Metabolomics Scientific Interest Group

Metabolomic and multi-omic data are increasingly being collected in basic, preclinical, and clinical research studies. Interpretation of these data though remains challenging. Common challenges include the difficulty in identifying metabolites and assigning unique identifiers, and the scarcity of resources that provide up-to-data comprehensive annotations and analysis tools on integrated genes/proteins and metabolites. To aid in interpreting these complex data, we developed RaMP-DB 2.0, a public resource that contains comprehensive biological, structural/chemical, disease, and ontology annotations for human metabolites and metabolic genes/proteins. The associated RaMP-DB 2.0 framework provides the ability to query those annotations and to perform pathway and chemical enrichment analysis on input multi-omic datasets. Since our first release, RaMP-DB 2.0 has been substantially upgraded and now includes an expanded breadth and depth of functional and chemical annotations, and a reproducible and semi-automated method for entity resolution of analytes across the different source databases pulled. The usability of the RaMP-DB 2.0 has also been improved through updates of pathway and chemical enrichment analysis methods, and a completely revamped web interface and associated public API for programmatic access. RaMP-DB 2.0 currently pulls information from HMDB, KEGG (through HMDB), Reactome, WikiPathways, Lipid-MAPS, and ChEBI and includes 254,860 chemical structures, of which 43,338 are lipids, 15,389 genes, 53,745 pathways, 807,362 metabolic enzyme/metabolite reactions, and 699 functional ontologies (biofluid, health condition, etc.). RaMP-DB 2.0 is available at https://rampdb.nih.gov/.

Over a lifetime, humans experience thousands of chemical exposures from multiple sources. A more complete estimate of environmental exposures across the lifespan would be a transformative research initiative. The use of high-resolution, mass spectrometry (HRMS) provides a key platform for assessing the exposome and provides measures of thousands of chemical signals in a single human sample. Untargeted assays for the exposome primarily rely on strategies that were initially developed for metabolomic analysis, resulting in sample preparation, data acquisition and processing, and compound identification methods optimized for high-abundance metabolites. Therefore, current approaches have limited capability to capture many of the 200,000 chemicals registered with the EPA, FDA or otherwise used in commercial products present in biological samples at concentrations orders of magnitude lower than endogenous metabolites and a critical need exists to optimize analytical procedures and data processing workflows for detection of low abundance exposome chemicals. This presentation will discuss the limitations of using analytical frameworks primarily optimized for metabolomics analysis for measuring the exposome and highlight recent advances that include incorporating multiple untargeted analytical platforms, sample preparation techniques, data processing and annotation strategies that allow improved capture of exposure biomarkers in human samples. The use of enhanced exposome analytical frameworks based upon untargeted HRMS are poised to provide a robust foundation for exposome research and facilitate development of a knowledge base of environmental chemicals, their products, distributions and associated effects.

Metabolic reprogramming has emerged as one prominent hallmark of cancers. While the link between metabolism and tumor growth was established over 100 years ago by Otto Warburg with his discovery that tumor cells prefer aerobic glycolysis over oxidative phosphorylation to produce energy, the recent discovery of mutations in metabolic genes has rekindled the interest in studying the metabolism of tumors. One classical example of the link between metabolism and tumors is the discovery that a metabolic alteration is an early event in lower grade glioma formation. Understanding metabolic reprogramming in gliomas, a devastating disease with no known curable treatment, has the potential to identify targeted therapies from within metabolic pathways. Dr. Larion’s group has developed methodologies to interrogate metabolism as a snapshot of metabolites in time via untargeted metabolomics, which can be used as a discovery step. Once the altered pathway is identified, Dr. Larion’s group develops detailed 13C tracing experiments which approximate the flux within that pathway. In addition, her group developed methods to study metabolism in single organelles of live cells as well as to image certain classes of metabolites in cells and tissues. All these techniques allow Dr. Larion’s group to interrogate questions from nutrient dependencies of tumor cells to diets, to lipid alterations associated with increased survival as well as to identify biomarkers of disease progression.

The U.S. Environmental Protection Agency’s (EPA) Computational Toxicology Program utilizes computational and data-driven approaches that integrate chemistry, exposure and biological data to help characterize potential risks from chemical exposure. The Center for Computational Toxicology and Exposure has measured, assembled and delivered an enormous quantity and diversity of data for the environmental sciences, including high-throughput in vitro screening data, in vivo and functional use data, exposure models and chemical databases with associated properties. The CompTox Chemicals Dashboard provides access to data associated with ~900,000 chemical substances. New data are added on an ongoing basis, including the registration of new and emerging chemicals, data extracted from the literature, chemicals studied in our labs, and data of interest to specific research projects at the EPA. Integrated modules include an interactive read-across module, real-time physicochemical and toxicity endpoint prediction and an integrated search to PubMed. This assembly of data, together with specific search capabilities, provides a basis to support the application of mass spectrometry techniques to help identify chemicals in various matrices and to prioritize them for further confirmation. This presentation will provide an overview of the CompTox Chemicals Dashboard and how it has developed into an integrated data hub for environmental data and how it provides a foundation to support both exposomics and metabolomics research.

This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

It is well established that the metabolism of cancer cells is reprogrammed to support the demands of rapid proliferation, however, a comprehensive map of metabolic adaptations that occur as a result of malignant transformation has yet to be achieved. The focus of this talk was the application of mass spectrometry-based metabolomics to broaden our understanding of metabolic alterations in cancer, with the ultimate goal of identifying biochemical liabilities that can be exploited therapeutically. To increase insight, data from multiple experimental paradigms of metabolomics was described in detail including: (i) global, untargeted profiling, (ii) isotope-tracer analysis, and (iii) dose-response metabolomics. Particular attention was dedicated to computational resources available for data processing, such as those supported by the NIH Metabolomics Common Fund. The workflow covering metabolic profiling to drug selection and target validation in animals was reviewed. Opportunities for polypharmacology was discussed.

Data preprocessing and compound identification are two important steps in the informatics pipeline for making sense of mass spectrometry-based untargeted metabolomics data. In this talk, Dr. Du presented a software tool, named ADAP, and an online resource, named ADAP-KDB that was developed by her group at the University of North Carolina at Charlotte. ADAP extracts compound information from untargeted liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) data through data preprocessing. It carries out a sequence of computational steps including peak picking, peak grouping, alignment, and spectral deconvolution. Dr. Du described principles of the computational algorithms that underlie these steps. ADAP-KDB is a spectral knowledgebase that uses information from publicly available data repositories such as the NIH’s Metabolomics Data Repository for prioritizing unknown compounds. It consists of a computational workflow and an online portal, with the former for extracting prioritization information and the latter for managing and allowing researchers to search against the information.

Dr. Tom Metz earned bachelor’s degrees in biology and chemistry before earning a Ph.D. in chemistry from the University of South Carolina, where he studied the role of Maillard chemistry in the development of diabetic complications via the chronic, cumulative, chemical modification of tissue proteins. In 2003, he joined Pacific Northwest National Laboratory for post-doctoral work in mass spectrometry with Dr. Richard D. Smith, where he focused on metabolomics. He became Staff Scientist and a Principal Investigator in the Integrative Omics Group in 2005 and is the Metabolomics Team Lead for a group of scientists that focuses on development and application of high throughput metabolomics and lipidomics methods to various biological questions. Dr. Metz’s research has focused primarily on applying MS-based omics approaches, including proteomics, in studies of diabetes mellitus and infectious diseases, resulting in over 150 publications to date. Currently, he is the Director of the Pacific Northwest Advanced Compound Identification Core within the NIH Common Fund Metabolomics Program.

Abstract: The capability to unambiguously and comprehensively identify thousands of metabolites and other chemicals in clinical samples, including the microbiome, will revolutionize the search for environmental, dietary, and metabolic determinants of health and disease. By comparison to near-comprehensive genetic information, comparatively little is understood of the totality of the human metabolome, largely due to insufficiencies in molecular identification methods. Through innovations in computational chemistry and advanced ion mobility separations coupled with mass spectrometry, we are overcoming a significant, long standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of metabolites without relying on data derived from analysis of authentic reference compounds. We use gas-phase molecular properties that can be both predicted computationally with high accuracy and experimentally measured with high precision, and which can thus be used for comprehensive identification of the metabolome without the need for reference libraries constructed through experimental analysis of authentic chemical standards. The benefits and remaining limitations of the standards-free metabolomics approach will be demonstrated in a variety of examples, including in analysis of blinded chemical mixtures as a part of the EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT) and in analysis of plasma samples from individuals subjected to simulated shift work.

Abstract: Eoin Fahy has a B.Sc. in Biochemistry from University College Galway and a Ph.D. in Chemistry from the University of British Columbia, specializing in the structure elucidation of marine natural products. He completed a post-doctoral appointment at the Scripps Institution of Oceanography in University of California, San Diego (UCSD). He has over 15 years’ experience in the biotechnology industry in the areas of organic chemistry, drug target discovery, molecular biology, proteomics, genomics and informatics. He has been involved with the LIPID MAPS consortium since 2003 where he serves as project coordinator for the bioinformatics efforts. His work in this area include development of a lipid classification system as a member of the International Lipid Classification and Nomenclature Committee (ILCNC), design and development of database infrastructures for lipidomics, development of mass spectrometry software for lipid research, design of novel lipid structure drawing tools and development of integrated pathway tools and resources. Since 2012, he has served as bioinformatic project coordinator for the NIH Common Fund Metabolomics program's National Metabolomics Data Repository (NMDR) at UCSD. He oversees the Metabolomics Workbench website which acts as a national and international repository for metabolomics data and metadata and provides analysis tools and access to metabolite standards, protocols, tutorials, training, and more.

Abstract: Variations in diet have long been thought to contribute to worldwide differences in cancer rates. Indeed, nearly 40 years ago, Doll and Peto published a landmark study estimating that one in three cancers could be attributed to diet. Since then, nutritional epidemiological studies have played a key role in identifying diet-related cancer risk factors, but the role of many dietary factors in cancer etiology remains unclear. Dr. Loftfield's research seeks to clarify diet-disease associations and elucidate biological mechanisms, principally relating to human and microbial metabolism, that underlie associations of diet with cancer and mortality. In recent years, high-throughput omics technologies have made this research feasible in large epidemiological studies. She presented results from our recent studies that use metabolomic data to better understand associations of coffee intake with cancer risk as well as results from ongoing studies on the interplay between diet, obesity, the gut microbiome and the metabolome.

Abstract: The Consortium of Metabolomics Studies (COMETS) comprises 50+ international prospective cohorts and cohort consortia that use metabolomics to study risk of incident disease. Early on, COMETS investigators determined that each site would perform its own analysis and that results would be sent centrally for meta-analysis. However, conducting multiple studies—each with multiple models and multiple stratified analyses—with this centralized approach is logistically complex. To coordinate and streamline consortium-based data analyses, we developed COMETS Analytics, a secure, online statistical analysis platform for metabolomics data analysis. The web based application performs 3 main tasks: 1) it harmonizes metabolite identifiers across cohorts; 2) it conducts statistical analyses in large batches of user-defined models; and 3) it produces standardized, meta-analysis ready output. We anticipate that conducting the analysis in this way will increase rates of participation, accelerate data analysis, and lower error rates as compared with more conventional approaches.

Abstract: In metabolomics, mass spectrometry is frequently coupled with a chromatographic separation technique to maximize analytical resolution. Although this provides a powerful approach, it necessitates the extraction of a sample and sometimes lengthy separation run. Direct injection and flow infusion mass spectrometry approaches have allowed higher throughput in metabolomic studies, but they still require sample extraction prior to mass spectrometric analysis. The field of ambient ionization holds promise in removing this requirement for extensive sample preparation by allowing ionization to occur outside of a vacuum, with the sample in its natural form. Rapid evaporative ionization mass spectrometry (REIMS) is an ambient ionization technique which generates ions through heating of a sample, with the resulting analyte-containing vapor aspirated directly to a mass spectrometer, allowing near real-time data collection. This webinar will introduce REIMS and detail its recent development into an automated, high-throughput system suitable for the analysis of a wide range of sample types, such as microorganisms, feces, sputum, blood plasma, urine, and food products. It will also cover the data analysis approaches employed and developing areas for new applications.

Dr. Nichole Reisdorph is an Associate Professor in the Department of Pharmaceutical Sciences at the University of Colorado Anschutz Medical Campus. She is also the Director of the Skaggs School of Pharmacy Mass Spectrometry Facility and serves as Treasurer on the Metabolomics Society Board of Directors. Nichole Received her PhD in Biochemistry and Molecular Biology from the University of South Dakota School of Medicine. She performed post doctoral work in Dr. Gary Suizdak’s lab at the Scripps Research Institute. Nichole’s main interest lies in applying mass spectrometry approaches to projects that may lead to new information and are of therapeutic relevance to human diseases. Her research program focuses on discovering mechanisms and markers of lung diseases including asthma and chronic obstructive pulmonary disease (COPD). Her asthma research utilizes clinical cohorts to discover biomarkers for response to medication and determining how the microbiome contributes to asthma pathogenesis and exacerbation. Her COPD research takes a systems approach to understanding pathogenesis in both mice and humans.

As a Core Director, Nichole collaborates on clinical projects spanning from epilepsy and diabetes to nutrition and aging; giving Nichole a broad perspective on several human diseases. Finally, Nichole’s lab offers training in the fields of proteomics and metabolomics. Their hands on workshops have reached over 400 international participants since 2004.

Abstract: Approximately 25 million Americans, including 8.4% of children, have asthma. Asthma is a complex disease, with multiple phenotypes including allergic, obesity-related, and exercise-induced asthma. Adding to the complexity is the fact that individuals respond differently to medication; this forces physicians to rely on step-wise treatments, whereby decisions are based solely on whether an individual continues to have symptoms while on a particular treatment. The end result is an extended period of treatment trial and error, during which time patients are at risk for serious exacerbations and hospitalizations. The goal of our research, based on preliminary data on a single biomarker leukotriene E4, is to determine if small molecules can be used to predict response to asthma medication. We used unbiased metabolomics profiling of urine and plasma from 230 pediatric asthma patients to address this question. The study was designed as a cross-over study where patients were placed on 3 different medications for 16 weeks each. The main clinical outcome tested was: Did the child have a differential response to the drugs and if so, which drug proved superior in relieving symptoms? A variety of informatics strategies, including o-PLS-DA, PCA, hierarchical clustering, and logistic regression were used to analyze data. A series of models were developed that support the use of small molecules to predict response to medication. Final models are currently being tested and identification of compounds is underway. Future work will include testing the strength of these markers in a larger population.

A complex network of host receptors and microbiota within the gastrointestinal tract work in concert to process and absorb dietary nutrients, detoxify xenobiotics, and establish a homeostatic system that regulates metabolism and inflammation. Emerging evidence suggests ligand-activated transcription factors of the nuclear receptor superfamily and the basic helix-loop-helix/per-arnt-sim (PAS) family not only receive and process chemical signals derived from microbial-dependent metabolic activity, but also transmit these signals to distant organs, including the liver. For example, small intestine signaling of the farnesoid X receptor (FXR), an essential regulator of bile acid, lipid, and glucose metabolism, is modulated through gut microbiome-dependent metabolism of bile acid metabolites produced in the liver. Additionally, studies of the aryl hydrocarbon receptor (AHR), a xenobiotic sensor, have revealed microbial metabolites derived from dietary nutrients including tryptophan as critical regulators of both intestinal and hepatic inflammation. Dissection of the host-metabolite-microbiome interaction was facilitated by use of transgenic mouse models, host and microbiome sequencing, and mass spectrometry- and NMR-based metabolomics. Identification and characterization of microbial metabolites and their relationship with host receptors has begun to provide new avenues for studying host-microbiota communication networks and identifying new therapeutics to modulate this interaction in human disease.

In the past fifteen years, the cost of mass spectrometry has come down by two orders of magnitude per volume of data that is collected. As the sensitivity of instrumentation increases by an order of magnitude, the number of unknowns doubles. One of the key limitations of untargeted mass spectrometry is the lack of data analysis reproducibility. If the same data is provided to different people, they have different outcomes. The second limitation is our ability to annotate molecules that can be observed. Currently in untargeted metabolomics, on average, only 2% of the data that is collected can be annotated.

To address these shortcomings, a team of investigators, including Dr. Dorrestein, launched a global data driven knowledge sharing and analysis infrastructure called global natural product social molecular networking or GNPS (https://www.nature.com/articles/nbt.3597). The GNPS community now counts 22,000 users from 128 countries. One of the key features of GNPS is that is allows public sharing of raw data. This is critical for scientific reproducibility and argue that only sharing of tables with m/z, features and annotations is not appropriate. When the raw data is not available, the results tables cannot be updated with the most advanced analysis tools and knowledge. This is important as new algorithms are rapidly advancing.

As of March 2017, there are 910 projects that have raw data in the public domain, more than 500 are contributed by the GNPS community. Within GNPS, one can perform molecular networking and annotate all MS/MS spectra against all publicly available libraries (~240,000 reference spectra) and the libraries contributed by the GNPS community has grown to 40,000. All searches are remembered in your personalized jobs tab and the links of the jobs can be shared in publications and cloned by others to promote reproducibility in the analysis. GNPS also changes the interaction with data. GNPS introduced the living data concept where subscribers to data are periodically updated with the latest knowledge about a given data set.

Finally, Dr. Dorrestein touched on FDR estimation in untargeted metabolomics and visualization tools such as 3D cartography to study the distributions of therapeutics, metabolites of microbial interactions, the microbiome and demonstrate how this may be achieved on clinically relevant time scales.

Dr. Pieter Dorrestein is Professor at the University of California - San Diego. Dr. Dorrestein is trained as a chemist with a focus on understanding how microbes made amino acids, vitamins and other small molecules such as virulence factors, quorum sensors and therapeutically valuable natural products. Currently, Dr. Dorrestein is the Director of the Collaborative Mass Spectrometry Innovation Center and a Co-Director, Institute for Metabolomics Medicine in the Skaggs School of Pharmacy & Pharmaceutical Sciences, and Department of Pharmacology and Pediatrics. Since his arrival to UCSD in 2006, Dr. Dorrestein has been pioneering the development of mass spectrometry methods to study the chemical ecological crosstalk between populations of microorganisms, including host interactions, for agricultural, diagnostic, clinical and therapeutic applications. For a more detailed biography see http://www.nature.com/news/the-man-who-can-map-the-chemicals-all-over-your-body-1.20035.

Healthy dietary patterns are related to lower chronic disease incidence and longer lifespan. However, the precise mechanisms involved are unclear. Furthermore, epidemiologic evidence for associations between dietary factors and breast cancer is weak and, where evidence exists, etiologic mechanisms are also unclear. Identifying biomarkers of dietary patterns and specific dietary exposures may provide tools to validate diet quality measurement, mitigate errors related to self-reported diet, and identify mechanistic mediators.

In two analyses of data from prospective studies, we measured whether four pre-defined diet quality indices (the Healthy Eating Index-2010, the Alternative Mediterranean Diet score, the World Health Organization Healthy Diet Indicator, and the Baltic Sea Diet) were cross-sectionally associated with baseline fasting serum metabolites among 1380 Finnish men participating in the Alpha-tocopherol, Beta-carotene Cancer Prevention Trial, and examined prediagnostically-measured, diet-related metabolites for their association with incident invasive breast cancer among 1,242 post-menopausal women in a nested case-control study within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

For both studies, metabolites were measured using mass spectrometry and diet was measured using validated food frequency questionnaires prior to study randomization. We found that diet quality, measured by healthy diet indices, is associated with serum metabolites. Rather than exerting a unifying metabolic response, the metabolite profiles of each diet index was driven by the components used to score adherence. We also found that prediagnostic serum concentrations of metabolites related to alcohol, vitamin E and animal fats were associated with estrogen receptor positive breast cancer risk.

Mary Playdon, Ph.D., M.P.H., joined the National Cancer Institute’s (NCI) Division of Cancer Epidemiology and Genetics (DCEG) in August 2014 as a predoctoral fellow through the Yale University-NCI Partnership Training Program. She completed a B.S. and an M.P.H. at the Queensland University of Technology, Australia. Dr. Playdon is a registered clinical dietitian, practicing in both Australia and England before coming to the U.S. She was previously Senior Clinical Research Dietitian for the Colorado State University Cancer Prevention Laboratory. Her research interests include nutritional epidemiology, metabolomics and etiology and survivorship of female cancers. In DCEG’s Metabolic Epidemiology Branch (MEB), under the mentorship of Steven C. Moore, Ph.D., M.P.H., investigator, and Rachael Stolzenberg-Solomon, Ph.D., M.P.H., R.D., senior investigator, Dr. Playdon’s dissertation work focused on exploring dietary metabolites as novel biomarkers of dietary intake and their role in breast cancer etiology. As a postdoctoral fellow, Dr. Playdon continues her work on nutritional metabolomics and cancer etiology, and has expanded her portfolio in collaboration with Britton Trabert, Ph.D., M.S., investigator, to include alcohol and obesity exposures and their relationships to hormone metabolism and other female cancers, including endometrial cancer.

Genome-wide association studies with concentrations of hundreds of small molecules in samples collected from thousands of individuals (mGWAS) access otherwise inaccessible natural genetic experiments and their influence on the metabolic capacities of the human body. By sampling the natural metabolic and genetic variability that is present in the general population, mGWAS identified so far over 150 associations between genetic variants and variation in the metabolic composition of human body fluids. Associations identified by mGWAS can reveal novel biochemical knowledge, such as the function of uncharacterized genes, the biochemical identity of small molecules, and the structure of entire biochemical pathways. Knowledge of genetic variation in metabolism has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic associations with clinical end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. mGWAS with growing sample sizes and increasingly complex phenotypic trait panels, such as proteomics and glycomics, are currently being conducted, allowing for more comprehensive and systems-based downstream analyses. In this presentation, Dr. Suhre reviewed work done by his group and collaborators in past and recent mGWAS, discuss extensions to multiomics phenotypes, and outlined possible ways to translate mGWAS findings to clinical and biomedical application.

Dr. Suhre is a Professor of Physiology and Biophysics at Weill Cornell Medicine and the Director of the Bioinformatics and Virtual Metabolomics Core at its branch campus in Doha, Qatar. He holds a Ph.D. in Atmospheric Chemistry and Meteorology from the University of Toulouse III, France, and graduated in Physics from the University of Osnabrück, Germany. Dr. Suhre’s research interests focus on metabolomics and genetic epidemiology, bioinformatics, functional and structural biology. On these subjects, he has taught multiple courses, published over 140 articles in peer-reviewed journals, and delivered numerous international presentations. He established the field of genome-wide association studies of human metabolism, which is reflected in several high-impact publications in the field of which he is the senior author. He is also interested in how genetic variation in human metabolism interacts with environmental challenges and lifestyle factors in the development of complex diseases, including diabetes, heart and kidney diseases, knowing that understanding of the genetic basis of metabolic individuality in humans generates many new hypotheses for biomedical and pharmaceutical research, and can potentially lead to new and individualized therapies. Dr. Suhre is currently involved in setting up clinical studies in cooperation with different health organizations in Qatar, including diabetes and cancer, which centrally involve his specific expertise in the field of metabolomics.

Healthy dietary patterns are related to lower chronic disease incidence and longer lifespan. However, the precise mechanisms involved are unclear. Furthermore, epidemiologic evidence for associations between dietary factors and breast cancer is weak and, where evidence exists, etiologic mechanisms are also unclear. Identifying biomarkers of dietary patterns and specific dietary exposures may provide tools to validate diet quality measurement, mitigate errors related to self-reported diet, and identify mechanistic mediators.

In two analyses of data from prospective studies, we measured whether four pre-defined diet quality indices (the Healthy Eating Index-2010, the Alternative Mediterranean Diet score, the World Health Organization Healthy Diet Indicator, and the Baltic Sea Diet) were cross-sectionally associated with baseline fasting serum metabolites among 1380 Finnish men participating in the Alpha-tocopherol, Beta-carotene Cancer Prevention Trial, and examined prediagnostically-measured, diet-related metabolites for their association with incident invasive breast cancer among 1,242 post-menopausal women in a nested case-control study within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

For both studies, metabolites were measured using mass spectrometry and diet was measured using validated food frequency questionnaires prior to study randomization. We found that diet quality, measured by healthy diet indices, is associated with serum metabolites. Rather than exerting a unifying metabolic response, the metabolite profiles of each diet index was driven by the components used to score adherence. We also found that prediagnostic serum concentrations of metabolites related to alcohol, vitamin E and animal fats were associated with estrogen receptor positive breast cancer risk.

Mary Playdon, Ph.D., M.P.H., joined the National Cancer Institute’s (NCI) Division of Cancer Epidemiology and Genetics (DCEG) in August 2014 as a predoctoral fellow through the Yale University-NCI Partnership Training Program. She completed a B.S. and an M.P.H. at the Queensland University of Technology, Australia. Dr. Playdon is a registered clinical dietitian, practicing in both Australia and England before coming to the U.S. She was previously Senior Clinical Research Dietitian for the Colorado State University Cancer Prevention Laboratory. Her research interests include nutritional epidemiology, metabolomics and etiology and survivorship of female cancers. In DCEG’s Metabolic Epidemiology Branch (MEB), under the mentorship of Steven C. Moore, Ph.D., M.P.H., investigator, and Rachael Stolzenberg-Solomon, Ph.D., M.P.H., R.D., senior investigator, Dr. Playdon’s dissertation work focused on exploring dietary metabolites as novel biomarkers of dietary intake and their role in breast cancer etiology. As a postdoctoral fellow, Dr. Playdon continues her work on nutritional metabolomics and cancer etiology, and has expanded her portfolio in collaboration with Britton Trabert, Ph.D., M.S., investigator, to include alcohol and obesity exposures and their relationships to hormone metabolism and other female cancers, including endometrial cancer.

Once thought of as a single disease, renal cell carcinoma (RCC) is now known to be several different types of cancer that are characterized by different genetic mutations, histologies, and responses to therapy. Many gene mutations in kidney cancer are known to have a direct and profound effect on cell metabolism, including oxygen sensing by the HIF pathway (VHL), nutrient sensing via the mTOR and other pathways (FLCN, MET, TFE3), and energy sensing as a result of direct disruption of the Krebs cycle (FH, SDH). We are utilizing stable isotope-resolved metabolomics (SIRM) to investigate altered metabolism in patient-derived cultured RCC cells, tumor xenografts in mice, thin tissue slices obtained during surgery, and intraoperative 13C-glucose infusion in patients to explore the unique metabolic phenotypes associated with the various genetic lesions that cause kidney cancer in humans. Untargeted isotopologue distributions in polar and non-polar metabolites are determined in extracts of cancer and normal kidney using 1H and 13C NMR spectroscopy and multiple mass spectrometry modalities to define the metabolic reprogramming in different RCCs. Preliminary analyses in fumarate hydratase (FH)-deficient patient tumors and cells have demonstrated that FH-deficient tumors contain a significant pool of fumarate that is not derived directly from the Krebs cycle via the succinate dehydrogenase reaction. These findings are being used to evaluate the effect of novel therapeutic approaches for kidney cancer that are tailored to the distinctive metabolism and genotype found in the diverse array of genetically-defined kidney cancers.

Dr. Crooks studies kidney cancer metabolism in the laboratory of W. Marston Linehan at the NCI Urologic Oncology Branch in the NIH Clinical Center, with the goal of identifying novel metabolic pathways that can be targeted by therapeutics in the clinic. Dr. Crooks is learning to apply Stable Isotope-Resolved Metabolomics (SIRM) techniques to study cell metabolism through a collaboration with Drs. Teresa Fan and Andrew Lane at University of Kentucky. He completed a B.A. in Molecular Biology and an M.S. in Environmental Toxicology at the University of California at Santa Cruz, and earned a Ph.D. in Biochemistry from Georgetown University while performing research at the NIH on cellular iron metabolism in developing red blood cells and in mitochondrial myopathy patients. His long-term research interests lie in developing new metabolomics techniques to study altered cellular metabolism and tissue specificity in human diseases.

Hypertension represents a major global disease burden but discovering molecular mechanisms underlying blood pressure (BP) regulation has been challenging. During this webinar, Dr. Cristina Menni described research using a metabolomics and interventional approach to identify novel metabolite markers for BP and BP phenotypes that were significantly associated with both BP and mortality. Evidence for a causal role was obtained in an animal model experiment that resulted in significant increases in BP, indicating that it was not a byproduct, but a cause of high BP. This research has also identified other metabolites strongly associated with cardiovascular traits.

Dr. Cristina Menni is a Research Fellow in the Twin Research & Genetic Epidemiology Department at King’s College London. She received her Master in Mathematics from the University of Cambridge and her Ph.D. in statistics from the University of Milan-Bicocca where she specialized in statistical genetics. Dr. Menni’s research is focused on identifying novel molecular markers associated with ageing and age-related diseases such as hypertension using metabolomics in conjunction with several other "omics" datasets. She has co-authored over 40 scientific articles in peer reviewed journals. She is involved in the data quality control, analysis, and interpretation for various European Union and Medical Research Council projects within King's College London.