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FooDB & The Construction of an International Consortium

FooDB & The Construction of an International Consortium. David Wishart University of Alberta, Edmonton, Canada The 3rd NUGO Workshop on Nutritional Metabolomics, July 1-2, Vlaardingen, The Netherlands. You Are What You Eat. 150+ Food Composition DBs. The Problem with Today’s FCDBs.

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FooDB & The Construction of an International Consortium

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  1. FooDB & The Construction of an International Consortium David Wishart University of Alberta, Edmonton, Canada The 3rd NUGO Workshop on Nutritional Metabolomics, July 1-2, Vlaardingen, The Netherlands

  2. You Are What You Eat

  3. 150+ Food Composition DBs

  4. The Problem with Today’s FCDBs • Highly distributed, not uniform in content, search capabilities or in user presentation • Breadth is good (lots of foods) depth is not (relatively few compounds) • Primary focus on AA’s, sugar, vitamins and general compound classes (i.e. “fats” or “lipids”), not on phytonutrients, micronutrients, aroma or flavour components • Were assembled using “old” technologies • Don’t relate composition data to chemistry, biology or physiology • Don’t provide data on nutrient metabolites or food consumption biomarkers

  5. The Power of Metabolomics Unknowns 4 LC-MS or DI-MS 3 GC-MS TOF # Metabolites or Features detected (Log10) 2 NMR 1 Knowns GC-MS Quad 0 M mM M nM pM fM Sensitivity or LDL

  6. The Power of Metabolomics Response Metabolomics Response Proteomics Response Genomics Time

  7. Toxins/Env. Chemicals Drug metabolites Food additives/Phytochemicals Drugs Endogenous metabolites Human Metabolomes 2900 (T3DB) 1500 (DrugMet) 30000 (FooDB) 1450 (DrugBank) 8000 (HMDB) M mM M nM pM fM

  8. The Food Metabolome Project • Experimentally characterize 3000+ metabolites in 30-40 representative raw, fermented and partially processed foods • Experimentally characterize 300+ food-derived metabolites in human blood & urine under controlled feeding conditions • Use data mining techniques to consolidate known food composition data, food metabolite data and food/health effects into a single “deep” web-accessible database • Combine all experimental & literature data into a database called FooDB $5 million over 3-5 years

  9. Ideal Food Database Content

  10. The Ideal Food Database • Searchable by name, text string, molecular weight, concentration range or structure • Given a compound name, what food products or plant species is it in and at what concentrations • Given a food product or plant species, what compounds are in it and at what concentrations • Given a compound, what are its (human) protein targets, transporters or mode of action • Given a compound, what are the (referenced) health claims and benefits • Given a compound, what are its metabolites

  11. A Blended Model http://www.phenol-explorer.eu http://www.hmdb.ca

  12. Challenge #1: Finding Food Compounds

  13. 8000 “housekeeping” metabolites in animals and plants 2000+ synthetic food additives 400,000+ plant species 7500 edible plant species 250 “common” edible plants (use this as a filter on phytoDBs) 13708 cmpds in DFC 8461 cmpds in Dr. Duke’s databases 1726 cmpds in KnapSack + KEGG 677 cmpds in Phenol Explorer Non-redundant = 19,767 cmpds 20,000 + 2000 + 8000 = 30,000 cmpds Counting Compounds

  14. Challenge #2: Finding Phytochemical Structures Journals Web Books 10317 Manual 9450 structures 1077 OSRA Mol files SMILES InChI InChI key … .png 8373 Food compounds: 19767

  15. Challenge #3: Annotating Cmpds • Use BioSpider to extract phys-chem data, formulas, names and/or synonyms, structures and descriptions • Modify databases it searches to include food component resources to extract concentration or content data • Calculate chemical class based on structure Prediction/Processing Engine

  16. Challenge #4: Finding Health Claims & Targets • Text Mining software called PolySearch • Given X find all associated Y’s • Searches PubMed abstracts to extract and compile data about associations between compounds & disease or health claims as well as compounds and protein targets http://wishart.biology.ualberta.ca/polysearch

  17. Sample Entry for Catechin

  18. When Will It Be Done? Apr. 2009 Aug. 2009 Dec. 2009 Apr. 2010 Aug. 2010 Dec. 2010 Database Selection BioSpider Modification Compound Cleanup Structure Generation & Annotation Health Claim & Content data Release Jan. 1, 2011

  19. Partnerships?

  20. Industry Buy-in?

  21. Acknowledgements Augustin Scalbert Vanessa Neveu Craig Knox Roman Eisner Edison Dong Paul Huang Russ Greiner Liang Li Hans Vogel The HMP & Panamp Team

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