--/--/2017

Application of Metabolomics

in the HPRU

Imperial College London

Professor Paul Elliott

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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

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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