Identification of disregulatory genes in parkinsons disease gene expression data using bioinformatics analysis

Project Title

Identification of disregulatory genes in parkinsons disease gene expression data using bioinformatics analysis

Project Area of Specialization Biomedical EngineeringProject Summary

ABSTRACT:

Parkinson’s disease (PD) affects millions of people worldwide and causes symptoms such as bradykinesia and disrupted speech. Parkinson’s disease is known to be characterized by the mass death of dopaminergic neurons in the substantia nigra region. In the status quo, PD is often diagnosed at late stages because obvious motor symptoms appear after the disease has progressed far. It is advantageous to diagnose PD before the onset of motor symptoms because treatments are often more effective at early stages. While motor symptoms usually manifest when over 50% of dopaminergic neurons in the substantia nigra are already lost, molecular signatures of PD may be present at early stages in patient blood. This study aimed to analyze several gene expression studies’ data for commonly differentially expressed genes (DEGs) in the blood of early stage PD patients. Sequencher software applied to analyze microarray expression of Parkinson’s disease. Sequencer working with microarrays expression data and looking to re-analyze pre-existing results or trying to perform concordance studies with RNA-Sequence. Moreover, functional gene annotation, pathway enrichment analysis, construction of protein-protein interaction (PPI) network and identification of small molecule were applied for exploring the potential biological roles of DEGs PD. The Connectivity Map (CMap) a database to assemble the entire transcriptome profile from cultured human cells and applies simple pattern-matching algorithms; both facilitate the detection of functional connections between genes, disease and drugs along with transient feature of changes in common-gene expression. Using connectivity map, DEGs will characterize into up and down-regulated genes. Using Gastroplus software 9.6 version to check accurately estimation of potential Drug-drug interaction through in-silico analysis for the identification of species-specific changes to estimate how drug affect on Parkinson’s patient.

Project Objectives

OBJECTIVES:

1. In proposed study “Parkinson’s Disease” was used as the keyword to search expression profiling of ‘PD’ in the NCBI Gene Expression Omnibus (GEO) database that provides a large collection of microarray expression data.  
2. Sequencer working with microarrays expression data and looking to re-analyze pre-existing results or trying to perform concordance studies with RNA-Sequence.
3. The raw expression datasets is download and preprocess by log2 transformation in R language. In Linux 05 The Linear Models ‘‘limma’’ package in R language  use to analyze the microarray datasets. The false discovery rate (FDR) is utilize for multiple testing corrections by using the Benjamini and Hochberg method.
4. DAVID (Database for annotation, visualization, and integrated discovery) is applied to investigate the DEGs at a function level and to cluster the genes according to the gene ontology (GO), biological process, and molecular function.
5. To assess the similarity of gene expression patterns between two samples, two-way hierarchical clustering analysis is perform using R language.
6. In order to understand the biological roles of DEGs, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) terms is enrich by the online tool GeneCoDis3.
7. The interaction between the up-and down-regulated DEGs can be explored using BioGRID, a database of known protein interactions, which is use to predict the PPI association among DEGs and the PPI interaction network is visualiz by Cytoscape.
8. The TFs in the human genome and the motifs of genomic binding sites are download from the TRANSFAC is a database covering details of TFs and their DNA binding sites. Promoter region of top 10 up- and down-regulated DEGs is download from UCSC (http://www.genome.ucsc.edu/cgi-bin/hgTables) and constructing regulatory network, and the network is visualiz by Cytoscape software (http://cytoscape.org/).
9. The connective map (CMap) a database to assembly the entrie transcriptome profile from cultured human cells and applies simple pattern-matching algorithms; both facilitate the detection of function connection between genes, disease, and drug along with the transient future of changes in common gene expression
10. Gastroplus software 9.6 version have ability to accurately estimate potential Drug-drug interaction through in-silico analysis for the identification of species-specific changes to estimate how drug affect on Parkinson’s patient.
11. Number of key DEGs in Parkinson’s disease based on the bioinformatics analysis is obtain. Box-plot, volcano plot, mean differences analysis is perform to preliminarily detect the expression levels of DEGs in peripheral blood cells of RA patients and health individuals.

Project Implementation Method

Methodology:

Data collection of microarray expression data:

The gene expression profile (GSE54536) was downloaded from the website https://www.ncbi.nlm.nih.gov/geo/  (Gene Expression Omnibus database). All 10 Parkinson’s disease (PD) samples were analyzed on the basis of GPL10558  [Illumina HumanHT-12 V4.0 expression beadchip].

Microarray expression analysis by Sequencher:

Sequencher software applied to analyze microarray expression of Parkinson’s disease. Sequencer working with microarrays expression data and looking to re-analyze pre-existing results or trying to perform concordance studies with RNA-Sequence.

Identification of differentially expressed genes (DEGs):

The linear model package of “limma” applied to analyze the microarray expression datasets. There are differentially expressed genes were found in PD patients compared to healthy individuals. The Benjamini and Hochberg procedure (False Discovery Rate -FDR) was applied for multiple testing modifications. FDR below 0.05 is considered as the DEGs threshold.

Gene Ontology (GO) analysis of DEGs:

DAVID (Database for Annotation, Visualization and Integrated Discovery) is used for the analysis of DEGs functions and classification of genes according to cellular component, biological process and molecular function.

Pathway Enrichment Analysis:  

The dysregulated biological pathways in Parkinson’s disease will investigate in depth to find the changes at functional level. Metabolic and non-metabolic pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG) database is applied as DAVID inputs for KEGG pathway enrichment analysis.

Protein-Protein network interaction association with DEGS:

The interaction between the up-regulated and down-regulated DEGs can be explored using BioGRID, a database of known protein interactions, which used to predict the PPI association among DEGs and the obtained PPI interaction network was visualized by Cytoscape software.

Identification of small molecules:

The Connectivity Map (CMap) a database to assemble the entire transcriptome profile from cultured human cells and applies simple pattern-matching algorithms; both facilitate the detection of functional connections between genes, disease and drugs along with transient feature of changes in common-gene expression. Using connectivity map, DEGs will characterize into up and down-regulated genes

PBPK modelling using Gastroplus stimulation software:

Gastroplus software 9.6 version have ability to accurately estimate potential Drug-drug interaction through in-silico analysis for the identification of species-specific changes to estimate physiological based pharmacokinetics and pharmacodynamics of Parkinson’s disease.

Benefits of the Project

Benefits:

Advancement in bioinformatics method has proved significant prospective in biomedical research. These approaches reduce the investigation time and provide probabilistic and biologically significant results. bioinformatics methods are designed for Parkinson’s disease to explore the pathogenesis and check the PBPK of Parkinson’s at the molecular level and predict the new therapeutic target for the disease.

Technical Details of Final Deliverable

1. TOOLS AND SOFTWARE:
2. DAVID: used for the analysis of DEGs functions and classification of genes according to cellular component, biological process and molecular function.
3. GASTROPLUS SOFTWARE: Gastroplus software 9.6 version have ability to accurately estimate potential Drug-drug interaction through in-silico analysis for the identification of species-specific changes to estimate how drug affect in the body
4. AUTODOCKING VINA SOFTWARE: used for molecular docking and virtual screening.
5. SEQUENCHER: Sequencer working with microarrays expression data and looking to re-analyze pre-existing results or trying to perform concordance studies with RNA-Sequence.
6. STRING TOOL: for retrieval of interacting genes and to estimate protein-protein netwrk information
7. CYTOSCAPE TOOL:Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data.
8. CONNECTIVITY MAP: Creating and analyzing large perturbational datasets to aid our understanding of human disease and to accelerate the discovery of novel therapeutics.
9. GENECODIS3:GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics
10. LIMMA PACKAGE:limma powers differential expression analyses for RNA-sequencing and microarray studies.

Final Deliverable of the Project Software SystemCore Industry MedicalOther Industries IT , Others , Health Core Technology Internet of Things (IoT)Other Technologies OthersSustainable Development Goals Good Health and Well-Being for People, Quality EducationRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	67000
Gastroplus software	Equipment	1	30000	30000
Seqencher software	Equipment	1	30000	30000
Hard drive	Equipment	1	7000	7000