An In-Silico Study on the Most Effective Growth Factors in Retinal Regeneration Utilizing Tissue Engineering Concepts

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.

Clinical Pharmacology Perspective on Development of Adeno‐Associated Virus Vector‐Based Retina Gene Therapy

“…75 A semimechanistic model was developed in rabbits to study ocular PK of various proteins administered IVT; the model was used to estimate half-life in retina, vitreous and aqueous humor in relation to protein size up to a hydrodynamic radius of 14 nm, which is the minimum radius for monomeric AAVs, and the authors suggested that the half-life in each compartment (retina, vitreous, and aqueous humor) could be geometrically scaled to humans. 76 Network systems biology is another promising technique that has been used to study ophthalmic indications for which gene therapy products are being developed; for example, networks of gene regulation have been constructed in glaucoma 9 and AMD, [77][78][79] providing insights into the mechanisms underlying these multifactorial diseases, as well as used to identify the most effective proteins for retinal regeneration. 78 Moreover, by coupling network systems biology models with complex QSP models, 75 one could study the drug effects on the downstream disease pathway.…”

“…76 Network systems biology is another promising technique that has been used to study ophthalmic indications for which gene therapy products are being developed; for example, networks of gene regulation have been constructed in glaucoma 9 and AMD, [77][78][79] providing insights into the mechanisms underlying these multifactorial diseases, as well as used to identify the most effective proteins for retinal regeneration. 78 Moreover, by coupling network systems biology models with complex QSP models, 75 one could study the drug effects on the downstream disease pathway.…”

Editorial — Systems Biology Approach for Identification of Essential Growth Factors in Retinal Regeneration

“…In the current issue of Journal of Ophthalmic and Vision Research, Beheshtizadeh et al report an in-silico study which was aimed to determine the most important growth factors in retinal tissue engineering. [3] Gene ontology (GO) and degree centrality analysis were used to identify the most effective proteins for retinal regeneration. Despite the remarkable results presented in the study, it should be stressed that the growth factors were determined only by degree centrality analysis.…”