Identifying metabolic markers of pre-obesity regulated by growth hormone using microarrays,...

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


Genomics and proteomics tells you what might happen, but metabolomics tells you what actually did happen.

- Bill Lasley, University of California, Davis


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


Roles of GHGrowth Hormone (GH) is the major regulator of somatic growth and metabolism.


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


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


Array data - metabolic pathway analysis

TCA cycle Fatty acid metabolism

Androgen and estrogen metabolism

Glutathione metabolism

Glycolysis/gluconesogenesis

Pathway Miner – www.biorag.org

updown


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?


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


PCA and PLS-DA analysis

PCA

PLS-DA

WT

569

391

taurine

TMA

Fatty acid

2-oxoglutarate


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


Changes of metabolism with age – metabolic trajectories

WT

569

391


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

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

2mth 4mth 10mth 13mthAge

% b

wt WT

K1

K2

KO

** *

Body composition

Subcutaneous fat Growth curve


Blood metabolites at 10-13 months

*

*

*

*

**

*

*

Pathways


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


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


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


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.


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.


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

Identifying metabolic markers of pre-obesity regulated by growth hormone using microarrays,...

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