metabolomics in the study of cns disorders: early lessons learned rima kaddurah-daouk,ph.d. nci, oct...
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![Page 1: Metabolomics in the study of CNS Disorders: Early Lessons Learned Rima Kaddurah-Daouk,Ph.D. NCI, Oct 2005 Duke University Medical Center](https://reader037.vdocuments.us/reader037/viewer/2022110323/56649d6c5503460f94a4b4ad/html5/thumbnails/1.jpg)
Metabolomics in the study of CNS Disorders: Early Lessons Learned
Rima Kaddurah-Daouk,Ph.D.NCI, Oct 2005
Duke University Medical Center
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CNS Disorders
• CNS disorders are poorly understood with no effective therapies• Include:
– Neurodegenerative (AD, HD, PD and MND)– Psychiatric (depressive disorders, schizophrenia, ADHD, addictive, and more)
• Genetic and Environmental factors• Several hypothesis few biochemical pathways mapped • Disease symptoms disease progression and response to therapy varies • Animal models are not optimal• Reliable biomarkers would help
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The Human CNS: A Hub of Metabolic Activity
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The Electro metabolomic Brain ?
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Preparing for the big battle in the future
Doraiswamy PM, 2003
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A global approach for the study of CNS disorders
• The scale up of data production and analysis in neuroscience presents great promise for understanding the complexities of the brain (Nature Neuroscience, Vol 7, Number 5, 2004)
• Genomics, comparative genomics, gene expression atlases, proteomics and imaging data are starting to build at a significant rate
• Adding metabolomics data is essential in this global effort towards understanding biological systems as integrated whole
• We plan to leverage these new technologies and system approaches towards better understanding and treating CNS disorders
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Which metabolomics technology to use?
• Probably more than one– Both Random and Targeted – MS, NMR, EC, Lipidomics, Tracers
• It depends on disease studied and pathways investigated
• Hypothesis generation vs. hypothesis testing mode
• We are building programs and databases
• Will make all publicly available
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Recchia, A. et al (2004)-The FASEB Journal. 2004;18:617-626
Metabolic Pathways in Parkinson Disease
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Cami, J. et al.(2003) N Engl J Med;349:975-986
Metabolic Pathways in Drug Abuse
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Two examples today
• Schizophrenia and a targeted approach for gaining insights into mechanisms of disease and metabolic syndrome development with anti psychotics
• MND and a random metabolomics approach for biomarker discovery
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Lipid profiles- signatures for schizophrenia (SCH) and for anti psychotic drugs
• SCH is relatively common, chronic and frequently devastating neuropsychiatric disorder
• No diagnostic test
• Positive symptoms (delusions, hallucinations) negative symptoms (impaired cognition and emotion)
• Hypothesis include – changes in dopamine, serotonin and glutamate neurotransmission
– Decreased synthesis and increased degradation of membrane phospholipids in certain regions of the brain
• Atypical antipsychotic drugs – are thought to target serotonin 5-HT2 receptors and dopamine D2 receptors to lesser extent
– many associated with metabolic disorders such as DM and metabolic syndrome
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Patients and drugs selected
• SCH patients selected were in acute psychotic episode who where medication free
• First episode patients with schizophrenia or patients who failed to take their prescribed medications
• Placed them on one of three anti psychotic drugs
• Plasma samples were collected fasting at base line and 2-4 weeks later
• We evaluated initially plasma samples from – 31 first episode SCH patients off medications
– 9 patients placed on risperidone, 14 patients on olanzapine, 4 patients on aripiprazole
– 16 controls matched for age and gender
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Lipid profiles and lipidomics signatures for schizophrenia (SCH) and for anti psychotic drugs
• We report early studies to exemplify power of approach
• Fatty acid/lipid profile data obtained
– Lipids extracted chloroform:methanol (2/1); individual lipid classes separated by preparative thin layer chromatography; lipid fractions scraped from plate;placed in 3N methanolic-HCl N2 atm 100degrees C 45 min; fatty acid methyl esters extracted in hexane 0.05%butylated hydroxytoluene; FA methyl esters were seperated and quantified by capillary GC
– concentration of more than 300 lipid metabolites within each of eight classes of lipids determined
– Quantitative (nmol fatty acid/g) or mole %
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Comparison of groups and visualization
• Significant differences between SCH patients and matched control group or upon treatment with anti psychotic drugs is determined
– unpaired Student's t-tests (P < 0.05)
– Many other statistical approaches are used to evaluate differences
• Quantitative data is visualized using the Lipomics Surveyor software
system – The system creates a "heat map" graph for significant differences between samples
– The brightness of each individual square denotes the magnitude of the difference, as displayed with each of the heat maps
– Differences not meeting P < 0.05 are shown in black
– Quantitative or mole % values shown
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Baseline vs. Control Quantitative
Nmoles/gram data, view percent difference, p<0.05
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Baseline vs. Control Mole Percent
Mole percent data, view percent difference, p<0.05
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Observations at Baseline
• Phosphatidylcholine and Phosphatidylethanolamine concentrations are down
• A clear pattern towards decrease in long chain polyunsaturated fatty acids suggests impairments in membrane structures
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Risperidone Signature-Post vs. Pre Quantitative
Nmoles/gram data, view percent difference, p<0.05
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Risperidone Signature Post vs. Pre Mole Percent
Mole percent data, view percent difference, p<0.05
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Observations with Risperidone
• Risperidone significantly decreased total free fatty acid concentrations
• increased triglycerides
• Increased lysophosphatidylcholine and phosphatidylethanolamine (Lysophosphatidylcholine is derived from phosphatidylcholine via the action of phospholipases)
• Other significant changes include changes in the concentration of 18:3n3 and 18:3n6 across multiple lipid classes.
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Olanzapine Signature-Post vs. Pre Quantitative
Nmoles/gram data, view percent difference, p<0.05
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Olanzapine Signature Post vs. Pre Mole Percent
Mole percent data, view percent difference, p<0.05
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Observations with Olanzapine
• Decreased total free fatty acids and Increased total triglycerides– suggest that there is a problem in turnover of fatty acids
• Increased phosphatidylcholine and phosphatidylethanolamine• Changes in the concentration of 20:4n3 and 20:3n6 across multiple lipid classes• From examination of the mole percent data we find
– composition of the free fatty acid, triglyceride, and phosphatidylethanolamine classes changed minimally
– the composition and concentration of phosphatidylcholine changed significantly
– production of arachadonic acid (20:4n6) may be altered by drug treatment (a desaturase enzyme involved ?)
25 lipid metabolites were significantly changed by both Risperidone and Olanzapine
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Aripiprazole Signature Post vs. Pre Quantitative
Nmoles/gram data, view percent difference, p<0.05
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Aripiprazole Signature Post vs. Pre Mole Percent
Mole percent data, view percent difference, p<0.05
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Observations with Aripiprazole
• Treatment with Aripiprazole had virtually no effect on plasma lipid metabolite concentrations or composition
• This drug has minimal metabolic side effects in schizophrenic patients
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A Biomarker program for the study of MND
• This program brings together MGH, Metabolon, UPMC, NIEHS and ALSA
• Proteomics and Metabolomics technologies for studying perturbations in networks and pathways in motor neuron disease (MND)
• Focus on biomarker discovery
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MND classification
Heterogeneous group of rare disorders with diverse signs and symptoms all effecting motor neurons
Terminology and classification is confusing
Unclear as to degree of relatedness at biochemical and physiologic level
Genetic susceptibility and environmental risk factors could contribute to disease
Disease progression varies
Clinical classification not precise• ALS• UMN • LMN
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Amyotrophic Lateral Sclerosis (ALS)
• The most common form of MND, large motor neurons, cerebral cortex, brain stem and spinal cord are affected
• There are familial and sporadic forms of ALS• Results in progressive wasting and paralysis of voluntary muscles ventilatory failure and 90% death within five years of onset of symptoms• No known treatment that prevents, halts or reverses the disease-Riluzole has
marginal delay on mortality• Many causes for ALS were proposed including: glutamate excitotoxcicity; oxidative
stress; mitochondrial dysfunction, autoimmune processes, cytoskeletal abnormalities, trophic factors deprivation
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Can Metabolomics help inthe management of MND?
• Enable global understanding of changes in biochemical and signaling pathways in MND towards– Better understanding of disease mechanism for new approaches for
drug design
– Improve diagnosis of disease and its progression
– Sub classification of MND
– Enable more effective clinical trials by stratifying patients and providing markers for detecting response to therapy
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Knowledge coming in stagesEarly days in metabolomics
• Stage I – Data is derived form HPLC-EC – Powerful platform but no structures– Simplistic in our design
• Stage II– Preliminary data derived from GC-MS, LC-MS will follow– Repeat study of samples used in HPLC-EC
• Stage III – Collaborative program– More sophisticated design– Mechanistic issues
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EC Metabolomics Platform
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EC Chromatograms
0.0 20.0 40.0 60.0 80.0 100.0
0.00
0.50
1.00
1.50
2.00
2.50
Retention tim e (m inutes)
Re
sp
on
se
(µ
A)
0.0 20.0 40.0 60.0 80.0 100.0
0.00
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1.00
1.50
2.00
2.50
Retention tim e (m inutes)
Re
sp
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se
(µ
A)
80 90
Retention time (minutes)
Control
ALS
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Data set I
• Profile Plasma• Simplistic design
– Selected 28 ALS and 30 controls
• Over 1500 peaks detected on HPLC-EC• 317 metabolites were selected for analysis• Post analysis we realized
– 23 SALS, 5LMN and 30 controls
– 16 of the MND patients were on Riluzole, 7 were off
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Data mining tools
• Three measure of class association– T-statistic– Pearson’s correlation coefficient– Relative class association (Golub et al.)
• All three resulted in similar ranking of metabolites by their level of association with MND
• Multivariate regression used PLS-DA
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Metabolites with significantly different concentrations in normal and MND plasmas in the first study
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PLS-DA distinguished subgroups of MND in early studies
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Data set II
• Profile Plasma
• Still simplistic in our experimental design– 19 ALS and 33 controls
• All MND patients were not on Riluzole
• Over 1500 peaks detected on HPLC-EC
• 317 metabolites were selected for analysis
• Post analysis we realized set included– 13 SALS
– 4FALS (1with SOD1, 3 without)
– 2UMN
– 33 controls
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Metabolites with significantly different concentrations
in normal controls and in MND patients not taking Riluzole
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PLS-DA distinguishes MND from controls in drug fee study
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On the identity of Two Riluzole Induced Peaks
• Selected two Riluzole induced peaks– Major peak at 80.2 min channel 5– Minor peak at 78.9 min channel 5
(Riluzole peak is at 72.5 min on channel 8)
• Isolated, purified and concentrated 100 fold• Made compatible with LC-MS• Two peaks with retention times of 4.43 and 9.58 min gave MS/MS
fragmentation patters that were not related to Riluzole (Riluzole elutes at 19.9 min)
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Extracted Ion Chromatograms and Spectra for Peak #1 @4.43 min
UNKNOWNMSMS01
09/17/2002 03:26:13 PM
RT:
0.00 - 15.05
SM:
3B
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Time (min)
10
20
30
40
50
60
70
80
90
100
RT: 4.47
RT: 9.53
NL:
2.21E7
TIC F: + c
Full ms [
75.00-
400.00] MS
UNKNOWNM
SMS01
UNKNOWNMSMS01
#
194-
RT:
4.17 -4.77
AV:
9
NL:
3.41E6
F:
+ c Full ms [ 75.00- 400.00]
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
400
m/z
0
20
40
60
80
100
357.37
264.34
340.21
282.21
378.42
UNKNOWNMSMS01
#
194-
RT:
4.14 --4.75
AV:
9
NL:
8.45E5
F:
+ c Full ms2 [email protected] [ 100.00- 400.00]
100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
400
m/z
0
20
40
60
80
100
282.16
340.13
322.29
264.32
[M+H]+
MS
MS/MS
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Extracted Ion Chromatograms and Spectra for Peak #2 @9.58 min
80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400m/z
0
20
40
60
80
100359.45
376.41
397.54
UNKNOWNMSMS01 RT: 9.37-9.85 AV: 10 NL: 1.33E5F:+ c Full ms2 [email protected] [ 100.00-450.00]
100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440m/z
0
20
40
60
80
100251.42
249.31 341.47267.30
127.27 145.26
[M+H]+
[M+NH4]+
[M+K]+
UNKNOWNMSMS01 09/17/2002 03:26:13 PM
RT: 0.00 - 15.05 SM: 3B
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Time (min)
0
10
20
30
40
50
60
70
80
90
100RT: 4.47
RT: 9.53
2.503.24 12.563.83 14.1013.0910.136.22 8.005.88 11.208.65 12.037.24 14.406.641.130.44 1.47
NL:2.21E7TIC F: + c Full ms [ 75.00-400.00] MS UNKNOWNMSMS01
UNKNOWNMSMS01 RT: 9.36-9.90 AV: 11 NL: 1.01E6F: + c Full ms [ 75.00-400.00]
MS
MS/MS
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a) Riluzole 2.4 ng and b) MS, MSMS, and MS3 RT: 0.00 - 23.89SM: 15B
0 2 4 6 8 10 12 14 16 18 20 22Time (min)
0
10
20
30
40
50
60
70
80
90
100RT: 19.61
19.51 19.77
NL:1.84E5m/z=
235.30-236.30 F: + c Full ms
[ 55.00-400.00]
MS 005
100 150 200 250 300 350 400m/z
0
50
1000
50
1000
50
100 235.76
208.56
193.48 235.73149.65 166.62
164.53
209.40
NL: 4.41E6001#1 RT: 0.01 AV: 1 T:
+ c Full ms [ 150.00-400.00]
NL: 1.41E4002#1 RT: 0.01 AV: 1 T:
+ c Full ms2 [email protected] [ 65.00-300.00]
NL: 3.13E3003#1 RT: 0.01 AV: 1 T:
+ c Full ms3 [email protected] [email protected] [ 55.00-300.00]
MS
MS/MS
MS3
(a)
(b)
F3CO
N
S
NH2
RiluzoleC8H5F3N2OS
Mol. Wt.: 234.20
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Summary of Phase I Metabolomics data
using HPLC/EC platform • Metabolic profiles based on approximately 300 metabolites permit
mathematical separation of MND and control subjects
• MND is associated more with a general down-regulation than elevation of metabolites
• A set of highly correlated metabolites is characteristic of a subset of MND patients that was significantly enriched for LMN disease
• 12 compounds are significantly up-regulated in MND patients taking Riluzole
• Metabolomics could help in the process of classification of MND
• Bigger sample sizes needed and better experimental design
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Phase II: MS based data in ALS metabolomics
• GC-MS and LC-MS platform• Complex and powerful• Key is to start the process of getting to structures and
pathways• Preliminary data will be shared in next set of slides
– GC-MS pilot study, use previous samples HPLC-EC to test the system
– LC-MS data in the works
– Complexities of design
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Classification of motor neuron diseases
Classical
ALS
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Motor Neuron DiseasesHow closely are they related as a class?
ALS
HTLVass
myel
UMN
LMN
PLS
HSP
Others
Kennsynd
PMA
SMA
Autoimmune
FALS
•Factors which influence disease course are unknown
age at onset, site of onset, delay from first symptoms to entering clinic, rate of change in motor and respiratory function
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Motor Neuron DiseasesHow closely are they related to other CNS disorders
HTLVUMN
PLS
HSP
Others
KennedyPMA
Autoimmune
LMN
ALS
AD
HDPD
FALS
Others
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Metabolic Signatures in CNSPlasma vs. CSF
• What does a signature in plasma mean? – What is contributed from the brain?
– Does it reflect the death of a motor neuron?
– What is a result from involvement of other organs such as muscle?
– Are we sure effects of all drugs and environmental variations are sorted out of the equation?
– What remains as a biomarker for the disease? What does that mean anyway?
– How do these signatures change as a function of course of disease?
• What does a signature in CSF mean? – Does CSF mimic better brain biochemistry?
– What is common between CFS and plasma signatures?
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Samples for determining ALS Signatures and its specificity and classification of MND
Plasma type Number of samples needed
NIEHS R21 (pending)
ALSA Grant Year I (funded)
ALSA Grant Year II (Contingent on I)
Samples still needed
SBIR Phase I
SBIR Phase II
Other Grants or funding Metabolon other studies
SALS 150 50 75 0 0 25 0 FALS-Non SOD1 FALS-SOD1
75
25
0
0
24 6
51
19
30
40
UMN 100 40 60 0 60 LMN 100 40 60 0 60 PN 100 25 50 25 0 AD 100 50 50 0 0 50 Myopathy 100 25 25 50 25 25 Normal controls 100 50 25 0 25 0 Total number of samples
850 200 75 210 290 130
185
Samples for determining ALS signatures and its specificity
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Conclusions
• Metabolomics has promise in helping us dissect MND• This is early days• Bigger sample sets of each MND and hence collaborations at a
national level• Integrating proteomic and metabolomic data in a system approach
will be challenging and interesting
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Collaborators and Consultants
Technology Group EC : BU/VAWayne Matson Mikhail Bogdanov
MS-MetabolonChris BeecherScott HarrisonLisa PaigeCorey DeHavenTom Barret
Robert H. Brown, Jr. Merit E. CudkowiczM. Flint Beal
INFORMATICSSteve RozenBruce Kristal
Scientific advisors for ALS programJeff RothsteinDon ClevelandLucie Bruijn
Paul VourosJimmy Flarakos
Clinical
Chemisty
Clinical data coordinationKristyn Newhall
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-200
-100
0
100
200
-200 -100 0 100 200
t[2]
t[1]
HD only all variables.M1 (PCA-X), Untitledt[Comp. 1]/t[Comp. 2]
H131
H132
H133
H134
H135
H136
H137H138
H139
H140
H219H220
H241
H242
H243
H244
H245
H246
H247
H248
H249
H250
H252
H257H274
H276AH276B
H323 H326
TWO METABOLOMIC GROUPS IN HUNTINGTONS DISEASE
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-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11
t[2]
t[1]
P_vs_W_transposed.M1 (PCA-X), Untitledt[Comp. 1]/t[Comp. 2]
P127_
P132_
P137_
P153_
P513_
P515_
P520_
P523_
P524_
P525_
P530_
W1A_
W2A_
W3A_
W4A_
W5A_W6A_
W7A_
GENE POSITIVE G93A MOUSE (P) vs. NORMAL PARENT (W)FRONTAL CORTEX 71 VARIABLES
GENOMIC METABOLOMIC CORRELLATION
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Pharmacogenomics and Metabolomics a natural fit
Complementary data towards understanding drug response
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Pharmacogenetics
The study of the role of inheritance in individual variation in drug response phenotype.
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GENOME
EN
VIR
ON
ME
NT
TRANSCRIPTOME
PROTEOMEPROTEOME
METABOLOMEMETABOLOME
HEALTHSTATE
HEALTHSTATE
Disease State
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Nature Reviews Genetics 5; 669-676 (2004);
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Evans: Nature, Volume 429(6990).May 27, 2004.464-468
Identifying genes influencing polygenic drug responses
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Molecular diagnostics of pharmacogenomic traits
Evans: Science, Volume 286(5439).October 15, 1999.487-491
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Add metabolomics data between genotype and phenotype
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Identify metabolites and pathways that influence drug response
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PharmacogenomicsMetabolomics-The Future
The Vision
The right drug, at the right dose for every patient.
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Table 1 The Duke Organizing Team
Metabolomics Technology
Pharmacogenomics PGRN members
Databases and Biochemical Modeling
Informatics
Experts and Links to Networks
Rima Kaddurah-Daouk (PI)
EC based Metabolomics Wayne Matson (VA) Misha Bogdanov (Cornell)
SSRIs Weinshilboum Mrazek (Mayo)
Pedro Mendes (VT)
Rozen (Whitehead)
SSRIs Rush---UT (Southwestern) national clinical trials with SSRIs
Ranga Krishnan (Chair of psychiatry)
MS based metabolomics Oliver Fiehn (UC Davis) Shulaev (VT)
SSRIs Wong (UCLA) Licinio (UCLA)
Kristal (Cornell)
Statins Dennis (UCDavis) Lipidomics Consortium
Kathie Piendl's (epidimiology)
Lipidomics Steve Watkins Rebecca Bailey (Lipomics Technology)
Statins Krauss (Oakland)
Industry Harrigan (Pfizer)
Maggie Kuchibatla (statistics)
SIDMAP (tracer) Laszlo Boros (UCLA)
Bruce Burchett (database)
Duke DCRI
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Metabolomics Society Organized 2004
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Our Mission
• "The mission of The Metabolomics Society, as listed in its Bye-Laws, shall be to promote the growth and development of the field of metabolomics nationally and internationally; to provide the opportunity for collaboration and association among the workers in that science and in related sciences and connections between academia, government and industry in the field of metabolomics; to provide opportunities for presentation of research achievements and creation of workshops, and to promote the publication of meritorious research in the field."
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Metabolomics Society Japan Meeting
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Metabolomics 2006 June 24-28The Conference Center at Harvard Medical School