Post on 03-Jul-2015
description
1
IMB, UQ, Brisbane
Brisbane
Dr A. M. Lichanska, UQ, Brisbane 2
Identifying metabolic markers of pre-obesity regulated by growth hormone
using microarrays, confirmation through metabonomics and use in
clinical applications.
Dr Agnieszka M. Lichanska, PhDOral Biology and Pathology, School of Dentistry and IMB,
University of Queensland,Brisbane, Australia
Dr A. M. Lichanska, UQ, Brisbane 3
Genomics and proteomics tells you what might happen, but metabolomics tells you what actually did happen.
- Bill Lasley, University of California, Davis
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Overview of the presentation• Background• Animal model• Array data - GO and metabolic pathway
analysis• Metabonomics platform• Data analysis and biomarker identification• Analysis of the metabolism• Clinical assessment of the biomarkers
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Roles of GHGrowth Hormone (GH) is the major regulator of somatic growth and metabolism.
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Materials and methods
• Microarray analysis– Male 42 days old mice– Liver RNA– Affymetrix U74v2A chips– MAS 5, RMA, GeneRaVE, Pathway Miner
• Metabonomics analysis– Fasted male mice 2-12 months old– Urine – 500MHz Brucker NMR spectrometer
• Clinical analysis– Human urine – Prader-Willi syndrome children– GH-deficient and GH-replacement patients
35 121 330Genes differentially expressed vs WT
WT Mutant 569 Mutant 391 GHR-/-mutant
0% JAK2
0% MAPK
0% STAT5
Animal model
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Egrf
Fabp5Socs2Keg1
Hsd3b5GhrCsadIgf1
Es31
Hsd3b2
Hao3
Fos
H19Igfb1Cyp2b9
Fmo3
Sult2a2
DbpRgs16
Mt1
110001G20Rik
569 WT 391 GHR-/-
RCK/p54
Markers identified by GeneRaVE analysis
GeneRaVE analysis - gene networks
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Array data - metabolic pathway analysis
TCA cycle Fatty acid metabolism
Androgen and estrogen metabolism
Glutathione metabolism
Glycolysis/gluconesogenesis
Pathway Miner – www.biorag.org
updown
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Potential biomarkers from the microarray analysis
• Es31 - esterase 31• Hsd3b5 – hydroxysteroid dehydrogenase• RCK/p54 – RNA helicase• 19 others: Csad, Igf1, Egfr, Keg1, Socs2, Fabp5,
Fos, Fmo3, Dbp, Rgs16, Hao3, H19, Igfbp1, Cyp2b9, Mt1, Sult2a2, Ghr, 1100001G20Rik
How do these markers help us in understanding the physiology of GH action?
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Metabonomic analysis using NMR platform
Metabolic changes
Genetic changes (mutations)
Drugs, diet
Changes in metabolite concentration
Blood Urine
NMR PCA/PLS-DA analysis
Identification of individual metabolites
other biofluids
H2O and urea excluded
Bucket table (bucket width 0.05ppm)1d Spectrum
Scores plot
Loadings plot
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PCA and PLS-DA analysis
PCA
PLS-DA
WT
569
391
taurine
TMA
Fatty acid
2-oxoglutarate
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Biomarker identification1H chemical shifts (ppm) Metabolite All 569/
WT391/WT
391/569
3.43t;3.27t Taurine -- -- -- --3.94s;3.04s Creatine + + + +4.06s;3.05s Creatinine + + ++ ++2.88s TMA ++ ++ ++ --3.28s TMAO -- 0 -- --2.30t;1.64m,1.63m,1.61m;1.6m,1.59m;0.88t
Fatty acid -- - -- ++
2.69d;2.55d Citrate - - - -3.37s Oxaloacetate -- 0 0 03.02t 2-oxoglutarate - -- - -2.41s Succinate - - 0 07.36m;5.39s Allantoin ++ 0 ++ ++2.45t;1.25d 3-hydroxybutyrate - - - 0
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Changes of metabolism with age – metabolic trajectories
WT
569
391
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Analysis of metabolism
WT 569 391 GHR-/-
GHR KI mice at 42 days
WT 569 391 GHR-/-
GHR KI mice at 10 months
SubQ fat pads
0.00
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2mth 4mth 10mth 13mthAge
% b
wt WT
K1
K2
KO
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Body composition
Subcutaneous fat Growth curve
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Blood metabolites at 10-13 months
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Pathways
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Potential biomarkers
Es31Hsd3b5RCK/p54Csad Igf-1
TaurineTMA, TMAO, DMAAllantoinHippurateCitrate (citrate/creatine)Creatine/creatinine
Arrays Metabonomics
Is it the individual biomarkers or rather the metabolic fingerprint that is going to be more
useful for diagnostics?
PathwaysSteroid metabolismTCA cycleFatty acid metabolismCarbohydrate metabolismGlutathione metabolism
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Assessment of biomarkers in clinical samples – taking results from mouse model
into the clinic
• GH-deficient patients– Children
• GH/GHR mutation, GH - insensitivity• Prader-Willi syndrome children
– Adults• Follow up of GH replacement therapy
– Children– Adults
• Identification of a type of GH-deficiency• Identification of pre-obesity state
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Assessment of biomarkers in clinical samples - testing clinical samples
Patients
Samples
Data
Cohorts
Urine Saliva Blood
NMR MS+
Metabolite database
Statistical analysis
BiomarkersMetabolic FingerprintsHealth/Disease Trajectories
ValidationDiagnostics
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Conclusions
• Microarray analysis has identified the metabolic processes affected by mutations in GHR and some potential biomarkers
• Metabonomic analysis has confirmed the predicted changes
• The small changes in microarrays lead to significant alterations in metabolism
• Both methods identified the intermediate phenotype represented by the mutant 569
• Metabolic profiling of human samples will focus first on the metabolites identified in mouse study.
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Take home message
• The markers identified in microarray studies have been useful for metabolic differentiation of individual groups using metabonomic analysis.
• Pre-obesity syndrome was identified using both microarrays and post-array NMR analysis.
• These results form basis of human clinical analysis using metabonomics.
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AcknowledgementsIMBHorst J. SchirraCameron AndersonLinda KerrSheryl MaherDavid J. CraikMike J. Waters Jenny Rowland
CSIROBill Wilson
Queensland Smart State Fellowship
Mater HospitalFrancis BowlingGary Leong
School of Dentistry/IMBShaffinaz Abd Rahman
University of OhioJohn Kopchik
Millenium ScienceRobert Henke