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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

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

the problem with today s fcdbs
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
the power of metabolomics
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

the power of metabolomics1
The Power of Metabolomics

Response

Metabolomics

Response

Proteomics

Response

Genomics

Time

human metabolomes

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

the food metabolome project
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

the ideal food database
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
a blended model
A Blended Model

http://www.phenol-explorer.eu

http://www.hmdb.ca

counting compounds
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
challenge 2 finding phytochemical structures
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

challenge 3 annotating cmpds
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

challenge 4 finding health claims targets
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

when will it be done
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

acknowledgements
Acknowledgements

Augustin Scalbert

Vanessa Neveu

Craig Knox

Roman Eisner

Edison Dong

Paul Huang

Russ Greiner

Liang Li

Hans Vogel

The HMP & Panamp Team