Nima Beheshtizadeh1,2, Alireza Baradaran-Rafii3, Maryam Sharifi Sistani1, Mahmoud Azami1. 1. Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran. 2. Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran. 3. Ocular Tissue Engineering Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract
PURPOSE: Considering the significance of retinal disorders and the growing need to employ tissue engineering in this field, in-silico studies can be used to establish a cost-effective method. This in-silico study was performed to find the most effective growth factors contributing to retinal tissue engineering. METHODS: In this study, a regeneration gene database was used. All 21 protein-coding genes participating in retinal regeneration were considered as a protein-protein interaction (PPI) network via the "STRING App" in "Cytoscape 3.7.2" software. The resultant graph possessed 21 nodes as well as 37 edges. Gene ontology (GO) analysis, as well as the centrality analysis, revealed the most effective proteins in retinal regeneration. RESULTS: According to the biological processes and the role of each protein in different pathways, selecting the correct one is possible through the information that the network provides. Eye development, detection of the visible light, visual perception, photoreceptor cell differentiation, camera-type eye development, eye morphogenesis, and angiogenesis are the major biological processes in retinal regeneration. Based on the GO analysis, SHH, STAT3, FGFR1, OPN4, ITGAV, RAX, and RPE65 are effective in retinal regeneration via the biological processes. In addition, based on the centrality analysis, four proteins have the greatest influence on retinal regeneration: SHH, IGF1, STAT3, and ASCL1. CONCLUSION: With the intention of applying the most impressive growth factors in retinal engineering, it seems logical to pay attention to SHH, STAT3, and RPE65. Utilizing these proteins can lead to fabricate high efficiency engineered retina via all aforementioned biological processes.
PURPOSE: Considering the significance of retinal disorders and the growing need to employ tissue engineering in this field, in-silico studies can be used to establish a cost-effective method. This in-silico study was performed to find the most effective growth factors contributing to retinal tissue engineering. METHODS: In this study, a regeneration gene database was used. All 21 protein-coding genes participating in retinal regeneration were considered as a protein-protein interaction (PPI) network via the "STRING App" in "Cytoscape 3.7.2" software. The resultant graph possessed 21 nodes as well as 37 edges. Gene ontology (GO) analysis, as well as the centrality analysis, revealed the most effective proteins in retinal regeneration. RESULTS: According to the biological processes and the role of each protein in different pathways, selecting the correct one is possible through the information that the network provides. Eye development, detection of the visible light, visual perception, photoreceptor cell differentiation, camera-type eye development, eye morphogenesis, and angiogenesis are the major biological processes in retinal regeneration. Based on the GO analysis, SHH, STAT3, FGFR1, OPN4, ITGAV, RAX, and RPE65 are effective in retinal regeneration via the biological processes. In addition, based on the centrality analysis, four proteins have the greatest influence on retinal regeneration: SHH, IGF1, STAT3, and ASCL1. CONCLUSION: With the intention of applying the most impressive growth factors in retinal engineering, it seems logical to pay attention to SHH, STAT3, and RPE65. Utilizing these proteins can lead to fabricate high efficiency engineered retina via all aforementioned biological processes.
Authors: Jie Zhu; Agustin Luz-Madrigal; Tracy Haynes; Julia Zavada; Amy K Burke; Katia Del Rio-Tsonis Journal: PLoS One Date: 2014-07-08 Impact factor: 3.240