Mostafa Rezaei-Tavirani1, Mona Zamanian-Azodi2, Sepideh Rajabi1, Ali Masoudi-Nejad3, Mohammad Rostami-Nejad4, Sara Rahmatirad5. 1. Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2. Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 3. Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. 4. Research Institute for Gastroenterology and Liver Diseases, Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 5. Department of Cell and Molecular Biology, Faculty of Science, University of Tehran, Tehran, Iran.
Abstract
BACKGROUND: Alzheimer and Parkinson diseases (AD and PD) are the two most important neurodegenerative disorders. This paper aims to determine the possible molecular linkage between these two common neurodegenerative diseases by a combination of computational investigations. METHODS: According to our aim, common sets of identified proteins from the KEGG database were further analyzed based on Gene Ontology (GO) annotation and sequence similarities by the agglomerative hierarchical clustering. Proteins possessing same characteristics were categorized based on biological features in distinct clusters using the R programming software. In addition to this, by the use of DAVID Program and PPI network analysis, more insight can be achieved. RESULTS: The results of this study indicated that 23 proteins are common between these two diseases. Their ontology evaluations by application of clustering methods showed that proteins belonging to a specific cluster indicate discrete properties that are different from other clusters. Furthermore, PPI network analysis confirms that the proteins with similarity ontology and sequence are also in close relationship. CONCLUSION: In conclusion, assessment of protein features supported the idea that mitochondria are the main malfunction compartment in AD and PD. Some of these common properties are apoptosis and mitochondria oxidation pathways that can be used for drug targeting. Moreover, examination of other neurodegenerative diseases can be helpful for comprehensive understanding of the origin of these diseases.
BACKGROUND:Alzheimer and Parkinson diseases (AD and PD) are the two most important neurodegenerative disorders. This paper aims to determine the possible molecular linkage between these two common neurodegenerative diseases by a combination of computational investigations. METHODS: According to our aim, common sets of identified proteins from the KEGG database were further analyzed based on Gene Ontology (GO) annotation and sequence similarities by the agglomerative hierarchical clustering. Proteins possessing same characteristics were categorized based on biological features in distinct clusters using the R programming software. In addition to this, by the use of DAVID Program and PPI network analysis, more insight can be achieved. RESULTS: The results of this study indicated that 23 proteins are common between these two diseases. Their ontology evaluations by application of clustering methods showed that proteins belonging to a specific cluster indicate discrete properties that are different from other clusters. Furthermore, PPI network analysis confirms that the proteins with similarity ontology and sequence are also in close relationship. CONCLUSION: In conclusion, assessment of protein features supported the idea that mitochondria are the main malfunction compartment in AD and PD. Some of these common properties are apoptosis and mitochondria oxidation pathways that can be used for drug targeting. Moreover, examination of other neurodegenerative diseases can be helpful for comprehensive understanding of the origin of these diseases.
Authors: Mostafa Rezaei-Tavirani; Majid Rezaei-Tavirani; Vahid Mansouri; Seyed Mohammad Mahdavi; Reza Valizadeh; Mohammad Rostami-Nejad; Mohammad Reza Zali Journal: Gastroenterol Hepatol Bed Bench Date: 2017