Professor D.Bonchev shows an in-depth look at how a systems biology approach was used to identify some of the critical aspects Parkinson's disease: molecular players, drug targets, and underlying biological processes.

2. A Network View on
Parkinson’s Disease
Danail Bonchev
Center for the Study of Biological Complexity
Virginia Commonwealth University
Elsevier, January 15, 2015
Based on: S. Chandrasekaran , D. Bonchev, Comput. Struct. Biotechn. J. 7(8), 2013, e201304004
(free downloads from researchgate.net/profile/Danail_Bonchev)
.

3. Introduction
 The Neurodegenerative Diseases – The Known and the Unknown
 The Network Approach: What could it help?
 Understanding better the molecular mechanisms of these
diseases will bring new effective drug candidates, as well as
molecular markers for early warning for incoming problems.
 This report presents the results for a detailed network analysis
of the Parkinson’s disease, as a part of a search for a common
underlying mechanism of neurodegenerative diseases, including
Alzheimer’s and Huntington’s diseases.
 The network analysis is performed using Pathway Studio
software as a basic tool, supplemented for some specific aims
by other software packages.

4. Figure 1. Biological processes and genes implicated in the Parkinson’s disease
What was known

5. Nine genes have previously been associated with
dominant or recessive form of Parkinson’s
disease:
9 genes: ATPI3A2, DJ-1 GIGYF2, HTRA2,
LRRK2, PARK (parkin), PINK1, SNCA , UCHL1
SNCA (α-synuclein or α-syn) is critical to the
early stage pathogenesis.
This list was updated after extensive online
search, so as to create one of our “seed genes”
list, a preliminary step in our approach.
Compiling a list of genes implicated
with Parkinson’s disease

6. Work Flow

7. A B
Figure 3. Four-set Venn diagram of the overlap of significantly differentially expressed genes (SDEGs) in
(a) GSE8397 HG-U133A (b) GSE8397 HG-U133B and (c) GSE20295 HG-133A gene expression datasets.
SFG, MSN and LSN,
stand for three types
of brain tissue
samples:
Superior Frontal Gyrus,
Medial and Lateral
Substantia nigra, resp.
Broadman Area 9 (BA9),
Putamen (PT) and
Substantia Nigra (SN)
C– Another three
affected brain areas:

8. Figure 4. Parkinson's disease direct interaction network
Node Colors: Blue- genes of interest from the SDEG set Green – known PD genes in SDEGs
Interaction colors: regulation – dashed grey, molecular transport – dotted red, co-expression : solid
blue, protein modification – solid green, and protein-protein binding – solid purple

9. Table 1. Summary of the genes of interest and
genes already known in Parkinson disease

10. Figure 5. Parkinson's disease compact shortest path network
Orange – gene of interest in connecting nodes
Red – known genes in connecting nodes
Interaction colors: protein modification – solid green,
promoter binding – dotted green and direct regulation – solid grey.
Key players
determined
by node degree,
closeness centrality,
and betweenness
centrality
(Pajek software)

11. Table 2. Gene Ontology enrichment analysis of Parkinson's disease compact shortest path network

12. Table 3. Genes of interest for Parkinson’s disease identified
by “guilt-by-association” with the known PD-related genes

13. Table 4. Enriched KEGG pathways in Parkinson’s from DAVID analysis
(Database for Annotation, Visualization and Integrated Discovery )

14. Figure 6. Parkinson’s disease enriched canonical pathways
as produced by IPA analysis

15. Figure 7. Integrated Parkinson’s disease mechanism
The 46 genes/proteins found in common in all 16 enriched KEGG pathways
Three routes emerged for triggering
the Parkinson’s disease mechanism
via one of the extra-cellular ligands
CX3CL1, IL12B and SEMA6D.

16. Figure 8. Parkinson’s disease microRNA regulatory network
The genes and miRNAs implicated in PD pathology are highlighted in green and the genes of
potential interest are highlighted in blue. Genes that code for transcription factors (TFs) are
highlighted in yellow. MiRNA-mRNA target interactions are represented using solid orange lines.

17. Table 5. Genes of interest determined from
Parkinson's disease microRNA regulatory network

18. Summary
Using Pathway Studio software and the ResNet database
three routes were identified for triggering the Parkinson’s
disease molecular mechanism via one of the extra-cellular
ligands CX3CL1, IL12B and SEMA6D.
28 genes of potential importance for Parkinson disease are
Identified proceeding from their close relations to known
Parkinson disease genes in shortest paths type networks.
17 genes of interest were determined from Parkinson's
disease microRNA regulatory network. Four of these
(PAK1, SYNJ1, UBE2N, NEDD4I) are also present in the
above list of 28 genes of interest, which makes these four
genes prime candidates for experimental studies.
These findings demonstrate the important role Pathway
Studio software plays in biomedical research as powerful
tool for build-up and analysis of biomolecular networks.

19. This study was essential part of the PhD Thesis
of my former student Dr. Sreedevi Chandrasekaran,
defended in May 2013.
Thank You for Your Attention

20. Thank you for joining our webinar today:
A Network View on Parkinson's Disease
with Professor Danail Bonchev
If you have any questions for our speaker, please type them into the
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If you would like more information about Pathway Studio, you can
contact:
Frank A White III, PhD
f.white.1@elsevier.com