How Bioinformatics Helped Reveal the Human Metabolome

How Bioinformatics HelpedReveal the Human Metabolome

David WishartUniversity of Alberta, Edmonton, Canada

InCoB 2007, Hong Kong Aug. 28, 2007

Inspiration for the Title

Outline

• Metabolomics - Backgrounder

• The Human Metabolome Project (HMP)

• Bioinformatics Challenges & the HMP

• HMP Resources

• Applications

• Conclusion & the Future

The Pyramid Of Life

35,000 Genes35,000 Genes

3500 Enzymes3500 Enzymes

3000 Chemicals

Metabolomics

Proteomics

Genomics

(the human metabolome project)

(the human proteome project)

(the human genome project)

Small Molecules Count…

• >95% of all diagnostic clinical assays test forsmall molecules

• 89% of all known drugs are small molecules

• 50% of all drugs are derived from pre-existing metabolites

• 30% of identified genetic disorders involvediseases of small molecule metabolism

• Metabolites serve as cofactors and signalingmolecules to 1000’s of proteins

Metabonomics & Metabolomics

• Metabonomics:The quantitative measurement

of the time-related “total” metabolic response

of vertebrates to pathophysiological

(nutritional, xenobiotic or toxic) stimuli

• Metabolomics:The quantitative measurement

of the metabolic profiles of model organisms

to characterize their phenotype or phenotypic

response to genetic or nutritional

perturbations

MetaboXomics

What is a Metabolite?

• Any organic molecule detectable in thebody with a MW < 1500 Da

• Includes peptides, oligonucleotides,sugars, nucelosides, organic acids,ketones, aldehydes, amines, aminoacids, lipids, steroids, alkaloids, foods,food additives and drugs (xenobiotics)

• Includes human & microbial products

• Concentration > detectable (1μM)

Defining Metabolites

M mM μM nM pM fM

Endogenous metabolites

Drugs

Food additives

Drug metabolites

Toxins/Env. Chemicals

The Metabolome is “Fuzzy”

What Can You Do WithMetabolomics?

• Generate metabolic “signatures”

• Monitor/measure metabolite flux

• Monitor enzyme/pathway kinetics

• Assess/identify phenotypes

• Monitor gene/environment interactions

• Track effects from toxins/perturbants

• Monitor consequences from gene KOs

• Identify functions of unknown genes

Other MetabolomicApplications

• Genetic Disease Tests

• Nutritional Analysis

• Clinical Blood Analysis

• Clinical Urinalysis

• Cholesterol Testing

• Drug Compliance

• Transplant Monitoring

• MRS and fMRI

• Toxicology Testing

• Clinical Trial Testing

• Fermentation Monitoring

• Food & Beverage Tests

• Nutraceutical Analysis

• Drug Phenotyping

• Water Quality Testing

• Petrochemical Analysis

What Are The Technologies?

• UPLC, HPLC

• CE/microfluidics

• LC-MS

• FT-MS

• QqQ-MS

• NMR spectroscopy

• X-ray crystallography

• GC-MS

• LIF detection

What Does the Data Look Like?

-25

-20

-15

-10

-5

0

5

10

15

20

25

-30 -20 -10 0 10

PC1

PC2

PAP

ANIT

Control

2 Routes to Metabolomics

1234567ppm

hippurate urea

allantoin creatininehippurate

2-oxoglutarate

citrate

TMAO

succinatefumarate

water

creatinine

taurine

1234567ppm

-25

-20

-15

-10

-5

0

5

10

15

20

25

-30 -20 -10 0 10

PC1

PC2

PAP

ANIT

Control

QuantitativeMethods

Chemometric (Pattern)Methods

Quantitative vs. Chemometric

• Identifies compounds

• Quantifies compds

• Concentration rangeof 1 μM to 1 M

• Handles wide range ofsamples/conditions

• Allows identification ofdiagnostic patterns

• Limited by DB size

• No compound ID

• No compound conc.

• No compoundconcentration range

• Requires strict sampleuniformity

• Allows identificationof diagnostic patterns

• Limited by training set

Both Methods Need A Metabolite “Parts List” to be Effective

The Human Metabolome Project

• $7.5 million Genome Canada Projectlaunched in Jan. 2005

• Mandate to quantify (normal andabnormal ranges) and identify allmetabolites in biofluids (urine, CSF,plasma) and tissues

• Make all data freely and electronicallyaccessible (HMDB, DrugBank, FooDB)

• Make all cmpds publicly available (HML)

HGP vs. HMP

• Focus on genome

• Alphabet’s known,need the sequence

• 3,000,000,000 bases

• Public Repository(GenBank)

• Lots of pre-existingelectronic data

• $1 billion

• Start 1990, end2003

• Focus on metabolome

• Alphabet’s not known,need concentrations

• 8000 compounds

• Public Repository(HMDB + others)

• Almost no pre-existing electronicrecords

• $7.5 million

• Start 2005, end 2008

Project Investigators

Project Resources

• 400 MHz NMR, 2x500MHz NMR, 2x600 MHzNMR, 700 MHz NMR,800 MHz NMR

• 9.4 Tesla FT-MS, 4 iontrap LC-MS, 2 QStar-TOFs, 1 triple quad MS

• 2 prep HPLC systems, 3nano-HPLC systems, 1UPLC system

• 30 staff and students

The Bionformatics Challenge

?

Challenge 1: Very Different Data Types

Genomics/Proteomics Metabolomics

InChI=1/C19H28O2/c1-18-9-7-13(20)11-

12(18)3-4-14-15-5-6-17(21)19(15,2)

10-8-16(14)18/h11,14-17,21H,3-10H2,1-

2H3/t14-,15-,16-,17-,18-,19-/m0/s1

Challenge 2: Very Little MetabolomicData is Electronically Accessible

• Sequence Databases

– GenBank, UniProt

• Expression Databases

– GEO, SMDB

• Structure Databases

– PDB, MSD, SCOP

• Pathway Databases

– KEGG, BioCarta

• Electronic Deposition

– GenBank, PDB, GEO

• Metabolite DBs

– ????

• Concentration DBs

– ????

• Spectral DBs

– ????

• Pathway DBs

– KEGG, BioCyc

• Electronic Deposition

– ????

Genomics/Proteomics Metabolomics

The Bioinformatics Challenge

• What’s already known?

• How to assemble, elaborate and linkchemical data with biological data?

• How to manage data flow in a multi-centred project?

• How to make this data public (anduseful)?

What’s Already Known?

• Databases (KEGG,HumanCyc) suggested ~800cmpds

• Comparative metabolomicsstudies with mouse, yeast &E. coli suggested ~1000cmpds

• #Peaks seen in NMR and FT-MS spectra suggested ~1000cmpds

• What about drugs?

• What about foods and foodadditives?

• What about environmentalchemicals?

Sherlock ChemLoc

• Build chemical name, drug name andmetabolite name recognition tools using“training sets” from existing databasesand concepts borrowed from proteinname recognition systems

• Scour Pubmed, Google, SProt, OMMBID

• Track all synonyms (>50,000 so far)

• Supplement with old-fashionedsleuthing

What We (and others) HaveFound…

M mM μM nM pM fM


Drugs

Food additives

Drug metabolites


2800 cmpds

1400 cmpds

2100 cmpds

900 cmpds

800 cmpds

Assembling & LinkingBiochemical Data - BioSpider

• Searches web databasesto extract and compilelarge volumes of dataabout metabolites, drugsand associated proteins

• Scans about 20 webdatabases

• Uses chemical propertypredictors & sketch tools

• Also assemblesprotein/gene/SNP data iftarget is ID’d

PubChem

KEGGSwissProt

Wikipedia

NIST-library PharmGKB

Prediction/Processing Engine

The BioSpider Server

www.biospider.ca

Assembling & Linking BiologicalData - PolySearch

• Searches PubMedabstracts to extract andcompile data aboutassociations betweendrugs, metabolites,genes, diseases,tissues, fluids andmutations

• Also searchesSwissProt, OMIM, ,DrugBank, GAD, HPRD,HMBD, HGMD, CGAP

http://wishart.biology.ualberta.ca/polysearch

Data Sources & Data Flow

• Backfilling (Phase I)

• Text mining (Phase II)

• Experiment (Phase III)

2 Sites, 18 months

3 Sites, 12 months

5 Sites, 36 months

A Common Problem

Collaborator

In Timbuktu

Sample

Collection

Sample

Analysis

Data Analysis

& Informatics

Solution? LIMS

What’s A LIMS?

MetaboLIMS

• Web-based, portable, robust, andscalable laboratory informationmanagement system designed to meetthe high throughput processing and/orclinical needs of laboratories involvedin metabolomics research

• Designed to be “generic” with somecustomizable features

• Very simple, intuitive interface

MetaboLIMS

• Compound and SampleTracking

• Electronic Notebook Entry

• GANTT Charting

• Audit Trail Tracking

• Text Data Entry

• Spectral Trace or ImageEntry

• Automated CompoundAnnotation

• Relational Query Searches

Making Data Accessible

• Need to create web-based databases

• Need to anticipate all possiblebrowsing and query options

• Need to make data views user friendlyand data content as appealing aspossible for a very broad audience

• Need to make data current and easilyupdatable (link to LIMS)

Where The Data’s Kept…

M mM μM nM pM fM


Drugs

Food additives

Drug metabolites


HMDB

DrugBank

FooDB

DrugMet

ToxDB

Human Metabolome Database

www.hmdb.ca

HMDB

• ~95 data fields for each metaboliteentry

• 30+ fields pertaining to chemical orphysico-chemical data

• 50+ fields pertaining to biological,biomedical or clinical data

• >550 reference and assigned NMRspectra, 1600+ MS spectra

• Extensive search and query tools

DrugBank

http://redpoll.pharmacy.ualberta.ca/drugbank/

FooDB (Beta)

http://hmdb.med.ualberta.ca/foodb

Human Metabolite Library

http://www.metabolibrary.com/

Comparing MetabolomeDatabases

METLIN

KEGG

HumanCyc

BiGG

HMP Databases

Applications

Improving Data Linkage

• HMDB, DrugBank, FooDB, etc. are NOT justlists of compounds, they are encyclopedias

• Extensive clinical and physiological data isprovided on many metabolites, foods anddrugs

• Extensive disease data (normal vs. abnormalcompound concentrations)

• Extensive protein, pathway, gene and SNPdata for metabolizing enzymes andmacromolecular interacting partners

Systems Biology Databases

Improving Data Linkage

Improving Targeted Profiling - TheHMDB Spectral Library

Characterizing Clinically ImportantBiofluids: The CSF Metabolome

NMR

(56 cmpds

16 unique)

GC-MS

(48 cmpds

12 unique)

LC-MS

(19 cmpds

6 unique)

NMR+GC

(18 cmpds)

LC+GC

(1 cmpd)

LC+NMR

(3 cmpds)

LC+NMR+GC

(9 cmpds)

274

cmpds

76 cmpds

from 3 methods

Providing Quantitative Datafor Metabolic Simulation

• Petri Nets

• Flux Balance Analysis

• ODE’s and PDE’s

• Pi Calculus

• S-equations

• Agent-based Methods

• Cellular Automata

• Boolean Networks

Facilitating Drug Discovery

PharmaBrowse ChemQuery

SeqSearch Data Extractor

Facilitating Drug Discovery

• Matching drugs/chemicals to protein targets

• BLAST new sequences (viral, bacterial) tofind similarities to known drug targets

• Query newly isolated/synthesizedcompounds to check for similarities toexisting drugs

• Generating novel drug “ideas” fromstructure/function comparisons to knownmetabolites and drugs

Finding Novel Drug ToxicityBiomarkers

Trimethylamine-N-oxide

Glucose

Creatine

Creatinine

Applications in Drug (Dosage)Monitoring

Drug Signature

Missed a dose

Conclusions• Bioinformatics proved integral (once

again) to nearly all aspects ofdevelopment and implementation of alarge scale “omics” project

• The tools and resources developed bythe HMP are mandated by GC to bemade freely available to all

• The lessons learned (and tools made)for the HMP may be useful for otherlarge scale metabolomics projects

Conclusions• Many of the bioinformatics needs in

metabolomics are only beginning to beaddressed -- lots of opportunity for

innovation and new ideas

• Technology changes in metabolomicsare quite rapid, requiring changes tothe computational/analytical tools

• Metabolomics is like proteomics ~1995or transcriptomics in ~1998

The Future of Metabolomics

1990 1995 2000 2005 2010 2015 2020

Genomics

Proteomics

Metabolomics???

Acknowledgements

• Craig Knox

• Roman Eisner

• Dean Cheng

• Russ Greiner

• An Chi Guo

• Savita Shrivastava

• Nelson Young

• Dan Tzur

• Leslie Jia

• David Arndt

• Ian Forsythe

How Bioinformatics Helped Reveal the Human Metabolome

Documents

Transcript of How Bioinformatics Helped Reveal the Human Metabolome