NMR and Mass NMR and Mass Spectrometry approaches Spectrometry approaches to metabolomics in man to metabolomics in man

and mouseand mouse

Dr. Julian GriffinDr. Julian Griffin

jlg40@mole.bio.cam.acjlg40@mole.bio.cam.ac.uk.uk

Dept of Biochemistry,Dept of Biochemistry,

University of University of CambridgeCambridge

OverviewOverview What is metabolomics and why do we need it?What is metabolomics and why do we need it?

Type II diabetesType II diabetes Man, mouse and ratMan, mouse and rat

CAD and cardiovascular diseaseCAD and cardiovascular disease Markers of drug efficacyMarkers of drug efficacy

Type I diabetes Type I diabetes Biomarker discoveryBiomarker discovery

The basis of The basis of metabolomicsmetabolomics

Metabolomics/Metabolomics/metabonomicsmetabonomics the quantitative the quantitative

measurement of measurement of metabolic responses metabolic responses to pathophysiological to pathophysiological stimuli or genetic stimuli or genetic modificationmodification

Measure small Measure small molecule molecule concentrations through concentrations through a global approacha global approach NMR spectroscopyNMR spectroscopy Mass SpectrometryMass Spectrometry

Use pattern recognition Use pattern recognition to define metabolism in to define metabolism in a multidimensional a multidimensional spacespace metabolic phenotypemetabolic phenotype metabotypemetabotype

Type II diabetesType II diabetes

Metabolism is very Metabolism is very easily compared across easily compared across animal models and back animal models and back to humansto humans With Roger Cox, With Roger Cox,

Michael Cheeseman Michael Cheeseman and Tertius Hough and Tertius Hough looked at the effects looked at the effects of age and gender on of age and gender on the profile of diabetic the profile of diabetic urineurine

Ignored glucose!Ignored glucose! Identified a number Identified a number

of novel of novel perturbationsperturbations E.g. E.g. NMN and

nucleotide metabolism

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

-1.00 -0.90 -0.80 -0.70 -0.60 -0.50 -0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

t[2]

t[1]

dbdb-0-4.24_ex_glucose_2.M1 (PCA-X), PCA allt[Comp. 1]/t[Comp. 2]Colored according to classes in M1

Class 1Class 2Class 3Class 4

PCA of 160 urine samples from a diabetic mouse model (dbdb mouse maintained at MRC Harwell). Class 1 – Male Wild Type/Heterozygous; Class 2 - Male Homozygous; Class 3 - Female WT/Heterozygous; Class 4 - Female Homozygous.

-0.8

-0.6

-0.4

-0.2

-0.0

0.2

0.4

0.6

-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

PL

S C

om

po

nen

t [2

]

PLS Component [1]

Class 1Class 2

Rat

Mouse Human

TCA cycle & Oxidative

phosphorylation

Amino acid metabolism

Taurine, bile acid & Sulfur metabolism

Propanoate, C5 branched dibasic acid & Butanoate

metabolism

Styrene degradation

Biotin metabolism

Urea cycle

Glyoxylate metabolism

Benzoate metabolism

Ascorbate and aldarate metabolism

Pyrimidine, purine & nicotine/nicotinamide

metabolism

Nitrogen metabolism

Methane metabolism

Beta-alanine metabolism

pyruvate metabolism & glycolysis/

gluconeogenesis

Salek RM, Physiol Genomics2007

CAD and cardiovascular diseaseCAD and cardiovascular disease

Predict the occurrence and Predict the occurrence and severity of coronary artery severity of coronary artery disease using blood serum. disease using blood serum.

Blood sera collected at Blood sera collected at Papworth hospital as part Papworth hospital as part of trials concerning statinsof trials concerning statins

Such systems may produce Such systems may produce significant financial savingssignificant financial savings angiography, currently angiography, currently

the gold standard for the gold standard for diagnosis.diagnosis.

Brindle JT et al., 2002. Nat Med. 8(12), 1439-45.

However, on closer However, on closer inspection:inspection: ‘‘Biomarkers’ are Biomarkers’ are

rather genericrather generic Gender and statin Gender and statin

treatment effect the treatment effect the same ‘biomarkers’ same ‘biomarkers’ of diseaseof disease

Groups must be Groups must be stratifiedstratified

Over fitting of the Over fitting of the pattern recognition pattern recognition models is a models is a problem problem Kirschenlohr et al.,

Nature Medicine, 2006

Mice - C57Bl/6, LDLR-/-

Diet - Control RM1 Diet (SDS), HFCC Diet (Hope Farms)

Blood plasma (and urine)

Mouse models of atherosclerosis

Class 2, ControlHigh fat diet

Class 4, LDLR -/-

High fat diet

Class 1, ControlNormal diet

Class 3, LDLR-/- Normal diet

Class 2, ControlNormal diet (Week 0)

Class 4, LDLR-/-

Normal diet (Week 0)

Cheng KK, Physiol Genomics, 2010

Source: Analytica Chimica Acta 629 (2008) 47-55

ANOVA-PCA

ANOVA-PCADiet + error

RM1 dietHFCC diet

Diet effectDiet effectRM1 diet HFCC diet

Genotype Genotype effecteffect

LDLR -/- B6

ANOVA-PCAGen + error

B6LDLR -/-

Variance components (case study)

52.65

11.84

28.50

7.01

0

10

20

30

40

50

60

Diet Gen DxG Within

Component

Va

ria

nc

e (

%)

Discussion & ConclusionDiscussion & Conclusion Metabolomics can now be used as a high Metabolomics can now be used as a high

throughput phenotyping tool in micethroughput phenotyping tool in mice Metabolism is also very translatable across Metabolism is also very translatable across

speciesspecies Reduced variability in phenotype can simplify Reduced variability in phenotype can simplify

biomarker discoverybiomarker discovery Mass spectrometry is much more sensitive Mass spectrometry is much more sensitive

if you know what you are looking forif you know what you are looking for Database tools are also in place to conduct Database tools are also in place to conduct

this across multiple sitesthis across multiple sites

AcknowledgementsJLG Group (present)

Zsuszi Ament Michael Baker Cecilia Castro Martin Coleston Sue Connor Melanie Gulston Cheng Kian Kai Steve Murphitt Lee Roberts Reza Salek Ben Tucker Baljit Ubhi Xinzhu Wang James West

CollaboratorsRoger Cox, Michael Cheeseman & Tertius

Hough, MRC Harwell

Anne Cooke & Paola Zaccone

Andy Nicholls & John Haselden, GSK

Funders: BBSRC, EU, BHF, GlaxoSmithKline, MRC, Syngenta, Unilever & Wellcome Trust.