik

Abstract

Laser post-ionization (MALDI-2) is a novel technique to boost ion yields for MALDI, which are known to be in the order of 10 -4–10 -7 for classical MALDI. MALDI-2 also ameliorates, if not eliminates, ion suppression effects, to which MALDI Imaging is known to be prone. Here, we characterize the sensitivity enhancement MALDI-2 provides for MALDI Imaging of small molecules. Trapped ion mobility further reveals the high number of molecular features detected. We investigated fundamental MALDI-2 parameters, such as delay, pressure and distance between sample desorption and MALDI-2 post-ionization (PI), and fluence of both the MALDI and the PI-Laser on exemplary lipids and small molecules.

The source from a standard timsTOF fleX was modified to accommodate the MALDI-2 laser. The second laser beam is directed parallel to the sample and intercepts the MALDI plume orthogonal to the plume expansion axis and is adjustable to a distance of ~500 µm–1000 µm from the desorption plane. The delay between desorption and PI laser pulse was adjustable from 0–200 µs via software. The MALDI laser was a Smartbeam 3D laser (Bruker) at 355 nm (max. rep. rate 10 kHz), the MALDI-2 laser was a frequency quadrupled Nd:YAG at 266 nm (1 kHz). Sagittal rat brain and porcine liver homogenate samples sections were covered with matrix by pneumatic spray and sublimation/recrystallisation. In other experiments, serial dilutions of reference metabolites were pipetted onto tissue and dried before applying matrix.

The MALDI-2 laser post-ionization effect produced results comparable to data using other MS platforms (Soltwisch et al., Ellis et al., Niehaus et al.). Ion signals for several lipids and small molecules were increased up to a factor of ~300. The full integration of MALDI-2 capabilities allowed for a simple and quick variation of experimental parameters and revealed for lipids that three laser power regimes existed, following largely a logarithmic function. At very low energy of PI-Laser pulses (<5 µJ), no post-ionization could be observed. At low PI-pulse energies (~5–100 µJ) a strong ion signal increase with PI energy was observed. In the range between ~100–300 µJ, the intensity benefit began to plateau such that above ~300 µJ only a minor signal increase could be observed. MALDI-2 was found to increase ion yield over a large range of desorption laser pulse energies, but worked best with desorption laser fluences slightly higher, than for “normal” MALDI. The effect of MALDI-2 for imaging was significant in that 5x-10x more images were detected, especially when trapped ion mobility separation was used to separate isobaric species.

The MANA SODA initiative is geared to build a community resource of interlinked software, dataset and data analysis results, with an ultimate aim to help researchers new to the field navigate the state-of-the art approaches to metabolomics data analysis. In this interactive workshop, our goal is show researchers how to submit software, data and results and query it, and to continue fostering communication and input from the community on how to develop this resource.

Interactive Forum Organizers: Dinesh Barupal, Tytus Mak, Ewy Mathé, Bob Powers

Contact: Ewy Mathe ewy.mathe@nih.gov

Metabolomics has the potential to advance understanding of the mechanisms of complex illnesses like cancer and pulmonary diseases in which multiple phenotypes likely exist. Furthermore, it can serve as an investigative avenue to identify diagnostic and prognostic biomarkers. Despite this potential, application of metabolomics to the clinical situation can bring challenges. These can be dictated by the clinical setting, patient population and the illness itself and involve everything from sample collection to the interpretation of data.

This interactive forum is designed to stimulate discussion between audience participants and speakers. This will be accomplished by short, thought provoking presentations by 3-4 speakers (taking ~30-40 min) that highlight successes and challenges of using metabolomics to understand human disease. The remaining time will be used for Q&A and discussion. This will be facilitated by the moderator who may ask probing questions to prompt audience participation.

The forum is expected to capture a range of perspectives and examples of successes in the clinical application of metabolomics. It will also convey possible solutions to overcoming challenges that certain clinical scenarios represent. Ultimately, it is our expectation that the interactive dialogue will assist investigators in advancing their own metabolomics research and approach to clinical application.

Speakers :

1. Oana Zeleznik: Metabolomics and Ovarian Cancer in Two Large Scale

2. Bing Yu: Genetics, Metabolomics and Coronary Heart Disease

3. Rachel Kelly: What Pharmacometabolomics Can Teach Us About Personalized Therapeutics: Examples from Asthma

Interactive forum organizers: Kathleen Stringer, chair (U Michigan), Tim Garrett (U Florida), Rachel Kelly (Harvard), Yuan Li (UNC), George Michailidis (U Florida/U Michigan), Mike Puskarich (U Minnesota), Baljit Ubhi (Sciex)

Contact: Kathleen Stringer stringek@umich.edu

“The TruQuant Workflow combined with Authentic compound libraries… unparalleled ID, QUANT and QC”

Abstract

What do we look for in a metabolomics Workflow? One of the biggest concerns we continuously battle is reproducibility. A typical experiment produces thousands of metabolic features. The Workflow should provide a means to differentiate these metabolic features (fragments, adducts, noise), provide data handing and analysis solutions including Q/C, identification, and removal of variances so that quantitation is not only accurate but reproducible.

"The Power of Precision – Tools to advance your, Lipidomics, Metabolomics and True Integrative Omics Analyses"

Presenters:

Raghav Seghal

Biological and Medical Ph.D Student, Yale University (Kibbey Lab) and ex- Product Manager at Elucidata

Mackenzie Pearson

Senior Applications Scientist and Global Lipidomics Lead, SCIEX

Abstract

The key to a successful Omics lab is not just about hardware tools anymore, software becomes more and more critical. We describe two workflows for metabolomics and for lipidomics which allow you to perform more precise and accurate analysis and gain insights at the same time from the large datasets generated.

In the first part of this member event learn about how TripleTOF and QTRAP Technology with Differential Mobility Spectrometry known as the SelexION is being used in the Kibbey lab for understanding and unraveling mechanisms of action of many FDA approved drugs for which this is unknown. For many more drugs, off-target effects remain poorly described. Compounds orphaned for one indication might have an on or off-target effect that benefit another indication in mono- or combination therapy. Raghav describes how the multi-omics (untargeted metabolomics, fluxomics, transcriptomics and phenomics) datasets generated are then turned into insights through an artificially intelligent-driven processing pipeline known as Polly (Elucidata).

In the second part, learn how to profiling the lipidome and achieve 20% more structural lipids quantified as well as achieving femtogram LLOQ detection of lipid mediators on the new SCIEX Triple Quad™ 7500 LC-MS/MS System.

"Achieving High Quality Data for High Quality Results in Metabolomics"

Host:

Amanda Souza
Metabolomics Program Manager, Thermo Fisher Scientific

Special Guests:

Dr. Kim Ekroos
Founder & CEO of Lipidomics Consulting Ltd.

Dr. Warwick Dunn
Professor of Analytical and Clinical Metabolomics, School of Biosciences, University of Birmingham, UK

Abstract

For metabolomics to provide valuable biological insights, robust reproducible measurements and confident metabolite identifications are required. During this interactive workshop we will explore critical steps before, during, and after LC/MS acquisition to ensure high quality metabolomics data. Please join us for a lively discussion with leaders in the field to learn how to avoid common pitfalls and optimize experimental design.

Authors

Jiangjiang (Chris) Zhu, The Ohio State University (Primary Presenter)

Xiaowei Sun, The Ohio State University

Abstract

Fecal metabolites, driven largely by the gut microbiota, are established to change in the presence of gastrointestinal disorders. Understanding these changes could help to diagnose various diseases and monitor drug treatment or food intervention. Compared with clinical analysis of other body fluids and biological specimens, volatile organic compound (VOC) analysis from the headspace of feces for gut metabolism assessment has the potential to save analysis time (from hours to minutes) and significantly reduce costs otherwise incurred from expensive blood chemistry or genomic approaches. However, VOC-based techniques are still far from reaching the maturity needed to widely support clinicians in making day-to-day patient care decisions. Therefore, the objective of the current project is to apply an innovative biological volatile organic compound (VOC) detection technology (i.e. Stable Isotope Labeling-Secondary Electrospray Ionization-High Resolution Mass Spectrometry; SIL-SESI-HRMS) to sensitively and specifically detect metabolic VOCs of gut microbes. A commercial SESI ionization chamber and a VOC sampling system was developed in collaboration with Fossil Ion Technology and installed to a Thermo QE Quadrupole-Orbitrap mass spectrometer. The integration of SESI ionization source and HRMS enables direct gas-phase sampling without any sample preparation, and provide sensitive and selective detection of charged ions from neutral gas samples. N2 carrier gas was used to flush through a water bottle to increase the humility, and then to flush through the sample bottle to transfer the VOCs in the headspace of the sampling vessel into the SESI ionization source. The VOCs undergo ionization (either positively or negatively) by passing through the electrospray and then enter the HRMS, and the charged VOC ions are then separated by their mass/charge ratio (m/z) for detection and identification. The whole system was incubated at 40 °C to increase the transfer efficiency of volatile metabolites. The whole system is airtight to ensure accurate signal response and to avoid potential environmental interference from room air. The performance of the SESI-HRMS system was verified by 3 representative VOCs commercially available standards from different compound classes. Diluted standard solutions were used to generate calibration curves and determine the limit of detection. Excellent linearity was obtained for these compounds with the correlation coefficients reported between 0.9971 (9-decen-1-ol) to 0.9997 (ethyl acetate). SIL-SESI-HRMS system was then introduced and tested with gut bacterial culture cultivated with both a non-labeled media and a ¹³C labeled media. The mixed gut bacteria culture from human fecal isolates was prepared at 37°C in anaerobic conditions for 24 hours before measuring the volatile metabolites. The headspace of the culture was introduced to SIL-SESI-HRMS for targeted detection of the previously tested VOCs (match accurate m/z). The culture signals obtained were subtracted from the signals generated by the media control to offset the signals from the growth media. The resulting mass spectra were also compared with standards and database spectrum to identify the biogenic VOCs which have the comparable spectrum pattern in ¹²C and ¹³C medium culture. While the non-biogenic VOC peaks did not appear in pairs or similar patterns.

Abstract

Background: Recent advances in newborn screening (NBS) have improved the diagnosis of inborn errors of metabolism (IEMs); however, metabolic screening for patients with a normal NBS who present with nonspecific neurological features including intellectual disability (ID), global developmental delays (GDD), or seizures, has not changed in >40 years. This standard metabolic screening does not identify many potentially treatable IEMs. Therefore, we aim to demonstrate the utility of untargeted metabolomics to serve as a primary screening tool for IEMs by comparing the diagnostic rate of clinical metabolomics with the recommended traditional metabolic screening approach including plasma amino acids (PAA), plasma acylcarnitine profiling (ACP) and urine organic acids (UOA).

Method: Data from 4464 clinical samples from 1483 unrelated families referred for traditional screening test between 2014 - 2018 were reviewed and analyzed to determine the diagnostic rate of this traditional screening approach. These data were then compared to analysis of 2000 consecutive plasma samples from 1807 unrelated families received for clinical metabolomic screening during a similar period. Metabolomic patients were primarily pediatric of diverse ethnicity, with 79.8% referred for neurological phenotypes. To assess clinical metabolomic screening outcomes, a global meta-analysis pipeline was developed by integrating all available genetics information from the patients to provide an in-depth analysis for every clinical sample.

Results: Of the 1483 families screened by the traditional approach, 18 families were identified with IEMs, giving a 1.2% diagnostic rate; 12 different IEMs were identified, with only two conditions not included in the recommended universal newborn screening panel (RUSP). Plasma untargeted metabolomic profiling of 2000 clinical samples from 1807 unrelated families identified 129 cases with IEMs, giving an overall diagnostic rate of 7.1%. Of these 129 positive cases, 63 subsequently had targeted quantitative biochemical testing that confirmed the metabolomic findings and diagnosis. In total, 70 different conditions were identified, including 49 conditions not presently included on the RUSP.

Conclusions: In our series, untargeted metabolomics provided a ~6-fold higher diagnostic yield as compared with the conventional screening approach and identified a broader spectrum of IEMs. Notably, with the expansion of newborn screening programs, traditional testing approaches identify few disorders outside of those covered on the newborn screen. These data support the capability of untargeted metabolomics as initial screening for IEMs in children with non-specific neurological phenotypes.

Authors

Anton Kozhinov, Spectroswiss, Lausanne, Switzerland

Konstantin O. Nagornov, Spectroswiss, Lausanne, Switzerland

Donald F. Smith, National High Magnetic Field Laboratory, Tallahassee, FL

Yury O. Tsybin, Spectroswiss, Lausanne, Switzerland

Franklin E. Leach III, Department of Environmental Health Science, University of Georgia (Primary Presenter)

Abstract

FTMS based approaches enable the highest levels of molecular information content based on the provision of high mass resolution and mass accuracy in combination with increased speed and sensitivity. A key technology hurdle in FTMS lies in the direct correlation between performance and ion detection time. To achieve high mass resolving power, an increasing portion of the instrument duty cycle is required. This necessity is at odds with current desires for high throughput analysis. To overcome this deficiency, advanced data acquisition strategies have been developed. Both LC-MS and mass spectrometry imaging (MSI) approaches can leverage these advances, in particular absorption mode signal processing, multiple frequency detection, and higher applied magnetic or electric fields during ion detection, which increase experimental throughput for Orbitrap and/or ICR FTMS.

Modern high-spectral resolution MALDI MSI is dominated by its implementation on the ICR platforms. The ability to routinely upgrade a solenoidal magnet is generally cost-prohibitive, and the ability to modify the detection circuitry of an existing instrument provides a more affordable alternative to achieve increased performance. For example, appropriate segmentation of excitation and detection plates to measure the third harmonic of the cyclotron frequency (3) can provide approximately 3x the mass resolving power for the same acquisition period or the same within 1/3 of the time. Recently, Orbitrap FTMS instruments have started their expansion into MALDI imaging field of application. However, unlike the present-generation Orbitraps, the originally introduced hybrid LTQ Orbitraps (equipped with D30 mass analyzers) are able to collect mass spectra exclusively in magnitude FT mode and demonstrate limited resolution performance. The addition of an auxiliary data acquisition system enables both absorption mode as well as extended transients for improvement in overall performance.

Here, we report on these two different strategies to achieve enhanced throughput and performance for MSI lipidomics and metabolomics performed on an ICR and Orbitrap. Selected examples include mouse lung and brain in addition to zebrafish. ICR data acquisition has employed multiple frequency detection (3) to increase throughput while maintaining high mass resolution and mass accuracy required to make confident molecular assignments during MSI. The acquisition of 1.55 s transients in magnitude mode can provide a mass resolving power of ~640,000 at m/z 800 that enables the resolution of 2.4 mDa mass differences in the lipid range of the mass spectrum. In comparison, we also demonstrate that even an LTQ Orbitrap XL platform boosted by an external advanced data acquisition system and allied data processing software can deliver a state-of-the-art performance in MALDI imaging. Our preliminary results demonstrate, for the first time, that the employed LTQ Orbitrap XL instrument can provide similar space charge capacity, dynamic range, and mass resolving power to that of an ICR operating at the cyclotron frequency for MSI. These results include the resolution of doublets with 8-10 mDa spacing in a 200-1500 m/z mass range with a spectral dynamic range up to 1000 (obtained in a single pixel) and mass error standard deviation not exceeding 1.5 ppm for very low abundance peaks after internal re-calibration.

Authors

Eric Gier, Georgia tech graduate student (Primary Presenter)

Clint Miles Alfaro, Georgia Institute of Technology

David Gaul, School of Chemistry and Biochemistry, Georgia Institute of Technology

Facundo M. Fernández, School of Chemistry and Biochemistry, Georgia Institute of Technology

Alexis Pulliam, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology

LaPlaca Michelle, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology

Abstract

Approximately 1.7 million people suffer a traumatic brain injury (TBI) annually in the United States, and one-third of injury related deaths are linked to TBI. Lipid biomarkers for TBI are understudied yet appealing due to the brain’s lipid content, and possible transport of lipids to the blood through the cerebrospinal fluid. We hypothesize that the presence and severity of mild TBI in Sprague-Dawley rodent models can be predicted by measuring lipidomic markers in brain tissue and serum. We are combining ultra-performance liquid chromatography (UPLC)-MS for serum and brain tissue analysis with matrix-assisted laser desorption/ionization (MALDI) for brain tissue MS imaging (MSI) analysis. This study will provide insight into lipidome changes associated with mild TBI and discovery of new serum biomarkers.

Sprague-Dawley rats (n=32), in compliance with an approved IACUC protocol, were impacted with a pneumatic piston on the crown of the skull. Experimental groups were sham controls (n=11), 1x impact (n=10), and 3x sequential impacts (n=11). Whole blood was collected at baseline, 4- and 24-hours post-injury. After 24 hours, rats were sacrificed, and brains were extracted. Whole blood was diluted with cold isopropanol, and the supernatants were analyzed with reversed-phase UPLC-MS/MS on a Thermofisher Scientific ID-X Tribrid mass spectrometer; data was processed and analyzed with Compound Discoverer (Thermofisher Scientific) and analyzed with the PLS_Toolbox (Eigenvector Research) within Matlab. The left hemisphere of the brain was homogenized for UPLC-MS/MS and the right hemisphere was cryosectioned for MALDI-MSI on a Bruker Rapiflex TOF-MS.

UPLC-MS of serum in both positive and negative modes yielded 13,130 features. Principal component analysis showed clustering of pooled quality control samples in the center of all study samples and showed separation between uninjured Abcam reference and study serums. To narrow the feature list, all features below five times blank normalized abundance or with CVQC values greater than 20% were removed. Unpaired t-tests were conducted and features with q-values below 0.05 were identified as features of interest. Further MS/MS experiments were conducted on 212 features of interest also exhibiting a 1.5-fold or greater increase post injury.

Twenty-three important variables were selected with genetic algorithms and inverse partial least squares analyses. This panel was first used in a binary comparison of baseline and injury timepoints and preformed with 100% sensitivity, 98.1% specificity, and 99.1% accuracy. Orthogonalized partial least squares discriminant analysis with venetian blinds cross validation and modeling of each timepoint as a separate class showed 77% proper class assignment over all four timepoints. Most misclassified samples, 65%, were collected at baseline and 30 minutes post injury. These early timepoint samples were improperly classified likely due to minimal differences in the plasma metabolome shortly after injury.

Several serum markers of interest, such as cholesterol sulfate, arachidonic acid, PE(36:4), PE(36:2), stearic acid, and docosapentaenoic acid, were detected in MALDI-MSI of a test rat brain. Future work will require comparing UPLC-MS results of the brain homogenates to the serum and identifying the locations of lipids of interest in the brain through MALDI-MSI.

Authors

Tanya Puccio, Philips Institute for Oral Health Research, Virginia Commonwealth University (Primary Presenter)

Biswapriya Biswavas Misra, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine

Todd Kitten, Philips Institute for Oral Health Research

Abstract

Introduction

Streptococcus sanguinis is an opportunistic pathogen and major etiological agent of infective endocarditis. Previous studies have implicated manganese acquisition as an important virulence determinant in streptococcal endocarditis. A knockout mutant lacking the primary manganese import system, SsaACB, is severely attenuated for virulence in an in vivo rabbit model. Manganese is a known cofactor for two important enzymes in S. sanguinis, superoxide dismutase, SodA, and the aerobic ribonucleotide reductase, NrdEF. Yet, little is known about how manganese impacts the cellular and extracellular metabolomes of streptococci.

Methods

We applied metabolomics to cells and Brain Heart Infusion spent media to understand the temporal metabolic changes resulting from manganese depletion. The metal chelator EDTA was added to the ΔssaACB mutant culture in aerobic fermentor conditions. Both cell and media samples were collected 20 min pre-EDTA and 25 and 50 min post-EDTA treatment. Pre-inoculation media was also sampled. Untargeted metabolomics data were generated through combined use of positive and negative mode liquid chromatography-tandem mass spectrometry (LC-MS/MS) platforms. Data were subjected to statistical processing using MetaboAnalyst and time-course analysis was performed using Short Time series Expression Miner (STEM) tool.

Results

EDTA treatment of ΔssaACB aerobic fermentor grown cells was previously shown to result in the depletion of manganese but not other biologically relevant metals, such as iron or zinc. From our metabolomics analysis, we observed changes in 534 and 422 metabolites in cells and media, respectively. Pathway enrichment analysis of the 13 significantly differential cellular metabolites were enriched only for purine and pyrimidine metabolism. Glutamine levels decreased post-EDTA treatment in the media. Unsupervised principal component analysis (PCA) revealed that metabolite abundances alone were able to discriminate between the three time points and explain the variation in the dataset by virtue of the first two PCs by 58.8% and 67.5% in cells and media, respectively. Multivariate supervised partial least squares discriminant analysis (PLS-DA) analysis derived variable importance in projection (VIP) analysis revealed that the top 15 metabolites were lipids, cCMP, cUMP, and redox metabolites in cells. Further, 7 VIP derived metabolites (glutamine, adenosine, adenine, glycerate, forminoglutamate, citrulline, and orotate) were shared between cells and media across all the time points. Time-course analysis of metabolite abundances revealed global scale changes in both cells and media in similar metabolic pathways. Thus, the impact of manganese depletion leads to changes is a multitude of metabolic pathways, likely due to reduced activity of as of yet unknown manganese-cofactored enzymes as well as a regulatory factor for metal-dependent regulatory systems.

Conclusions

Our analysis provided evidence for a manganese-dependent shifts in the membrane lipid profiles, sugar and nucleoside metabolism, redox metabolism in oxidative stress. Future studies will attempt to confirm the role of manganese as either a cofactor for key enzymes or as a regulatory molecule that leads to the changes observed in these pathways. This study provides further evidence for the extensive involvement of this trace element in streptococcal biology, as well as a better understanding of the complete metabolome of S. sanguinis.

Authors

Raziyeh Abooshahab, Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences (Primary Presenter)

Kourosh Hooshmand, Department of Agroecology, Aarhus University, Slagelse, Denmark

S. Adeleh Razavi, Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences

Morteza Gholami, Department of Chemistry, Faculty of Science, Golestan University, Gorgan, Iran

Maryam Sanoie, Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences

Mehdi Hedayati, Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences

Abstract

Background: One of the challenges in the diagnostic area of thyroid cancer is a preoperative diagnosis of thyroid nodules with indeterminate cytology. Herein, we report an untargeted metabolomics analysis to identify circulating thyroid nodules metabolic signatures for finding the new metabolic biomarkers.

Methods: Untargeted gas chromatography-quadrupole-mass spectrometry (GC-Q-MS) was used to ascertain the specific plasma metabolic changes of thyroid nodule patients consisted of papillary thyroid carcinoma (PTC; n = 19), and multinodular goiter (MNG; n = 16) compared to healthy subjects (n=20). Diagnostic models constructed using multivariate analysis such as principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and univariate analysis including One-way ANOVA and volcano plot by MetaboAnalyst and SIMCA software. Because of the multiple-testing issue, false discovery rate (FDR) p-values are also computed for this functions.

Results: A combination of univariate and multivariate statistical analyses identified 28 differentially significant metabolites with variable importance in the projection (VIP) value greater than 0.8 and P value less than 0.05 in plasma samples of patients with thyroid nodules compared with those of healthy persons. The differentiating metabolites were involved in amino acids metabolism, tricarboxylic acid cycle, fatty acids metabolism, and purine and pyrimidine metabolism, including cysteine, cystine, glutamic acid, α-ketoglutarate, 3-hydroxybutyric acid, adenosine-5-monophosphate and uracil respectively. Further, sucrose metabolism differed profoundly between thyroid nodule patients and healthy subjects. Moreover, according to the receiver operating characteristic (ROC) curve analysis, sucrose could discriminate PTC from MNG (area under ROC curve value = 0.92).

Conclusion: The study has gone some way towards enhancing the understanding of the metabolic changes taking place in relevant biochemical pathways in thyroid nodules with considerable distinguishing between patients and healthy subjects. In addition, our study showed extensive sucrose metabolism in the plasma of thyroid nodules patients, which provides a new metabolic signature of the thyroid nodules tumorigenesis. Accordingly, it suggests that sucrose can be considered as circulating biomarkers for differential diagnosis between malignancy and benignity in indeterminate thyroid nodules.

Keywords: Thyroid nodules, Papillary thyroid cancer, Multinodular goiter, Metabolomics, GC-MS

Authors

Skyler Kramer, Univ. of Missouri

Feng Qiu, University of Missouri

Barbara Sumner, University of Missouri Metabolomics Center

Ryan Renslow, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Sean Colby, Pacific Northwest National Laboratory

Thomas Metz, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Lloyd W. Sumner, University of Missouri (Primary Presenter)

Abstract

The confident identification of metabolites is one of the greatest challenges in non-targeted metabolomics research. Typically, metabolite identification depends on matching compound retention time, accurate mass, and/or fragmentation patterns to authentic compound reference libraries. However, current reference libraries only cover a small proportion of biochemical space, and authentic standards are not always available. In the absence of authentic compounds, additional physical measurements, such as collisional cross section (CCS), can help provide more confident identifications. In this study, we present novel supervised machine learning-based techniques to predict negative ion CCS values for metabolites/natural products, which are incorporated into a complimentary searchable library.

Metabolite structures in the form of CAS, InChI, and/or SMILES codes along with experimentally measured [M-H]- CCS values (n = 794) were obtained from three sources: the Bruker-Sumner natural products library (n = 120), the Pacific Northwest National Laboratory database (n = 341), and the MetCCS database (n = 333). These experimental data were used to construct and test predictive models. Forty-three physicochemical features were calculated using ALOGPS and/or the Pharmaceutical Data Exploration Library (PaDEL-Descriptor) software. These values were further used by an L1-regularized epsilon support vector machine coupled to a Gaussian activation function (ε-SVM) to make predictions. Hyperparameters used in the model (cost and epsilon) and the activation function (gamma) were tuned by 10-fold cross-validated hybridized genetic algorithms (CV-HGA). We further found that the separation of training data based upon chemical superclass as determined by ClassyFire could improve model performance. Mean absolute error percentages within the validation sets ranged from 2.7% - 11.2%. Finally, the suite of models were used to predict CCS values for a large number of metabolites. All structures from Mass Bank of North America (MoNA), Human Metabolome Database (HMDB), Phenol-Explorer, Chemical Entities of Biological Interest (ChEBI), and FooDB were downloaded. The ‘webchem’, ‘ChemmineR’, and ‘ChemmineOB’ packages in R were used to generate SMILES strings and InChI codes/keys where they were missing. Then SMILES codes were converted to their canonical forms prior to eliminating duplicate compounds and entered into the model to predict CCS values for all structures within the above-named databases.

Authors

John R. Cort, Pacific Northwest National Laboratory (Primary Presenter)

Roger Linington, Simon Fraser University

Ryan Renslow, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

David Wishart, University of Alberta

Lloyd W. Sumner, University of Missouri

Abstract

For several decades, NMR spectroscopy has been essential to the discovery and structure elucidation of natural products and specialized metabolites. Numerous advances in NMR hardware and software have helped immeasurably to move the field move forward. However, a widely-recognized deficiency has been the lack of a central source or database for the NMR data used in determining the structures of tens of thousands of natural products. This deficiency creates well-known frustrations for natural products chemists, perhaps most notably in dereplication. This presentation will introduce the Natural Products Magnetic Resonance Database (NP-MRD, www.np-mrd.org), a recently launched database and repository for natural products NMR data that aspires to fill this important need and help natural products research and discovery to flourish. The core of the NP-MRD is an open-access, web-enabled, community-focused, FAIR (findable, accessible, interoperable, reusable)-compliant database that eventually will contain NMR data and structures for an estimated 350,000 natural products. The database contains (1) legacy NMR data derived from the literature, existing public databases, and other archival sources; (2) new NMR data, particularly for novel NPs, submitted by depositors through a simple and intuitive interface; and (3) calculated or predicted NMR data for most NPs. The NP-MRD will integrate closely with other databases containing mass spectrometry, biosynthetic gene cluster, and bioactivity data. It will provide rigorous validation, data checking, and powerful database search, filtering, and querying tools. In addition to data storage, retrieval, and curation, the NP-MRD will host software tools to enable NMR-based natural products research (for example: automated assignments, computer-aided structure elucidation, automated dereplication).

Authors

Lisa Bramer, Pacific Northwest National Laboraotry (Primary Presenter)

David Degnan, Pacific Northwest National Laboraotry

Yuri Corilo, Pacific Northwest National Laboraotry

Allison M Thompson, Pacific Northwest National Laboraotry

Chaevien Clendinen, Pacific Northwest National Laboraotry

Abstract

The major challenge with analysis of GC-MS metabolomics datasets is the confident identification of features that are usually matched to one or more metabolites in a database or library. Current scoring approaches and manual vetting can lead to errors in data interpretation and is a major factor in our lack of understanding of many biological and environmental systems. GC-MS-based metabolomics studies employ standards (FAMES or n-alkane) that are run and processed with each sample set to facilitate determination of retention indices¹ which, when coupled with fragmentation spectra, results in better database matches.² Datasets of varying complexity with ground truth collected at PNNL have been compiled and used to evaluate the quality of current scoring approaches and the assumptions in variation made by RI scores. Currently, observed RI scores are based on the observed RI’s position on a normal distribution curve, centered at the expected RI, where the variance of the distribution is assumed to be equal across metabolites. We evaluate these assumptions of current RI scoring methodology and demonstrate that RI distributional properties vary and that such assumptions can result in incorrect assignments. We demonstrate a RI scoring methodology with corrected distributional assumptions through associations of distributional and chemical properties, allowing for improvement in RI scoring. We further present a similar investigation in spectral similarity scoring methods.

[1] Kovats, v. E., Helvetica Chimica Acta 1958, 41 (7), 1915-1932

[2] Fiehn, O., Curr Protoc Mol Biol 2016, 114, 30 4 1-30 4 32.

Authors

Susanta Das, Postdoc, Michigan State University (Primary Presenter)

Kiyoto Aramis Tanemura, Michigan State University

Arthur S Edison, University of Georgia

Facundo M. Fernández, School of Chemistry and Biochemistry, Georgia Institute of Technology

Kenneth M. Merz, Jr., Professor, Department of Chemistry, Department of Biochemistry and Molecular Biology, Michigan State University.

Abstract

Ion mobility coupled to mass spectrometry (IM‐MS) is an analytical technique with growing popularity that is currently gaining attraction for unknown metabolite structure identification. IM separates gas‐phase ions based on their shape, size and charge.1 Unlike many other biophysical techniques that provide structural information, such as X‐ray crystallography and NMR spectroscopy, IM‐MS is a rapid experiment and requires no previous purification or crystallization of the target compound. However, the interpretation of IM‐MS data is still a challenge and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low pressure drift chamber.2,3 For any given molecular geometry, the CCS of the target ion can be accurately calculated, with an appropriate treatment of the collisions between the molecular ion and the buffer gas, for subsequent comparison with experimental values. The sensitivity and reliability of computational prediction of CCS values depends on how large the conformational space is, and how accurately the molecular state is modeled. In this work, we developed an efficient CCS computational workflow and pipeline encompassing the following steps: First, we generate the conformations of the individual metabolites using the RDKit tool.4 Each generated conformer is then optimized with the ASE_ANI QM machine learning model.5 All the optimized structures are then clustered using our in-house automated clustering code (viz. AutoGraph) to identify chemically unique conformations. A geometry optimization and Mulliken atomic charge calculation is then performed on a representative conformation of each identified cluster at the M06-2X/6-31+G(2d, p) level of theory using the Gaussian 16 software package. The input file for the CCS calculation using the HPCCS code developed by Zanotto et al.6 is prepared by extracting the geometry and atomic charges from the DFT computation using a python based script. Finally, the room temperature N2-based trajectory method is used to calculate the average collisional cross-sectional areas and to predict the structure of the targeted metabolites by comparing the computed results using a Boltzmann weighted average over multiple conformers with experimental CCS values. The complete protocol is modeled in such a manner that makes the computation of CCS values tractable for large number of conformationally flexible metabolites with complex molecular structures.

Reference:

(1) Lanucara, F.; Holman, S. W.; Gray, C. J.; Eyers, C. E. Nature Chemistry 2014, 6, 281-294.

(2) May, J. C.; McLean, J. A. Anal Chem 2015, 87, 1422-1436.

(3) Zang, X.; Monge, M. E.; Gaul, D. A.; Fernandez, F. M. Anal Chem 2018, 90, 13767-13774.

(4) Ebejer, J. P.; Morris, G. M.; Deane, C. M. J Chem Inf Model 2012, 52, 1146-1158.

(5) Smith, J. S.; Nebgen, B. T.; Zubatyuk, R.; Lubbers, N.; Devereux, C.; Barros, K.; Tretiak, S.; Isayev, O.; Roitberg, A. E. Nat Commun 2019, 10, 2903-2911.

(6) Zanotto, L.; Heerdt, G.; Souza, P. C. T.; Araujo, G.; Skaf, M. S. J Comput Chem 2018, 39, 1675-1681.

Authors

Jacob Kempa, Montana State Universty

Corinne Elise Moss, Montana State University (Primary Presenter)

Tami Peters, Chemistry & Biochemistry Department, Montana State University, Bozeman, MT 59718

Lexi Kyro, Montana State University

Brian Tripet, Montana State University

Brian Eilers, Chemistry & Biochemistry Department, Montana State University, Bozeman, MT 59718

Seth Pincus, Department of Chemistry & Biochemistry, Montana State University, Bozeman, MT 59718

Valerie Copie, Montana State University

Abstract

Ricin toxin, derived from the seed of the castor oil plant, is a ribosome-inactivating protein. As a highly toxic, readily available, and chemically stable molecule, ricin is a potential agent of bioterrorism. Ricin is configured as an A-B toxin, in which the B chain binds promiscuously to mammalian cells, and the A chain comprises the ribosome-inactivating agent. The A chain has also been studied for therapeutic use as a component of immunotoxins. Previous research by our group has demonstrated that parenteral administration of ricin can cause lethal hypoglycemia. To aid in the treatment and prevention of ricin toxicity, research efforts have focused on better understanding the ricin-induced metabolic state of hypoglycemia. These studies may also aid in better understanding the systemic, pleiotropic effects of hypoglycemia, and how to exploit the toxicity of ricin A chain for immunotherapy.

We have used a mouse model to characterize liver metabolome changes associated with hypoglycemia induced by two different environmental conditions: ricin treatment and fasting. To monitor the progression of ricin toxicity over time, mice were given intraperitoneal injections of ricin at lethal and sub-lethal doses for 2 hours, 8 hours, and overnight. To understand how ricin-induced hypoglycemia differs from the hypoglycemic state induced by fasting, another group of mice had food withheld for 8-hours and overnight. 1H NMR-based metabolomics was performed on polar molecules extracted from the livers of these mice, and metabolites annotated and quantified using the Chenomx software. Metabolite IDs were further validated using 2D-1H-1H/13C NMR or spiking of metabolite standards into the samples. Metabolite level changes were examined using multivariate statistical analysis, and MetaboAnalyst was employed to characterize liver metabolome changes between the different treatment groups.

While similar decreases in blood glucose were observed between ricin treatment and fasting, results from NMR analyses indicate that the metabolic state of ricin-induced hypoglycemia differs significantly from that of fasting. NMR-based experiments identified 59 polar metabolites in all groups. Multivariate statistical analyses were employed to evaluate global differences including hierarchical clustering analysis (HCA), unsupervised principal component analysis (2D-PCA), and supervised partial-least-squares discriminant analysis (2D-PLS-DA). All three analyses demonstrated the divergence of fasted and ricin-treated liver metabolic profiles at 8h and overnight.

Liver metabolites were also extracted and analyzed using HPLC-MS. Profiling and statistical analyses were performed using XCMS and Metaboanalyst software. Multivariate statistical analyses of the MS data confirmed metabolite IDs and global patterns observed in metabolic states by NMR. Our results support previous findings of pronounced hypoglycemia associated with ricin toxicity, and demonstrate that, while both ricin and fasting induce hypoglycemia, the metabolic states resulting from these two conditions are different. Further analyses may give insights into mechanisms of ricin toxicity, specific metabolic pathways that are altered, and potential treatments for hypoglycemia.

Authors

Lucy Woods, Bruker Daltonics (Primary Presenter)

Abstract

The highly confident annotation of metabolites requires the combination of many properties of the ion. In this study, we compared the results from multiple systems using PASEF acquisition. PASEF utilizes trapped ion mobility spectrometry (TIMS) separation, which separates metabolites based on their shape. In utilizing the fast PASEF MS/MS mode, up to x10 more compounds can be fragmented producing more MS/MS spectra which are "mobility cleaned" using TIMS separation, allowing for the unambiguous determination of a metabolite. Moreover, PASEF gives CCS values for each ion, allowing an additional parameter to be used for identification of structure. The determined CCS values were highly reproducible for intra- and inter-lab measurements (< 1 %) showing the widescale use of CCS values in libraries can be reliably used. This was further proven by comparing the measured CCS value to those already in the literature, showing similar trends and a deviation of <1%

Experimental

PASEF acquisition mode was used to separate co-eluting compounds and utilize the additional separation dimension to give clean MS/MS spectra. On average15 MS/MS spectra were acquired per PASEF scan. This leads to a high coverage of precursor peaks in each MS spectrum, leading to about 80 % of metabolomics features with assigned MS/MS spectra. A further central topic of this poster will be the inter-lab comparison of the determined CCS values. For all identified metabolites the CCS values determined by TIMS deviated by < 1 % on average from published DT-IMS reference CCS values (examples: Theobromine, Riboflavine, 1-Methyladenosin, Caffeine), showing the ability to compare CCS values determined by TIMS with DT-IMS values. The CCS value deviation filter was combined with filters for retention time deviation, precursor mass error, precursor isotopic pattern and MS/MS spectrum in an Annotation quality (AQ) scoring symbol. This reliability provides high confidence in annotations of target molecules and shows the benefit of PASEF spectra for metabolomics experiments for increasing confidence in IDs.

Authors

Radin Sadre, Michigan State University (Primary Presenter)

Thilani Anthony, Department Of Biochemistry and Molecular Biology, Michigan State University

Josh Grabar, Michigan State University

Matthew Bedewitz, University of Colorado Boulder

Arthur Daniel Jones, Michigan State University

Cornelius Barry, Department of Horticulture, Michigan State University

Abstract

In the past decade, the medicinal plant Atropa belladonna (Deadly Nightshade) has become a model plant to study tropane alkaloid biosynthesis in the Solanaceae family. So far, research efforts primarily focused on understanding the biosynthesis of hyoscyamine and scopolamine, clinically important anticholinergic and antispasmodic tropane alkaloids. Cytochromes P450 represent the largest protein superfamily in plants and are a major driving force for the structural diversification of plant specialized metabolites. The A. belladonna transcriptome encodes 180 unique transcripts for cytochromes P450 of which many are produced in root, the major site for the biosynthesis of tropane alkaloids. Functional characterization of the membrane-bound plant cytochromes P450 is often limited by low production, poor activity and/or stability of recombinant proteins in heterologous non-plant hosts, and limited knowledge about the enzymes’ substrates. Therefore, we combined transcript analyses with silencing of candidate cytochrome P450 genes and LC/MS-based untargeted metabolite profiling to investigate the functions of candidate enzymes in planta. This approach identified two cytochromes P450, essential for the production of non-acylated and acylated alkaloids in A. belladonna. Besides the known tropane alkaloids, comparative analysis of the metabolic changes in gene silenced vs. wild-type A. belladonna lines led to annotation of more than 40 additional alkaloids. Most of these metabolites are novel or not previously reported in A. belladonna. We established an Agrobacterium-mediated transient expression system to produce and assay the activity of the cytochromes P450 in leaves of Nicotiana benthamiana, a non-tropane producing species in the Solanaceae family. This system represents an invaluable tool to test hypotheses regarding the functions of candidate enzymes. Leaves producing the cytochromes P450 were infiltrated with candidate substrates selected as potential intermediates in the biosynthesis of modified tropane alkaloids. The transient assays provided further evidence for the distinct catalytic activities of the cytochromes P450 as revealed in the formation of hydroxylated and demethylated alkaloids. Overall, our study provides insight into how networked alkaloid metabolic pathways respond to flux changes and offers applications for engineered production of modified tropane alkaloids.

Authors

Sneha Couvillion, Biological Sciences Division, Pacific Northwest National Laboratory (Primary Presenter)

Dan Naylor, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA

Ruonan Wu, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA

Montana Smith, Pacific Northwest National Laboratory

Vanessa Paurus, Pacific Northwest National Laboratory

Kent Bloodsworth, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Kelly Stratton, National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, USA

Lisa Bramer, Pacific Northwest National Laboraotry

Mary S Lipton, Pacific Northwest National Laboratory

Jansson Janet, Pacific Northwest National Laboratory

Thomas Metz, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Kirsten Hofmockel, Pacific Northwest National Laboratory

Abstract

Soil environments are rich in microbial diversity and biomass, where microbial communities play a critical role in biogeochemical processes that sustain ecosystem health. Shifting environmental conditions (temperature, moisture, pH, salinity, nutrient/resource availability etc.) associated with global climate change can be stressful for microorganisms, which must adapt their metabolic processes and membrane properties in order to survive and remain active. Reliable prediction of the ecosystem level consequences of climate change will require a more thorough understanding of how soil microbial communities respond to abiotic drivers that affect microbial physiological processes and community composition. Despite the recognition that lipids are key orchestrators of cellular function and homeostasis, intact lipids still remain understudied in soil. While PLFA (phospholipid fatty acid) analysis is a widely used technique to characterize microbial groups, it cannot provide information on the lipid head group and focuses only on phospholipids, thereby omitting other potentially interesting and informative lipid classes.

Here, we performed untargeted lipidomics analyses of soil to advance understanding of the role of lipids in the microbial response to abiotic stress induced by drying and rewetting soil. We simulated a drought and subsequent rewetting event to investigate the immediate (0-3 hr) physiological stress response of the microbial community in an arid grassland soil. Soil aliquots were dried for a week and sampled immediately prior to rewetting (0 min) and at 5 timepoints after water addition (10, 20, 30, 90, 180 min). Metabolites and lipids were simultaneously extracted using MPLEx, a modified Folch extraction technique. 16S and ITS amplicon sequencing data were also collected to understand compositional changes in bacterial and fungal communities. Our untargeted lipidomics approach captured a variety of lipid classes including glycerolipids (triacylglycerols, diacylglycerols, sulfoquinovosyl diacylglycerols, betaine lipids), glycerophospholipids (diacylglycerophosphocholines, diacylglycerophosphoethanolmines, diacylglycerophosphoglycerols, diacylglycerophosphoinositols) and sphingolipids (ceramides, hexosylceramides, phytoceramides). A total of 524 features were annotated with 884 unique lipids identified from LC-MS/MS data in positive and negative ionization modes. To the best of our knowledge, this represents the largest soil lipidome reported to date. Comparisons between the dry soil group and the later time point groups were performed via ANOVA with a Dunnett correction. Distinct changes in the lipidome (Dunnet corrected p-value<0.05) were observed as early as 10 minutes after wet-up, pointing to remodeling of membrane lipids as an adaptive response to osmotic stress. Additionally, changes in the soil lipidome are indicative of community shifts due to the differential response of various microbial groups in soil. Our results show that, for lipids that belong to subclasses diacylglycerophosphocholine, triacylglycerols and diacylglycerophosphoethanolmines, those with lower total numbers of fatty acid carbons, significantly increased upon rewetting, whereas lipids with higher total fatty acid carbons decreased. Lipid ontology enrichment analysis enabled biological information to be extracted from the lipid name, highlighting significant trends. Polyunsaturated glycerophospholipids and triacylglycerols were enriched in dry soil and saturated and monounsaturated triacylglycerols and glycerophospholipids were enriched after wetting. Our findings demonstrate that membrane, signaling and storage lipids can be valuable indicators of the physiological status of soil microorganisms in response to environmental perturbations and stress.

Authors

Xinyu Fu, MSU-DOE Plant Research Laboratory, Michigan State University (Primary Presenter)

Berkley Walker, MSU-DOE Plant Research Laboratory, Department of Plant Biology, Michigan State University, East Lansing MI, USA

Abstract

Quantifying fluxes through the plant metabolic network is fundamental for engineering plants with improved photosynthetic efficiency and productivity. Recent advances in metabolomics and computational modeling have enabled us to map carbon fluxes through photosynthetic metabolism using 13CO2 as a tracer. However, experimental and computational challenges have limited its application to an integrated understanding of carbon partitioning within the complex plant metabolic network. Here, we presented an improved framework for isotopically nonstationary metabolic flux analysis (INST-MFA), interfaced with gas exchange measurements, to quantify the roles of photorespiration on the carbon economy of the photosynthetic cells. To perform in vivo 13CO2 labeling of Nicotiana tabacum leaves, we outfitted a gas exchange cuvette with a custom gas mixing system and a cryospray injection port to quench metabolism within 0.3 second. 13CO2 labeling experiments were performed on leaves acclimated at high (40% O2), normal (21% O2), and low photorespiratory (2% O2) conditions. Using various GC-MS and LC-MS/MS platforms, we analyzed the mass isotopomer distributions of 50 fragment ions from 35 metabolites, representing intermediates involved in the Calvin Benson cycle, photorespiration, tricarboxylicacid cycle, and synthesis of starch and sucrose. Despite a lower net CO2 assimilation rate with 40% O2 acclimation, glycine and serine became isotopically enriched at a faster rate, whereas UDP-glucose, glucose 6-phosphate, pyruvate, and alanine became isotopically enriched at a slower rate. Metabolic models were solved to estimate ~100 network-wide fluxes based on the isotopomer measurements, the net CO2 assimilation, the synthesis rates of starch and sucrose, and the levels of vascular sucrose and amino acids. Rates of rubisco carboxylation and oxygenation were estimated from gas exchange data to provide accurate constraints to the final flux solutions. This study expands the potential for INST-MFA to provide a mechanistic understanding of photorespiration and how it interacts with central metabolism, possibly elucidating routes for improving the efficiency of carbon fixation.

Authors

Emma Bilbrey, The Ohio State University

Kathryn Williamson, The Ohio State University

Diane Miller, The Ohio State University, Horticulture and Crop Science

Emmanouil Chatzakis, The Ohio State University

Jonathan Fresnedo Ramírez, The Ohio State University

Jessica Cooperstone, The Ohio State University (Primary Presenter)

Abstract

Apples are the most commonly consumed fruit in America, and “an apple a day keeps the doctor away” is a well-known adage. The commercial and nutritional importance of apples has prompted interest in varietal improvement. However, progress is limited by heterozygosity, obligate out-crossing, and a long juvenile period, which delays fruit evaluation for quality traits. To minimize these drawbacks, apple breeders have begun using marker-assisted selection (MAS) for some traits, but breeding strategies for fruit phytochemicals have yet to be developed.

In response, we have developed an integrated genomic-metabolomic platform to better understand gene-phytochemical associations in breeding-relevant apple germplasm. Phytochemicals that are potentially health beneficial, contribute disease resistance, or improve fruit quality can be characterized using metabolomics, providing a foundation to study apple’s breeding potential. The platform is based on high-throughput genomic and metabolomic assessment of 124 unique apples, including members of three pedigree-connected families alongside diverse and wild selections. Single nucleotide polymorphism (SNP) data was obtained from the 20K SNP array for apple and integrated with metabolomic datasets from high-resolution mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyses of polar/semi-polar apple fruit extracts. Metabolite genome-wide association studies (mGWAS) were conducted with 11,165 SNPs for two LC-MS data sets of 4,000+ features each and an NMR data set of 756 bins. Novel schemes for prioritizing results from mGWAS indicated 519 (LC-MS (+)), 726 (LC-MS (-)), and 177 (NMR) significant marker-trait associations across the apple genome (LC-MS: p < .00001, NMR: p < .0001). These results were then sifted to select features to analyze with a more powerful pedigree-based analysis (PBA) in FlexQTL™ with 6,034 SNPs to identify metabolite quantitative trait loci (mQTL), genomic areas exerting genetic control over phytochemical production. Putative mQTL were detected on each chromosome with hotspots on chromosomes 16 and 17. An mQTL for chlorogenic acid was identified on the bottom of chromosome 17 across all three metabolomic data sets and was used as a proof-of-concept example to demonstrate the applicability of the platform. This workflow includes multi-platform metabolomics data acquisition, and allows a pseudo-self-validating approach to profile phytochemicals and unravel genetic coordination, enabling continued study and crop improvement. Determining gene-phytochemical relationships in apple will inform breeding and facilitate future MAS for improved nutrition along with attributes related to flavor and disease resistance etiology.

Authors

Maris Angela Cinelli, Department of Biochemistry and Molecular Biology, Michigan State University (Primary Presenter)

Josh Grabar, Michigan State University

Hannah Makayla Parks, Michigan State University

Cornelius Barry, Department of Horticulture, Michigan State University

Arthur Daniel Jones, Michigan State University

Abstract

Plants in the genus Datura (Solanaceae) are characterized by the production of tropane alkaloids including hyoscyamine, scopolamine, littorine, and others. Because of the presence of these alkaloids, Daturaspecies have been used both medicinally and recreationally, and these plants have also historically held cultural and religious significance. As such, the discovery of novel alkaloids from this genus could have implications in drug or pesticide discovery or serve as a launching point for transcriptomic analysis and gene discovery useful in breeding for plant chemical defenses. We performed untargeted LC-ESIMS-based metabolite profiling using MSE followed by data dependent-analysis (DDA) MS/MS of extracts of Datura stramonium, a widespread American Datura species also known as Jimsonweed. By analyzing different tissues of the plant at varied stages of development, we observed that D. stramonium produces a stunning assortment of hydroxylated and acylated tropane alkaloids, with the roots possessing the highest number of different compounds (approximately 80 compounds observed in roots were identified by MS/MS as having tropane fragments). Surprisingly, D. stramonium roots also contain a unique class of tropane alkaloids with odd-mass MH+ values, suggesting (along with their MS/MS product ion spectra) the presence of an extra nitrogen in the acyl portion. Tropane alkaloids containing nitrogenous acylations are uncommon, and the proposed structures of these novel alkaloids are unprecedented across the Solanaceae, Erythroxylaceae, and Convolvulaceae families. These alkaloids are found exclusively in the roots and accumulate as the plant ages. Additionally, this class of compounds is functionalized (hydroxylated and acylated) in a manner analogous to hyoscyamine and/or littorine, suggesting that they may be produced using common biosynthetic machinery. The discovery of these alkaloids highlights the utility of untargeted metabolite profiling followed by DDA MS/MS for the identification of new plant specialized metabolites.

Authors

Ryland Giebelhaus, The University of British Columbia (Primary Presenter)

Susan Murch, University of British Columbia Okanagan

Abstract

In recent years, online metabolomics tools have become increasingly common, with the majority of resources focused on human metabolites, or compounds directly related to human health. Fewer resources are available to fully understand plant metabolomes and metabolism. Our overall goal was to develop a new tool for metabolomics analysis of plant growth regulators (PGRs) and signaling metabolites in untargeted plant metabolomes. PGRs play key roles in mediating plant growth, development and , and the production of valuable secondary metabolites. To develop a searchable hormonomics database, our specific goals were: (1) to catalogue a list of known and putative PGRs, (2) to validate the database toolbox with untargeted and targeted UPLC – MS/MS datasets and (3) to develop an open source online tool to match experimental data with a library of known and predicted PGRs by mass and RT.

We created a new metabolomics tool dubbed ‘HormonomicsDB’ that contains a library of 250 physiologically relevant PGRs and their metabolites including the name, abbreviation, monoisotopic mass, M+H mass, adducts and biotransformations arising from the predicted enzymatic addition or removal of natural moieties.. An untargeted UPLC-MS/MS method was developed to validate the predicted database with experimental data (Acquity I-Class UPLC; Xevo TQ-S tandem mass spectrometer; Waters) with separation on a BEH C18 column (15 mm x 2.1 mm 1.7 µm) with linear gradient elution (A: 0.25% formic acid in water | B: acetonitrile; 0.0 - 10.0 min, 95:5-5:95 v/v; 10.0-15.0 min, 5:95 v/v; 15.0-20.0 min 5:95-95:5 v/v; 20.0-25.0 min, 95:5 v/v; 25 minute run time, injection volume 5 µL, column temperature 30° C). |Compounds were ionized by ESI (positive ion mode) using optimized conditions for 9 known metabolites. A standard HormonomicsDB operating protocol was developed in the validation. The experimental data coupled with the predicted values were combined to create the HormonomicsDB tool as a searchable metabolite database accessible via an R-Shiny package (v1.5.0) using a custom R script for data analysis. Users can upload data via a CSV file in standard format. The database uses multiple parameters such as mass to charge ratios (m/z), elution time, elution order and cluster relationships to determine compound identification. Validation of the database used a test set of >12 plant species and determined a confidence interval for predicted compound identification. HormonomicsDB will provide researchers with a new resource for metabolomics analysis to understand and interpret untargeted plant datasets and will give new insights into plant growth responses, plant morphogenesis, plant perception of changes in their environments and plant responses to environmental cues.

Microbiomes are now understood to influence every aspect of our world and lives, from our bodies to the most extreme environments on Earth. In realizing the complexity of these microbial communities and the breadth of interactions between community members and their hosts, we are now poised to harness microbiomes for improved ecosystem and human health. This is of particular interest because microbial communities have an expansive metabolic capacity for biosynthesis of diverse primary and secondary metabolites that indirectly and directly affect community and host health.

This interactive forum will feature talks from two experts in environmental microbiome research followed by a fireside chat-style discussion on the utility of metabolomics in characterizing microbial communities and assessing overall community and ecosystem health. We aim to engage our audience in an inspirational discussion to identify ways to advance this emerging topic.

Interactive forum organizers: Maryam Goudarzi, Kehau Hagiwara, Ewy Mathe, Thomas Metz

Contact: Maryam Goudarzi goudarm@ccf.org

Internal standards play multiple roles in metabolomics studies, ranging from quantitation and quality control to feature alignment and compound identification. The use of internal standards is far from uniform in the metabolomics research community, with no agreed-upon best practices regarding their preparation and usage. This session will discuss the current use of internal standards in participants’ labs and brainstorm potential improvements that could enhance the utility and consistency of internal standards from a community perspective. We will seek to encourage participation and highlight perspectives from scientists in academic labs, industry, chemical vendors, and all other groups.

Interactive forum organizers: Charles Evans (U Michigan), Tim Garrett (U Florida)

Contact: Charles Evans chevans@med.umich.edu, Tim Garrett tgarrett@ufl.edu

We plan to have an expert panel discussion that will address topics like visa issues, COVID restrictions on job searches, funding, hiring freezes, etc., and virtual self-promotion.

Interactive Forum Organizers: Brianna Garcia (UGA), Kehau Hagiwara (NIST), Candice Ulmer (CDC), Christina Jones (NIST), Franklin Leach (UGA), Yuan Li (UNC), Fariba Tayyari (U Iowa), Arpana Vaniya (UC, Davis), Oana Zeleznik (Harvard)

Contact: Kehau Hagiwara kehau.usui@nist.gov

Authors

Gayatri Iyer, University of Michigan - Ann Arbor (Primary Presenter)

Janis Wigginton, Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI

William L Duren, Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI and Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI

Marci Brandenburg, Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI and Taubman Health Sciences Library, University of Michigan Medical School, Ann Arbor, MI

Jennifer LaBarre, Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor MI

Charles Burant, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor MI

George Michailidis, Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI and Department of Statistics, University of Florida, Gainsville, FL

Alla Karnovsky, Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI

Abstract

We present a novel bioinformatics tool for the analysis and interpretation of Mass Spectrometry data. Based on our previously published DNEA (Differential Network Enrichment Analysis) algorithm, we have built a Java-based, user-friendly software that performs joint estimation of the partial correlation network topology from the input data, identifies subnetworks via consensus clustering, and determines enrichment using the NetGSA algorithm. We incorporated additional functionalities into the tool, allowing it to handle realistic sample sizes and experimental design in the data. In a high dimensionality setting when the number of metabolites is much larger than the number of samples in the input data, the DNEA tool uses a combined knowledge- and data-driven approach to aggregate highly correlated metabolites into singular features to make the feature space comparable to the sample space, hence overcoming the sparsity issue and achieving higher statistical power. Additionally, we use a subsampling-based procedure to recover highly robust edges in the network from datasets with extremely imbalanced experimental groups.

We applied the DNEA tool to analyze metabolomics data from Type 1 diabetic mice and humans with incident Type 2 Diabetes. Our analysis revealed enriched subnetworks related to oxidative stress, nucleotide and energy metabolism. We also identified enriched lipid subnetworks composed of Lysophosphatidylcholines and Lysophosphatidylethanolamines in pregnant mothers’ plasma that had a strong association with the birthweight of their infants, suggesting that subnetworks identified by DNEA are not only biologically relevant to the disease under study, but are also predictive of other phenotypes of interest. Such enriched subnetworks from partial correlations therefore have the potential to provide deeper insights into disease mechanisms as well as changes in the biochemistry under different physiological states.

Authors

Aleksandr Smirnov, University of North Carolina at Charlotte (Primary Presenter)

Yunfei Liao, University of North Carolina at Charlotte

Eoin Fahy, University of California San Diego

Shankar Subramaniam, University of California San Diego

Xiuxia Du, University of North Carolina at Charlotte

Abstract

The NIH's Metabolomics Data Repository (NMDR) has been growing steadily with data from an ever-increasing number of studies that use the liquid chromatography (LC-) and gas chromatography coupled to mass spectrometry (GC-MS) analytical platforms. Accompanying this growth is the enormous number of known and unknown compounds contained in that data. Tracking these compounds across multiple studies and making them searchable is an essential step towards an efficient sharing and aggregating the vast volume of data available in the metabolomics community. ADAP Spectral Knowledgebase (ADAP-KDB) has been developed to address this need. It uses raw data that is publicly available through NMDR to construct a library of consensus GC-MS and LC-MS/MS spectra of compounds observed in multiple studies. The mass spectra derived from studies in NMDR and used to construct the consensus spectra in ADAP-KDB can be traced back to NMDR with the URLs provided.

The constructed library of consensus spectra is available through ADAP-KDB online portal, where users can browse consensus spectra, upload their GC-MS or LC-MS/MS spectra, and match them to the library using the spectral similarity. Furthermore, users can filter the matching results based on the species, sample source, and disease information collected for each study. Finally, ADAP-KDB provides users with quantitative measures that could be used for prioritizing unknown mass spectra for subsequent compound identification. These quantitative measures include p-values of the ANOVA significance tests performed for each mass spectrum in every study, Gini-Simpson diversity index, and the Chi-Squared goodness-of-fit tests performed on distributions of the meta information collected for each study. This meta information includes information about species, sample source, treatment, MS instrument name and type, chromatography system, and column.

In order to build ADAP-KDB, 55 studies with raw untargeted GC-MS data were downloaded from NMDR. Their raw data was processed using the algorithms of ADAP-GC 4.0 workflow. As a result of the data processing, 20085 pure fragmentation mass spectra were constructed and then annotated by matching them against NIST EI Spectral Library with the matching score 800 or higher. Next, both annotated and unannotated spectra were uploaded to ADAP-KDB together with the meta information about each study. Then, all spectra from all studies were clustered based on their spectral similarity. Finally, a consensus spectrum was constructed for each cluster. The resulting 16878 consensus spectra are searchable and available through the online portal of ADAP-KDB.

Thus far, the following NMDR studies have been processed and the resulting spectra have been added to the ADAP-KDB: ST000025, ST000048, ST000054, ST000058, ST000061, ST000063, ST000065, ST000083, ST000084, ST000085, ST000087, ST000092, ST000117, ST000118, ST000189, ST000215, ST000302, ST000396, ST000417, ST000481, ST000495, ST000548, ST000603, ST000622, ST000658, ST000660, ST000832, ST000833, ST000878, ST000884, ST000889, ST000889, ST000910, ST000949, ST000950, ST000951, ST000952, ST000964, ST000965, ST000967, ST000968, ST000969, ST000979, ST000981, ST001034, ST001037, ST001044, ST001045, ST001046, ST001049, ST001050, ST001056, ST001075, ST001117, ST001190. Currently, we are working on processing studies with LC-MS/MS data from NMDR and expect to add LC-MS/MS spectra to ADAP-KDB in the coming months.

Authors

Sandi Azab, Department of Chemistry and Chemical Biology, McMaster University (Primary Presenter)

Philip Britz-McKibbin, McMaster University

Rebecca Hum, McMaster University

Abstract

Perfluoroalkyl substances (PFASs) are a major contaminant class due to their ubiquitous prevalence, persistence and putative endocrine disrupting activity that may contribute to chronic disease risk notably with exposures early in life. Herein, multisegment injection-nonaqueous capillary electrophoresis-tandem mass spectrometry (MSI-NACE-MS/MS) is introduced as a high throughput approach for PFAS screening in serum samples following a simple methyl-tert-butyl ether (MTBE) liquid extraction. Separation and ionization conditions were optimized to quantify low nanomolar concentration levels of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) from serum extracts when using multiple reaction monitoring under negative ion mode conditions. Multiplexed separations of PFOA and PFOS were achieved with excellent throughput (< 3 min/sample), adequate concentration sensitivity (LOD ~ 20 nM, SNR= 3) and good technical precision over three consecutive days of analysis (mean CV = 9.1%, n = 84). Accurate quantification of PFASs was demonstrated in maternal serum samples (n = 16) when using MSI-CE-MS/MS following pre-column sample enrichment with median concentrations of 3.46 nM (0.7 to 9.0 nM) and 3.29 nM (1.5 to 6.6 nM) for PFOA and PFOS, respectively. This was lower than average PFAS exposures assessed for pregnant women who had serum collected prior to 2009 due to subsequent phase out of their production. Overall, this method offers a convenient approach for large-scale biomonitoring of environmental exposures to legacy PFASs and their emerging replacements that is relevant to maternal health and chronic disease risk in children.

Authors

Baljit Ubhi, SCIEX (Primary Presenter)

Robert Proos, Sciex

Darren Dumlao, SCIEX

Zuzana Demianova, SCIEX

Abstract

Metabolomics provides a snapshot of the metabolic system and is an incredibly useful tool for advancing precision/personalized medicine. The challenge with LC-MS/MS metabolomics is the number of different column chemistries which are employed to retain metabolites because of their varying size, polarities, solubility’s and charge – therefore no single method can capture all metabolites.

Twelve varying column chemistries from multiple vendors were assessed, namely; Acquity BEH Amide and XBridge Amino (Waters, MA), Luna NH2, Luna Polar C18, Luna PS C18, Luna PFP(2), Luna Omega, Kinetex Evo C18, Kinetex Biphenyl and Kinetex F5 (all Phenomenex, CA), the YMC NH2 (YMC, Japan) and zic-pHILIC (SeQuant, US). Each column was tested using buffers appropriate for their column chemistry.

A targeted panel of biochemically relevant metabolites was generated using pure standards by using the Mass Spectrometry Metabolite Library of Standards (MSMLS) available from IROA Technologies (MA, USA). Multiple MRMs per metabolite were optimized using the standards and a single targeted multiple reaction monitoring (MRM) method using LC-MS/MS was developed on a QTRAP® 7500 System and used to evaluate performance of a given chromatographic method. The assay allowed fast positive/negative ion mode switching allowing for a single injection for capturing all relevant metabolites. These ranged from amino acids, nucleotides, organic acids, sugars, etc. To optimize each chromatographic method a mixture of the MSMLS compounds were made combining metabolites of similar solubility. Fourteen mixtures of 270 metabolites we evaluated, however data for only mixtures 1 and 2 are shown in this presentation. Once the column was finalized, LC buffers, flow rate, gradient, column temperature and source/gas conditions were optimized for reproducibility, sensitivity, resolution of isomers and peak shape. All data exploration/analysis was completed in PeakViewTM and MutiQuantTM Software.

We present an extensive evaluation of a variety of column chemistries where the goal was to retain as many biologically relevant metabolites as possible as part of a targeted metabolomics assay. The most commonly used column chemistries were evaluated as well as others available on the market. We report on an alternative column which provides superior sensitivity (s/n), peak shape and retains the largest number of metabolites. We highlight the utilization of this method on the new 7500 LC-MS/MS platform which has allowed for an overall improvement in sensitivity and linear dynamic range equating to more metabolite identified in NIST 1950 plasma as well as other matrices tested.

Authors

Felicity Nielson, Pacific Northwest National Laboratory (Primary Presenter)

Dennis Thomas, Pacific Northwest National Laboratory

Sean Colby, Pacific Northwest National Laboratory

Ryan Renslow, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Thomas Metz, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Abstract

A conformer, or 3D structure of a molecule, can drastically alter structure-dependent molecular properties. With a growing presence of computationally derived metabolite reference libraries, subtle differences between metabolite conformers can lead to misidentification in platforms such as ion mobility spectrometry (IMS), infrared spectroscopy, and nuclear magnetic resonance spectroscopy, where two unique metabolites might be neighbors in the property space being measured. For example, subtle differences in ion structure, even the rotation of a methyl group, can lead to significantly different measurements of molecular collisional cross sections (CCS). CCS is a measure that depends on shape, size, and chemistry of a molecular ion and is reported as a cross sectional area (Å2). It can be determined using IMS, which separates ions based on the extent of their interactions with a buffer gas (usually N2 or He) as they travel under the influence of an electric field. CCS is highly sensitive to molecular shape, showing measurable differences even between conformers.

The structural dependence of chemical reactions and property predictions makes choosing correct conformers for computational simulations critical. But to date, there has not been a clear study on the most appropriate method for conformer selection in computational pipelines. In order to increase calculated property accuracy, which is critical for metabolomics, small molecule design, biological activity assays, and many other research fields, we need to gain a better understanding of the best approaches for conformer selection. Here, we employed Monte Carlo simulation and physics-based approaches to perform an in-depth analysis of conformer sampling trends in the context of CCS prediction for a set of 18 molecules. We tested the performance of various conformer selection techniques – Boltzmann weighting, lowest energy selection, random selection, energy thresholds, and RMSD-based similarity down-selection – on structures generated by molecular dynamics (MD). While different selection techniques on MD conformers had different effects on precision and accuracy, no single technique or combination of techniques was sufficient to obtain levels of accuracy within experimental error. We found this was due to the randomness of their generation, even when sampling 50,000 conformers. As a small test, we ran Density Functional Theory (DFT) geometry optimization on 25,000-50,000 conformers of three small molecules and found this significantly improved the precision and accuracy of the selection techniques. We present our findings on which selection methodology performed best and discuss the current necessity of quantum chemistry geometry optimization or more efficient conformer generation tools.

Authors

Marc McCann, University of Michigan (Primary Presenter)

Theodore Salvatore Jennaro, University of Michigan College of Pharmacy

Alan E. Jones, University of Mississippi Medical Center

Michael A Puskarich, University of Minnesota - School of Medicine

Kathleen A. Stringer, University of Michigan

Abstract

Authors

Arpana Vaniya, NIH-West Coast Metabolomics Center, Univeristy of California, Davis (Primary Presenter)

Ying Yng Yng Choy, UC Davis

Alberto Valdés, NIH-West Coast Metabolomics Center, University of California, Davis, United States

Sajjan Mehta, UC Davis West Coast Metabolomics Center

John de la Parra, Harvard Univeristy

Carol D. Stroble, NIH-West Coast Metabolomics Center, University of California, Davis, United States

Tong Shen, NIH-West Coast Metabolomics Center, University of California, Davis

Luis M. Valdiviez, NIH-West Coast Metabolomics Center, University of California, Davis, United States

Michael Sebek, Center for Complex Network Research Northeastern University

Rebekah Carlson, Open Agriculture Foundation

Caleb Harper, Open Agriculture Foundation

Oliver Fiehn, UC Davis

Abstract

Chemical constituents in food can be measured by the same methods as endogenous mammalian metabolites. Analyzing the complex diversity in food natural products is important to understand its impact to health, for example, through changes in gut microbiomes. Yet, compound identification in plant products is more challenging than human metabolomics because many compounds are specifically produced only by one or a few plants. To overcome this challenge, we have developed an m/z-RT library of more than 2,500 natural product standards that complements our previously reported MS/MS reference library. We show how confident compound annotations are achieved despite the large chemical diversity in foods. From frequently purchased produce items; garlic, basil, lettuce, apple, tomato, and strawberry were selected. Produce were homogenized and lyophilized before analysis. For complex lipids, 2 mg of produce was extracted using 3:10:2.5 MeOH/MTBE/H2O. For primary metabolites and biogenic amines, 2 mg of produce was extracted using 5:2:2 MeOH/CHCl3/H2O. For polyphenols, 20 mg of each produce was extracted using 80:20 MeOH/H2O and for comparison 20 mg of produce was extracted using 50:50 ethyl acetate/H2O. An m/z-RT library was developed by injecting mixes of 50 compounds each using an Agilent HPLC 1290 using a Kinetex PFP column coupled to an Agilent 6530 Q-TOF. First, we acquired over 33,000 MS/MS spectra for more than 2,500 authentic standards on Q-TOF and Q Exactive MS instruments using three different collision energies. Spectra were uploaded to the freely available MassBank of North America (MoNA). For the improved MS/MS library, retention time was also acquired using a Kinetex PFP column. Our m/z-RT polyphenol library contains 1,540 m/z-RT MS/MS spectra in positive ion mode covering common molecular adducts, including protonated, ammoniated, and sodiated species. In negative ion mode, the polyphenol library contains 1,327 m/z-RT MS/MS spectra covering deprotonated, formiated, and chlorinated species. When applying the MoNA libraries to the six food products, we identified more than 2,000 metabolites using four untargeted metabolomics platforms. Of the 2,000 metabolite; 637 complex lipids were separated on a Waters Acquity UPLC CSH C18 column, 648 biogenic amines were separated on a Waters Acquity UPLC BEH Amide column, and 918 polyphenols were separated on a Kinetex PFP column. Each liquid chromatography method was coupled to a high resolution Thermo Q Exactive HFmass spectrometer using both (+) and (-) ESI modes. For GC-TOF-MS, 168 primary metabolites were annotated on a LECO Pegasus III TOF MS using a Restek Rtx-5Sil MS column. Lipidomics data showed that garlic, lettuce, apple shared similarities in complex lipids, while strawberries and tomatoes were vastly different. For biogenic amines, the profiles varied for each type of produce item. Interestingly, we found that the distribution of the types of polyphenols from the RP-PFP analysis also varied from produce to produce when comparing the different extraction methods.

Authors

Robert Quinn, Department of Biochemistry and Molecular Biology (Primary Presenter)

Christian Martin H., 1 Department of Biochemistry and Molecular Biology. Michigan State University

Ty Roach, Hawaii Institute of Marine Biology

Ford Drury, Hawaii Institute of Marine Biology

Arthur Daniel Jones, Michigan State University

Jenna Dilworth, Hawaii Institute of Marine Biology

Abstract

Coral bleaching, a process where corals expel their photosynthetic symbionts, has a profound impact on the health and function of coral reefs. As global ocean temperatures continue to rise, bleaching poses the greatest threat to coral reef ecosystems. Here, untargeted metabolomics was used to analyze the biochemicals in pairs of adjacent corals from a patch reef in Kāneʻohe Bay, Hawaiʻi where one colony in the pair bleached (in 2015) and recovered while the other did not bleach. There was a strong metabolomic signature of prior bleaching history four years after recovery found in both the host and its algal symbionts. Machine learning analysis determined that the strongest metabolite drivers of the difference in bleaching phenotype were a group of betaine lipids. Those with saturated fatty acids were significantly enriched in thermally tolerant corals and those with longer, unsaturated and diacyl forms were enriched in historically bleached corals. Host immune response molecules, Lyso-PAF and PAF, were also altered by bleaching history and were strongly correlated with symbiont community and algal-derived metabolites suggesting a role of coral immune modulation in symbiont choice and bleaching response. To validate these findings, we tested a separate in situ set of corals and were able to predict the bleaching phenotype with 100% accuracy. Furthermore, corals subjected to an experimental temperature stress had strong phenotype-specific responses in all components of the holobiont, which served to further increase the differences between historical bleaching phenotypes. Thus, we show that natural bleaching susceptibility is simultaneously manifested in the biochemistry of the coral animal and the algal symbiont and that this bleaching history results in different physiological responses to temperature stress. This work provides insight into the biochemical mechanisms involved in coral bleaching and presents metabolomics as a valuable new tool for resilience-based reef restoration.

"Advances in Targeted Metabolomics: Applications in Parkinson’s Disease."

Presenter:

Katherine Black, Ph.D

Abstract

Tryptophan catabolism has an important role in orchestrating metabolic crosstalk between various tissues within the host, as well as between the host and its microbial inhabitants. Moreover, tryptophan and its downstream metabolites have been implicated as robust biomarkers for several clinical indications, and thus it is crucial to monitor the activity of this pathway when developing various therapeutic strategies. In this webinar event, we will highlight the power of biocrates’ standardized targeted metabolomics technology, with an emphasis on the newly launched Tryptophan metabolism assay and its potential to aid us in the fight against neurodegenerative disease.

During this event, you will learn:

• How targeted metabolomics enables absolute quantitation of biological metabolites with superior reproducibility, and thereby empowers the rapid discovery of deeper pathophysiological insights with direct clinical relevance.

• The advantages associated with a highly reproducible and reliable approach that offers exceptional comparability of analyte concentrations across unique metabolomic assays.

• The pivotal physiological function of the tryptophan metabolism pathway in health and disease, with a special focus on its role in monitoring treatment response of neurodegenerative disorders.

"Improving your confidence; how highly reproducible NMR & highly confident 4D-MS annotations can advance your metabolomics research Applications"

Host:

Lucy Woods, PhD
Business Unit Manager Phenomics and Metabolomics, Bruker Daltonics

Presenters:

Amy Freund, Ph.D.
Senior Applications Scientist II / Applications Development Product Manager Bruker BioSpin

Sven Meyer, Ph.D.
Senior Scientist · Solutions Development OMICs
Bruker Daltonik GmbH

Eduardo Nascimento, Ph.D.
Field Application Scientist – AIC (Applied, Industrial & Clinical MR Market Division) Bruker BioSpin Corp.

Abstract

NMR is an intrinsically quantitative analytical tool highly suited to metabolomic analysis. A single NMR spectrum can describe the physiological state of the individual; from small metabolites to larger lipo-proteins, and many, many compounds in between. That same NMR spectrum can deliver quantitative information of both known and unknown metabolites. With careful attention to SOP’s, high equality spectral data can be acquired, and compared with other spectra from labs around the globe. With access to spectra and metadata from many metabolomics projects, we can build models and develop comprehensive databases.

Additionally, the annotation of lipids can be very demanding due to the high number of structural variations. Mass spectrometry-based identification typically relies on characteristic fragments from lipid headgroups and side chains obtained from MS/MS experiments. Depending on the quality of the MS/MS spectra , the depth of the structure elucidation can cover different levels like molecular formula level, chain composition level, etc. While the matching of MS/MS spectral libraries such gives a broad and quick overview on the lipid content, the detail of the annotation level can be too high.

This workshop will focus on demonstrating the reproducibility of NMR and the versatility of the 1D proton spectrum for both targeted and non-targeted analysis. The complete suite of tools Bruker brings to your metabolomic project including hardware, software, and standardized solutions will be outlined. Furthermore, novel tools embedded within the MetaboScape MS software will be presented. By making use of selected fragmentation rules and by enabling visual investigation of retention time and Collisional Cross Section consistency within lipid groups, automatic identification can be used to simplify lipid annotation workflows and provide confident annotation within Bruker’s MetaboScape software package.

"Simplifying Metabolomics with the QReSS Mix – Development and Applications"

Presenters:

Andrew Percy, PhD
Senior Applications Chemist, Mass Spectrometry; Cambridge Isotope Laboratories, Inc.

Robert Proos
Senior Applications Scientist; SCIEX

Zuzana Demianova, PhD
Senior BioPharma Scientist; Sciex

Abstract

The aim of this session is to introduce you to a new product, called QReSS, that is designed in a surefire manner to help overcome obstacles in the MS metabolomics field. QReSS stands for Quantification, Retention, and System Suitability. As its name suggests, this product (configured as stable isotope-labeled metabolite mixes) is intended to be used in qualification/quantification studies as well as in performance tracking exercises. An overview of QReSS spanning its development to application will be presented. Come find out how QReSS can simplify implementation and routine evaluations in MS metabolomics.