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Bioinformatic and Microarray
Strategies to Identify Peripheral
Biomarkers for Parkinson’s Disease
Bruce Chase
University of Nebraska - Omaha
Identifying Peripheral Biomarkers for PD
Parkinson’s Disease (PD) as a complex syndrome
How might peripheral biomarkers be useful?
Is there evidence for peripheral biomarkers?
Bioinformatic/Microarray approaches
Proof of concept
Parkinson’s Disease Is A Complex Syndrome
Cardinal Features
Resting tremor
Rigidity
Bradykinesia
Postural instability
Positive and longlasting response to
levodopa
Parkinson’s Plus
Syndromes
poor or short-lived
response to levodopa
autonomic dysfunction
dementia
ophthalmoplegia
amyotrophy
dystonia
depression
ataxia
Neuronal
Complexity in PD
Neurodegeneration
Progressive loss of
dopaminergic
neurons in the
substantia nigra
Formation of Lewy
bodies
Impacts multiple
neurochemical
pathways
dopamine
norepinephrine
serotonin
acetylcholine
GABA
glutamate
Lewy bodies
Clinical Spectrum of Lewy Body Disorders
Behavioral Abnormalities
Memory Disorder
DLB
Extrapyramidal Disorder
PD
PD With
Dementia
Visual Hallucinations
LB Variant
Of AD
AD
Modified from Arch Neurol 2001; 58:186
Genetic Complexity In Parkinson’s Disease
Common Idiopathic Forms
Unknown cause
Environmental (+ Genetic?)
Less Common Monogenic Forms
-synuclein (PARK1)
Parkin (PARK2)
UCH-L1 (PARK5)
Tau
>4 others
Molecular Complexity: -Synuclein
Main component of intracellular fibrillar protein
deposits in affected brain regions in multiple
neurodegenerative disorders
Parkinson’s disease (Lewy bodies)
Alzheimer disease (plaques)
Multiple system atrophy
Amyotrophic lateral sclerosis
Mutations in the coding region and gene
triplications only cause familial Parkinson’s
disease
Molecular Complexity: -Synuclein
-Synuclein interactions
b-amyloid
tau
parkin
phospholipase D2
transcription factor Elk-1
dopamine transporter
tyrosine hydroxylase
lipids
Biophysical properties
Can exist in multiple conformations
Affected by environment and mutations
Can form protofibrils and fibrils
Affected by lipid binding
Identifying Peripheral Biomarkers for PD
Parkinson’s Disease (PD) as a complex syndrome
How might peripheral biomarkers be useful?
Is there evidence for peripheral biomarkers?
Bioinformatic/Microarray approaches
Proof of concept
How Might Peripheral Biomarkers Be Useful?
Clinical Issues in PD
Etiology of PD is largely unknown
Biomarkers could aid in understanding PD etiology
PD is a chronic, progressive and complex syndrome
where diagnosis is subjective, confirmable only at
autospy, and disease progression is variable
Biomarkers could discriminate between forms of PD,
support early diagnosis, document stage
Peripheral biomarkers are evaluated using relatively
noninvasive methods
Therapy is based solely on symptoms, and requires
periodic adjustment
Biomarkers could aid in design/implementation of optimal
therapeutic regimens
Identifying Peripheral Biomarkers for PD
Parkinson’s Disease (PD) as a complex syndrome
How might peripheral biomarkers be useful?
Is there evidence for peripheral biomarkers?
Bioinformatic/Microarray approaches
Proof of Concept
Test Case: Do -Synuclein Expression
Levels Serve as a Biomarker?
-Synuclein expression in lymphocytes
Low levels: RT-PCR
Lanes 1-4: lymphocyte RNA
Lanes 5-7: Lymphoblastoid cell lines
Lanes 8-9: Negative controls
Do levels vary with disease status?
Assess levels of mutant and normal gene products as
a function of disease status
Assess -Synuclein Expression Levels In
Kindreds Transmitting -Synuclein Mutations
Autosomal dominant mutations
Variable expressivity
Age of onset
Disease severity/duration
Presence of dementia
Pathological findings
Within & between kindreds
G88C
exon 3
G209A exon 4
G209A exon 4
Mutant Alleles Show Reduced Expression In
Late-Stage Familial Parkinson’s Disease
Direct G209A
sequencing
of RT-PCR
products
RFLP
RT-PCR
G88C
G209A
qRT-PCR
G88C
Identifying Peripheral Biomarkers for PD
Parkinson’s Disease (PD) as a complex syndrome
How might peripheral biomarkers be useful?
Is there evidence for peripheral biomarkers?
Bioinformatic/Microarray approaches
Proof of concept
Bioinformatic/Microarray Approaches
Evaluate gene expression profiles to identify a molecular
signature associated with PD stages/forms
Targets identified using bioinformatic approach: all genes in
pathways previously suggested relevant to PD
Alternative: Assess all genes without an initial bias
Concerns:
Power: What constitutes a biological replicate in RNA samples?
What are normal levels of variation?
Are parkinsonian individuals more variable?
Affected individuals fluctuate in disease severity
Disease symptoms vary widely in idiopathic disease
Genetic/environmental background effects (noise) could be as large as
disease effects (signal)
Statistical evaluation
Relevance to neuronal function
Kindred Members As “Biological” Replicates
Pseudosolution:
Reduce genetic (and possibly
environmental) variation
Compare profiles obtained from
nuclear families transmitting a
dominant mutation
Use UPDRS (Unified Parkinson’s
Disease Rating Scale) to score
disease severity
Compare first-degree relatives
who are
Symptomatic gene-positive vs.
gene-negative
Symptomatic vs. asymptomatic
gene-positive
G209A exon 4
G88C exon 3
Identifying Peripheral Biomarkers for PD
Parkinson’s Disease (PD) as a complex syndrome
How might peripheral biomarkers be useful?
Is there evidence for peripheral biomarkers?
Bioinformatic/Microarray approaches
Proof of concept
Trial Design
Extract RNA from G209A/ + heterozygotes
Label RNA from a severely symptomatic individual with Cy5
Label RNA from mildly symptomatic and asymptomatic individuals
with Cy3
Probe cDNA spotted arrays; Affymetrix chips
Sample Label Gene Status
Symptoms
1
Cy3
G209A/+
Severe
2
Cy5
G209A/+
Mild
3
Cy5
G209A/+
None
Multiple Processes Appear Affected
Energy/metabolism
ATP synthase, ATPase
cytochrome C oxidase
NADH dehydrogenase
Neurotransmission
GABA-A receptor subunits, associated proteins
DOPA decarboxylase
Catechol-O-methyltransferase
Chloride channel
Neurodegeneration / protein degradation / apoptosis
alpha-Synuclein interacting protein (synphilin)
Huntingtin interacting protein C
Tumor necrosis factor receptor superfamily, members
E3 ubiquitin ligase
Apoptosis-inducing serine-threonine kinase
Transcriptional regulation / Development
Heterogeneous nuclear ribonucleoprotein H1
Bicaudal
Translation
Eukaryotic translation initiation and elongation factors
Gene Expression In Cell Lines Established From
G209A Heterozygotes Differing in Disease Status
#2 (Mild) : #1 (Severe)
#3 (Asymptomatic) : #1 (Severe)
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3.5
Summary
Parkinson’s Disease is a complex syndrome
Biomarkers hold promise for aiding diagnosis and
implementing treatment regimens
Peripheral biomarkers are likely to exist
Microarray-based approaches hold promise for peripheral
biomarker development
Comparisons between nuclear family members in FPD
kindreds may serve to increase power and reduce
environmental and genetic effects in the initial
identification of peripheral biomarkers
Acknowledgments
Collaborators
Katerina Markopoulou, UNMC, Omaha
Zbigniew Wszolek, Mayo Clinic, Jacksonville
Lola Katechalidou, ELPIS Hospital, Athens
Nobu Hattori, Juntendo Medical School, Tokyo
Microarray consultants
Jim Eudy, UNMC, Omaha
Dan Bosinov, UNMC, Omaha
Funding
NIH/NINDS
NE-BRIN