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Metabolomics in Nutrition Research, and Implications in Blood Type Research

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Metabolomics in Nutrition Research, and Implications in Blood Type Research Metabolomics in Nutrition Research, and Implications in Blood Type Research Susan Sumner, PhD Director, NIH Eastern Regional Metabolomics Resource Center Director, Systems and Translational Sciences Center 1 RTI International is a registered trademark and a trade name of Research Triangle Institute. www.rti.org Learning Objectives § Where to learn more: NIH Common Fund Metabolomics Program § What is Metabolomics and when is it useful? § Study Design Considerations for Clinical Trials § Metabolomics Experimental Workflow and Data Interpretation § This presentation will cover several applications of metabolomics § Responsivity to healthy life-style weight loss § Impact of sub-therapeutic doses of antibiotics § Weight status and the response to vaccination § Diet and ovarian health § Pregnancy complications and target identification § Autism and nutritional supplementation § Individual Variability 2 NIH Common Fund Program – Building Metabolomics Capabilities Technology Training Development Research Cores Data Sharing Reference and Standards Collaboration NIH Common Fund Metabolomics Centers NIH Metabolomics Centers Ramp Up | November 4, 2013 Issue - Vol. 91 Issue 44 | Chemical & Engineering News. by Jyllian Kemsley Metabolomics Workbench http://www.metabolomicsworkbench.org/ Shankar Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C, Edison A, Fiehn O, Higashi R, Nair KS, Sumner S, Subramaniam S. SubramaniamMetabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 2015 Oct 13. pii: gkv1042. Hands on Training Stephen Barnes Stephen Barnes, University of Alabama – Week Long Course- June/July – Experimental design, sample collection and storage, data capture, processing, statistical and multivariate analysis – Mass Spectrometry and NMR Metabolomics – http://www.uab.edu/proteomics/metabolomics/workshop/workshop Web-based Metabolomics Learning § Martin Kohlmeier, University of North Carolina at Chapel Hill http://metabolomicsinmedicine.org/ Martin Kohlmeier Metabolomics § Metabolomics involves the analysis of the low molecular weight complement of cells, tissues, or biological fluids. § Metabolomics makes it feasible to uniquely profile the biochemistry of an individual or system. – Metabonomics is used to determine the pattern of changes (and related metabolites) arising from disease, dysfunction, disorder, or from the therapeutic or adverse effects of xenobiotics § This leading-edge method has come to the fore to reveal biomarkers for the early detection and diagnosis of disease, to monitor therapeutic treatments, and to provide insights into biological mechanisms. 7 Why Metabolomics? § Specific genes can be identified that define individuals’ at risk for a disease, dysfunction, or disorder, or response to treatments. § Diseases or responses can occur at the level of the proteome; and proteins and metabolites can inform us about the state of a disease, dysfunction or disorder at any given time. Metabolomics of Cells, Tissues, and Biological Fluids Cells / Organ System Released metabolites Serum Urine Saliva Breath Plasma CSF Feces Signatures or Profiles Cytosolic metabolites Discrete peaks Diagnostics Comparing States of Wellness and Sickness More sensitive Proteins and early markers for disease detection and staging Pathways Markers to monitor Metabolite • efficacy • adverse response • relapse • transplantation Traditional Clinical Mechanistic Parameters insights from pathway analysis Target Identification Metabotype Studies have shown metabolomics signatures (the metabotype) to correlate with gender, race, age, ethnicity, drugs, chemicals, stress, weight status, mental health status, blood pressure, many disease states, behaviors, nutrition, gut microbiome…. Study Design Considerations § Study Design – Gender, race, ethnicity, age, exposures (drugs, chemicals, stress, city, etc..) all contribute to the metabotype § Sample Collection and Storage – Consistency in collection and processing § blood to serum (over ice?), or blood to plasma (anticoagulant?) – Storage consistency (vials, temperature, freeze thaws, etc.) – Selection of chemicals for extraction of samples 12 NIH Common Fund Eastern Regional Metabolomics Core Data Reduction & Sample Sample Discovery Experimental Visualization Receipt Preparation Data Capture & Communicating Design Entry QC Standards & Storage Empirical & Pathway Results Pooled Samples Standards Mapping Library Predictive Modeling T UPLC-MS/MS, UPLC-TOF-MS, ORBITRAP BROAD A AK_011413_87.raw : 1 319680 287.1012 R 100 130.0512 SPECTRUM % 136.0765 288.1032 G 84.0456 195.0868 0 m/z Multivariate & 100 150 200 250 300 4.75 E 5452 Statistical Analysis 100 7.71 T 3960 Endocannabinoids % 4.47 7.06 1455 6.44 1655 1109 8.19 8.82 Lipidomics E 762 308 0 Time 4.00 5.00 6.00 7.00 8.00 9.00 D Biocrates Panels Neurotransmitters Pathway Mapping 16 Channel CoulArray Environmental Panels 2D-GC-TOF-MS, GC-MS ICP-MS Metals NIH C-F Metabolite Synthesis Core NIH C-F 1U24DK097193; Sumner, PI Metalomics Analysis Considerations § Instrument Response and Drift – Consistency in parameters for each sample run – Create phenotypic pools as well pre- and post- run standards § Data Analysis – Data quality, spectral alignment, formatting, etc. – Check the standards and the pooled samples!! 14 Quality Control Pool Samples § Aliquots from each sample in the study phenotype are pooled (phenotypic pool) § Equal amount of each phenotypic pools are pooled (Combined phenotypic pool) Phenotype 1 Phenotype 2 § Replicates of pools are processed and randomized with the study samples Equal amounts Phenotype 1 Pool QC Phenotype 1 Phenotype 2 t2 Pooled QC Pooled QC Combined Phenotypes Combined Phenotypes Pool QC Phenotype 2 Pooled QC t1 Pool QC Data Analysis Methods Data analysis methods can include: – Descriptive Statistics – Hypothesis Testing – Multivariate Analyses – Linear Regression – Logistic Regression – Structural Equation Modeling – Integration of Data (e.g., genomics, microbiome) – Pathway Analyses 16 Multivariate Analysis Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components 17 Research Areas Application Areas § Treatment § Intervention Sample Types Over 150 Research Collaborations § Foods § Serum – Fat, Soy, Casein, Organizations § Plasma Rice Harvard ECU § Feces Origin § Exposure Columbia WFU § Urine Humans – Metals, PAHs, Wood UPenn NCA&T § Saliva § Elderly UDC Smoke, PM2.5 LRRI § Sweat § Adults UCSD § Mental Health RTI § Kidney § Children – Schizophrenia, Duke NYU § Liver § Neonate Bipolar Disorder, UNC-CH U Iowa § Pregnant Anxiety NCSU NCRC § Brain § Models § Development U Louisville UAB Ovary Fort Bragg § § Primates § Reproduction U Montanna Eye § § Rodents § Cancer Vanderbilt Lung Johns Hopkins § Aquatic § Climate Change § Muscle Nationwide Children’s Hospital § Insects § Rare Disease Mussel NC Museum of Sciences § Cells § Infection Rice Howard University § Opthamology Moffiat Cancer Targeted and Untargeted Analysis § Dentistry Nutrition Research Applications § In nutrition research, metabolomics holds promise for determining biomarkers for the early diagnosis of disease, for understanding how weight and diet influence health outcomes and the responsivity to treatment, and for determining perturbations in biochemical pathways related to exposure, or disease, for informing the development of intervention strategies § Responsivity to healthy life-style weight loss program § Impact of sub-therapeutic doses of antibiotics § Weight status and the response to vaccination § Diet and ovarian health § Pregnancy complications and target identification § Autism and nutritional supplementation. § While we are aware that the biochemistry of blood groups differ, research on the metabotype of blood type is at its infancy. – provide compelling evidence that the metabolic profiles of individuals differs by blood type, and that these biochemical difference may be associated with known increased risks for disease, and provide a means for intervention strategy. 19 in utero Exposure to Chemicals and Health Outcomes § Phthalates, ubiquitous in the environment, have been characterized as endocrine disruptors. § Pregnant rats were dosed with BBP for during gestation (gd 18-21): control, low dose (25 mg/kg), high dose (250 mg/kg). § Urine was collected from dams gd 18 and pnd 21, and from pups after weaning but before puberty (pnd 26), and organ tissues were collected at study termination. Differences (control vs BBP dose groups)) were noted in levels of Dams that received the highCitrate Cycle dose, the low dose, or the • (quinolatemetabolites in the brain (male and vehicle at 21 days past • Glycine, Serine, Threonine female pups, not dams), testes, and Nicotinamide uterus (dam but not pup). exposure were distinguished• Tryptophan and • Nicotinate • Nanoparticles hyrocarbons • Arsenic • Polycyclic aromatic Urine from pups, on pnd 26, were distinguished by the dose the 20 dam received during gestation. Male pup brain Testes Sumner et al., 2009. Metabolomics in the assessment of chemical-induced reproductive and developmental outcomes using non- invasive biological fluids: application to the study of butylbenzyl phthalate. Journal of Applied Toxicology and Banerjee et al., 2012. Metabolomics of brain and reproductive organs: characterizing
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