--/--/2017
Application of Metabolomics
in the HPRU
Imperial College London
Professor Paul Elliott
0 200 400 600 800
France
Portugal
Spain
Italy
Greece
Sw itzerland
Netherlands
Albania
Denmark
Norw ay
Germany
Sw eden
Austria
Poland
UK
Ireland
Croatia
Bulgaria
Finland
Czech Rep
Romania
Hungary
Slovakia
Lithuania
Latvia
Estonia
Russia
Kazakhstan
Ukraine
Belarus
Female
Male
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Ezzati M & Riboli E New Engl J Med 2013
The challenge…
To a systems biologist pathology is just a change in phenotype!
Most human diseases are connected at some genetic level
Cardio-vascular
CancerMetabolic
Disease gene network
Barabasi et al (2007)
To a systems biologist pathology is just a change in phenotype!
Most human diseases are connected at some genetic level
Cardio-vascular
CancerMetabolic
Disease gene network
Barabasi et al (2007)
“Genetics loads the gun, but
Environment pulls the trigger”After Elliott Proctor Joslin MD, Br Med J 1991; 302: 1231
The main determinants of health
Whitehead M & Marmot M. Lessening inequalities and effect on coronary heart
disease. In: Marmot M & Elliott P. Coronary Heart Disease Epidemiology. From
Aetiology to Public Health, OUP 2005.
While genetic data are a (fixed) digital read-out...
Environmental exposure data vary over time, are continuously distributed, with wide dynamic range...
And difficult to measure....
Such that ... the quality of the exposure data has been called the Achilles heel of environmental epidemiology
New approaches required!
Genome
Epigenome
Transcriptome
Proteome
Metabolome
Microbiome
Pollution
Xenobiotics
Lifestyle
Stress
Diet
EN
VIR
ON
MEN
TO
MIC
S
Metabolic and
physiological phenotypeS
yste
ms
ap
pro
ach
Exposome concept
After Rappaport & Smith Science 2010; 330:460-1
Epidemiology
Molecular
PhenotypingBiomarkers
Systems
Toxicology
Approach
Pathways
Environment
Health
Policy & Action
Molecular
Insights
Metabolomics
• Measurement of small molecules in biological samples (e.g. blood, urine)
• Metabolites represent downstream biochemical end products that are close to the phenotype
• Link between environmental stressors, intrinsic metabolism, genetic information, health and disease
Holmes et al. Cell, 2008, 134: 714 - 717
The effects of the internal and external exposome on
metabolic phenotypes can be assessed with global
metabolic profiling
Genome
Polymorphisms, SNPs, copy
number variation
Epigenome
DNA methylation
Transcriptome
RNA seq, exome,
microarrays
Proteome
Proteomics (MS)
Metabolome
Metabolomics (NMR, MS)
Metabolic pathways
Phenome
Disease phenotype
Age, gender, ethnicity, diet, physical activity, stress, in utero effects, air
pollution, geographic location, drugs, gut microflora
Tzoulaki et al AJE 2014
Exposome
Metabolome over the lifecourse
Unhealthy
metabolome
Metabolic Profiling
System Environment
CELL
STRESSOR
Knock-out / strain
Disease
Toxicity
GUT MICROBES DIET
TISSUEBIOFLUID
DRUGS & OTHER CHEMICALS
METABOLIC PROFILE
LC-MS
NMR
Urinary NMR spectrum
Metabolic profiling can run in either targeted or
untargeted modes
• Targeted profiling (ARIC, Framingham, others) separates a
limited number of specific metabolites of known identity, is
optimized for these metabolites, and is based on a priori
hypotheses
• Untargeted profiling (e.g. COMBI-BIO) involves multiple
assays (NMR, MS) to measure as many metabolites as
possible. The chemical identity of the peaks are not known a
priori (with a few exceptions) and post hoc analyses are
required for identification
• Untargeted profiling is used for novel metabolic biomarker
discovery. Data output is high and metabolite identification is
not guaranteed.
The Challenge of Metabolomic Data
~1000s signals,100s metabolites
NMR LC-MS
~10,000s signals,1000s (?) metabolites
• Complex, but information rich• How do we retrieve the information?
Better Instrumentation, Higher Throughput,
More Integration
Advancement & application of metabolic profiling methods &
technologies
• State-of-the-art (mass spectrometric and NMR spectroscopic) analyses for metabolic finger-printing of biofluids
• Combine metabolic analyses with other clinical, lifestyle and –omicsdatasets
• A national resource and research capacity, enabling researchers to derive clinically-relevant insights to identify bio-markers or profiles
• Develop new methods and technologies
• International phenome centres – common methods
AN
ALY
TIC
AL
DA
TAB
IOIN
FOR
MA
TIC
SA
ND
MO
DEL
LIN
G
NMR UPLC-MS
STATISTICALSPECTROSCOPY
EXPERT SYSTEMS
OMICS INTEGRATION - THE INTERACTOME
SPECTRA
CLASSIFICATION & PREDICTION
IMAGE RECONSTRUCTION
SPEC
TRO
SCO
PIC
P
LATF
OR
M
NMR data preprocessing
AnalyticsNMR / MS
Putative Biomarker Identification
StructuralElucidation
Independent Cohort Validation
Pharmacokinetics
BiomarkerDiscovery
BiomarkerValidation
BiomarkerCharacterization
Measuring The Metabolome
Untargeted metabolomics
can lead to novel metabolic
biomarker discovery. BUT
requires structural
elucidation and validation.
Biomarker identification – value from analysisFeature(s) of
interest arising from
chemometricanalysis
Spectral matching to databases
Internal:Small molecules
Lipids
Purchased:NIST 14
Openly available:
LipidMapsMetlinHMDB
Massbank
Definitive match Comparison to authentic reference material
Typically from NPC library or commercially
obtained
Ambiguous match/no
match
Requires further
elucidation
Advanced elucidation
pipeline
In-house automated multi-stage purification
Product analysis:NMR
Ion mobilityFTICR-MS
M. Lewis, J. Pearce - NPCSeptember 09, 2016
Biomarker Identification – Example Marker
Biomarker flagged in HILIC analysis of both
plasma and urine
Database searches return multiple possible
matches, and reference materials are not
commercially available
Ion exchange and up-scaled version of HILIC analysis used to purify unknown from human
urine
Advanced formula and structure elucidation techniques identify unknown. In this process, many additional features observed in the HILIC
analysis are characterised
! MetlinMassBank
NIST 14
2D-NMR
ID’d moleculeEIC = 118.087
Synthesis, spike, ID!
?
M. Lewis, J. Pearce - NPCSeptember 09, 2016
Metabolic profilingMBX
• Mass Spectrometry (HILIC +/-, Lipid +/-)
• Nuclear Magnetic Resonance Spectroscopy (NOESY, CPMG)
“Top down” systems biology Cohorts
Rotterdam Study – 2,000 samplesEMC
LOLIPOP – 2,000 samplesICL
MESA – 4,000 samplesNWU
Management ICL
Analytical evaluation
Statistics
Epidemiology
Multi-omics
ICL
ICL
EMC
HMGU
UoI
Modelling
Pathway modelling
Development and modelling of risk scores
HMGU
UoI NWU EMC
Clinical translation, commercialisationMBX
The COMBI-BIO consortium
Combi-Bio Study Design & Analysis
New Opportunities, New Challenges
• New approaches (metabolomics, other omics) to capitalise on well-phenotypedcohorts and biobanks with long-term follow-up
• New insights on pathways and mechanisms linking environmental exposures to disease (exposome)
• Integrating, analysing and obtaining new knowledge from this wealth of information ─ computational challenge!
• Requires new integrated inter-disciplinary approaches
Genome
Gene Regulation
Proteins Metabolism
MetabolomicsProteomics
Transcriptomics
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