Proliferative Diabetic Retinopathy from a Network Biology Perspective
Journal of Ophthalmic and Optometric Sciences,
Vol. 3 No. 4 (2019),
5 October 2019
,
Page 29-41
https://doi.org/10.22037/joos.v3i4.37319
Abstract
Background: Proliferative Diabetic Retinopathy (PDR) is the advanced version of Diabetic Retinopathy in which, new, fragile blood vessels can start to develop in the retina and into the vitreous, the gel-like fluid that fills the back of the eye.
Material and Methods: Here we study PDR from a whole system viewpoint in which network science is utilized for the system representation. Our objective is to explore the role of differentially expressed genes in the development of PDR. For this purpose, we have designed a framework in which the genes with high differential expression are identified and their PPI networks are regenerated. Next, influential dominating nodes are specified in the resulting network. With the enrichment analyses, the output set is validated and its role in the PDR is studied.
Results: These results suggest that the output gene set has a significant association with the disease of study. Additionally, we identify miRNAs regulating the transcription of genes inside the explored module as biomarkers affecting the progress of PDR.
Keywords: Diabetic Retinopathy; Network; Systems Biology; Differential Gene Expression.
- Diabetic Retinopathy
- Network
- Systems Biology
- Differential Gene Expression
How to Cite
References
Li JQ, Welchowski T, Schmid M, Letow J, Wolpers C, Pascual-Camps I, et al. Prevalence, incidence and future projection of diabetic eye disease in Europe: a systematic review and meta-analysis. Eur J Epidemiol. 2020;35(1):11-23.
Kertes PJ, Johnson TM. Evidence-based eye care: Lippincott Williams & Wilkins; 2007.
Zhao Y, Singh RP. The role of anti-vascular endothelial growth factor (anti-VEGF) in the management of proliferative diabetic retinopathy. Drugs in context. 2018;7.
Sun JK, Glassman AR, Beaulieu WT, Stockdale CR, Bressler NM, Flaxel C, et al. Rationale and application of the protocol S anti–vascular endothelial growth factor algorithm for proliferative diabetic retinopathy. Ophthalmology. 2019;126(1):87-95.
Zhu X-R, Yang F-y, Lu J, Zhang H-r, Sun R, Zhou J-B, et al. Plasma metabolomic profiling of proliferative diabetic retinopathy. Nutr Metab (Lond). 2019;16(1):1-11.
Pan J, Chen D, Yang X, Zou R, Zhao K, Cheng D, et al. Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography. Am J Ophthalmol. 2018;192:146-56.
Shao Y, Dong L-j, Takahashi Y, Chen J, Liu X, Chen Q, et al. miRNA-451a regulates RPE function through promoting mitochondrial function in proliferative diabetic retinopathy. American Journal of Physiology-Endocrinology and Metabolism. 2019;316(3):E443-E52.
Wu F, Lamy R, Ma D, Laotaweerungsawat S, Chen Y, Zhao T, et al. Correlation of aqueous, vitreous, and plasma cytokine levels in patients with proliferative diabetic retinopathy. Invest Ophthalmol Vis Sci. 2020;61(2):26-.
Suresh R, Hannah JY, Thoveson A, Swisher J, Apolinario M, Zhou B, et al. Loss to follow-up among patients with proliferative diabetic retinopathy in clinical practice. Am J Ophthalmol. 2020;215:66-71.
Qiao L, Zhu Y, Zhou H. Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access. 2020;8:104292-302.
Yang Y, Liu Y, Li Y, Chen Z, Xiong Y, Zhou T, et al. MicroRNA-15b targets VEGF and inhibits angiogenesis in proliferative diabetic retinopathy. The Journal of Clinical Endocrinology & Metabolism. 2020;105(11):3404-15.
Nagasawa T, Tabuchi H, Masumoto H, Enno H, Niki M, Ohara Z, et al. Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy. Int Ophthalmol. 2019;39(10):2153-9.
Mousavian Z, Díaz J, Masoudi-Nejad A. Information theory in systems biology. Part II: protein-protein interaction and signaling networks. Seminars in cell & developmental biology. 2016;51:14-23. doi: 10.1016/J.SEMCDB.2015.12.006.
Mousavian Z, Kavousi K, Masoudi-Nejad A. Information theory in systems biology. Part I: Gene regulatory and metabolic networks. Seminars in Cell and Developmental Biology. 2016;51:3-13. doi: 10.1016/j.semcdb.2015.12.007.
Torkamanian-Afshar M, Lanjanian H, Nematzadeh S, Tabarzad M, Najafi A, Kiani F, et al. RPINBASE: An online toolbox to extract features for predicting RNA-protein interactions. Genomics. 2020;112(3). doi: 10.1016/j.ygeno.2020.02.013.
Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. Trader as a new optimization algorithm predicts drug-target interactions efficiently. Scientific Reports 2019 9:1. 2019;9(1):1-14. doi: 10.1038/s41598-019-45814-8.
H L, S N, S H, M T-A, F K, M M-J, et al. High-throughput analysis of the interactions between viral proteins and host cell RNAs. Computers in biology and medicine. 2021;135:104611-. doi: 10.1016/J.COMPBIOMED.2021.104611.
Aflakparast M, Salimi H, Gerami A, Dubé MP, Visweswaran S, Masoudi-Nejad A. Cuckoo search epistasis: a new method for exploring significant genetic interactions. Heredity 2014 112:6. 2014;112(6):666-74. doi: 10.1038/hdy.2014.4.
Ghasemi M, Seidkhani H, Tamimi F, Rahgozar M, Masoudi-Nejad A. Centrality Measures in Biological Networks. Current Bioinformatics. 2014;9(4):426-41. doi: 10.2174/15748936113086660013.
Pournoor E, Elmi N, Masoudi-Nejad A. CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks. (1389-2029 (Print)).
Pournoor E, Mousavian Z, Dalini AN, Masoudi-Nejad A. Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework. (2045-2322 (Electronic)).
Barabasi A-L, Oltvai ZN. Network biology: understanding the cell's functional organization. Nature reviews genetics. 2004;5(2):101-13.
Li Y, Chen D, Sun L, Wu Y, Zou Y, Liang C, et al. Induced expression of VEGFC, ANGPT, and EFNB2 and their receptors characterizes neovascularization in proliferative diabetic retinopathy. Invest Ophthalmol Vis Sci. 2019;60(13):4084-96.
Smyth GK. Limma: linear models for microarray data. Bioinformatics and computational biology solutions using R and Bioconductor: Springer; 2005. p. 397-420.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-40.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2014;43(D1):D447-D52.
Esfahanian AH. Connectivity algorithms. Topics in structural graph theory: Cambridge University Press; 2013. p. 268-81.
Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90-W7.
Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 2019;47(D1):D419-D26.
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30.
Taliun SAG, VandeHaar P, Boughton AP, Welch RP, Taliun D, Schmidt EM, et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat Genet. 2020;52(6):550-2.
Bastian M, Heymann S, Jacomy M, editors. Gephi: an open source software for exploring and manipulating networks. Third international AAAI conference on weblogs and social media; 2009.
Huang H-Y, Lin Y-C-D, Li J, Huang K-Y, Shrestha S, Hong H-C, et al. miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic Acids Res. 2020;48(D1):D148-D54.
Krishna Vadlapatla R, Dutt Vadlapudi A, Mitra AK. Hypoxia-inducible factor-1 (HIF-1): a potential target for intervention in ocular neovascular diseases. Curr Drug Targets. 2013;14(8):919-35.
Lavoz C, Rayego-Mateos S, Orejudo M, Opazo-Ríos L, Marchant V, Marquez-Exposito L, et al. Could IL-17A be a novel therapeutic target in diabetic nephropathy? Journal of clinical medicine. 2020;9(1):272.
Huang H, Gandhi JK, Zhong X, Wei Y, Gong J, Duh EJ, et al. TNFα is required for late BRB breakdown in diabetic retinopathy, and its inhibition prevents leukostasis and protects vessels and neurons from apoptosis. Invest Ophthalmol Vis Sci. 2011;52(3):1336-44.
Liang Z, Gao KP, Wang YX, Liu ZC, Tian L, Yang XZ, et al. RNA sequencing identified specific circulating miRNA biomarkers for early detection of diabetes retinopathy. American Journal of Physiology-Endocrinology and Metabolism. 2018;315(3):E374-E85.
Khan R, Kadamkode V, Kesharwani D, Purkayastha S, Banerjee G, Datta M. Circulatory miR-98-5p levels are deregulated during diabetes and it inhibits proliferation and promotes apoptosis by targeting PPP1R15B in keratinocytes. RNA Biol. 2020;17(2):188-201.
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