1. Bioinformatic and Microarray Strategies to Identify Peripheral Biomarkers for Parkinson’s Disease Bruce Chase University of Nebraska - Omaha

2. 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

3. Parkinson’s Disease Is A Complex Syndrome Cardinal Features Resting tremor Rigidity Bradykinesia Postural instability Positive and long- lasting response to levodopa Parkinson’s Plus Syndromes poor or short-lived response to levodopa autonomic dysfunction dementia ophthalmoplegia amyotrophy dystonia depression ataxia

4. 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

5. Lewy bodies

6. Clinical Spectrum of Lewy Body Disorders Modified from Arch Neurol 2001; 58:186 DLB Visual Hallucinations Behavioral Abnormalities PDPD With Dementia LB Variant Of AD AD Extrapyramidal Disorder Memory Disorder

7. 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

8. 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

9. Molecular Complexity:  -Synuclein -Synuclein interactions -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

10. 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

11. 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

12. 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

13. 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

14. 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 G209A exon 4 G88C exon 3 G209A exon 4

15. Mutant Alleles Show Reduced Expression In Late-Stage Familial Parkinson’s Disease Direct sequencing of RT-PCR products G209AG88C RFLP RT-PCR G209A G88C qRT-PCR

16. 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

17. 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

18. Kindred Members As “Biological” Replicates G209A exon 4G88C exon 3 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

19. 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

20. 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

21. 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

23. 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

24. 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