Metabolomics in the study of CNS Disorders: Early Lessons Learned

Rima Kaddurah-Daouk,Ph.D.NCI, Oct 2005

Duke University Medical Center

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

The Human CNS: A Hub of Metabolic Activity

The Electro metabolomic Brain ?

Preparing for the big battle in the future

Doraiswamy PM, 2003

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

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

Recchia, A. et al (2004)-The FASEB Journal. 2004;18:617-626

Metabolic Pathways in Parkinson Disease

Cami, J. et al.(2003) N Engl J Med;349:975-986

Metabolic Pathways in Drug Abuse

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

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

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

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 %

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

Baseline vs. Control Quantitative

Nmoles/gram data, view percent difference, p<0.05

Baseline vs. Control Mole Percent

Mole percent data, view percent difference, p<0.05

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

Risperidone Signature-Post vs. Pre Quantitative

Nmoles/gram data, view percent difference, p<0.05

Risperidone Signature Post vs. Pre Mole Percent

Mole percent data, view percent difference, p<0.05

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.

Olanzapine Signature-Post vs. Pre Quantitative

Nmoles/gram data, view percent difference, p<0.05

Olanzapine Signature Post vs. Pre Mole Percent

Mole percent data, view percent difference, p<0.05

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

Aripiprazole Signature Post vs. Pre Quantitative

Nmoles/gram data, view percent difference, p<0.05

Aripiprazole Signature Post vs. Pre Mole Percent

Mole percent data, view percent difference, p<0.05

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

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

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

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

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

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

EC Metabolomics Platform

EC Chromatograms

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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

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

Metabolites with significantly different concentrations in normal and MND plasmas in the first study

PLS-DA distinguished subgroups of MND in early studies

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

Metabolites with significantly different concentrations

in normal controls and in MND patients not taking Riluzole

PLS-DA distinguishes MND from controls in drug fee study

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)

Extracted Ion Chromatograms and Spectra for Peak #1 @4.43 min

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Extracted Ion Chromatograms and Spectra for Peak #2 @9.58 min

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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

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

Classification of motor neuron diseases

Classical

ALS

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

Motor Neuron DiseasesHow closely are they related to other CNS disorders

HTLVUMN

PLS

HSP

Others

KennedyPMA

Autoimmune

LMN

ALS

AD

HDPD

FALS

Others

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?

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

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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

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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TWO METABOLOMIC GROUPS IN HUNTINGTONS DISEASE

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GENE POSITIVE G93A MOUSE (P) vs. NORMAL PARENT (W)FRONTAL CORTEX 71 VARIABLES

GENOMIC METABOLOMIC CORRELLATION

Pharmacogenomics and Metabolomics a natural fit

Complementary data towards understanding drug response

Pharmacogenetics

The study of the role of inheritance in individual variation in drug response phenotype.

GENOME

EN

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ON

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NT

TRANSCRIPTOME

PROTEOMEPROTEOME

METABOLOMEMETABOLOME

HEALTHSTATE

HEALTHSTATE

Disease State

Nature Reviews Genetics 5; 669-676 (2004);

Evans: Nature, Volume 429(6990).May 27, 2004.464-468

Identifying genes influencing polygenic drug responses

Molecular diagnostics of pharmacogenomic traits

Evans: Science, Volume 286(5439).October 15, 1999.487-491

Add metabolomics data between genotype and phenotype

Identify metabolites and pathways that influence drug response

PharmacogenomicsMetabolomics-The Future

The Vision

The right drug, at the right dose for every patient.

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

Metabolomics Society Organized 2004

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."

Metabolomics Society Japan Meeting

Metabolomics 2006 June 24-28The Conference Center at Harvard Medical School