NMR and Mass Spectrometry
approaches to metabolomics in man
and mouse
Dr. Julian Griffin
jlg40@mole.bio.cam.ac.uk
Dept of Biochemistry,
University of Cambridge
Overview

What is metabolomics and why do we need it?

Type II diabetes


CAD and cardiovascular disease


Man, mouse and rat
Markers of drug efficacy
Type I diabetes

Biomarker discovery
The basis of metabolomics

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Metabolomics/metabonomics
 the quantitative
measurement of metabolic
responses to
pathophysiological stimuli
or genetic modification
Measure small molecule
concentrations through a
global approach
 NMR spectroscopy
 Mass Spectrometry
Use pattern recognition to
define metabolism in a
multidimensional space
 metabolic phenotype
 metabotype
Type II diabetes
Class 1
dbdb-0-4.24_ex_glucose_2.M1 (PCA-X), PCA all
t[Comp. 1]/t[Comp. 2]
Colored according to classes in M1
1.00
Rat
0.4
0.80
t[2]
Metabolism is very easily
compared across animal models
and back to humans
 With Roger Cox, Michael
Cheeseman and Tertius
Hough looked at the effects
of age and gender on the
profile of diabetic urine
 Ignored glucose!
 Identified a number of
novel perturbations
 E.g. NMN and
nucleotide metabolism
0.6
PLS Component [2]

Class 2
Class
1
Class 2
Class 3
Class 4
0.2
0.60
0.40
-0.0
0.20
-0.2
0.00
Mouse
Human
-0.20
-0.4
-0.40
-0.6
-0.60
-0.8
-0.80
-0.9
-0.8 -0.80
-0.7 -0.70
-0.6 -0.60
-0.5-0.50-0.4-0.40-0.3
-1.00 -0.90
-0.30 -0.2
-0.20 -0.1
-0.10 0.0
0.00
0.1
0.10
0.2 0.30
0.3 0.400.4 0.500.50.60 0.60.70 0.7
0.20
0.80 0.8
0.90
Beta-alanine metabolism
Methane metabolism
Nitrogen metabolism
TCA cycle & Oxidative
phosphorylation
Pyrimidine, purine &
nicotine/nicotinamide
metabolism
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.
Ascorbate and aldarate
metabolism
Benzoate metabolism
Amino acid metabolism
Glyoxylate metabolism
Urea cycle
Salek RM, Physiol Genomics
2007
0.9
1.00
PLS Component
[1]
t[1]
Biotin metabolism
Styrene degradation
pyruvate metabolism &
glycolysis/
gluconeogenesis
Taurine, bile acid & Sulfur
metabolism
Propanoate, C5 branched
dibasic acid & Butanoate
metabolism
CAD and cardiovascular disease



Predict the occurrence and
severity of coronary artery
disease using blood serum.
Blood sera collected at
Papworth hospital as part of
trials concerning statins
Such systems may produce
significant financial savings
 angiography, currently the
gold standard for diagnosis.
Brindle JT et al., 2002. Nat Med. 8(12), 1439-45.

However, on closer
inspection:


‘Biomarkers’ are rather
generic
Gender and statin
treatment effect the same
‘biomarkers’ of disease


Groups must be stratified
Over fitting of the
pattern recognition
models is a problem
Kirschenlohr et al.,
Nature Medicine, 2006
Mouse models of atherosclerosis



Mice - C57Bl/6, LDLR-/Diet - Control RM1 Diet (SDS), HFCC Diet (Hope Farms)
Blood plasma (and urine)
Class 2, Control
High fat diet
Class 2, Control
Normal diet (Week 0)
Class 1, Control
Normal diet
Class 4, LDLR-/Normal diet (Week 0)
Class 3, LDLR-/Normal diet
Class 4, LDLR -/High fat diet
Cheng KK, Physiol Genomics, 2010
ANOVA-PCA
Source: Analytica Chimica Acta 629 (2008) 47-55
Diet effect
RM1 diet
RM1 diet
HFCC diet
HFCC diet
ANOVA-PCA
Diet + error
Genotype effect
LDLR -/LDLR -/-
B6
B6
ANOVA-PCA
Gen + error
Variance components (case study)
60
52.65
Variance (%)
50
40
28.50
30
20
11.84
7.01
Gen
DxG
10
0
Diet
Component
Within
Discussion & Conclusion



Metabolomics can now be used as a high throughput
phenotyping tool in mice
 Metabolism is also very translatable across species
Reduced variability in phenotype can simplify biomarker
discovery
 Mass spectrometry is much more sensitive if you know
what you are looking for
Database tools are also in place to conduct this across
multiple sites
Acknowledgements
JLG Group (present)
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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
Collaborators
Roger 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.