nmr and mass spectrometry approaches to metabolomics in man and mouse dr. julian griffin...
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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
[email protected]@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.